http://arxiv.org/abs/2404.18567
Assessing Cybersecurity Vulnerabilities in Code Large Language Models. (99%)
Md Imran Hossen; Jianyi Zhang; Yinzhi Cao; Xiali Hei

http://arxiv.org/abs/2404.18514
A Systematic Evaluation of Adversarial Attacks against Speech Emotion Recognition Models. (99%)
Nicolas Facchinetti; Federico Simonetta; Stavros Ntalampiras

http://arxiv.org/abs/2404.18791
Certification of Speaker Recognition Models to Additive Perturbations. (54%)
Dmitrii Korzh; Elvir Karimov; Mikhail Pautov; Oleg Y. Rogov; Ivan Oseledets

http://arxiv.org/abs/2404.18702
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots. (13%)
Xi Xin; Fei Huang; Giles Hooker

http://arxiv.org/abs/2404.18541
Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research. (3%)
Daniel Gibert

http://arxiv.org/abs/2404.18649
Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection. (1%)
Konstantinos Tsigos; Evlampios Apostolidis; Spyridon Baxevanakis; Symeon Papadopoulos; Vasileios Mezaris

http://arxiv.org/abs/2404.18825
Harmonic Machine Learning Models are Robust. (1%)
Nicholas S. Kersting; Yi Li; Aman Mohanty; Oyindamola Obisesan; Raphael Okochu

http://arxiv.org/abs/2404.17844
Towards Robust Recommendation: A Review and an Adversarial Robustness Evaluation Library. (92%)
Lei Cheng; Xiaowen Huang; Jitao Sang; Jian Yu

http://arxiv.org/abs/2404.17970
Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness. (70%)
Ali Reza Ghavamipour; Benjamin Zi Hao Zhao; Oguzhan Ersoy; Fatih Turkmen

http://arxiv.org/abs/2404.17947
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks. (67%)
Yassine Abbahaddou; Sofiane Ennadir; Johannes F. Lutzeyer; Michalis Vazirgiannis; Henrik Boström

http://arxiv.org/abs/2404.17867
Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics. (2%)
Xiaoshuai Wu; Xin Liao; Bo Ou; Yuling Liu; Zheng Qin

http://arxiv.org/abs/2404.17760
Adversarial Examples: Generation Proposal in the Context of Facial Recognition Systems. (92%)
Marina Fuster; Ignacio Vidaurreta

http://arxiv.org/abs/2404.17196
Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications. (54%)
Quan Zhang; Binqi Zeng; Chijin Zhou; Gwihwan Go; Heyuan Shi; Yu Jiang

http://arxiv.org/abs/2404.17399
Evaluations of Machine Learning Privacy Defenses are Misleading. (3%)
Michael Aerni; Jie Zhang; Florian Tramèr

http://arxiv.org/abs/2404.17225
Enhancing Privacy and Security of Autonomous UAV Navigation. (2%)
Vatsal Aggarwal; Arjun Ramesh Kaushik; Charanjit Jutla; Nalini Ratha

http://arxiv.org/abs/2404.17275
Adversarial Reweighting with $\alpha$-Power Maximization for Domain Adaptation. (1%)
Xiang Gu; Xi Yu; Yan Yang; Jian Sun; Zongben Xu

http://arxiv.org/abs/2404.17020
Generating Minimalist Adversarial Perturbations to Test Object-Detection Models: An Adaptive Multi-Metric Evolutionary Search Approach. (98%)
Cristopher McIntyre-Garcia; Adrien Heymans; Beril Borali; Won-Sook Lee; Shiva Nejati

http://arxiv.org/abs/2404.16452
PAD: Patch-Agnostic Defense against Adversarial Patch Attacks. (92%)
Lihua Jing; Rui Wang; Wenqi Ren; Xin Dong; Cong Zou

http://arxiv.org/abs/2404.17092
Defending Spiking Neural Networks against Adversarial Attacks through Image Purification. (84%)
Weiran Chen; Qi Sun; Qi Xu

http://arxiv.org/abs/2404.16369
Don't Say No: Jailbreaking LLM by Suppressing Refusal. (67%)
Yukai Zhou; Wenjie Wang

http://arxiv.org/abs/2404.16656
A Self-Organizing Clustering System for Unsupervised Distribution Shift Detection. (12%)
Sebastián Basterrech; Line Clemmensen; Gerardo Rubino

http://arxiv.org/abs/2404.16417
Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learning. (2%)
David Winderl; Nicola Franco; Jeanette Miriam Lorenz

http://arxiv.org/abs/2404.16557
Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples. (2%)
Kuofeng Gao; Jindong Gu; Yang Bai; Shu-Tao Xia; Philip Torr; Wei Liu; Zhifeng Li

http://arxiv.org/abs/2404.17120
Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs. (1%)
Valeriia Cherepanova; James Zou

http://arxiv.org/abs/2404.15881
Steal Now and Attack Later: Evaluating Robustness of Object Detection against Black-box Adversarial Attacks. (99%)
Erh-Chung Chen; Pin-Yu Chen; I-Hsin Chung; Che-Rung Lee

http://arxiv.org/abs/2404.16212
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape. (99%)
Sifat Muhammad Abdullah; Aravind Cheruvu; Shravya Kanchi; Taejoong Chung; Peng Gao; Murtuza Jadliwala; Bimal Viswanath

http://arxiv.org/abs/2404.15784
An Empirical Study of Aegis. (98%)
Daniel Saragih; Paridhi Goel; Tejas Balaji; Alyssa Li

http://arxiv.org/abs/2404.15744
A General Black-box Adversarial Attack on Graph-based Fake News Detectors. (96%)
Peican Zhu; Zechen Pan; Yang Liu; Jiwei Tian; Keke Tang; Zhen Wang

http://arxiv.org/abs/2404.15656
MISLEAD: Manipulating Importance of Selected features for Learning Epsilon in Evasion Attack Deception. (83%)
Vidit Khazanchi; Pavan Kulkarni; Yuvaraj Govindarajulu; Manojkumar Parmar

http://arxiv.org/abs/2404.16154
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models. (83%)
Maximilian Wendlinger; Kilian Tscharke; Pascal Debus

http://arxiv.org/abs/2404.16020
Universal Adversarial Triggers Are Not Universal. (16%)
Nicholas Meade; Arkil Patel; Siva Reddy

http://arxiv.org/abs/2404.16251
Investigating the prompt leakage effect and black-box defenses for multi-turn LLM interactions. (9%)
Divyansh Agarwal; Alexander R. Fabbri; Philippe Laban; Ben Risher; Shafiq Joty; Caiming Xiong; Chien-Sheng Wu

http://arxiv.org/abs/2404.15854
CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning. (2%)
Haolin Wu; Jing Chen; Ruiying Du; Cong Wu; Kun He; Xingcan Shang; Hao Ren; Guowen Xu

http://arxiv.org/abs/2404.15587
Security Analysis of WiFi-based Sensing Systems: Threats from Perturbation Attacks. (61%)
Hangcheng Cao; Wenbin Huang; Guowen Xu; Xianhao Chen; Ziyang He; Jingyang Hu; Hongbo Jiang; Yuguang Fang

http://arxiv.org/abs/2404.14942
Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures. (61%)
Thanh Toan Nguyen; Quoc Viet Hung Nguyen; Thanh Tam Nguyen; Thanh Trung Huynh; Thanh Thi Nguyen; Matthias Weidlich; Hongzhi Yin

http://arxiv.org/abs/2404.15611
PoisonedFL: Model Poisoning Attacks to Federated Learning via Multi-Round Consistency. (54%)
Yueqi Xie; Minghong Fang; Neil Zhenqiang Gong

http://arxiv.org/abs/2404.15081
Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models. (47%)
Jingyao Xu; Yuetong Lu; Yandong Li; Siyang Lu; Dongdong Wang; Xiang Wei

http://arxiv.org/abs/2404.14795
Talk Too Much: Poisoning Large Language Models under Token Limit. (38%)
Jiaming He; Wenbo Jiang; Guanyu Hou; Wenshu Fan; Rui Zhang; Hongwei Li

http://arxiv.org/abs/2404.15042
Leverage Variational Graph Representation For Model Poisoning on Federated Learning. (10%)
Kai Li; Xin Yuan; Jingjing Zheng; Wei Ni; Falko Dressler; Abbas Jamalipour

http://arxiv.org/abs/2404.15065
Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structure. (2%)
Tobias Ladner; Michael Eichelbeck; Matthias Althoff

http://arxiv.org/abs/2404.14943
Does It Make Sense to Explain a Black Box With Another Black Box? (1%)
Julien Delaunay; Luis Galárraga; Christine Largouët

http://arxiv.org/abs/2404.14928
Graph Machine Learning in the Era of Large Language Models (LLMs). (1%)
Wenqi Fan; Shijie Wang; Jiani Huang; Zhikai Chen; Yu Song; Wenzhuo Tang; Haitao Mao; Hui Liu; Xiaorui Liu; Dawei Yin; Qing Li

http://arxiv.org/abs/2404.14309
Towards Better Adversarial Purification via Adversarial Denoising Diffusion Training. (98%)
Yiming Liu; Kezhao Liu; Yao Xiao; Ziyi Dong; Xiaogang Xu; Pengxu Wei; Liang Lin

http://arxiv.org/abs/2404.14693
Double Privacy Guard: Robust Traceable Adversarial Watermarking against Face Recognition. (91%)
Yunming Zhang; Dengpan Ye; Sipeng Shen; Caiyun Xie; Ziyi Liu; Jiacheng Deng; Long Tang

http://arxiv.org/abs/2404.14042
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction. (74%)
Wenhao Lan; Yijun Yang; Haihua Shen; Shan Li

http://arxiv.org/abs/2404.13879
Explicit Lipschitz Value Estimation Enhances Policy Robustness Against Perturbation. (67%)
Xulin Chen; Ruipeng Liu; Garrett E. Katz

http://arxiv.org/abs/2404.13914
Audio Anti-Spoofing Detection: A Survey. (62%)
Menglu Li; Yasaman Ahmadiadli; Xiao-Ping Zhang

http://arxiv.org/abs/2404.13946
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning. (41%)
Rong Wang; Guichen Zhou; Mingjun Gao; Yunpeng Xiao

http://arxiv.org/abs/2404.13968
Protecting Your LLMs with Information Bottleneck. (26%)
Zichuan Liu; Zefan Wang; Linjie Xu; Jinyu Wang; Lei Song; Tianchun Wang; Chunlin Chen; Wei Cheng; Jiang Bian

http://arxiv.org/abs/2404.14461
Competition Report: Finding Universal Jailbreak Backdoors in Aligned LLMs. (13%)
Javier Rando; Francesco Croce; Kryštof Mitka; Stepan Shabalin; Maksym Andriushchenko; Nicolas Flammarion; Florian Tramèr

http://arxiv.org/abs/2404.14265
Deep Learning as Ricci Flow. (2%)
Anthony Baptista; Alessandro Barp; Tapabrata Chakraborti; Chris Harbron; Ben D. MacArthur; Christopher R. S. Banerji

http://arxiv.org/abs/2404.14406
Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing. (1%)
Kartik Narayan; Vishal M. Patel

http://arxiv.org/abs/2404.13948
Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations. (1%)
Sukmin Cho; Soyeong Jeong; Jeongyeon Seo; Taeho Hwang; Jong C. Park

http://arxiv.org/abs/2404.14389
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction. (1%)
Zifan Zhang; Minghong Fang; Jiayuan Huang; Yuchen Liu

http://arxiv.org/abs/2404.13621
Attack on Scene Flow using Point Clouds. (98%)
Haniyeh Ehsani Oskouie; Mohammad-Shahram Moin; Shohreh Kasaei

http://arxiv.org/abs/2404.13631
Fermi-Bose Machine. (87%)
Mingshan Xie; Yuchen Wang; Haiping Huang

http://arxiv.org/abs/2404.15373
Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model. (50%)
Shadi Sartipi; Mujdat Cetin

http://arxiv.org/abs/2404.16873
AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs. (47%)
Anselm Paulus; Arman Zharmagambetov; Chuan Guo; Brandon Amos; Yuandong Tian

http://arxiv.org/abs/2404.13827
Swap It Like Its Hot: Segmentation-based spoof attacks on eye-tracking images. (26%)
Anish S. Narkar; Brendan David-John

http://arxiv.org/abs/2404.13660
Trojan Detection in Large Language Models: Insights from The Trojan Detection Challenge. (1%)
Narek Maloyan; Ekansh Verma; Bulat Nutfullin; Bislan Ashinov

http://arxiv.org/abs/2404.13518
Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion. (99%)
Hongyu Zhu; Sichu Liang; Wentao Hu; Fangqi Li; Ju Jia; Shilin Wang

http://arxiv.org/abs/2404.13277
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives. (99%)
Chenxi Yang; Yujia Liu; Dingquan Li; Yan Zhong; Tingting Jiang

http://arxiv.org/abs/2404.13320
Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think. (99%)
Haotian Xue; Yongxin Chen

http://arxiv.org/abs/2404.13279
Backdoor Attacks and Defenses on Semantic-Symbol Reconstruction in Semantic Communications. (41%)
Yuan Zhou; Rose Qingyang Hu; Yi Qian

http://arxiv.org/abs/2404.12653
How Real Is Real? A Human Evaluation Framework for Unrestricted Adversarial Examples. (99%)
Dren Fazlija; Arkadij Orlov; Johanna Schrader; Monty-Maximilian Zühlke; Michael Rohs; Daniel Kudenko

http://arxiv.org/abs/2404.12635
AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation. (99%)
Heqi Peng; Yunhong Wang; Ruijie Yang; Beichen Li; Rui Wang; Yuanfang Guo

http://arxiv.org/abs/2404.12704
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only. (75%)
Jiazhu Dai; Haoyu Sun

http://arxiv.org/abs/2404.12916
Physical Backdoor Attack can Jeopardize Driving with Vision-Large-Language Models. (5%)
Zhenyang Ni; Rui Ye; Yuxi Wei; Zhen Xiang; Yanfeng Wang; Siheng Chen

http://arxiv.org/abs/2404.12679
MLSD-GAN -- Generating Strong High Quality Face Morphing Attacks using Latent Semantic Disentanglement. (3%)
Aravinda Reddy PN; Raghavendra Ramachandra; Krothapalli Sreenivasa Rao; Pabitra Mitra

http://arxiv.org/abs/2404.13224
Model-Based Counterfactual Explanations Incorporating Feature Space Attributes for Tabular Data. (1%)
Yuta Sumiya; Hayaru shouno

http://arxiv.org/abs/2404.12852
LSP Framework: A Compensatory Model for Defeating Trigger Reverse Engineering via Label Smoothing Poisoning. (1%)
Beichen Li; Yuanfang Guo; Heqi Peng; Yangxi Li; Yunhong Wang

http://arxiv.org/abs/2404.12120
Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors. (99%)
Raz Lapid; Almog Dubin; Moshe Sipper

http://arxiv.org/abs/2404.12274
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing. (98%)
Jiabao Ji; Bairu Hou; Zhen Zhang; Guanhua Zhang; Wenqi Fan; Qing Li; Yang Zhang; Gaowen Liu; Sijia Liu; Shiyu Chang

http://arxiv.org/abs/2404.12612
SA-Attack: Speed-adaptive stealthy adversarial attack on trajectory prediction. (98%)
Huilin Yin; Jiaxiang Li; Pengju Zhen; Jun Yan

http://arxiv.org/abs/2404.12014
Enhance Robustness of Language Models Against Variation Attack through Graph Integration. (33%)
Zi Xiong; Lizhi Qing; Yangyang Kang; Jiawei Liu; Hongsong Li; Changlong Sun; Xiaozhong Liu; Wei Lu

http://arxiv.org/abs/2404.12512
Proteus: Preserving Model Confidentiality during Graph Optimizations. (15%)
Yubo Gao; Maryam Haghifam; Christina Giannoula; Renbo Tu; Gennady Pekhimenko; Nandita Vijaykumar

http://arxiv.org/abs/2404.12038
Uncovering Safety Risks in Open-source LLMs through Concept Activation Vector. (13%)
Zhihao Xu; Ruixuan Huang; Xiting Wang; Fangzhao Wu; Jing Yao; Xing Xie

http://arxiv.org/abs/2404.12139
Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models. (2%)
Shouwei Ruan; Yinpeng Dong; Hanqing Liu; Yao Huang; Hang Su; Xingxing Wei

http://arxiv.org/abs/2404.11265
The Victim and The Beneficiary: Exploiting a Poisoned Model to Train a Clean Model on Poisoned Data. (83%)
Zixuan Zhu; Rui Wang; Cong Zou; Lihua Jing

http://arxiv.org/abs/2404.11538
GenFighter: A Generative and Evolutive Textual Attack Removal. (82%)
Md Athikul Islam; Edoardo Serra; Sushil Jajodia

http://arxiv.org/abs/2404.11819
Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement. (80%)
Pushkar Shukla; Dhruv Srikanth; Lee Cohen; Matthew Turk

http://arxiv.org/abs/2404.11665
Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers. (75%)
Mohammad Javad Askarizadeh; Ebrahim Farahmand; Jorge Castro-Godinez; Ali Mahani; Laura Cabrera-Quiros; Carlos Salazar-Garcia

http://arxiv.org/abs/2404.11207
Exploring the Transferability of Visual Prompting for Multimodal Large Language Models. (2%)
Yichi Zhang; Yinpeng Dong; Siyuan Zhang; Tianzan Min; Hang Su; Jun Zhu

http://arxiv.org/abs/2404.11330
Toward Understanding the Disagreement Problem in Neural Network Feature Attribution. (1%)
Niklas Koenen; Marvin N. Wright

http://arxiv.org/abs/2404.11357
Detector Collapse: Backdooring Object Detection to Catastrophic Overload or Blindness. (1%)
Hangtao Zhang; Shengshan Hu; Yichen Wang; Leo Yu Zhang; Ziqi Zhou; Xianlong Wang; Yanjun Zhang; Chao Chen

http://arxiv.org/abs/2404.15360
Towards Robust and Interpretable EMG-based Hand Gesture Recognition using Deep Metric Meta Learning. (1%)
Simon Tam; Shriram Tallam Puranam Raghu; Étienne Buteau; Erik Scheme; Mounir Boukadoum; Alexandre Campeau-Lecours; Benoit Gosselin

http://arxiv.org/abs/2404.10335
Efficiently Adversarial Examples Generation for Visual-Language Models under Targeted Transfer Scenarios using Diffusion Models. (99%)
Qi Guo; Shanmin Pang; Xiaojun Jia; Qing Guo

http://arxiv.org/abs/2404.10408
Adversarial Identity Injection for Semantic Face Image Synthesis. (38%)
Giuseppe Tarollo; Tomaso Fontanini; Claudio Ferrari; Guido Borghi; Andrea Prati

http://arxiv.org/abs/2404.10499
Robust Noisy Label Learning via Two-Stream Sample Distillation. (1%)
Sihan Bai; Sanping Zhou; Zheng Qin; Le Wang; Nanning Zheng

http://arxiv.org/abs/2404.10796
Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective. (99%)
Khushnaseeb Roshan; Aasim Zafar

http://arxiv.org/abs/2404.10202
Towards a Novel Perspective on Adversarial Examples Driven by Frequency. (99%)
Zhun Zhang; Yi Zeng; Qihe Liu; Shijie Zhou

http://arxiv.org/abs/2404.09961
Ti-Patch: Tiled Physical Adversarial Patch for no-reference video quality metrics. (83%)
Victoria Leonenkova; Ekaterina Shumitskaya; Anastasia Antsiferova; Dmitriy Vatolin

http://arxiv.org/abs/2404.09475
Improving Weakly-Supervised Object Localization Using Adversarial Erasing and Pseudo Label. (1%)
Byeongkeun Kang; Sinhae Cha; Yeejin Lee

http://arxiv.org/abs/2404.09599
Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation. (1%)
Shangqing Liu; Wei Ma; Jian Wang; Xiaofei Xie; Ruitao Feng; Yang Liu

http://arxiv.org/abs/2404.10193
Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering. (1%)
Zaid Khan; Yun Fu

http://arxiv.org/abs/2404.09352
Counteracting Concept Drift by Learning with Future Malware Predictions. (96%)
Branislav Bosansky; Lada Hospodkova; Michal Najman; Maria Rigaki; Elnaz Babayeva; Viliam Lisy

http://arxiv.org/abs/2404.09401
Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models. (96%)
Peifei Zhu; Tsubasa Takahashi; Hirokatsu Kataoka

http://arxiv.org/abs/2404.09349
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. (76%)
Brian R. Bartoldson; James Diffenderfer; Konstantinos Parasyris; Bhavya Kailkhura

http://arxiv.org/abs/2404.09193
FaceCat: Enhancing Face Recognition Security with a Unified Generative Model Framework. (22%)
Jiawei Chen; Xiao Yang; Yinpeng Dong; Hang Su; Jianteng Peng; Zhaoxia Yin

http://arxiv.org/abs/2404.08980
Stability and Generalization in Free Adversarial Training. (96%)
Xiwei Cheng; Kexin Fu; Farzan Farnia

http://arxiv.org/abs/2404.09005
Proof-of-Learning with Incentive Security. (2%)
Zishuo Zhao; Zhixuan Fang; Xuechao Wang; Yuan Zhou

http://arxiv.org/abs/2404.10789
PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis. (99%)
Dipkamal Bhusal; Md Tanvirul Alam; Monish K. Veerabhadran; Michael Clifford; Sara Rampazzi; Nidhi Rastogi

http://arxiv.org/abs/2404.08341
Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts. (99%)
Yang Li; Songlin Yang; Wei Wang; Ziwen He; Bo Peng; Jing Dong

http://arxiv.org/abs/2404.08273
Struggle with Adversarial Defense? Try Diffusion. (99%)
Yujie Li; Yanbin Wang; Haitao xu; Bin Liu; Jianguo Sun; Zhenhao Guo; Wenrui Ma

http://arxiv.org/abs/2404.10790
Multimodal Attack Detection for Action Recognition Models. (83%)
Furkan Mumcu; Yasin Yilmaz

http://arxiv.org/abs/2404.08285
A Survey of Neural Network Robustness Assessment in Image Recognition. (83%)
Jie Wang; Jun Ai; Minyan Lu; Haoran Su; Dan Yu; Yutao Zhang; Junda Zhu; Jingyu Liu

http://arxiv.org/abs/2404.08255
Practical Region-level Attack against Segment Anything Models. (81%)
Yifan Shen; Zhengyuan Li; Gang Wang

http://arxiv.org/abs/2404.08631
FCert: Certifiably Robust Few-Shot Classification in the Era of Foundation Models. (69%)
Yanting Wang; Wei Zou; Jinyuan Jia

http://arxiv.org/abs/2404.14418
Mitigating Cascading Effects in Large Adversarial Graph Environments. (2%)
James D. Cunningham; Conrad S. Tucker

http://arxiv.org/abs/2404.08540
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation. (1%)
Agneet Chatterjee; Tejas Gokhale; Chitta Baral; Yezhou Yang

http://arxiv.org/abs/2404.08818
Empowering Malware Detection Efficiency within Processing-in-Memory Architecture. (1%)
Sreenitha Kasarapu; Sathwika Bavikadi; Sai Manoj Pudukotai Dinakarrao

http://arxiv.org/abs/2404.08069
Persistent Classification: A New Approach to Stability of Data and Adversarial Examples. (98%)
Brian Bell; Michael Geyer; David Glickenstein; Keaton Hamm; Carlos Scheidegger; Amanda Fernandez; Juston Moore

http://arxiv.org/abs/2404.08154
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization. (98%)
Runqi Lin; Chaojian Yu; Tongliang Liu

http://arxiv.org/abs/2404.07863
Backdoor Contrastive Learning via Bi-level Trigger Optimization. (96%)
Weiyu Sun; Xinyu Zhang; Hao Lu; Yingcong Chen; Ting Wang; Jinghui Chen; Lu Lin

http://arxiv.org/abs/2404.15344
Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers. (92%)
Nayan Moni Baishya; B. R. Manoj

http://arxiv.org/abs/2404.07921
AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs. (12%)
Zeyi Liao; Huan Sun

http://arxiv.org/abs/2404.07878
LeapFrog: The Rowhammer Instruction Skip Attack. (4%)
Andrew Adiletta; Caner Tol; Berk Sunar

http://arxiv.org/abs/2404.08197
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies. (1%)
Zichao Li; Cihang Xie; Ekin Dogus Cubuk

http://arxiv.org/abs/2404.06776
Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data. (99%)
Yu Qiao; Chaoning Zhang; Apurba Adhikary; Choong Seon Hong

http://arxiv.org/abs/2404.07153
Lost in Translation: Modern Neural Networks Still Struggle With Small Realistic Image Transformations. (82%)
Ofir Shifman; Yair Weiss

http://arxiv.org/abs/2404.06957
Adversarial purification for no-reference image-quality metrics: applicability study and new methods. (26%)
Aleksandr Gushchin; Anna Chistyakova; Vladislav Minashkin; Anastasia Antsiferova; Dmitriy Vatolin

http://arxiv.org/abs/2404.06838
Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora? (2%)
Miriam Anschütz; Edoardo Mosca; Georg Groh

http://arxiv.org/abs/2404.06971
TrajPRed: Trajectory Prediction with Region-based Relation Learning. (1%)
Chen Zhou; Ghassan AlRegib; Armin Parchami; Kunjan Singh

http://arxiv.org/abs/2404.08690
Towards Building a Robust Toxicity Predictor. (99%)
Dmitriy Bespalov; Sourav Bhabesh; Yi Xiang; Liutong Zhou; Yanjun Qi

http://arxiv.org/abs/2404.06313
On adversarial training and the 1 Nearest Neighbor classifier. (99%)
Amir Hagai; Yair Weiss

http://arxiv.org/abs/2404.06247
LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks. (80%)
Jianlang Chen; Xuhong Ren; Qing Guo; Felix Juefei-Xu; Di Lin; Wei Feng; Lei Ma; Jianjun Zhao

http://arxiv.org/abs/2404.06236
Towards Robust Domain Generation Algorithm Classification. (80%)
Arthur Drichel; Marc Meyer; Ulrike Meyer

http://arxiv.org/abs/2404.06666
SafeGen: Mitigating Unsafe Content Generation in Text-to-Image Models. (38%)
Xinfeng Li; Yuchen Yang; Jiangyi Deng; Chen Yan; Yanjiao Chen; Xiaoyu Ji; Wenyuan Xu

http://arxiv.org/abs/2404.07242
Sandwich attack: Multi-language Mixture Adaptive Attack on LLMs. (31%)
Bibek Upadhayay; Vahid Behzadan

http://arxiv.org/abs/2404.06230
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning. (26%)
Emre Ozfatura; Kerem Ozfatura; Alptekin Kupcu; Deniz Gunduz

http://arxiv.org/abs/2404.06694
How to Craft Backdoors with Unlabeled Data Alone? (1%)
Yifei Wang; Wenhan Ma; Yisen Wang

http://arxiv.org/abs/2404.05350
Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing. (99%)
Chengyan Fu; Wenjie Wang

http://arxiv.org/abs/2404.05688
David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge. (99%)
Miguel Costa; Sandro Pinto

http://arxiv.org/abs/2404.05311
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack. (99%)
Viet Quoc Vo; Ehsan Abbasnejad; Damith C. Ranasinghe

http://arxiv.org/abs/2404.05703
Case Study: Neural Network Malware Detection Verification for Feature and Image Datasets. (98%)
Preston K. Robinette; Diego Manzanas Lopez; Serena Serbinowska; Kevin Leach; Taylor T. Johnson

http://arxiv.org/abs/2404.05219
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey. (98%)
Naveen Karunanayake; Ravin Gunawardena; Suranga Seneviratne; Sanjay Chawla

http://arxiv.org/abs/2404.05824
Quantum Adversarial Learning for Kernel Methods. (75%)
Giuseppe Montalbano; Leonardo Banchi

http://arxiv.org/abs/2404.05639
Investigating the Impact of Quantization on Adversarial Robustness. (50%)
Qun Li; Yuan Meng; Chen Tang; Jiacheng Jiang; Zhi Wang

http://arxiv.org/abs/2404.05680
SphereHead: Stable 3D Full-head Synthesis with Spherical Tri-plane Representation. (1%)
Heyuan Li; Ce Chen; Tianhao Shi; Yuda Qiu; Sizhe An; Guanying Chen; Xiaoguang Han

http://arxiv.org/abs/2404.05159
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods. (99%)
Roopkatha Dey; Aivy Debnath; Sayak Kumar Dutta; Kaustav Ghosh; Arijit Mitra; Arghya Roy Chowdhury; Jaydip Sen

http://arxiv.org/abs/2404.05130
Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning. (15%)
Yu Bi; Yekai Li; Xuan Feng; Xianghang Mi

http://arxiv.org/abs/2404.05088
How much reliable is ChatGPT's prediction on Information Extraction under Input Perturbations? (5%)
Ishani Mondal; Abhilasha Sancheti

http://arxiv.org/abs/2404.04963
SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials. (1%)
Mael Jullien; Marco Valentino; André Freitas

http://arxiv.org/abs/2404.04648
CANEDERLI: On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems. (86%)
Francesco Marchiori; Mauro Conti

http://arxiv.org/abs/2404.04662
Learning Minimal NAP Specifications for Neural Network Verification. (78%)
Chuqin Geng; Zhaoyue Wang; Haolin Ye; Saifei Liao; Xujie Si

http://arxiv.org/abs/2404.07234
Goal-guided Generative Prompt Injection Attack on Large Language Models. (67%)
Chong Zhang; Mingyu Jin; Qinkai Yu; Chengzhi Liu; Haochen Xue; Xiaobo Jin

http://arxiv.org/abs/2404.04714
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods. (67%)
Elita Lobo; Harvineet Singh; Marek Petrik; Cynthia Rudin; Himabindu Lakkaraju

http://arxiv.org/abs/2404.04647
Structured Gradient-based Interpretations via Norm-Regularized Adversarial Training. (61%)
Shizhan Gong; Qi Dou; Farzan Farnia

http://arxiv.org/abs/2404.04601
Exploiting Sequence Number Leakage: TCP Hijacking in NAT-Enabled Wi-Fi Networks. (3%)
Yuxiang Yang; Xuewei Feng; Qi Li; Kun Sun; Ziqiang Wang; Ke Xu

http://arxiv.org/abs/2404.04245
Evaluating Adversarial Robustness: A Comparison Of FGSM, Carlini-Wagner Attacks, And The Role of Distillation as Defense Mechanism. (99%)
Trilokesh Ranjan Sarkar; Nilanjan Das; Pralay Sankar Maitra; Bijoy Some; Ritwik Saha; Orijita Adhikary; Bishal Bose; Jaydip Sen

http://arxiv.org/abs/2404.04188
Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection. (98%)
João Vitorino; Miguel Silva; Eva Maia; Isabel Praça

http://arxiv.org/abs/2404.04375
Compositional Estimation of Lipschitz Constants for Deep Neural Networks. (13%)
Yuezhu Xu; S. Sivaranjani

http://arxiv.org/abs/2404.04139
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning. (12%)
K Naveen Kumar; C Krishna Mohan; Aravind Machiry

http://arxiv.org/abs/2404.03340
Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks. (99%)
Lei Zhang; Yuhang Zhou; Yi Yang; Xinbo Gao

http://arxiv.org/abs/2404.03225
FACTUAL: A Novel Framework for Contrastive Learning Based Robust SAR Image Classification. (98%)
Xu Wang; Tian Ye; Rajgopal Kannan; Viktor Prasanna

http://arxiv.org/abs/2404.03233
Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning. (16%)
Hongsheng Hu; Shuo Wang; Tian Dong; Minhui Xue

http://arxiv.org/abs/2404.03411
Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks? (2%)
Shuo Chen; Zhen Han; Bailan He; Zifeng Ding; Wenqian Yu; Philip Torr; Volker Tresp; Jindong Gu

http://arxiv.org/abs/2404.03348
Knowledge Distillation-Based Model Extraction Attack using Private Counterfactual Explanations. (2%)
Fatima Ezzeddine; Omran Ayoub; Silvia Giordano

http://arxiv.org/abs/2404.02660
Adversarial Attacks and Dimensionality in Text Classifiers. (99%)
Nandish Chattopadhyay; Atreya Goswami; Anupam Chattopadhyay

http://arxiv.org/abs/2404.02585
Unsegment Anything by Simulating Deformation. (97%)
Jiahao Lu; Xingyi Yang; Xinchao Wang

http://arxiv.org/abs/2404.02832
"Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages. (81%)
Ying Yuan; Qingying Hao; Giovanni Apruzzese; Mauro Conti; Gang Wang

http://arxiv.org/abs/2404.03027
JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks. (75%)
Weidi Luo; Siyuan Ma; Xiaogeng Liu; Xiaoyu Guo; Chaowei Xiao

http://arxiv.org/abs/2404.02532
Learn to Disguise: Avoid Refusal Responses in LLM's Defense via a Multi-agent Attacker-Disguiser Game. (11%)
Qianqiao Xu; Zhiliang Tian; Hongyan Wu; Zhen Huang; Yiping Song; Feng Liu; Dongsheng Li

http://arxiv.org/abs/2404.02462
A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability. (9%)
Jie Zhu; Jirong Zha; Ding Li; Leye Wang

http://arxiv.org/abs/2404.02889
Steganographic Passport: An Owner and User Verifiable Credential for Deep Model IP Protection Without Retraining. (1%)
Qi Cui; Ruohan Meng; Chaohui Xu; Chip-Hong Chang

http://arxiv.org/abs/2404.01907
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack. (99%)
Ying Zhou; Ben He; Le Sun

http://arxiv.org/abs/2404.01642
ADVREPAIR:Provable Repair of Adversarial Attack. (99%)
Zhiming Chi; Jianan Ma; Pengfei Yang; Cheng-Chao Huang; Renjue Li; Xiaowei Huang; Lijun Zhang

http://arxiv.org/abs/2404.02928
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models. (97%)
Jiachen Ma; Anda Cao; Zhiqing Xiao; Jie Zhang; Chao Ye; Junbo Zhao

http://arxiv.org/abs/2404.02287
One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation. (92%)
Mehmet Ergezer; Phat Duong; Christian Green; Tommy Nguyen; Abdurrahman Zeybey

http://arxiv.org/abs/2404.01828
Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay. (88%)
Yuhang Zhou; Zhongyun Hua

http://arxiv.org/abs/2404.02151
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks. (82%)
Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion

http://arxiv.org/abs/2404.02931
READ: Improving Relation Extraction from an ADversarial Perspective. (81%)
Dawei Li; William Hogan; Jingbo Shang

http://arxiv.org/abs/2404.02356
Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-Backdoors. (64%)
Victoria Graf; Qin Liu; Muhao Chen

http://arxiv.org/abs/2404.02067
Red-Teaming Segment Anything Model. (45%)
Krzysztof Jankowski; Bartlomiej Sobieski; Mateusz Kwiatkowski; Jakub Szulc; Michal Janik; Hubert Baniecki; Przemyslaw Biecek

http://arxiv.org/abs/2404.02242
Towards Robust 3D Pose Transfer with Adversarial Learning. (31%)
Haoyu Chen; Hao Tang; Ehsan Adeli; Guoying Zhao

http://arxiv.org/abs/2404.02440
Designing a Photonic Physically Unclonable Function Having Resilience to Machine Learning Attacks. (12%)
Elena R. Henderson; Jessie M. Henderson; Hiva Shahoei; William V. Oxford; Eric C. Larson; Duncan L. MacFarlane; Mitchell A. Thornton

http://arxiv.org/abs/2404.02406
Exploring Backdoor Vulnerabilities of Chat Models. (2%)
Yunzhuo Hao; Wenkai Yang; Yankai Lin

http://arxiv.org/abs/2404.02388
CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation. (1%)
Townim Faisal Chowdhury; Kewen Liao; Vu Minh Hieu Phan; Minh-Son To; Yutong Xie; Kevin Hung; David Ross; Anton van den Hengel; Johan W. Verjans; Zhibin Liao

http://arxiv.org/abs/2404.01356
The Double-Edged Sword of Input Perturbations to Robust Accurate Fairness. (99%)
Xuran Li; Peng Wu; Yanting Chen; Xingjun Ma; Zhen Zhang; Kaixiang Dong

http://arxiv.org/abs/2404.01574
Multi-granular Adversarial Attacks against Black-box Neural Ranking Models. (99%)
Yu-An Liu; Ruqing Zhang; Jiafeng Guo; Rijke Maarten de; Yixing Fan; Xueqi Cheng

http://arxiv.org/abs/2404.00924
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks. (93%)
Zhiyuan Cheng; Zhaoyi Liu; Tengda Guo; Shiwei Feng; Dongfang Liu; Mingjie Tang; Xiangyu Zhang

http://arxiv.org/abs/2404.01177
Poisoning Decentralized Collaborative Recommender System and Its Countermeasures. (33%)
Ruiqi Zheng; Liang Qu; Tong Chen; Kai Zheng; Yuhui Shi; Hongzhi Yin

http://arxiv.org/abs/2404.01509
Can Biases in ImageNet Models Explain Generalization? (10%)
Paul Gavrikov; Janis Keuper

http://arxiv.org/abs/2404.01101
UFID: A Unified Framework for Input-level Backdoor Detection on Diffusion Models. (10%)
Zihan Guan; Mengxuan Hu; Sheng Li; Anil Vullikanti

http://arxiv.org/abs/2404.01231
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models. (2%)
Yuxin Wen; Leo Marchyok; Sanghyun Hong; Jonas Geiping; Tom Goldstein; Nicholas Carlini

http://arxiv.org/abs/2404.01109
An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks. (1%)
Abdullah H Alqahtani

http://arxiv.org/abs/2404.00828
PID Control-Based Self-Healing to Improve the Robustness of Large Language Models. (75%)
Zhuotong Chen; Zihu Wang; Yifan Yang; Qianxiao Li; Zheng Zhang

http://arxiv.org/abs/2404.00897
Machine Learning Robustness: A Primer. (62%)
Houssem Ben Braiek; Foutse Khomh

http://arxiv.org/abs/2404.00362
STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario. (99%)
Renyang Liu; Kwok-Yan Lam; Wei Zhou; Sixing Wu; Jun Zhao; Dongting Hu; Mingming Gong

http://arxiv.org/abs/2404.00540
Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches. (98%)
Lingxuan Wu; Xiao Yang; Yinpeng Dong; Liuwei Xie; Hang Su; Jun Zhu

http://arxiv.org/abs/2404.00461
Shortcuts Arising from Contrast: Effective and Covert Clean-Label Attacks in Prompt-Based Learning. (5%)
Xiaopeng Xie; Ming Yan; Xiwen Zhou; Chenlong Zhao; Suli Wang; Yong Zhang; Joey Tianyi Zhou

http://arxiv.org/abs/2404.00185
On Inherent Adversarial Robustness of Active Vision Systems. (99%)
Amitangshu Mukherjee; Timur Ibrayev; Kaushik Roy

http://arxiv.org/abs/2403.20254
Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions. (68%)
Runhao Zeng; Xiaoyong Chen; Jiaming Liang; Huisi Wu; Guangzhong Cao; Yong Guo

http://arxiv.org/abs/2404.00114
Deepfake Sentry: Harnessing Ensemble Intelligence for Resilient Detection and Generalisation. (8%)
Liviu-Daniel University "Politehnica" of Bucharest, Romania Ştefan; Dan-Cristian University "Politehnica" of Bucharest, Romania Stanciu; Mihai University "Politehnica" of Bucharest, Romania Dogariu; Mihai Gabriel University "Politehnica" of Bucharest, Romania Constantin; Andrei Cosmin University "Politehnica" of Bucharest, Romania Jitaru; Bogdan University "Politehnica" of Bucharest, Romania Ionescu

http://arxiv.org/abs/2403.20127
The Impact of Prompts on Zero-Shot Detection of AI-Generated Text. (2%)
Kaito Taguchi; Yujie Gu; Kouichi Sakurai

http://arxiv.org/abs/2404.00095
GDA: Generalized Diffusion for Robust Test-time Adaptation. (1%)
Yun-Yun Tsai; Fu-Chen Chen; Albert Y. C. Chen; Junfeng Yang; Che-Chun Su; Min Sun; Cheng-Hao Kuo

http://arxiv.org/abs/2404.00108
Efficient Data-Free Model Stealing with Label Diversity. (1%)
Yiyong Liu; Rui Wen; Michael Backes; Yang Zhang

http://arxiv.org/abs/2403.20056
Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets. (1%)
Shadi Manafi; Nikhil Krishnaswamy

http://arxiv.org/abs/2403.19150
Towards Understanding Dual BN In Hybrid Adversarial Training. (82%)
Chenshuang Zhang; Chaoning Zhang; Kang Zhang; Axi Niu; Junmo Kim; In So Kweon

http://arxiv.org/abs/2403.19559
Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset. (82%)
Janis Goldzycher; Paul Röttger; Gerold Schneider

http://arxiv.org/abs/2403.19510
On the Robustness of LDP Protocols for Numerical Attributes under Data Poisoning Attacks. (41%)
Xiaoguang Li; Zitao Li; Ninghui Li; Wenhai Sun

http://arxiv.org/abs/2403.19326
MedBN: Robust Test-Time Adaptation against Malicious Test Samples. (10%)
Hyejin Park; Jeongyeon Hwang; Sunung Mun; Sangdon Park; Jungseul Ok

http://arxiv.org/abs/2404.00076
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks. (1%)
Orson Mengara

http://arxiv.org/abs/2403.19254
Imperceptible Protection against Style Imitation from Diffusion Models. (1%)
Namhyuk Ahn; Wonhyuk Ahn; KiYoon Yoo; Daesik Kim; Seung-Hun Nam

http://arxiv.org/abs/2403.18318
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks. (99%)
Tian Ye; Rajgopal Kannan; Viktor Prasanna; Carl Busart

http://arxiv.org/abs/2403.18554
CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection. (99%)
Jiayi Zhu; Qing Guo; Felix Juefei-Xu; Yihao Huang; Yang Liu; Geguang Pu

http://arxiv.org/abs/2403.19080
MMCert: Provable Defense against Adversarial Attacks to Multi-modal Models. (98%)
Yanting Wang; Hongye Fu; Wei Zou; Jinyuan Jia

http://arxiv.org/abs/2403.18309
Bayesian Learned Models Can Detect Adversarial Malware For Free. (97%)
Bao Gia Doan; Dang Quang Nguyen; Paul Montague; Tamas Abraham; Vel Olivier De; Seyit Camtepe; Salil S. Kanhere; Ehsan Abbasnejad; Damith C. Ranasinghe

http://arxiv.org/abs/2403.18580
MisGUIDE : Defense Against Data-Free Deep Learning Model Extraction. (95%)
Mahendra Gurve; Sankar Behera; Satyadev Ahlawat; Yamuna Prasad

http://arxiv.org/abs/2403.19009
Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning. (83%)
Syed Mhamudul Hasan; Abdur R. Shahid; Ahmed Imteaj

http://arxiv.org/abs/2403.18674
Deep Learning for Robust and Explainable Models in Computer Vision. (82%)
Mohammadreza Amirian

http://arxiv.org/abs/2403.18423
SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks. (81%)
Brian Formento; Wenjie Feng; Chuan Sheng Foo; Luu Anh Tuan; See-Kiong Ng

http://arxiv.org/abs/2404.01318
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. (50%)
Patrick Chao; Edoardo Debenedetti; Alexander Robey; Maksym Andriushchenko; Francesco Croce; Vikash Sehwag; Edgar Dobriban; Nicolas Flammarion; George J. Pappas; Florian Tramer; Hamed Hassani; Eric Wong

http://arxiv.org/abs/2403.18624
Vulnerability Detection with Code Language Models: How Far Are We? (26%)
Yangruibo Ding; Yanjun Fu; Omniyyah Ibrahim; Chawin Sitawarin; Xinyun Chen; Basel Alomair; David Wagner; Baishakhi Ray; Yizheng Chen

http://arxiv.org/abs/2403.18607
Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices. (15%)
Hanqing Fu; Gaolei Li; Jun Wu; Jianhua Li; Xi Lin; Kai Zhou; Yuchen Liu

http://arxiv.org/abs/2403.18985
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning. (15%)
Soumyendu Sarkar; Ashwin Ramesh Babu; Sajad Mousavi; Vineet Gundecha; Avisek Naug; Sahand Ghorbanpour

http://arxiv.org/abs/2403.18587
The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision. (11%)
Andreas Müller; Erwin Quiring

http://arxiv.org/abs/2403.18671
Fact Checking Beyond Training Set. (1%)
Payam Karisani; Heng Ji

http://arxiv.org/abs/2403.18373
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection. (1%)
Changshun Wu; Weicheng He; Chih-Hong Cheng; Xiaowei Huang; Saddek Bensalem

http://arxiv.org/abs/2403.17755
DataCook: Crafting Anti-Adversarial Examples for Healthcare Data Copyright Protection. (92%)
Sihan Shang; Jiancheng Yang; Zhenglong Sun; Pascal Fua

http://arxiv.org/abs/2403.17494
FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids. (78%)
Emad Efatinasab; Francesco Marchiori; Alessandro Brighente; Mirco Rampazzo; Mauro Conti

http://arxiv.org/abs/2403.17520
Boosting Adversarial Training via Fisher-Rao Norm-based Regularization. (69%)
Xiangyu Yin; Wenjie Ruan

http://arxiv.org/abs/2403.18872
Targeted Visualization of the Backbone of Encoder LLMs. (9%)
Isaac Roberts; Alexander Schulz; Luca Hermes; Barbara Hammer

http://arxiv.org/abs/2403.17710
Optimization-based Prompt Injection Attack to LLM-as-a-Judge. (4%)
Jiawen Shi; Zenghui Yuan; Yinuo Liu; Yue Huang; Pan Zhou; Lichao Sun; Neil Zhenqiang Gong

http://arxiv.org/abs/2403.18144
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning. (1%)
Joshua C. Zhao; Ahaan Dabholkar; Atul Sharma; Saurabh Bagchi

http://arxiv.org/abs/2403.17860
Exploring LLMs as a Source of Targeted Synthetic Textual Data to Minimize High Confidence Misclassifications. (1%)
Philip Lippmann; Matthijs Spaan; Jie Yang

http://arxiv.org/abs/2403.16432
$\textit{LinkPrompt}$: Natural and Universal Adversarial Attacks on Prompt-based Language Models. (99%)
Yue Xu; Wenjie Wang

http://arxiv.org/abs/2403.17301
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving. (98%)
Junhao Zheng; Chenhao Lin; Jiahao Sun; Zhengyu Zhao; Qian Li; Chao Shen

http://arxiv.org/abs/2403.16782
The Anatomy of Adversarial Attacks: Concept-based XAI Dissection. (87%)
Georgii Mikriukov; Gesina Schwalbe; Franz Motzkus; Korinna Bade

http://arxiv.org/abs/2403.16768
DeepKnowledge: Generalisation-Driven Deep Learning Testing. (82%)
Sondess Missaoui; Simos Gerasimou; Nikolaos Matragkas

http://arxiv.org/abs/2403.16569
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors. (70%)
Md Abdul Kadir; GowthamKrishna Addluri; Daniel Sonntag

http://arxiv.org/abs/2403.17188
LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning. (69%)
Siyuan Cheng; Guanhong Tao; Yingqi Liu; Guangyu Shen; Shengwei An; Shiwei Feng; Xiangzhe Xu; Kaiyuan Zhang; Shiqing Ma; Xiangyu Zhang

http://arxiv.org/abs/2403.16479
Model-less Is the Best Model: Generating Pure Code Implementations to Replace On-Device DL Models. (1%)
Mingyi Zhou; Xiang Gao; Pei Liu; John Grundy; Chunyang Chen; Xiao Chen; Li Li

http://arxiv.org/abs/2403.16176
Subspace Defense: Discarding Adversarial Perturbations by Learning a Subspace for Clean Signals. (99%)
Rui Zheng; Yuhao Zhou; Zhiheng Xi; Tao Gui; Qi Zhang; Xuanjing Huang

http://arxiv.org/abs/2403.16405
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low Curvature Models. (98%)
Kaikang Zhao; Xi Chen; Wei Huang; Liuxin Ding; Xianglong Kong; Fan Zhang

http://arxiv.org/abs/2403.16067
Robust Diffusion Models for Adversarial Purification. (73%)
Guang Lin; Zerui Tao; Jianhai Zhang; Toshihisa Tanaka; Qibin Zhao

http://arxiv.org/abs/2403.16257
Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning. (5%)
Siyuan Liang; Kuanrong Liu; Jiajun Gong; Jiawei Liang; Yuan Xun; Ee-Chien Chang; Xiaochun Cao

http://arxiv.org/abs/2403.16206
Rumor Detection with a novel graph neural network approach. (4%)
Tianrui Liu; Qi Cai; Changxin Xu; Bo Hong; Fanghao Ni; Yuxin Qiao; Tsungwei Yang

http://arxiv.org/abs/2403.16365
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. (2%)
Hossein Souri; Arpit Bansal; Hamid Kazemi; Liam Fowl; Aniruddha Saha; Jonas Geiping; Andrew Gordon Wilson; Rama Chellappa; Tom Goldstein; Micah Goldblum

http://arxiv.org/abs/2403.16050
A General and Efficient Federated Split Learning with Pre-trained Image Transformers for Heterogeneous Data. (1%)
Yifan Shi; Yuhui Zhang; Ziyue Huang; Xiaofeng Yang; Li Shen; Wei Chen; Xueqian Wang

http://arxiv.org/abs/2403.15918
An Embarrassingly Simple Defense Against Backdoor Attacks On SSL. (70%)
Aryan Satpathy; Nilaksh; Dhruva Rajwade

http://arxiv.org/abs/2403.15786
Adversarial Defense Teacher for Cross-Domain Object Detection under Poor Visibility Conditions. (64%)
Kaiwen Wang; Yinzhe Shen; Martin Lauer

http://arxiv.org/abs/2403.15207
Robust optimization for adversarial learning with finite sample complexity guarantees. (96%)
André Bertolace; Konstatinos Gatsis; Kostas Margellos

http://arxiv.org/abs/2403.15365
A Transfer Attack to Image Watermarks. (95%)
Yuepeng Hu; Zhengyuan Jiang; Moyang Guo; Neil Gong

http://arxiv.org/abs/2403.15271
From Hardware Fingerprint to Access Token: Enhancing the Authentication on IoT Devices. (26%)
Yue Xiao; Yi He; Xiaoli Zhang; Qian Wang; Renjie Xie; Kun Sun; Ke Xu; Qi Li

http://arxiv.org/abs/2403.15010
Clean-image Backdoor Attacks. (12%)
Dazhong Rong; Guoyao Yu; Shuheng Shen; Xinyi Fu; Peng Qian; Jianhai Chen; Qinming He; Xing Fu; Weiqiang Wang

http://arxiv.org/abs/2403.15603
Forward Learning for Gradient-based Black-box Saliency Map Generation. (1%)
Zeliang Zhang; Mingqian Feng; Jinyang Jiang; Rongyi Zhu; Yijie Peng; Chenliang Xu

http://arxiv.org/abs/2403.14778
Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking. (99%)
Qianyu Guo; Jiaming Fu; Yawen Lu; Dongming Gan

http://arxiv.org/abs/2403.14774
Few-Shot Adversarial Prompt Learning on Vision-Language Models. (98%)
Yiwei Zhou; Xiaobo Xia; Zhiwei Lin; Bo Han; Tongliang Liu

http://arxiv.org/abs/2403.14731
Reversible Jump Attack to Textual Classifiers with Modification Reduction. (98%)
Mingze Ni; Zhensu Sun; Wei Liu

http://arxiv.org/abs/2403.14772
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures. (82%)
Sayanton V. Dibbo; Adam Breuer; Juston Moore; Michael Teti

http://arxiv.org/abs/2403.14489
Adversary-Robust Graph-Based Learning of WSIs. (45%)
Saba Heidari Gheshlaghi; Milan Aryal; Nasim Yahyasoltani; Masoud Ganji

http://arxiv.org/abs/2403.14250
Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations. (4%)
Xun Lin; Yi Yu; Song Xia; Jue Jiang; Haoran Wang; Zitong Yu; Yizhong Liu; Ying Fu; Shuai Wang; Wenzhong Tang; Alex Kot

http://arxiv.org/abs/2403.13507
FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs. (97%)
Jinmin Li; Kuofeng Gao; Yang Bai; Jingyun Zhang; Shu-tao Xia; Yisen Wang

http://arxiv.org/abs/2403.13322
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation. (96%)
Yifan Wu; Jiawei Du; Ping Liu; Yuewei Lin; Wenqing Cheng; Wei Xu

http://arxiv.org/abs/2403.13867
Capsule Neural Networks as Noise Stabilizer for Time Series Data. (93%)
Soyeon Kim; Jihyeon Seong; Hyunkyung Han; Jaesik Choi

http://arxiv.org/abs/2403.13502
Adversarial Attacks and Defenses in Automated Control Systems: A Comprehensive Benchmark. (70%)
Vitaliy Pozdnyakov; Aleksandr Kovalenko; Ilya Makarov; Mikhail Drobyshevskiy; Kirill Lukyanov

http://arxiv.org/abs/2403.13778
Certified Human Trajectory Prediction. (61%)
Mohammadhossein Bahari; Saeed Saadatnejad; Amirhossein Asgari Farsangi; Seyed-Mohsen Moosavi-Dezfooli; Alexandre Alahi

http://arxiv.org/abs/2403.13523
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning. (54%)
Gaspari Fabio De; Dorjan Hitaj; Luigi V. Mancini

http://arxiv.org/abs/2403.14720
Defending Against Indirect Prompt Injection Attacks With Spotlighting. (31%)
Keegan Hines; Gary Lopez; Matthew Hall; Federico Zarfati; Yonatan Zunger; Emre Kiciman

http://arxiv.org/abs/2403.15467
Don't be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks. (11%)
Seunguk Yu; Juhwan Choi; Youngbin Kim

http://arxiv.org/abs/2403.13355
BadEdit: Backdooring large language models by model editing. (1%)
Yanzhou Li; Tianlin Li; Kangjie Chen; Jian Zhang; Shangqing Liu; Wenhan Wang; Tianwei Zhang; Yang Liu

http://arxiv.org/abs/2403.13590
Teacher-Student Training for Debiasing: General Permutation Debiasing for Large Language Models. (1%)
Adian Liusie; Yassir Fathullah; Mark J. F. Gales

http://arxiv.org/abs/2403.13682
Threats, Attacks, and Defenses in Machine Unlearning: A Survey. (1%)
Ziyao Liu; Huanyi Ye; Chen Chen; Kwok-Yan Lam

http://arxiv.org/abs/2403.12693
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks? (99%)
Anjun Hu; Jindong Gu; Francesco Pinto; Konstantinos Kamnitsas; Philip Torr

http://arxiv.org/abs/2403.12445
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory. (99%)
Sensen Gao; Xiaojun Jia; Xuhong Ren; Ivor Tsang; Qing Guo

http://arxiv.org/abs/2403.13196
ADAPT to Robustify Prompt Tuning Vision Transformers. (98%)
Masih Eskandar; Tooba Imtiaz; Zifeng Wang; Jennifer Dy

http://arxiv.org/abs/2403.13031
RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content. (8%)
Zhuowen Yuan; Zidi Xiong; Yi Zeng; Ning Yu; Ruoxi Jia; Dawn Song; Bo Li

http://arxiv.org/abs/2403.13134
Robust NAS under adversarial training: benchmark, theory, and beyond. (2%)
Yongtao Wu; Fanghui Liu; Carl-Johann Simon-Gabriel; Grigorios G Chrysos; Volkan Cevher

http://arxiv.org/abs/2403.12777
Discover and Mitigate Multiple Biased Subgroups in Image Classifiers. (1%)
Zeliang Zhang; Mingqian Feng; Zhiheng Li; Chenliang Xu

http://arxiv.org/abs/2403.11981
Diffusion Denoising as a Certified Defense against Clean-label Poisoning. (99%)
Sanghyun Hong; Nicholas Carlini; Alexey Kurakin

http://arxiv.org/abs/2403.11833
SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator. (99%)
Javad Rafiei Asl; Mohammad H. Rafiei; Manar Alohaly; Daniel Takabi

http://arxiv.org/abs/2403.11656
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model. (99%)
Yuxin Cao; Jinghao Li; Xi Xiao; Derui Wang; Minhui Xue; Hao Ge; Wei Liu; Guangwu Hu

http://arxiv.org/abs/2403.13018
Invisible Backdoor Attack Through Singular Value Decomposition. (96%)
Wenmin Chen; Xiaowei Xu

http://arxiv.org/abs/2403.11830
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks. (88%)
Andrea Venturi; Dario Stabili; Mirco Marchetti

http://arxiv.org/abs/2403.13017
Impart: An Imperceptible and Effective Label-Specific Backdoor Attack. (83%)
Jingke Zhao; Zan Wang; Yongwei Wang; Lanjun Wang

http://arxiv.org/abs/2403.11515
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications. (78%)
Amira Guesmi; Muhammad Abdullah Hanif; Ihsen Alouani; Bassem Ouni; Muhammad Shafique

http://arxiv.org/abs/2403.12399
Electioneering the Network: Dynamic Multi-Step Adversarial Attacks for Community Canvassing. (61%)
Saurabh Sharma; Ambuj SIngh

http://arxiv.org/abs/2403.12371
Advancing Time Series Classification with Multimodal Language Modeling. (1%)
Mingyue Cheng; Yiheng Chen; Qi Liu; Zhiding Liu; Yucong Luo

http://arxiv.org/abs/2403.11397
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization. (99%)
Yujia Liu; Chenxi Yang; Dingquan Li; Jianhao Ding; Tingting Jiang

http://arxiv.org/abs/2403.11297
A Modified Word Saliency-Based Adversarial Attack on Text Classification Models. (99%)
Hetvi Waghela; Sneha Rakshit; Jaydip Sen

http://arxiv.org/abs/2403.11448
Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSM. (99%)
Linyu Tang; Lei Zhang

http://arxiv.org/abs/2403.11265
Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation. (76%)
Silvia Corbara; Alejandro Moreo

http://arxiv.org/abs/2403.11082
RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning. (50%)
Javad Rafiei Asl; Prajwal Panzade; Eduardo Blanco; Daniel Takabi; Zhipeng Cai

http://arxiv.org/abs/2403.11348
COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits. (22%)
Mintong Kang; Nezihe Merve Gürel; Linyi Li; Bo Li

http://arxiv.org/abs/2403.13010
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats. (9%)
Md. Ashraf Uddin; Sunil Aryal; Mohamed Reda Bouadjenek; Muna Al-Hawawreh; Md. Alamin Talukder

http://arxiv.org/abs/2403.13013
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis. (2%)
Md. Ashraf Uddin; Sunil Aryal; Mohamed Reda Bouadjenek; Muna Al-Hawawreh; Md. Alamin Talukder

http://arxiv.org/abs/2403.11206
CBR - Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution. (1%)
Amir Lukach; Ran Dubin; Amit Dvir; Chen Hajaj

http://arxiv.org/abs/2403.11166
Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption. (1%)
Xuanqi Liu; Zhuotao Liu; Qi Li; Ke Xu; Mingwei Xu

http://arxiv.org/abs/2403.10801
Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples. (98%)
Ziqi Zhou; Minghui Li; Wei Liu; Shengshan Hu; Yechao Zhang; Wei Wan; Lulu Xue; Leo Yu Zhang; Dezhong Yang; Hai Jin

http://arxiv.org/abs/2403.10935
Understanding Robustness of Visual State Space Models for Image Classification. (98%)
Chengbin Du; Yanxi Li; Chang Xu

http://arxiv.org/abs/2403.10883
Improving Adversarial Transferability of Visual-Language Pre-training Models through Collaborative Multimodal Interaction. (92%)
Jiyuan Fu; Zhaoyu Chen; Kaixun Jiang; Haijing Guo; Jiafeng Wang; Shuyong Gao; Wenqiang Zhang

http://arxiv.org/abs/2403.10995
Edge Private Graph Neural Networks with Singular Value Perturbation. (11%)
Tingting Tang; Yue Niu; Salman Avestimehr; Murali Annavaram

http://arxiv.org/abs/2403.10076
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks. (99%)
Chong Wang; Yi Yu; Lanqing Guo; Bihan Wen

http://arxiv.org/abs/2403.10330
Towards Non-Adversarial Algorithmic Recourse. (99%)
Tobias Leemann; Martin Pawelczyk; Bardh Prenkaj; Gjergji Kasneci

http://arxiv.org/abs/2403.10021
Time-Frequency Jointed Imperceptible Adversarial Attack to Brainprint Recognition with Deep Learning Models. (99%)
Hangjie Yi; Yuhang Ming; Dongjun Liu; Wanzeng Kong

http://arxiv.org/abs/2403.10461
Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness. (87%)
Mohamed elShehaby; Aditya Kotha; Ashraf Matrawy

http://arxiv.org/abs/2403.10073
Revisiting Adversarial Training under Long-Tailed Distributions. (80%)
Xinli Yue; Ningping Mou; Qian Wang; Lingchen Zhao

http://arxiv.org/abs/2403.10045
Towards Adversarially Robust Dataset Distillation by Curvature Regularization. (54%)
Eric Xue; Yijiang Li; Haoyang Liu; Yifan Shen; Haohan Wang

http://arxiv.org/abs/2403.10313
Interactive Trimming against Evasive Online Data Manipulation Attacks: A Game-Theoretic Approach. (50%)
Yue Fu; Qingqing Ye; Rong Du; Haibo Hu

http://arxiv.org/abs/2403.10005
Securing Federated Learning with Control-Flow Attestation: A Novel Framework for Enhanced Integrity and Resilience against Adversarial Attacks. (12%)
Zahir Alsulaimawi

http://arxiv.org/abs/2403.10499
Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study. (11%)
Chenguang Wang; Ruoxi Jia; Xin Liu; Dawn Song

http://arxiv.org/abs/2403.10663
Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View Data. (4%)
Yuxuan Li; Sarthak Kumar Maharana; Yunhui Guo

http://arxiv.org/abs/2403.10717
Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency. (3%)
Soumyadeep Pal; Yuguang Yao; Ren Wang; Bingquan Shen; Sijia Liu

http://arxiv.org/abs/2403.10698
Robust Influence-based Training Methods for Noisy Brain MRI. (1%)
Minh-Hao Van; Alycia N. Carey; Xintao Wu

http://arxiv.org/abs/2403.09766
An Image Is Worth 1000 Lies: Adversarial Transferability across Prompts on Vision-Language Models. (99%)
Haochen Luo; Jindong Gu; Fengyuan Liu; Philip Torr

http://arxiv.org/abs/2403.10562
Counter-Samples: A Stateless Strategy to Neutralize Black Box Adversarial Attacks. (99%)
Roey Bokobza; Yisroel Mirsky

http://arxiv.org/abs/2403.09441
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency. (98%)
Hallgrimur Thorsteinsson; Valdemar J Henriksen; Tong Chen; Raghavendra Selvan

http://arxiv.org/abs/2403.09101
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement. (83%)
Daiwei Yu; Zhuorong Li; Lina Wei; Canghong Jin; Yun Zhang; Sixian Chan

http://arxiv.org/abs/2403.09901
Robust Subgraph Learning by Monitoring Early Training Representations. (80%)
Sepideh Neshatfar; Salimeh Yasaei Sekeh

http://arxiv.org/abs/2403.09351
LDPRecover: Recovering Frequencies from Poisoning Attacks against Local Differential Privacy. (76%)
Xinyue Sun; Qingqing Ye; Haibo Hu; Jiawei Duan; Tianyu Wo; Jie Xu; Renyu Yang

http://arxiv.org/abs/2403.09513
AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Shield Prompting. (74%)
Yu Wang; Xiaogeng Liu; Yu Li; Muhao Chen; Chaowei Xiao

http://arxiv.org/abs/2403.09863
Towards White Box Deep Learning. (15%)
Maciej Satkiewicz

http://arxiv.org/abs/2403.10570
Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare. (13%)
Tao Li; Quanyan Zhu

http://arxiv.org/abs/2403.09562
PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps. (8%)
Ruixuan Liu; Tianhao Wang; Yang Cao; Li Xiong

http://arxiv.org/abs/2403.09346
AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Adversarial Visual-Instructions. (2%)
Hao Zhang; Wenqi Shao; Hong Liu; Yongqiang Ma; Ping Luo; Yu Qiao; Kaipeng Zhang

http://arxiv.org/abs/2403.10573
Medical Unlearnable Examples: Securing Medical Data from Unauthorized Traning via Sparsity-Aware Local Masking. (1%)
Weixiang Sun; Yixin Liu; Zhiling Yan; Kaidi Xu; Lichao Sun

http://arxiv.org/abs/2403.08294
Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation. (92%)
Tianyi Chu; Wei Xing; Jiafu Chen; Zhizhong Wang; Jiakai Sun; Lei Zhao; Haibo Chen; Huaizhong Lin

http://arxiv.org/abs/2403.08333
Fast Inference of Removal-Based Node Influence. (54%)
Weikai Li; Zhiping Xiao; Xiao Luo; Yizhou Sun

http://arxiv.org/abs/2403.08424
Tastle: Distract Large Language Models for Automatic Jailbreak Attack. (31%)
Zeguan Xiao; Yan Yang; Guanhua Chen; Yun Chen

http://arxiv.org/abs/2403.10558
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack. (8%)
Yinggui Wang; Yuanqing Huang; Jianshu Li; Le Yang; Kai Song; Lei Wang

http://arxiv.org/abs/2403.08383
RAF-GI: Towards Robust, Accurate and Fast-Convergent Gradient Inversion Attack in Federated Learning. (2%)
Can Liu; Jin Wang; Dongyang Yu

http://arxiv.org/abs/2403.08618
Verifix: Post-Training Correction to Improve Label Noise Robustness with Verified Samples. (1%)
Sangamesh Kodge; Deepak Ravikumar; Gobinda Saha; Kaushik Roy

http://arxiv.org/abs/2403.08170
Versatile Defense Against Adversarial Attacks on Image Recognition. (99%)
Haibo Zhang; Zhihua Yao; Kouichi Sakurai

http://arxiv.org/abs/2403.07673
Towards Model Extraction Attacks in GAN-Based Image Translation via Domain Shift Mitigation. (61%)
Di Mi; Yanjun Zhang; Leo Yu Zhang; Shengshan Hu; Qi Zhong; Haizhuan Yuan; Shirui Pan

http://arxiv.org/abs/2403.07463
Backdoor Attack with Mode Mixture Latent Modification. (8%)
Hongwei Zhang; Xiaoyin Xu; Dongsheng An; Xianfeng Gu; Min Zhang

http://arxiv.org/abs/2403.13000
Duwak: Dual Watermarks in Large Language Models. (2%)
Chaoyi Zhu; Jeroen Galjaard; Pin-Yu Chen; Lydia Y. Chen

http://arxiv.org/abs/2403.14678
Towards a Framework for Deep Learning Certification in Safety-Critical Applications Using Inherently Safe Design and Run-Time Error Detection. (2%)
Romeo Valentin

http://arxiv.org/abs/2403.07588
Visual Privacy Auditing with Diffusion Models. (1%)
Kristian Schwethelm; Johannes Kaiser; Moritz Knolle; Daniel Rueckert; Georgios Kaissis; Alexander Ziller

http://arxiv.org/abs/2403.06428
Intra-Section Code Cave Injection for Adversarial Evasion Attacks on Windows PE Malware File. (99%)
Kshitiz Aryal; Maanak Gupta; Mahmoud Abdelsalam; Moustafa Saleh

http://arxiv.org/abs/2403.06661
epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition. (99%)
Batuhan Cengiz; Mert Gulsen; Yusuf H. Sahin; Gozde Unal

http://arxiv.org/abs/2403.06668
PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor. (98%)
Jaewon Jung; Hongsun Jang; Jaeyong Song; Jinho Lee

http://arxiv.org/abs/2403.06798
Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification. (97%)
Shuai Li; Xiaoguang Ma; Shancheng Jiang; Lu Meng

http://arxiv.org/abs/2403.07261
Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation. (96%)
Chengxing Jia; Fuxiang Zhang; Yi-Chen Li; Chen-Xiao Gao; Xu-Hui Liu; Lei Yuan; Zongzhang Zhang; Yang Yu

http://arxiv.org/abs/2403.06698
PCLD: Point Cloud Layerwise Diffusion for Adversarial Purification. (86%)
Mert Gulsen; Batuhan Cengiz; Yusuf H. Sahin; Gozde Unal

http://arxiv.org/abs/2403.07095
Overcoming the Paradox of Certified Training with Gaussian Smoothing. (81%)
Stefan Balauca; Mark Niklas Müller; Yuhao Mao; Maximilian Baader; Marc Fischer; Martin Vechev

http://arxiv.org/abs/2403.06610
Real is not True: Backdoor Attacks Against Deepfake Detection. (78%)
Hong Sun; Ziqiang Li; Lei Liu; Bin Li

http://arxiv.org/abs/2403.07078
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning. (61%)
Fuseinin Mumuni; Alhassan Mumuni

http://arxiv.org/abs/2403.06634
Stealing Part of a Production Language Model. (33%)
Nicholas Carlini; Daniel Paleka; Krishnamurthy Dj Dvijotham; Thomas Steinke; Jonathan Hayase; A. Feder Cooper; Katherine Lee; Matthew Jagielski; Milad Nasr; Arthur Conmy; Eric Wallace; David Rolnick; Florian Tramèr

http://arxiv.org/abs/2403.06430
AS-FIBA: Adaptive Selective Frequency-Injection for Backdoor Attack on Deep Face Restoration. (9%)
Zhenbo Song; Wenhao Gao; Kaihao Zhang; Wenhan Luo; Zhaoxin Fan; Jianfeng Lu

http://arxiv.org/abs/2404.00011
A novel interface for adversarial trivia question-writing. (3%)
Jason Liu

http://arxiv.org/abs/2403.06462
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation. (2%)
Xiaoyang Wang; Huihui Bai; Limin Yu; Yao Zhao; Jimin Xiao

http://arxiv.org/abs/2403.06869
Learning with Noisy Foundation Models. (1%)
Hao Chen; Jindong Wang; Zihan Wang; Ran Tao; Hongxin Wei; Xing Xie; Masashi Sugiyama; Bhiksha Raj

http://arxiv.org/abs/2403.06581
DNNShield: Embedding Identifiers for Deep Neural Network Ownership Verification. (1%)
Jasper Stang; Torsten Krauß; Alexandra Dmitrienko

http://arxiv.org/abs/2403.06388
A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Grid. (22%)
Md. Shirajum Munir; Sravanthi Proddatoori; Manjushree Muralidhara; Walid Saad; Zhu Han; Sachin Shetty

http://arxiv.org/abs/2403.06014
Hard-label based Small Query Black-box Adversarial Attack. (99%)
Jeonghwan Park; Paul Miller; Niall McLaughlin

http://arxiv.org/abs/2403.05847
MirrorAttack: Backdoor Attack on 3D Point Cloud with a Distorting Mirror. (81%)
Yuhao Bian; Shengjing Tian; Xiuping Liu

http://arxiv.org/abs/2403.05955
IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics. (78%)
Ekaterina Shumitskaya; Anastasia Antsiferova; Dmitriy Vatolin

http://arxiv.org/abs/2403.07942
Attacking Transformers with Feature Diversity Adversarial Perturbation. (70%)
Chenxing Gao; Hang Zhou; Junqing Yu; YuTeng Ye; Jiale Cai; Junle Wang; Wei Yang

http://arxiv.org/abs/2403.05247
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds. (99%)
Tianrui Lou; Xiaojun Jia; Jindong Gu; Li Liu; Siyuan Liang; Bangyan He; Xiaochun Cao

http://arxiv.org/abs/2403.05100
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume. (98%)
Ping Guo; Cheng Gong; Xi Lin; Zhiyuan Yang; Qingfu Zhang

http://arxiv.org/abs/2403.05666
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm. (93%)
Ziyu Zhang; Johann Laconte; Daniil Lisus; Timothy D. Barfoot

http://arxiv.org/abs/2403.05181
Adversarial Sparse Teacher: Defense Against Distillation-Based Model Stealing Attacks Using Adversarial Examples. (92%)
Eda Yilmaz; Hacer Yalim Keles

http://arxiv.org/abs/2403.05422
EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV. (81%)
Huiming Sun; Jiacheng Guo; Zibo Meng; Tianyun Zhang; Jianwu Fang; Yuewei Lin; Hongkai Yu

http://arxiv.org/abs/2403.05530
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. (73%)
Machel Reid; Nikolay Savinov; Denis Teplyashin; Dmitry Lepikhin; Timothy Lillicrap; Jean-baptiste Alayrac; Radu Soricut; Angeliki Lazaridou; Orhan Firat; Julian Schrittwieser; Ioannis Antonoglou; Rohan Anil; Sebastian Borgeaud; Andrew Dai; Katie Millican; Ethan Dyer; Mia Glaese; Thibault Sottiaux; Benjamin Lee; Fabio Viola; Malcolm Reynolds; Yuanzhong Xu; James Molloy; Jilin Chen; Michael Isard; Paul Barham; Tom Hennigan; Ross McIlroy; Melvin Johnson; Johan Schalkwyk; Eli Collins; Eliza Rutherford; Erica Moreira; Kareem Ayoub; Megha Goel; Clemens Meyer; Gregory Thornton; Zhen Yang; Henryk Michalewski; Zaheer Abbas; Nathan Schucher; Ankesh Anand; Richard Ives; James Keeling; Karel Lenc; Salem Haykal; Siamak Shakeri; Pranav Shyam; Aakanksha Chowdhery; Roman Ring; Stephen Spencer; Eren Sezener; Luke Vilnis; Oscar Chang; Nobuyuki Morioka; George Tucker; Ce Zheng; Oliver Woodman; Nithya Attaluri; Tomas Kocisky; Evgenii Eltyshev; Xi Chen; Timothy Chung; Vittorio Selo; Siddhartha Brahma; Petko Georgiev; Ambrose Slone; Zhenkai Zhu; James Lottes; Siyuan Qiao; Ben Caine; Sebastian Riedel; Alex Tomala; Martin Chadwick; Juliette Love; Peter Choy; Sid Mittal; Neil Houlsby; Yunhao Tang; Matthew Lamm; Libin Bai; Qiao Zhang; Luheng He; Yong Cheng; Peter Humphreys; Yujia Li; Sergey Brin; Albin Cassirer; Yingjie Miao; Lukas Zilka; Taylor Tobin; Kelvin Xu; Lev Proleev; Daniel Sohn; Alberto Magni; Lisa Anne Hendricks; Isabel Gao; Santiago Ontañón; Oskar Bunyan; Nathan Byrd; Abhanshu Sharma; Biao Zhang; Mario Pinto; Rishika Sinha; Harsh Mehta; Dawei Jia; Sergi Caelles; Albert Webson; Alex Morris; Becca Roelofs; Yifan Ding; Robin Strudel; Xuehan Xiong; Marvin Ritter; Mostafa Dehghani; Rahma Chaabouni; Abhijit Karmarkar; Guangda Lai; Fabian Mentzer; Bibo Xu; YaGuang Li; Yujing Zhang; Tom Le Paine; Alex Goldin; Behnam Neyshabur; Kate Baumli; Anselm Levskaya; Michael Laskin; Wenhao Jia; Jack W. Rae; Kefan Xiao; Antoine He; Skye Giordano; Lakshman Yagati; Jean-Baptiste Lespiau; Paul Natsev; Sanjay Ganapathy; Fangyu Liu; Danilo Martins; Nanxin Chen; Yunhan Xu; Megan Barnes; Rhys May; Arpi Vezer; Junhyuk Oh; Ken Franko; Sophie Bridgers; Ruizhe Zhao; Boxi Wu; Basil Mustafa; Sean Sechrist; Emilio Parisotto; Thanumalayan Sankaranarayana Pillai; Chris Larkin; Chenjie Gu; Christina Sorokin; Maxim Krikun; Alexey Guseynov; Jessica Landon; Romina Datta; Alexander Pritzel; Phoebe Thacker; Fan Yang; Kevin Hui; Anja Hauth; Chih-Kuan Yeh; David Barker; Justin Mao-Jones; Sophia Austin; Hannah Sheahan; Parker Schuh; James Svensson; Rohan Jain; Vinay Ramasesh; Anton Briukhov; Da-Woon Chung; Glehn Tamara von; Christina Butterfield; Priya Jhakra; Matthew Wiethoff; Justin Frye; Jordan Grimstad; Beer Changpinyo; Charline Le Lan; Anna Bortsova; Yonghui Wu; Paul Voigtlaender; Tara Sainath; Charlotte Smith; Will Hawkins; Kris Cao; James Besley; Srivatsan Srinivasan; Mark Omernick; Colin Gaffney; Gabriela Surita; Ryan Burnell; Bogdan Damoc; Junwhan Ahn; Andrew Brock; Mantas Pajarskas; Anastasia Petrushkina; Seb Noury; Lorenzo Blanco; Kevin Swersky; Arun Ahuja; Thi Avrahami; Vedant Misra; Liedekerke Raoul de; Mariko Iinuma; Alex Polozov; Sarah York; George van den Driessche; Paul Michel; Justin Chiu; Rory Blevins; Zach Gleicher; Adrià Recasens; Alban Rrustemi; Elena Gribovskaya; Aurko Roy; Wiktor Gworek; Séb Arnold; Lisa Lee; James Lee-Thorp; Marcello Maggioni; Enrique Piqueras; Kartikeya Badola; Sharad Vikram; Lucas Gonzalez; Anirudh Baddepudi; Evan Senter; Jacob Devlin; James Qin; Michael Azzam; Maja Trebacz; Martin Polacek; Kashyap Krishnakumar; Shuo-yiin Chang; Matthew Tung; Ivo Penchev; Rishabh Joshi; Kate Olszewska; Carrie Muir; Mateo Wirth; Ale Jakse Hartman; Josh Newlan; Sheleem Kashem; Vijay Bolina; Elahe Dabir; Amersfoort Joost van; Zafarali Ahmed; James Cobon-Kerr; Aishwarya Kamath; Arnar Mar Hrafnkelsson; Le Hou; Ian Mackinnon; Alexandre Frechette; Eric Noland; Xiance Si; Emanuel Taropa; Dong Li; Phil Crone; Anmol Gulati; Sébastien Cevey; Jonas Adler; Ada Ma; David Silver; Simon Tokumine; Richard Powell; Stephan Lee; Michael Chang; Samer Hassan; Diana Mincu; Antoine Yang; Nir Levine; Jenny Brennan; Mingqiu Wang; Sarah Hodkinson; Jeffrey Zhao; Josh Lipschultz; Aedan Pope; Michael B. Chang; Cheng Li; Laurent El Shafey; Michela Paganini; Sholto Douglas; Bernd Bohnet; Fabio Pardo; Seth Odoom; Mihaela Rosca; Cicero Nogueira dos Santos; Kedar Soparkar; Arthur Guez; Tom Hudson; Steven Hansen; Chulayuth Asawaroengchai; Ravi Addanki; Tianhe Yu; Wojciech Stokowiec; Mina Khan; Justin Gilmer; Jaehoon Lee; Carrie Grimes Bostock; Keran Rong; Jonathan Caton; Pedram Pejman; Filip Pavetic; Geoff Brown; Vivek Sharma; Mario Lučić; Rajkumar Samuel; Josip Djolonga; Amol Mandhane; Lars Lowe Sjösund; Elena Buchatskaya; Elspeth White; Natalie Clay; Jiepu Jiang; Hyeontaek Lim; Ross Hemsley; Jane Labanowski; Cao Nicola De; David Steiner; Sayed Hadi Hashemi; Jacob Austin; Anita Gergely; Tim Blyth; Joe Stanton; Kaushik Shivakumar; Aditya Siddhant; Anders Andreassen; Carlos Araya; Nikhil Sethi; Rakesh Shivanna; Steven Hand; Ankur Bapna; Ali Khodaei; Antoine Miech; Garrett Tanzer; Andy Swing; Shantanu Thakoor; Zhufeng Pan; Zachary Nado; Stephanie Winkler; Dian Yu; Mohammad Saleh; Loren Maggiore; Iain Barr; Minh Giang; Thais Kagohara; Ivo Danihelka; Amit Marathe; Vladimir Feinberg; Mohamed Elhawaty; Nimesh Ghelani; Dan Horgan; Helen Miller; Lexi Walker; Richard Tanburn; Mukarram Tariq; Disha Shrivastava; Fei Xia; Chung-Cheng Chiu; Zoe Ashwood; Khuslen Baatarsukh; Sina Samangooei; Fred Alcober; Axel Stjerngren; Paul Komarek; Katerina Tsihlas; Anudhyan Boral; Ramona Comanescu; Jeremy Chen; Ruibo Liu; Dawn Bloxwich; Charlie Chen; Yanhua Sun; Fangxiaoyu Feng; Matthew Mauger; Xerxes Dotiwalla; Vincent Hellendoorn; Michael Sharman; Ivy Zheng; Krishna Haridasan; Gabe Barth-Maron; Craig Swanson; Dominika Rogozińska; Alek Andreev; Paul Kishan Rubenstein; Ruoxin Sang; Dan Hurt; Gamaleldin Elsayed; Renshen Wang; Dave Lacey; Anastasija Ilić; Yao Zhao; Lora Aroyo; Chimezie Iwuanyanwu; Vitaly Nikolaev; Balaji Lakshminarayanan; Sadegh Jazayeri; Raphaël Lopez Kaufman; Mani Varadarajan; Chetan Tekur; Doug Fritz; Misha Khalman; David Reitter; Kingshuk Dasgupta; Shourya Sarcar; Tina Ornduff; Javier Snaider; Fantine Huot; Johnson Jia; Rupert Kemp; Nejc Trdin; Anitha Vijayakumar; Lucy Kim; Christof Angermueller; Li Lao; Tianqi Liu; Haibin Zhang; David Engel; Somer Greene; Anaïs White; Jessica Austin; Lilly Taylor; Shereen Ashraf; Dangyi Liu; Maria Georgaki; Irene Cai; Yana Kulizhskaya; Sonam Goenka; Brennan Saeta; Kiran Vodrahalli; Christian Frank; Cesare Dario de; Brona Robenek; Harry Richardson; Mahmoud Alnahlawi; Christopher Yew; Priya Ponnapalli; Marco Tagliasacchi; Alex Korchemniy; Yelin Kim; Dinghua Li; Bill Rosgen; Zoe Ashwood; Kyle Levin; Jeremy Wiesner; Praseem Banzal; Praveen Srinivasan; Hongkun Yu; Çağlar Ünlü; David Reid; Zora Tung; Daniel Finchelstein; Ravin Kumar; Andre Elisseeff; Jin Huang; Ming Zhang; Rui Zhu; Ricardo Aguilar; Mai Giménez; Jiawei Xia; Olivier Dousse; Willi Gierke; Soheil Hassas Yeganeh; Damion Yates; Komal Jalan; Lu Li; Eri Latorre-Chimoto; Duc Dung Nguyen; Ken Durden; Praveen Kallakuri; Yaxin Liu; Matthew Johnson; Tomy Tsai; Alice Talbert; Jasmine Liu; Alexander Neitz; Chen Elkind; Marco Selvi; Mimi Jasarevic; Livio Baldini Soares; Albert Cui; Pidong Wang; Alek Wenjiao Wang; Xinyu Ye; Krystal Kallarackal; Lucia Loher; Hoi Lam; Josef Broder; Dan Holtmann-Rice; Nina Martin; Bramandia Ramadhana; Daniel Toyama; Mrinal Shukla; Sujoy Basu; Abhi Mohan; Nick Fernando; Noah Fiedel; Kim Paterson; Hui Li; Ankush Garg; Jane Park; DongHyun Choi; Diane Wu; Sankalp Singh; Zhishuai Zhang; Amir Globerson; Lily Yu; John Carpenter; Félix de Chaumont Quitry; Carey Radebaugh; Chu-Cheng Lin; Alex Tudor; Prakash Shroff; Drew Garmon; Dayou Du; Neera Vats; Han Lu; Shariq Iqbal; Alex Yakubovich; Nilesh Tripuraneni; James Manyika; Haroon Qureshi; Nan Hua; Christel Ngani; Maria Abi Raad; Hannah Forbes; Anna Bulanova; Jeff Stanway; Mukund Sundararajan; Victor Ungureanu; Colton Bishop; Yunjie Li; Balaji Venkatraman; Bo Li; Chloe Thornton; Salvatore Scellato; Nishesh Gupta; Yicheng Wang; Ian Tenney; Xihui Wu; Ashish Shenoy; Gabriel Carvajal; Diana Gage Wright; Ben Bariach; Zhuyun Xiao; Peter Hawkins; Sid Dalmia; Clement Farabet; Pedro Valenzuela; Quan Yuan; Chris Welty; Ananth Agarwal; Mia Chen; Wooyeol Kim; Brice Hulse; Nandita Dukkipati; Adam Paszke; Andrew Bolt; Elnaz Davoodi; Kiam Choo; Jennifer Beattie; Jennifer Prendki; Harsha Vashisht; Rebeca Santamaria-Fernandez; Luis C. Cobo; Jarek Wilkiewicz; David Madras; Ali Elqursh; Grant Uy; Kevin Ramirez; Matt Harvey; Tyler Liechty; Heiga Zen; Jeff Seibert; Clara Huiyi Hu; Mohamed Elhawaty; Andrey Khorlin; Maigo Le; Asaf Aharoni; Megan Li; Lily Wang; Sandeep Kumar; Alejandro Lince; Norman Casagrande; Jay Hoover; Dalia El Badawy; David Soergel; Denis Vnukov; Matt Miecnikowski; Jiri Simsa; Anna Koop; Praveen Kumar; Thibault Sellam; Daniel Vlasic; Samira Daruki; Nir Shabat; John Zhang; Guolong Su; Jiageng Zhang; Jeremiah Liu; Yi Sun; Evan Palmer; Alireza Ghaffarkhah; Xi Xiong; Victor Cotruta; Michael Fink; Lucas Dixon; Ashwin Sreevatsa; Adrian Goedeckemeyer; Alek Dimitriev; Mohsen Jafari; Remi Crocker; Nicholas FitzGerald; Aviral Kumar; Sanjay Ghemawat; Ivan Philips; Frederick Liu; Yannie Liang; Rachel Sterneck; Alena Repina; Marcus Wu; Laura Knight; Marin Georgiev; Hyo Lee; Harry Askham; Abhishek Chakladar; Annie Louis; Carl Crous; Hardie Cate; Dessie Petrova; Michael Quinn; Denese Owusu-Afriyie; Achintya Singhal; Nan Wei; Solomon Kim; Damien Vincent; Milad Nasr; Christopher A. Choquette-Choo; Reiko Tojo; Shawn Lu; Diego de Las Casas; Yuchung Cheng; Tolga Bolukbasi; Katherine Lee; Saaber Fatehi; Rajagopal Ananthanarayanan; Miteyan Patel; Charbel Kaed; Jing Li; Jakub Sygnowski; Shreyas Rammohan Belle; Zhe Chen; Jaclyn Konzelmann; Siim Põder; Roopal Garg; Vinod Koverkathu; Adam Brown; Chris Dyer; Rosanne Liu; Azade Nova; Jun Xu; Slav Petrov; Demis Hassabis; Koray Kavukcuoglu; Jeffrey Dean; Oriol Vinyals

http://arxiv.org/abs/2403.05365
The Impact of Quantization on the Robustness of Transformer-based Text Classifiers. (45%)
Seyed Parsa Neshaei; Yasaman Boreshban; Gholamreza Ghassem-Sani; Seyed Abolghasem Mirroshandel

http://arxiv.org/abs/2404.16851
EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems. (38%)
Kongyang Chen; Yi Lin; Hui Luo; Bing Mi; Yatie Xiao; Chao Ma; Jorge Sá Silva

http://arxiv.org/abs/2403.05030
Defending Against Unforeseen Failure Modes with Latent Adversarial Training. (83%)
Stephen Casper; Lennart Schulze; Oam Patel; Dylan Hadfield-Menell

http://arxiv.org/abs/2403.04954
Fooling Neural Networks for Motion Forecasting via Adversarial Attacks. (33%)
Edgar Medina; Leyong Loh

http://arxiv.org/abs/2403.04957
Automatic and Universal Prompt Injection Attacks against Large Language Models. (31%)
Xiaogeng Liu; Zhiyuan Yu; Yizhe Zhang; Ning Zhang; Chaowei Xiao

http://arxiv.org/abs/2403.04701
ObjectCompose: Evaluating Resilience of Vision-Based Models on Object-to-Background Compositional Changes. (31%)
Hashmat Shadab Malik; Muhammad Huzaifa; Muzammal Naseer; Salman Khan; Fahad Shahbaz Khan

http://arxiv.org/abs/2403.04837
Cell reprogramming design by transfer learning of functional transcriptional networks. (1%)
Thomas P. Wytock; Adilson E. Motter

http://arxiv.org/abs/2403.04257
Towards Robustness Analysis of E-Commerce Ranking System. (1%)
Ningfei Wang; Yupin Huang; Han Cheng; Jiri Gesi; Xiaojie Wang; Vivek Mittal

http://arxiv.org/abs/2403.03674
Adversarial Infrared Geometry: Using Geometry to Perform Adversarial Attack against Infrared Pedestrian Detectors. (99%)
Kalibinuer Tiliwalidi

http://arxiv.org/abs/2403.04070
Improving Adversarial Training using Vulnerability-Aware Perturbation Budget. (99%)
Olukorede Fakorede; Modeste Atsague; Jin Tian

http://arxiv.org/abs/2403.03967
Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability. (92%)
Rajdeep Haldar; Yue Xing; Qifan Song

http://arxiv.org/abs/2403.04050
Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations. (16%)
Xiaolin Sun; Zizhan Zheng

http://arxiv.org/abs/2403.03846
On the Effectiveness of Distillation in Mitigating Backdoors in Pre-trained Encoder. (2%)
Tingxu Han; Shenghan Huang; Ziqi Ding; Weisong Sun; Yebo Feng; Chunrong Fang; Jun Li; Hanwei Qian; Cong Wu; Quanjun Zhang; Yang Liu; Zhenyu Chen

http://arxiv.org/abs/2403.03773
Verified Training for Counterfactual Explanation Robustness under Data Shift. (2%)
Anna P. Meyer; Yuhao Zhang; Aws Albarghouthi; Loris D'Antoni

http://arxiv.org/abs/2403.02803
Towards Robust Federated Learning via Logits Calibration on Non-IID Data. (99%)
Yu Qiao; Apurba Adhikary; Chaoning Zhang; Choong Seon Hong

http://arxiv.org/abs/2403.02995
Mitigating Label Flipping Attacks in Malicious URL Detectors Using Ensemble Trees. (96%)
Ehsan Nowroozi; Nada Jadalla; Samaneh Ghelichkhani; Alireza Jolfaei

http://arxiv.org/abs/2403.02723
Minimum Topology Attacks for Graph Neural Networks. (83%)
Mengmei Zhang; Xiao Wang; Chuan Shi; Lingjuan Lyu; Tianchi Yang; Junping Du

http://arxiv.org/abs/2403.02983
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks. (82%)
Ehsan Nowroozi; Imran Haider; Rahim Taheri; Mauro Conti

http://arxiv.org/abs/2403.02950
A general approach to enhance the survivability of backdoor attacks by decision path coupling. (68%)
Yufei Zhao; Dingji Wang; Bihuan Chen; Ziqian Chen; Xin Peng

http://arxiv.org/abs/2403.03149
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks. (61%)
Yichang Xu; Ming Yin; Minghong Fang; Neil Zhenqiang Gong

http://arxiv.org/abs/2403.02692
Uplift Modeling for Target User Attacks on Recommender Systems. (12%)
Wenjie Wang; Changsheng Wang; Fuli Feng; Wentao Shi; Daizong Ding; Tat-Seng Chua

http://arxiv.org/abs/2403.02846
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models. (11%)
Younghan Lee; Yungi Cho; Woorim Han; Ho Bae; Yunheung Paek

http://arxiv.org/abs/2403.02691
InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents. (11%)
Qiusi Zhan; Zhixiang Liang; Zifan Ying; Daniel Kang

http://arxiv.org/abs/2403.02955
XAI-Based Detection of Adversarial Attacks on Deepfake Detectors. (8%)
Ben Pinhasov; Raz Lapid; Rony Ohayon; Moshe Sipper; Yehudit Aperstein

http://arxiv.org/abs/2403.01896
Robustness Bounds on the Successful Adversarial Examples: Theory and Practice. (99%)
Hiroaki Maeshima; Akira Otsuka

http://arxiv.org/abs/2403.01849
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models. (99%)
Lin Li; Haoyan Guan; Jianing Qiu; Michael Spratling

http://arxiv.org/abs/2403.12988
Improving the Robustness of Object Detection and Classification AI models against Adversarial Patch Attacks. (99%)
Roie Kazoom; Raz Birman; Ofer Hadar

http://arxiv.org/abs/2403.02329
COMMIT: Certifying Robustness of Multi-Sensor Fusion Systems against Semantic Attacks. (96%)
Zijian Huang; Wenda Chu; Linyi Li; Chejian Xu; Bo Li

http://arxiv.org/abs/2403.02116
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks. (26%)
Sayedeh Leila Noorbakhsh; Binghui Zhang; Yuan Hong; Binghui Wang

http://arxiv.org/abs/2403.02637
BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object Detection. (16%)
Yu Chen; Liyan Ma; Liping Jing; Jian Yu

http://arxiv.org/abs/2403.02172
Mirage: Defense against CrossPath Attacks in Software Defined Networks. (3%)
Shariq Murtuza; Krishna Asawa

http://arxiv.org/abs/2403.01446
GuardT2I: Defending Text-to-Image Models from Adversarial Prompts. (9%)
Yijun Yang; Ruiyuan Gao; Xiao Yang; Jianyuan Zhong; Qiang Xu

http://arxiv.org/abs/2403.01210
SAR-AE-SFP: SAR Imagery Adversarial Example in Real Physics domain with Target Scattering Feature Parameters. (99%)
Jiahao Cui; Jiale Duan; Binyan Luo; Hang Cao; Wang Guo; Haifeng Li

http://arxiv.org/abs/2403.01218
Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy. (68%)
Jamie Hayes; Ilia Shumailov; Eleni Triantafillou; Amr Khalifa; Nicolas Papernot

http://arxiv.org/abs/2403.04786
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models. (56%)
Arijit Ghosh Chowdhury; Md Mofijul Islam; Vaibhav Kumar; Faysal Hossain Shezan; Vaibhav Kumar; Vinija Jain; Aman Chadha

http://arxiv.org/abs/2403.01118
Adversarial Testing for Visual Grounding via Image-Aware Property Reduction. (11%)
Zhiyuan Chang; Mingyang Li; Junjie Wang; Cheng Li; Boyu Wu; Fanjiang Xu; Qing Wang

http://arxiv.org/abs/2403.01155
Query Recovery from Easy to Hard: Jigsaw Attack against SSE. (2%)
Hao Nie; Wei Wang; Peng Xu; Xianglong Zhang; Laurence T. Yang; Kaitai Liang

http://arxiv.org/abs/2403.00420
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey. (91%)
Lucas Schott; Josephine Delas; Hatem Hajri; Elies Gherbi; Reda Yaich; Nora Boulahia-Cuppens; Frederic Cuppens; Sylvain Lamprier

http://arxiv.org/abs/2403.00942
Resilience of Entropy Model in Distributed Neural Networks. (67%)
Milin Zhang; Mohammad Abdi; Shahriar Rifat; Francesco Restuccia

http://arxiv.org/abs/2403.00464
Attacking Delay-based PUFs with Minimal Adversary Model. (45%)
Hongming Fei; Owen Millwood; Prosanta Gope; Jack Miskelly; Biplab Sikdar

http://arxiv.org/abs/2402.19355
Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification. (99%)
Sonal Joshi; Thomas Thebaud; Jesús Villalba; Najim Dehak

http://arxiv.org/abs/2402.19027
How to Train your Antivirus: RL-based Hardening through the Problem-Space. (99%)
Jacopo Cortellazzi; Ilias Tsingenopoulos; Branislav Bošanský; Simone Aonzo; Davy Preuveneers; Wouter Joosen; Fabio Pierazzi; Lorenzo Cavallaro

http://arxiv.org/abs/2403.00103
On Robustness and Generalization of ML-Based Congestion Predictors to Valid and Imperceptible Perturbations. (88%)
Chester Holtz; Yucheng Wang; Chung-Kuan Cheng; Bill Lin

http://arxiv.org/abs/2402.19076
Pointing out the Shortcomings of Relation Extraction Models with Semantically Motivated Adversarials. (76%)
Gennaro Nolano; Moritz Blum; Basil Ell; Philipp Cimiano

http://arxiv.org/abs/2402.19401
Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance. (38%)
Huakun Shen; Boyue Caroline Hu; Krzysztof Czarnecki; Lina Marsso; Marsha Chechik

http://arxiv.org/abs/2402.19322
Verification of Neural Networks' Global Robustness. (38%)
Anan Kabaha; Dana Drachsler-Cohen

http://arxiv.org/abs/2402.19334
Here's a Free Lunch: Sanitizing Backdoored Models with Model Merge. (2%)
Ansh Arora; Xuanli He; Maximilian Mozes; Srinibas Swain; Mark Dras; Qiongkai Xu

http://arxiv.org/abs/2403.00867
Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes. (1%)
Xiaomeng Hu; Pin-Yu Chen; Tsung-Yi Ho

http://arxiv.org/abs/2402.18787
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense. (99%)
Qiao Han; yong huang; xinling Guo; Yiteng Zhai; Yu Qin; Yao Yang

http://arxiv.org/abs/2402.18792
MPAT: Building Robust Deep Neural Networks against Textual Adversarial Attacks. (99%)
Fangyuan Zhang; Huichi Zhou; Shuangjiao Li; Hongtao Wang

http://arxiv.org/abs/2402.18211
Catastrophic Overfitting: A Potential Blessing in Disguise. (98%)
Mengnan Zhao; Lihe Zhang; Yuqiu Kong; Baocai Yin

http://arxiv.org/abs/2402.18329
Living-off-The-Land Reverse-Shell Detection by Informed Data Augmentation. (76%)
Dmitrijs Trizna; Luca Demetrio; Battista Biggio; Fabio Roli

http://arxiv.org/abs/2402.18649
A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems. (64%)
Fangzhou Wu; Ning Zhang; Somesh Jha; Patrick McDaniel; Chaowei Xiao

http://arxiv.org/abs/2402.18104
Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction. (33%)
Tong Liu; Yingjie Zhang; Zhe Zhao; Yinpeng Dong; Guozhu Meng; Kai Chen

http://arxiv.org/abs/2402.18162
Out-of-Distribution Detection using Neural Activation Prior. (1%)
Weilin Wan; Weizhong Zhang; Cheng Jin

http://arxiv.org/abs/2402.17390
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates. (99%)
Daniele Angioni; Luca Demetrio; Maura Pintor; Luca Oneto; Davide Anguita; Battista Biggio; Fabio Roli

http://arxiv.org/abs/2402.17509
Extreme Miscalibration and the Illusion of Adversarial Robustness. (99%)
Vyas Raina; Samson Tan; Volkan Cevher; Aditya Rawal; Sheng Zha; George Karypis

http://arxiv.org/abs/2402.17533
Black-box Adversarial Attacks Against Image Quality Assessment Models. (99%)
Yu Ran; Ao-Xiang Zhang; Mingjie Li; Weixuan Tang; Yuan-Gen Wang

http://arxiv.org/abs/2402.17976
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks. (99%)
Zhewei Wu; Ruilong Yu; Qihe Liu; Shuying Cheng; Shilin Qiu; Shijie Zhou

http://arxiv.org/abs/2402.17916
LLM-Resistant Math Word Problem Generation via Adversarial Attacks. (87%)
Roy Xie; Chengxuan Huang; Junlin Wang; Bhuwan Dhingra

http://arxiv.org/abs/2402.18027
Breaking the Black-Box: Confidence-Guided Model Inversion Attack for Distribution Shift. (83%)
Xinhao Liu; Yingzhao Jiang; Zetao Lin

http://arxiv.org/abs/2402.17465
Model X-ray:Detect Backdoored Models via Decision Boundary. (67%)
Yanghao Su; Jie Zhang; Ting Xu; Tianwei Zhang; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2402.17729
Towards Fairness-Aware Adversarial Learning. (11%)
Yanghao Zhang; Tianle Zhang; Ronghui Mu; Xiaowei Huang; Wenjie Ruan

http://arxiv.org/abs/2402.17223
Time-Restricted Double-Spending Attack on PoW-based Blockchains. (1%)
Yiming Jiang; Jiangfan Zhang

http://arxiv.org/abs/2402.16586
Improving the JPEG-resistance of Adversarial Attacks on Face Recognition by Interpolation Smoothing. (99%)
Kefu Guo; Fengfan Zhou; Hefei Ling; Ping Li; Hui Liu

http://arxiv.org/abs/2402.16430
Improving behavior based authentication against adversarial attack using XAI. (99%)
Dong Qin; George Amariucai; Daji Qiao; Yong Guan

http://arxiv.org/abs/2402.18370
Adversarial example soups: averaging multiple adversarial examples improves transferability without increasing additional generation time. (99%)
Bo Yang; Hengwei Zhang; Chenwei Li; Jindong Wang

http://arxiv.org/abs/2402.17018
A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection. (98%)
Leonid Boytsov; Ameya Joshi; Filipe Condessa

http://arxiv.org/abs/2402.17104
Adversarial Perturbations of Physical Signals. (92%)
Robert L. Bassett; Dellen Austin Van; Anthony P. Austin

http://arxiv.org/abs/2402.16470
Unveiling Vulnerability of Self-Attention. (87%)
Khai Jiet Liong; Hongqiu Wu; Hai Zhao

http://arxiv.org/abs/2402.16479
Edge Detectors Can Make Deep Convolutional Neural Networks More Robust. (83%)
Jin Ding; Jie-Chao Zhao; Yong-Zhi Sun; Ping Tan; Jia-Wei Wang; Ji-En Ma; You-Tong Fang

http://arxiv.org/abs/2402.16397
Investigating Deep Watermark Security: An Adversarial Transferability Perspective. (64%)
Biqing Qi; Junqi Gao; Yiang Luo; Jianxing Liu; Ligang Wu; Bowen Zhou

http://arxiv.org/abs/2402.16459
Defending LLMs against Jailbreaking Attacks via Backtranslation. (33%)
Yihan Wang; Zhouxing Shi; Andrew Bai; Cho-Jui Hsieh

http://arxiv.org/abs/2402.17012
Pandora's White-Box: Increased Training Data Leakage in Open LLMs. (13%)
Jeffrey G. Wang; Jason Wang; Marvin Li; Seth Neel

http://arxiv.org/abs/2402.16965
WIPI: A New Web Threat for LLM-Driven Web Agents. (8%)
Fangzhou Wu; Shutong Wu; Yulong Cao; Chaowei Xiao

http://arxiv.org/abs/2402.16431
RoCoIns: Enhancing Robustness of Large Language Models through Code-Style Instructions. (4%)
Yuansen Zhang; Xiao Wang; Zhiheng Xi; Han Xia; Tao Gui; Qi Zhang; Xuanjing Huang

http://arxiv.org/abs/2402.17092
An Innovative Information Theory-based Approach to Tackle and Enhance The Transparency in Phishing Detection. (1%)
Van Nguyen; Tingmin Wu; Xingliang Yuan; Marthie Grobler; Surya Nepal; Carsten Rudolph

http://arxiv.org/abs/2402.16006
From Noise to Clarity: Unraveling the Adversarial Suffix of Large Language Model Attacks via Translation of Text Embeddings. (98%)
Hao Wang; Hao Li; Minlie Huang; Lei Sha

http://arxiv.org/abs/2402.16912
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection. (92%)
João Vitorino; Miguel Silva; Eva Maia; Isabel Praça

http://arxiv.org/abs/2402.16192
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing. (76%)
Jiabao Ji; Bairu Hou; Alexander Robey; George J. Pappas; Hamed Hassani; Yang Zhang; Eric Wong; Shiyu Chang

http://arxiv.org/abs/2402.16005
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation. (73%)
Xiaohui Chen; Tie Luo

http://arxiv.org/abs/2403.12077
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions. (13%)
Xuming Hu; Xiaochuan Li; Junzhe Chen; Yinghui Li; Yangning Li; Xiaoguang Li; Yasheng Wang; Qun Liu; Lijie Wen; Philip S. Yu; Zhijiang Guo

http://arxiv.org/abs/2402.16914
DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers. (2%)
Xirui Li; Ruochen Wang; Minhao Cheng; Tianyi Zhou; Cho-Jui Hsieh

http://arxiv.org/abs/2404.16847
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey. (1%)
Chaoyu Zhang

http://arxiv.org/abs/2402.16918
m2mKD: Module-to-Module Knowledge Distillation for Modular Transformers. (1%)
Ka Man Lo; Yiming Liang; Wenyu Du; Yuantao Fan; Zili Wang; Wenhao Huang; Lei Ma; Jie Fu

http://arxiv.org/abs/2402.15911
PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails. (87%)
Neal Mangaokar; Ashish Hooda; Jihye Choi; Shreyas Chandrashekaran; Kassem Fawaz; Somesh Jha; Atul Prakash

http://arxiv.org/abs/2402.15727
LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper. (86%)
Daoyuan Wu; Shuai Wang; Yang Liu; Ning Liu

http://arxiv.org/abs/2402.15853
RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation. (82%)
Jiawei Zhou; Linye Lyu; Daojing He; Yu Li

http://arxiv.org/abs/2402.15959
Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks. (76%)
Zhiying Jiang; Xingyuan Li; Jinyuan Liu; Xin Fan; Risheng Liu

http://arxiv.org/abs/2402.15808
Optimal Zero-Shot Detector for Multi-Armed Attacks. (50%)
Federica Granese; Marco Romanelli; Pablo Piantanida

http://arxiv.org/abs/2402.15751
Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning. (1%)
Yong Liu; Zirui Zhu; Chaoyu Gong; Minhao Cheng; Cho-Jui Hsieh; Yang You

http://arxiv.org/abs/2402.15586
Distilling Adversarial Robustness Using Heterogeneous Teachers. (99%)
Jieren Deng; Aaron Palmer; Rigel Mahmood; Ethan Rathbun; Jinbo Bi; Kaleel Mahmood; Derek Aguiar

http://arxiv.org/abs/2402.15570
Fast Adversarial Attacks on Language Models In One GPU Minute. (98%)
Vinu Sankar Sadasivan; Shoumik Saha; Gaurang Sriramanan; Priyatham Kattakinda; Atoosa Chegini; Soheil Feizi

http://arxiv.org/abs/2402.15267
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing. (98%)
Daniel Gibert; Giulio Zizzo; Quan Le; Jordi Planes

http://arxiv.org/abs/2402.15429
ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation. (93%)
Yi Zhang; Yun Tang; Wenjie Ruan; Xiaowei Huang; Siddartha Khastgir; Paul Jennings; Xingyu Zhao

http://arxiv.org/abs/2402.15152
On the Duality Between Sharpness-Aware Minimization and Adversarial Training. (92%)
Yihao Zhang; Hangzhou He; Jingyu Zhu; Huanran Chen; Yifei Wang; Zeming Wei

http://arxiv.org/abs/2402.15653
Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm. (87%)
Yanqi Qiao; Dazhuang Liu; Rui Wang; Kaitai Liang

http://arxiv.org/abs/2402.15555
Deep Networks Always Grok and Here is Why. (76%)
Ahmed Imtiaz Humayun; Randall Balestriero; Richard Baraniuk

http://arxiv.org/abs/2402.15218
BSPA: Exploring Black-box Stealthy Prompt Attacks against Image Generators. (67%)
Yu Tian; Xiao Yang; Yinpeng Dong; Heming Yang; Hang Su; Jun Zhu

http://arxiv.org/abs/2402.15617
Reinforcement Learning-Based Approaches for Enhancing Security and Resilience in Smart Control: A Survey on Attack and Defense Methods. (61%)
Zheyu Zhang

http://arxiv.org/abs/2402.15180
Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks with Self-Refinement. (5%)
Heegyu Kim; Sehyun Yuk; Hyunsouk Cho

http://arxiv.org/abs/2402.15425
Prime+Retouch: When Cache is Locked and Leaked. (2%)
Jaehyuk Lee; Fan Sang; Taesoo Kim

http://arxiv.org/abs/2402.14937
SoK: Analyzing Adversarial Examples: A Framework to Study Adversary Knowledge. (99%)
Lucas Fenaux; Florian Kerschbaum

http://arxiv.org/abs/2402.14648
Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off. (98%)
Futa Waseda; Isao Echizen

http://arxiv.org/abs/2402.14494
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task. (83%)
Jinxu Zhao; Guanting Dong; Yueyan Qiu; Tingfeng Hui; Xiaoshuai Song; Daichi Guo; Weiran Xu

http://arxiv.org/abs/2402.14899
Stop Reasoning! When Multimodal LLMs with Chain-of-Thought Reasoning Meets Adversarial Images. (81%)
Zefeng Wang; Zhen Han; Shuo Chen; Fan Xue; Zifeng Ding; Xun Xiao; Volker Tresp; Philip Torr; Jindong Gu

http://arxiv.org/abs/2402.14968
Mitigating Fine-tuning Jailbreak Attack with Backdoor Enhanced Alignment. (75%)
Jiongxiao Wang; Jiazhao Li; Yiquan Li; Xiangyu Qi; Junjie Hu; Yixuan Li; Patrick McDaniel; Muhao Chen; Bo Li; Chaowei Xiao

http://arxiv.org/abs/2403.00794
Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models. (26%)
Zachary Horvitz; Jingru Chen; Rahul Aditya; Harshvardhan Srivastava; Robert West; Zhou Yu; Kathleen McKeown

http://arxiv.org/abs/2402.13946
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning. (99%)
Vasudev Gohil; Satwik Patnaik; Dileep Kalathil; Jeyavijayan Rajendran

http://arxiv.org/abs/2402.13987
A Simple and Yet Fairly Effective Defense for Graph Neural Networks. (98%)
Sofiane Ennadir; Yassine Abbahaddou; Johannes F. Lutzeyer; Michalis Vazirgiannis; Henrik Boström

http://arxiv.org/abs/2402.13629
Adversarial Purification and Fine-tuning for Robust UDC Image Restoration. (98%)
Zhenbo Song; Zhenyuan Zhang; Kaihao Zhang; Wenhan Luo; Zhaoxin Fan; Jianfeng Lu

http://arxiv.org/abs/2402.13651
Robustness of Deep Neural Networks for Micro-Doppler Radar Classification. (80%)
Mikolaj Czerkawski; Carmine Clemente; Craig MichieCraig Michie; Christos Tachtatzis

http://arxiv.org/abs/2402.14016
Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment. (75%)
Vyas Raina; Adian Liusie; Mark Gales

http://arxiv.org/abs/2402.13575
Flexible Physical Camouflage Generation Based on a Differential Approach. (38%)
Yang Li; Wenyi Tan; Chenxing Zhao; Shuangju Zhou; Xinkai Liang; Quan Pan

http://arxiv.org/abs/2402.13851
VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models. (10%)
Jiawei Liang; Siyuan Liang; Man Luo; Aishan Liu; Dongchen Han; Ee-Chien Chang; Xiaochun Cao

http://arxiv.org/abs/2402.14872
Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMs. (8%)
Xiaoxia Li; Siyuan Liang; Jiyi Zhang; Han Fang; Aishan Liu; Ee-Chien Chang

http://arxiv.org/abs/2402.14020
Coercing LLMs to do and reveal (almost) anything. (4%)
Jonas Geiping; Alex Stein; Manli Shu; Khalid Saifullah; Yuxin Wen; Tom Goldstein

http://arxiv.org/abs/2402.14167
T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching. (1%)
Zizheng Pan; Bohan Zhuang; De-An Huang; Weili Nie; Zhiding Yu; Chaowei Xiao; Jianfei Cai; Anima Anandkumar

http://arxiv.org/abs/2402.12950
QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems. (92%)
Jinjing Shi; Zimeng Xiao; Heyuan Shi; Yu Jiang; Xuelong Li

http://arxiv.org/abs/2402.13148
Defending Jailbreak Prompts via In-Context Adversarial Game. (76%)
Yujun Zhou; Yufei Han; Haomin Zhuang; Taicheng Guo; Kehan Guo; Zhenwen Liang; Hongyan Bao; Xiangliang Zhang

http://arxiv.org/abs/2402.13517
Round Trip Translation Defence against Large Language Model Jailbreaking Attacks. (74%)
Canaan Yung; Hadi Mohaghegh Dolatabadi; Sarah Erfani; Christopher Leckie

http://arxiv.org/abs/2402.13006
Investigating the Impact of Model Instability on Explanations and Uncertainty. (69%)
Sara Vera Marjanović; Isabelle Augenstein; Christina Lioma

http://arxiv.org/abs/2402.13457
LLM Jailbreak Attack versus Defense Techniques -- A Comprehensive Study. (54%)
Zihao Xu; Yi Liu; Gelei Deng; Yuekang Li; Stjepan Picek

http://arxiv.org/abs/2402.13459
Learning to Poison Large Language Models During Instruction Tuning. (13%)
Yao Qiang; Xiangyu Zhou; Saleh Zare Zade; Mohammad Amin Roshani; Douglas Zytko; Dongxiao Zhu

http://arxiv.org/abs/2402.13487
Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits. (3%)
Zhiwei Wang; Huazheng Wang; Hongning Wang

http://arxiv.org/abs/2402.13518
RITFIS: Robust input testing framework for LLMs-based intelligent software. (1%)
Mingxuan Xiao; Yan Xiao; Hai Dong; Shunhui Ji; Pengcheng Zhang

http://arxiv.org/abs/2402.12329
Query-Based Adversarial Prompt Generation. (99%)
Jonathan Hayase; Ema Borevkovic; Nicholas Carlini; Florian Tramèr; Milad Nasr

http://arxiv.org/abs/2402.12187
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training. (99%)
Leo Hyun Park; Jaeuk Kim; Myung Gyo Oh; Jaewoo Park; Taekyoung Kwon

http://arxiv.org/abs/2402.12338
An Adversarial Approach to Evaluating the Robustness of Event Identification Models. (98%)
Obai Bahwal; Oliver Kosut; Lalitha Sankar

http://arxiv.org/abs/2402.11940
AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization. (98%)
Jiyao Li; Mingze Ni; Yifei Dong; Tianqing Zhu; Wei Liu

http://arxiv.org/abs/2402.12673
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies. (97%)
Xiangyu Liu; Chenghao Deng; Yanchao Sun; Yongyuan Liang; Furong Huang

http://arxiv.org/abs/2402.11953
Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels. (92%)
Shubhi Shukla; Manaar Alam; Pabitra Mitra; Debdeep Mukhopadhyay

http://arxiv.org/abs/2402.12426
Attacks on Node Attributes in Graph Neural Networks. (83%)
Ying Xu; Michael Lanier; Anindya Sarkar; Yevgeniy Vorobeychik

http://arxiv.org/abs/2402.12626
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. (68%)
Yiwei Lu; Matthew Y. R. Yang; Gautam Kamath; Yaoliang Yu

http://arxiv.org/abs/2402.11837
Self-Guided Robust Graph Structure Refinement. (67%)
Yeonjun In; Kanghoon Yoon; Kibum Kim; Kijung Shin; Chanyoung Park

http://arxiv.org/abs/2402.12336
Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models. (50%)
Christian Schlarmann; Naman Deep Singh; Francesco Croce; Matthias Hein

http://arxiv.org/abs/2402.12168
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning. (15%)
Shuai Zhao; Leilei Gan; Luu Anh Tuan; Jie Fu; Lingjuan Lyu; Meihuizi Jia; Jinming Wen

http://arxiv.org/abs/2402.12189
Amplifying Training Data Exposure through Fine-Tuning with Pseudo-Labeled Memberships. (1%)
Myung Gyo Oh; Hong Eun Ahn; Leo Hyun Park; Taekyoung Kwon

http://arxiv.org/abs/2402.11557
Evaluating Adversarial Robustness of Low dose CT Recovery. (92%)
Kanchana Vaishnavi Gandikota; Paramanand Chandramouli; Hannah Droege; Michael Moeller

http://arxiv.org/abs/2402.11469
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models. (92%)
Cuong Dang; Dung D. Le; Thai Le

http://arxiv.org/abs/2402.11687
Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks. (83%)
Satwik Kundu; Debarshi Kundu; Swaroop Ghosh

http://arxiv.org/abs/2402.11733
The Effectiveness of Random Forgetting for Robust Generalization. (75%)
Vijaya Raghavan T Ramkumar; Bahram Zonooz; Elahe Arani

http://arxiv.org/abs/2402.11473
Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection. (26%)
Jiawei Liang; Siyuan Liang; Aishan Liu; Xiaojun Jia; Junhao Kuang; Xiaochun Cao

http://arxiv.org/abs/2402.11637
Poisoning Federated Recommender Systems with Fake Users. (5%)
Ming Yin; Yichang Xu; Minghong Fang; Neil Zhenqiang Gong

http://arxiv.org/abs/2402.11755
SPML: A DSL for Defending Language Models Against Prompt Attacks. (1%)
Reshabh K Sharma; Vinayak Gupta; Dan Grossman

http://arxiv.org/abs/2402.12406
Teacher as a Lenient Expert: Teacher-Agnostic Data-Free Knowledge Distillation. (1%)
Hyunjune Shin; Dong-Wan Choi

http://arxiv.org/abs/2402.11196
Maintaining Adversarial Robustness in Continuous Learning. (75%)
Xiaolei Ru; Xiaowei Cao; Zijia Liu; Jack Murdoch Moore; Xin-Ya Zhang; Xia Zhu; Wenjia Wei; Gang Yan

http://arxiv.org/abs/2402.11237
Be Persistent: Towards a Unified Solution for Mitigating Shortcuts in Deep Learning. (22%)
Hadi M. Dolatabadi; Sarah M. Erfani; Christopher Leckie

http://arxiv.org/abs/2402.11208
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents. (2%)
Wenkai Yang; Xiaohan Bi; Yankai Lin; Sishuo Chen; Jie Zhou; Xu Sun

http://arxiv.org/abs/2402.11423
VoltSchemer: Use Voltage Noise to Manipulate Your Wireless Charger. (2%)
Zihao Zhan; Yirui Yang; Haoqi Shan; Hanqiu Wang; Yier Jin; Shuo Wang

http://arxiv.org/abs/2402.11120
DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation. (99%)
Yunjuan Wang; Hussein Hazimeh; Natalia Ponomareva; Alexey Kurakin; Ibrahim Hammoud; Raman Arora

http://arxiv.org/abs/2402.10470
Theoretical Understanding of Learning from Adversarial Perturbations. (98%)
Soichiro Kumano; Hiroshi Kera; Toshihiko Yamasaki

http://arxiv.org/abs/2402.10527
Zero-shot sampling of adversarial entities in biomedical question answering. (92%)
R. Patrick Xian; Alex J. Lee; Vincent Wang; Qiming Cui; Russell Ro; Reza Abbasi-Asl

http://arxiv.org/abs/2402.11083
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models. (92%)
Ziyi Yin; Muchao Ye; Tianrong Zhang; Jiaqi Wang; Han Liu; Jinghui Chen; Ting Wang; Fenglong Ma

http://arxiv.org/abs/2402.11082
The AI Security Pyramid of Pain. (47%)
Chris M. Ward; Josh Harguess; Julia Tao; Daniel Christman; Paul Spicer; Mike Tan

http://arxiv.org/abs/2402.10773
AIM: Automated Input Set Minimization for Metamorphic Security Testing. (2%)
Nazanin Bayati Chaleshtari; Yoann Marquer; Fabrizio Pastore; Lionel C. Briand

http://arxiv.org/abs/2402.10882
Universal Prompt Optimizer for Safe Text-to-Image Generation. (1%)
Zongyu Wu; Hongcheng Gao; Yueze Wang; Xiang Zhang; Suhang Wang

http://arxiv.org/abs/2402.09874
Camouflage is all you need: Evaluating and Enhancing Language Model Robustness Against Camouflage Adversarial Attacks. (62%)
Álvaro Huertas-García; Alejandro Martín; Javier Huertas-Tato; David Camacho

http://arxiv.org/abs/2402.10340
On the Safety Concerns of Deploying LLMs/VLMs in Robotics: Highlighting the Risks and Vulnerabilities. (31%)
Xiyang Wu; Ruiqi Xian; Tianrui Guan; Jing Liang; Souradip Chakraborty; Fuxiao Liu; Brian Sadler; Dinesh Manocha; Amrit Singh Bedi

http://arxiv.org/abs/2402.10283
Backdoor Attack against One-Class Sequential Anomaly Detection Models. (9%)
He Cheng; Shuhan Yuan

http://arxiv.org/abs/2402.10196
A Trembling House of Cards? Mapping Adversarial Attacks against Language Agents. (5%)
Lingbo Mo; Zeyi Liao; Boyuan Zheng; Yu Su; Chaowei Xiao; Huan Sun

http://arxiv.org/abs/2402.10082
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning. (3%)
Enrique Mármol Campos; Aurora González Vidal; José Luis Hernández Ramos; Antonio Skarmeta

http://arxiv.org/abs/2402.10983
Quantum-Inspired Analysis of Neural Network Vulnerabilities: The Role of Conjugate Variables in System Attacks. (1%)
Jun-Jie Zhang; Deyu Meng

http://arxiv.org/abs/2402.09132
Exploring the Adversarial Capabilities of Large Language Models. (98%)
Lukas Struppek; Minh Hieu Le; Dominik Hintersdorf; Kristian Kersting

http://arxiv.org/abs/2402.09674
PAL: Proxy-Guided Black-Box Attack on Large Language Models. (92%)
Chawin Sitawarin; Norman Mu; David Wagner; Alexandre Araujo

http://arxiv.org/abs/2402.09316
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models. (92%)
Weiheng Chai; Brian Testa; Huantao Ren; Asif Salekin; Senem Velipasalar

http://arxiv.org/abs/2402.09546
How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments? (80%)
Congcong Wen; Jiazhao Liang; Shuaihang Yuan; Hao Huang; Yi Fang

http://arxiv.org/abs/2402.09023
Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems. (76%)
Shiyi Yang; Lina Yao; Chen Wang; Xiwei Xu; Liming Zhu

http://arxiv.org/abs/2402.09154
Attacking Large Language Models with Projected Gradient Descent. (67%)
Simon Geisler; Tom Wollschläger; M. H. I. Abdalla; Johannes Gasteiger; Stephan Günnemann

http://arxiv.org/abs/2402.08986
Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary Statistics. (47%)
Wenwei Zhao; Xiaowen Li; Shangqing Zhao; Jie Xu; Yao Liu; Zhuo Lu

http://arxiv.org/abs/2402.08983
SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding. (38%)
Zhangchen Xu; Fengqing Jiang; Luyao Niu; Jinyuan Jia; Bill Yuchen Lin; Radha Poovendran

http://arxiv.org/abs/2402.09179
Rapid Adoption, Hidden Risks: The Dual Impact of Large Language Model Customization. (9%)
Rui Zhang; Hongwei Li; Rui Wen; Wenbo Jiang; Yuan Zhang; Michael Backes; Yun Shen; Yang Zhang

http://arxiv.org/abs/2402.09695
Reward Poisoning Attack Against Offline Reinforcement Learning. (5%)
Yinglun Xu; Rohan Gumaste; Gagandeep Singh

http://arxiv.org/abs/2403.12075
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation. (3%)
Jessica Quaye; Alicia Parrish; Oana Inel; Charvi Rastogi; Hannah Rose Kirk; Minsuk Kahng; Liemt Erin van; Max Bartolo; Jess Tsang; Justin White; Nathan Clement; Rafael Mosquera; Juan Ciro; Vijay Janapa Reddi; Lora Aroyo

http://arxiv.org/abs/2402.09199
Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling. (2%)
Yuhui Shi; Qiang Sheng; Juan Cao; Hao Mi; Beizhe Hu; Danding Wang

http://arxiv.org/abs/2402.08991
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. (1%)
Chenlu Ye; Jiafan He; Quanquan Gu; Tong Zhang

http://arxiv.org/abs/2402.09303
Immediate generalisation in humans but a generalisation lag in deep neural networks$\unicode{x2014}$evidence for representational divergence? (1%)
Lukas S. Huber; Fred W. Mast; Felix A. Wichmann

http://arxiv.org/abs/2402.09091
Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues. (1%)
Zhiyuan Chang; Mingyang Li; Yi Liu; Junjie Wang; Qing Wang; Yang Liu

http://arxiv.org/abs/2402.08586
Faster Repeated Evasion Attacks in Tree Ensembles. (96%)
Lorenzo Cascioli; Laurens Devos; Ondřej Kuželka; Jesse Davis

http://arxiv.org/abs/2402.08648
Generating Universal Adversarial Perturbations for Quantum Classifiers. (93%)
Gautham Anil; Vishnu Vinod; Apurva Narayan

http://arxiv.org/abs/2402.08763
Enhancing Robustness of Indoor Robotic Navigation with Free-Space Segmentation Models Against Adversarial Attacks. (83%)
Qiyuan An; Christos Sevastopoulos; Fillia Makedon

http://arxiv.org/abs/2402.09478
Data Reconstruction Attacks and Defenses: A Systematic Evaluation. (76%)
Sheng Liu; Zihan Wang; Qi Lei

http://arxiv.org/abs/2402.08679
COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability. (62%)
Xingang Guo; Fangxu Yu; Huan Zhang; Lianhui Qin; Bin Hu

http://arxiv.org/abs/2402.08577
Test-Time Backdoor Attacks on Multimodal Large Language Models. (56%)
Dong Lu; Tianyu Pang; Chao Du; Qian Liu; Xianjun Yang; Min Lin

http://arxiv.org/abs/2402.08768
Adversarially Robust Feature Learning for Breast Cancer Diagnosis. (33%)
Degan Hao; Dooman Arefan; Margarita Zuley; Wendie Berg; Shandong Wu

http://arxiv.org/abs/2402.08567
Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast. (31%)
Xiangming Gu; Xiaosen Zheng; Tianyu Pang; Chao Du; Qian Liu; Ye Wang; Jing Jiang; Min Lin

http://arxiv.org/abs/2402.08845
Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation. (1%)
Xuexin Chen; Ruichu Cai; Zhengting Huang; Yuxuan Zhu; Julien Horwood; Zhifeng Hao; Zijian Li; Jose Miguel Hernandez-Lobato

http://arxiv.org/abs/2402.07496
Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment. (99%)
Xabier Echeberria-Barrio; Amaia Gil-Lerchundi; Jon Egana-Zubia; Raul Orduna-Urrutia

http://arxiv.org/abs/2402.07480
Topological safeguard for evasion attack interpreting the neural networks' behavior. (89%)
Xabier Echeberria-Barrio; Amaia Gil-Lerchundi; Iñigo Mendialdua; Raul Orduna-Urrutia

http://arxiv.org/abs/2402.07867
PoisonedRAG: Knowledge Poisoning Attacks to Retrieval-Augmented Generation of Large Language Models. (83%)
Wei Zou; Runpeng Geng; Binghui Wang; Jinyuan Jia

http://arxiv.org/abs/2402.07687
Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience. (33%)
Ethan Wilson; Azim Ibragimov; Michael J. Proulx; Sai Deep Tetali; Kevin Butler; Eakta Jain

http://arxiv.org/abs/2402.07689
OrderBkd: Textual backdoor attack through repositioning. (13%)
Irina Alekseevskaia; Konstantin Arkhipenko

http://arxiv.org/abs/2402.07639
Tighter Bounds on the Information Bottleneck with Application to Deep Learning. (10%)
Nir Weingarten; Zohar Yakhini; Moshe Butman; Ran Gilad-Bachrach

http://arxiv.org/abs/2402.08125
Customizable Perturbation Synthesis for Robust SLAM Benchmarking. (9%)
Xiaohao Xu; Tianyi Zhang; Sibo Wang; Xiang Li; Yongqi Chen; Ye Li; Bhiksha Raj; Matthew Johnson-Roberson; Xiaonan Huang

http://arxiv.org/abs/2402.08070
Multi-Attribute Vision Transformers are Efficient and Robust Learners. (8%)
Hanan Gani; Nada Saadi; Noor Hussein; Karthik Nandakumar

http://arxiv.org/abs/2402.07498
Accelerated Smoothing: A Scalable Approach to Randomized Smoothing. (3%)
Devansh Bhardwaj; Kshitiz Kaushik; Sarthak Gupta

http://arxiv.org/abs/2402.08695
Game of Trojans: Adaptive Adversaries Against Output-based Trojaned-Model Detectors. (3%)
Dinuka Sahabandu; Xiaojun Xu; Arezoo Rajabi; Luyao Niu; Bhaskar Ramasubramanian; Bo Li; Radha Poovendran

http://arxiv.org/abs/2402.08191
THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation. (2%)
Wilbert Pumacay; Ishika Singh; Jiafei Duan; Ranjay Krishna; Jesse Thomason; Dieter Fox

http://arxiv.org/abs/2402.07718
Local Centrality Minimization with Quality Guarantees. (1%)
Atsushi Miyauchi; Lorenzo Severini; Francesco Bonchi

http://arxiv.org/abs/2402.07841
Do Membership Inference Attacks Work on Large Language Models? (1%)
Michael Duan; Anshuman Suri; Niloofar Mireshghallah; Sewon Min; Weijia Shi; Luke Zettlemoyer; Yulia Tsvetkov; Yejin Choi; David Evans; Hannaneh Hajishirzi

http://arxiv.org/abs/2402.07506
NeuralSentinel: Safeguarding Neural Network Reliability and Trustworthiness. (1%)
Xabier Echeberria-Barrio; Mikel Gorricho; Selene Valencia; Francesco Zola

http://arxiv.org/abs/2402.08183
Pixel Sentence Representation Learning. (1%)
Chenghao Xiao; Zhuoxu Huang; Danlu Chen; G Thomas Hudson; Yizhi Li; Haoran Duan; Chenghua Lin; Jie Fu; Jungong Han; Noura Al Moubayed

http://arxiv.org/abs/2402.07183
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense. (99%)
Ryota Iijima; Sayaka Shiota; Hitoshi Kiya

http://arxiv.org/abs/2402.07347
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensemble. (98%)
Yunzhe Xue; Usman Roshan

http://arxiv.org/abs/2402.06922
Whispers in the Machine: Confidentiality in LLM-integrated Systems. (26%)
Jonathan Evertz; Merlin Chlosta; Lea Schönherr; Thorsten Eisenhofer

http://arxiv.org/abs/2402.06957
Architectural Neural Backdoors from First Principles. (26%)
Harry Langford; Ilia Shumailov; Yiren Zhao; Robert Mullins; Nicolas Papernot

http://arxiv.org/abs/2402.06249
Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks. (98%)
Nandish Chattopadhyay; Amira Guesmi; Muhammad Shafique

http://arxiv.org/abs/2402.06255
Studious Bob Fight Back Against Jailbreaking via Prompt Adversarial Tuning. (95%)
Yichuan Mo; Yuji Wang; Zeming Wei; Yisen Wang

http://arxiv.org/abs/2402.06827
RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations. (83%)
Enyi Jiang; Gagandeep Singh

http://arxiv.org/abs/2402.06846
System-level Analysis of Adversarial Attacks and Defenses on Intelligence in O-RAN based Cellular Networks. (82%)
Azuka Chiejina; Brian Kim; Kaushik Chowhdury; Vijay K. Shah

http://arxiv.org/abs/2402.06357
The SpongeNet Attack: Sponge Weight Poisoning of Deep Neural Networks. (69%)
Jona te Lintelo; Stefanos Koffas; Stjepan Picek

http://arxiv.org/abs/2402.06734
Corruption Robust Offline Reinforcement Learning with Human Feedback. (67%)
Debmalya Mandal; Andi Nika; Parameswaran Kamalaruban; Adish Singla; Goran Radanović

http://arxiv.org/abs/2402.06244
Quantifying and Enhancing Multi-modal Robustness with Modality Preference. (56%)
Zequn Yang; Yake Wei; Ce Liang; Di Hu

http://arxiv.org/abs/2402.06363
StruQ: Defending Against Prompt Injection with Structured Queries. (45%)
Sizhe Chen; Julien Piet; Chawin Sitawarin; David Wagner

http://arxiv.org/abs/2402.06289
Evaluating Membership Inference Attacks and Defenses in Federated Learning. (4%)
Gongxi Zhu; Donghao Li; Hanlin Gu; Yuxing Han; Yuan Yao; Lixin Fan; Qiang Yang

http://arxiv.org/abs/2402.06855
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation. (1%)
Muthu Chidambaram; Rong Ge

http://arxiv.org/abs/2402.05668
Comprehensive Assessment of Jailbreak Attacks Against LLMs. (99%)
Junjie Chu; Yugeng Liu; Ziqing Yang; Xinyue Shen; Michael Backes; Yang Zhang

http://arxiv.org/abs/2402.05493
Investigating White-Box Attacks for On-Device Models. (93%)
Mingyi Zhou; Xiang Gao; Jing Wu; Kui Liu; Hailong Sun; Li Li

http://arxiv.org/abs/2402.06132
TETRIS: Towards Exploring the Robustness of Interactive Segmentation. (81%)
Andrey Moskalenko; Vlad Shakhuro; Anna Vorontsova; Anton Konushin; Anton Antonov; Alexander Krapukhin; Denis Shepelev; Konstantin Soshin

http://arxiv.org/abs/2402.05521
Linearizing Models for Efficient yet Robust Private Inference. (68%)
Sreetama Sarkar; Souvik Kundu; Peter A. Beerel

http://arxiv.org/abs/2402.05674
A High Dimensional Model for Adversarial Training: Geometry and Trade-Offs. (26%)
Kasimir Tanner; Matteo Vilucchio; Bruno Loureiro; Florent Krzakala

http://arxiv.org/abs/2402.05675
Is Adversarial Training with Compressed Datasets Effective? (10%)
Tong Chen; Raghavendra Selvan

http://arxiv.org/abs/2402.05541
Reinforcement Learning as a Catalyst for Robust and Fair Federated Learning: Deciphering the Dynamics of Client Contributions. (9%)
Jialuo He; Wei Chen; Xiaojin Zhang

http://arxiv.org/abs/2402.04660
Adversarial Robustness Through Artifact Design. (99%)
Tsufit Shua; Mahmood Sharif

http://arxiv.org/abs/2402.04699
EvoSeed: Unveiling the Threat on Deep Neural Networks with Real-World Illusions. (98%)
Shashank Kotyan; PoYuan Mao; Danilo Vasconcellos Vargas

http://arxiv.org/abs/2402.05284
Analyzing Adversarial Inputs in Deep Reinforcement Learning. (96%)
Davide Corsi; Guy Amir; Guy Katz; Alessandro Farinelli

http://arxiv.org/abs/2402.05162
Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications. (1%)
Boyi Wei; Kaixuan Huang; Yangsibo Huang; Tinghao Xie; Xiangyu Qi; Mengzhou Xia; Prateek Mittal; Mengdi Wang; Peter Henderson

http://arxiv.org/abs/2402.03951
Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping. (98%)
Qinliang Lin; Cheng Luo; Zenghao Niu; Xilin He; Weicheng Xie; Yuanbo Hou; Linlin Shen; Siyang Song

http://arxiv.org/abs/2403.08806
Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning. (98%)
Sarwar Khan

http://arxiv.org/abs/2402.04038
PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural Network. (75%)
Tan Sun; Junhong Lin

http://arxiv.org/abs/2402.04325
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons. (74%)
Zhenyu Liu; Garrett Gagnon; Swagath Venkataramani; Liu Liu

http://arxiv.org/abs/2402.03741
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems. (67%)
Oubo Ma; Yuwen Pu; Linkang Du; Yang Dai; Ruo Wang; Xiaolei Liu; Yingcai Wu; Shouling Ji

http://arxiv.org/abs/2402.03740
BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning. (64%)
Mohammad Majid Akhtar; Navid Shadman Bhuiyan; Rahat Masood; Muhammad Ikram; Salil S. Kanhere

http://arxiv.org/abs/2402.04421
Studying Vulnerable Code Entities in R. (10%)
Zixiao Zhao; Millon Madhur Das; Fatemeh H. Fard

http://arxiv.org/abs/2402.03760
DeMarking: A Defense for Network Flow Watermarking in Real-Time. (10%)
Yali Yuan; Jian Ge; Guang Cheng

http://arxiv.org/abs/2402.04249
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal. (2%)
Mantas Mazeika; Long Phan; Xuwang Yin; Andy Zou; Zifan Wang; Norman Mu; Elham Sakhaee; Nathaniel Li; Steven Basart; Bo Li; David Forsyth; Dan Hendrycks

http://arxiv.org/abs/2402.02732
A Generative Approach to Surrogate-based Black-box Attacks. (99%)
Raha Moraffah; Huan Liu

http://arxiv.org/abs/2402.03095
Transcending Adversarial Perturbations: Manifold-Aided Adversarial Examples with Legitimate Semantics. (99%)
Shuai Li; Xiaoyu Jiang; Xiaoguang Ma

http://arxiv.org/abs/2402.03477
Arabic Synonym BERT-based Adversarial Examples for Text Classification. (99%)
Norah Alshahrani; Saied Alshahrani; Esma Wali; Jeanna Matthews

http://arxiv.org/abs/2402.03576
Generalization Properties of Adversarial Training for $\ell_0$-Bounded Adversarial Attacks. (92%)
Payam Delgosha; Hamed Hassani; Ramtin Pedarsani

http://arxiv.org/abs/2402.03705
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution. (89%)
Qi Zhou; Dongxia Wang; Tianlin Li; Zhihong Xu; Yang Liu; Kui Ren; Wenhai Wang; Qing Guo

http://arxiv.org/abs/2402.02886
Time-Distributed Backdoor Attacks on Federated Spiking Learning. (83%)
Gorka Abad; Stjepan Picek; Aitor Urbieta

http://arxiv.org/abs/2402.06659
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models. (83%)
Yuancheng Xu; Jiarui Yao; Manli Shu; Yanchao Sun; Zichu Wu; Ning Yu; Tom Goldstein; Furong Huang

http://arxiv.org/abs/2402.03627
Partially Recentralization Softmax Loss for Vision-Language Models Robustness. (81%)
Hao Wang; Xin Zhang; Jinzhe Jiang; Yaqian Zhao; Chen Li

http://arxiv.org/abs/2402.03214
Organic or Diffused: Can We Distinguish Human Art from AI-generated Images? (31%)
Anna Yoo Jeong Ha; Josephine Passananti; Ronik Bhaskar; Shawn Shan; Reid Southen; Haitao Zheng; Ben Y. Zhao

http://arxiv.org/abs/2402.02739
DisDet: Exploring Detectability of Backdoor Attack on Diffusion Models. (12%)
Yang Sui; Huy Phan; Jinqi Xiao; Tianfang Zhang; Zijie Tang; Cong Shi; Yan Wang; Yingying Chen; Bo Yuan

http://arxiv.org/abs/2402.03481
FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning. (1%)
Sejoon Oh; Berk Ustun; Julian McAuley; Srijan Kumar

http://arxiv.org/abs/2402.02629
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks. (99%)
Ziquan Liu; Zhuo Zhi; Ilija Bogunovic; Carsten Gerner-Beuerle; Miguel Rodrigues

http://arxiv.org/abs/2402.02554
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision Transformers. (99%)
Oryan Yehezkel; Alon Zolfi; Amit Baras; Yuval Elovici; Asaf Shabtai

http://arxiv.org/abs/2402.06655
Adversarial Text Purification: A Large Language Model Approach for Defense. (99%)
Raha Moraffah; Shubh Khandelwal; Amrita Bhattacharjee; Huan Liu

http://arxiv.org/abs/2402.02695
Exploiting Class Probabilities for Black-box Sentence-level Attacks. (75%)
Raha Moraffah; Huan Liu

http://arxiv.org/abs/2402.02600
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach. (41%)
Brian Etter; James Lee Hu; Mohammedreza Ebrahimi; Weifeng Li; Xin Li; Hsinchun Chen

http://arxiv.org/abs/2402.02699
Adversarial Data Augmentation for Robust Speaker Verification. (1%)
Zhenyu Zhou; Junhui Chen; Namin Wang; Lantian Li; Dong Wang

http://arxiv.org/abs/2402.02154
Evaluating the Robustness of Off-Road Autonomous Driving Segmentation against Adversarial Attacks: A Dataset-Centric analysis. (96%)
Pankaj Deoli; Rohit Kumar; Axel Vierling; Karsten Berns

http://arxiv.org/abs/2402.02316
Your Diffusion Model is Secretly a Certifiably Robust Classifier. (80%)
Huanran Chen; Yinpeng Dong; Shitong Shao; Zhongkai Hao; Xiao Yang; Hang Su; Jun Zhu

http://arxiv.org/abs/2402.02263
MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers. (76%)
Yatong Bai; Mo Zhou; Vishal M. Patel; Somayeh Sojoudi

http://arxiv.org/abs/2402.02145
Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models. (68%)
Alapan Kuila; Somnath Jena; Sudeshna Sarkar; Partha Pratim Chakrabarti

http://arxiv.org/abs/2402.02034
Universal Post-Training Reverse-Engineering Defense Against Backdoors in Deep Neural Networks. (16%)
Xi Li; Hang Wang; David J. Miller; George Kesidis

http://arxiv.org/abs/2402.02165
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error. (10%)
Haoran Li; Zicheng Zhang; Wang Luo; Congying Han; Yudong Hu; Tiande Guo; Shichen Liao

http://arxiv.org/abs/2402.02227
Invisible Finger: Practical Electromagnetic Interference Attack on Touchscreen-based Electronic Devices. (9%)
Haoqi Shan; Boyi Zhang; Zihao Zhan; Dean Sullivan; Shuo Wang; Yier Jin

http://arxiv.org/abs/2402.02207
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models. (5%)
Yongshuo Zong; Ondrej Bohdal; Tingyang Yu; Yongxin Yang; Timothy Hospedales

http://arxiv.org/abs/2402.02160
Data Poisoning for In-context Learning. (5%)
Pengfei He; Han Xu; Yue Xing; Hui Liu; Makoto Yamada; Jiliang Tang

http://arxiv.org/abs/2402.02095
Seeing is not always believing: The Space of Harmless Perturbations. (2%)
Lu Chen; Shaofeng Li; Benhao Huang; Fan Yang; Zheng Li; Jie Li; Yuan Luo

http://arxiv.org/abs/2402.01879
$\sigma$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples. (99%)
Antonio Emanuele Cinà; Francesco Villani; Maura Pintor; Lea Schönherr; Battista Biggio; Marcello Pelillo

http://arxiv.org/abs/2402.01806
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text. (99%)
Han Liu; Zhi Xu; Xiaotong Zhang; Feng Zhang; Fenglong Ma; Hongyang Chen; Hong Yu; Xianchao Zhang

http://arxiv.org/abs/2402.01227
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition. (99%)
Yi Chang; Zhao Ren; Zixing Zhang; Xin Jing; Kun Qian; Xi Shao; Bin Hu; Tanja Schultz; Björn W. Schuller

http://arxiv.org/abs/2402.01220
Delving into Decision-based Black-box Attacks on Semantic Segmentation. (93%)
Zhaoyu Chen; Zhengyang Shan; Jingwen Chang; Kaixun Jiang; Dingkang Yang; Yiting Cheng; Wenqiang Zhang

http://arxiv.org/abs/2402.01340
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding. (92%)
Chanho Park; Namyoon Lee

http://arxiv.org/abs/2402.02028
Unlearnable Examples For Time Series. (86%)
Yujing Jiang; Xingjun Ma; Sarah Monazam Erfani; James Bailey

http://arxiv.org/abs/2402.01920
Preference Poisoning Attacks on Reward Model Learning. (83%)
Junlin Wu; Jiongxiao Wang; Chaowei Xiao; Chenguang Wang; Ning Zhang; Yevgeniy Vorobeychik

http://arxiv.org/abs/2402.01894
S2malloc: Statistically Secure Allocator for Use-After-Free Protection And More. (3%)
Ruizhe Wang; Meng Xu; N. Asokan

http://arxiv.org/abs/2402.01546
Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting. (3%)
Yi Dong; Yingjie Wang; Mariana Gama; Mustafa A. Mustafa; Geert Deconinck; Xiaowei Huang

http://arxiv.org/abs/2402.01369
Cheating Suffix: Targeted Attack to Text-To-Image Diffusion Models with Multi-Modal Priors. (2%)
Dingcheng Yang; Yang Bai; Xiaojun Jia; Yang Liu; Xiaochun Cao; Wenjian Yu

http://arxiv.org/abs/2402.01865
What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement. (1%)
Xisen Jin; Xiang Ren

http://arxiv.org/abs/2402.00418
Benchmarking Transferable Adversarial Attacks. (98%)
Zhibo Jin; Jiayu Zhang; Zhiyu Zhu; Huaming Chen

http://arxiv.org/abs/2402.00412
Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection. (70%)
Xinlin Peng; Ying Zhou; Ben He; Le Sun; Yingfei Sun

http://arxiv.org/abs/2402.01114
Double-Dip: Thwarting Label-Only Membership Inference Attacks with Transfer Learning and Randomization. (64%)
Arezoo Rajabi; Reeya Pimple; Aiswarya Janardhanan; Surudhi Asokraj; Bhaskar Ramasubramanian; Radha Poovendran

http://arxiv.org/abs/2402.00626
Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks. (45%)
Maan Qraitem; Nazia Tasnim; Piotr Teterwak; Kate Saenko; Bryan A. Plummer

http://arxiv.org/abs/2402.00695
Approximating Optimal Morphing Attacks using Template Inversion. (9%)
Laurent Colbois; Hatef Otroshi Shahreza; Sébastien Marcel

http://arxiv.org/abs/2402.01096
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance. (8%)
Wenqi Wei; Ling Liu

http://arxiv.org/abs/2402.01012
algoXSSF: Detection and analysis of cross-site request forgery (XSRF) and cross-site scripting (XSS) attacks via Machine learning algorithms. (1%)
Naresh Kshetri; Dilip Kumar; James Hutson; Navneet Kaur; Omar Faruq Osama

http://arxiv.org/abs/2402.00176
Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis. (95%)
Petros Georgiou; Sharu Theresa Jose; Osvaldo Simeone

http://arxiv.org/abs/2402.00304
Invariance-powered Trustworthy Defense via Remove Then Restore. (70%)
Xiaowei Fu; Yuhang Zhou; Lina Ma; Lei Zhang

http://arxiv.org/abs/2402.00906
BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks. (13%)
Hamed Poursiami; Ihsen Alouani; Maryam Parsa

http://arxiv.org/abs/2401.17723
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning Attacks. (9%)
Kaike Zhang; Qi Cao; Yunfan Wu; Fei Sun; Huawei Shen; Xueqi Cheng

http://arxiv.org/abs/2401.17746
Logit Poisoning Attack in Distillation-based Federated Learning and its Countermeasures. (4%)
Yonghao Yu; Shunan Zhu; Jinglu Hu

http://arxiv.org/abs/2401.17865
Manipulating Predictions over Discrete Inputs in Machine Teaching. (1%)
Xiaodong Wu; Yufei Han; Hayssam Dahrouj; Jianbing Ni; Zhenwen Liang; Xiangliang Zhang

http://arxiv.org/abs/2401.17606
Ambush from All Sides: Understanding Security Threats in Open-Source Software CI/CD Pipelines. (1%)
Ziyue Pan; Wenbo Shen; Xingkai Wang; Yutian Yang; Rui Chang; Yao Liu; Chengwei Liu; Yang Liu; Kui Ren

http://arxiv.org/abs/2401.17196
Single Word Change is All You Need: Designing Attacks and Defenses for Text Classifiers. (99%)
Lei Xu; Sarah Alnegheimish; Laure Berti-Equille; Alfredo Cuesta-Infante; Kalyan Veeramachaneni

http://arxiv.org/abs/2401.17263
Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks. (98%)
Andy Zhou; Bo Li; Haohan Wang

http://arxiv.org/abs/2401.17038
Towards Assessing the Synthetic-to-Measured Adversarial Vulnerability of SAR ATR. (98%)
Bowen Peng; Bo Peng; Jingyuan Xia; Tianpeng Liu; Yongxiang Liu; Li Liu

http://arxiv.org/abs/2401.17499
AdvGPS: Adversarial GPS for Multi-Agent Perception Attack. (95%)
Jinlong Li; Baolu Li; Xinyu Liu; Jianwu Fang; Felix Juefei-Xu; Qing Guo; Hongkai Yu

http://arxiv.org/abs/2401.17523
Game-Theoretic Unlearnable Example Generator. (92%)
Shuang Liu; Yihan Wang; Xiao-Shan Gao

http://arxiv.org/abs/2401.17405
Camouflage Adversarial Attacks on Multiple Agent Systems. (87%)
Ziqing Lu; Guanlin Liu; Lifeng Lai; Weiyu Xu

http://arxiv.org/abs/2401.17256
Weak-to-Strong Jailbreaking on Large Language Models. (76%)
Xuandong Zhao; Xianjun Yang; Tianyu Pang; Chao Du; Lei Li; Yu-Xiang Wang; William Yang Wang

http://arxiv.org/abs/2401.17133
A Proactive and Dual Prevention Mechanism against Illegal Song Covers empowered by Singing Voice Conversion. (75%)
Guangke Chen; Yedi Zhang; Fu Song; Ting Wang; Xiaoning Du; Yang Liu

http://arxiv.org/abs/2401.17498
Improving QA Model Performance with Cartographic Inoculation. (26%)
Allen UT Austin Chen; Okan UT Austin Tanrikulu

http://arxiv.org/abs/2401.17497
Towards Visual Syntactical Understanding. (4%)
Sayeed Shafayet Chowdhury; Soumyadeep Chandra; Kaushik Roy

http://arxiv.org/abs/2401.16820
Provably Robust Multi-bit Watermarking for AI-generated Text via Error Correction Code. (2%)
Wenjie Qu; Dong Yin; Zixin He; Wei Zou; Tianyang Tao; Jinyuan Jia; Jiaheng Zhang

http://arxiv.org/abs/2401.16001
LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection. (99%)
Jiwei Tian; Chao Shen; Buhong Wang; Xiaofang Xia; Meng Zhang; Chenhao Lin; Qian Li

http://arxiv.org/abs/2401.16352
Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization. (92%)
Guang Lin; Chao Li; Jianhai Zhang; Toshihisa Tanaka; Qibin Zhao

http://arxiv.org/abs/2401.16687
Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks. (68%)
Lulu Xue; Shengshan Hu; Ruizhi Zhao; Leo Yu Zhang; Shengqing Hu; Lichao Sun; Dezhong Yao

http://arxiv.org/abs/2401.16011
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling. (5%)
Wei Ju; Yiyang Gu; Zhengyang Mao; Ziyue Qiao; Yifang Qin; Xiao Luo; Hui Xiong; Ming Zhang

http://arxiv.org/abs/2402.00888
Security and Privacy Challenges of Large Language Models: A Survey. (1%)
Badhan Chandra Das; M. Hadi Amini; Yanzhao Wu

http://arxiv.org/abs/2401.15615
Addressing Noise and Efficiency Issues in Graph-Based Machine Learning Models From the Perspective of Adversarial Attack. (81%)
Yongyu Wang

http://arxiv.org/abs/2401.15817
Transparency Attacks: How Imperceptible Image Layers Can Fool AI Perception. (75%)
Forrest McKee; David Noever

http://arxiv.org/abs/2401.15335
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks. (98%)
Ping Guo; Fei Liu; Xi Lin; Qingchuan Zhao; Qingfu Zhang

http://arxiv.org/abs/2401.14961
Set-Based Training for Neural Network Verification. (99%)
Lukas Koller; Tobias Ladner; Matthias Althoff

http://arxiv.org/abs/2401.14707
Mitigating Feature Gap for Adversarial Robustness by Feature Disentanglement. (91%)
Nuoyan Zhou; Dawei Zhou; Decheng Liu; Xinbo Gao; Nannan Wang

http://arxiv.org/abs/2401.15295
Multi-Trigger Backdoor Attacks: More Triggers, More Threats. (82%)
Yige Li; Xingjun Ma; Jiabo He; Hanxun Huang; Yu-Gang Jiang

http://arxiv.org/abs/2401.14780
Adversarial Attacks and Defenses in 6G Network-Assisted IoT Systems. (81%)
Bui Duc Son; Nguyen Tien Hoa; Chien Trinh Van; Waqas Khalid; Mohamed Amine Ferrag; Wan Choi; Merouane Debbah

http://arxiv.org/abs/2401.14948
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training. (62%)
Shruthi Gowda; Bahram Zonooz; Elahe Arani

http://arxiv.org/abs/2401.15262
Asymptotic Behavior of Adversarial Training Estimator under $\ell_\infty$-Perturbation. (22%)
Yiling Xie; Xiaoming Huo

http://arxiv.org/abs/2401.15248
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective. (22%)
Yue Xing; Xiaofeng Lin; Qifan Song; Yi Xu; Belinda Zeng; Guang Cheng

http://arxiv.org/abs/2401.15239
MEA-Defender: A Robust Watermark against Model Extraction Attack. (13%)
Peizhuo Lv; Hualong Ma; Kai Chen; Jiachen Zhou; Shengzhi Zhang; Ruigang Liang; Shenchen Zhu; Pan Li; Yingjun Zhang

http://arxiv.org/abs/2401.15002
BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning. (2%)
Baoyuan Wu; Hongrui Chen; Mingda Zhang; Zihao Zhu; Shaokui Wei; Danni Yuan; Mingli Zhu; Ruotong Wang; Li Liu; Chao Shen

http://arxiv.org/abs/2401.14031
Sparse and Transferable Universal Singular Vectors Attack. (99%)
Kseniia Kuvshinova; Olga Tsymboi; Ivan Oseledets

http://arxiv.org/abs/2401.14184
Friendly Attacks to Improve Channel Coding Reliability. (54%)
Anastasiia Kurmukova; Deniz Gunduz

http://arxiv.org/abs/2401.14440
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models. (16%)
Erik Arakelyan; Zhaoqi Liu; Isabelle Augenstein

http://arxiv.org/abs/2401.14027
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness. (2%)
Mengyao Du; Miao Zhang; Yuwen Pu; Kai Xu; Shouling Ji; Quanjun Yin

http://arxiv.org/abs/2401.14033
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations. (1%)
Patricia Pauli; Aaron Havens; Alexandre Araujo; Siddharth Garg; Farshad Khorrami; Frank Allgöwer; Bin Hu

http://arxiv.org/abs/2401.14583
Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation. (1%)
Jing Long; Tong Chen; Guanhua Ye; Kai Zheng; Nguyen Quoc Viet Hung; Hongzhi Yin

http://arxiv.org/abs/2401.13624
Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint. (86%)
Zhongjie Shi; Fanghui Liu; Yuan Cao; Johan A. K. Suykens

http://arxiv.org/abs/2401.13751
A Systematic Approach to Robustness Modelling for Deep Convolutional Neural Networks. (83%)
Charles Meyers; Mohammad Reza Saleh Sedghpour; Tommy Löfstedt; Erik Elmroth

http://arxiv.org/abs/2401.13578
WPDA: Frequency-based Backdoor Attack with Wavelet Packet Decomposition. (76%)
Zhengyao Song; Yongqiang Li; Danni Yuan; Li Liu; Shaokui Wei; Baoyuan Wu

http://arxiv.org/abs/2401.13801
Exploring Adversarial Threat Models in Cyber Physical Battery Systems. (76%)
Shanthan Kumar Padisala; Shashank Dhananjay Vyas; Satadru Dey

http://arxiv.org/abs/2402.01702
Fluent dreaming for language models. (64%)
T. Ben Confirm Labs Thompson; Zygimantas Confirm Labs Straznickas; Michael Confirm Labs Sklar

http://arxiv.org/abs/2401.13205
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size. (99%)
Junlin Liu; Xinchen Lyu

http://arxiv.org/abs/2401.12700
Securing Recommender System via Cooperative Training. (80%)
Qingyang Wang; Chenwang Wu; Defu Lian; Enhong Chen

http://arxiv.org/abs/2401.13171
Compositional Generative Inverse Design. (56%)
Tailin Wu; Takashi Maruyama; Long Wei; Tao Zhang; Yilun Du; Gianluca Iaccarino; Jure Leskovec

http://arxiv.org/abs/2401.13212
AdCorDA: Classifier Refinement via Adversarial Correction and Domain Adaptation. (33%)
Lulan Shen; Ali Edalati; Brett Meyer; Warren Gross; James J. Clark

http://arxiv.org/abs/2401.12578
ToDA: Target-oriented Diffusion Attacker against Recommendation System. (13%)
Xiaohao Liu; Zhulin Tao; Ting Jiang; He Chang; Yunshan Ma; Xianglin Huang; Xiang Wang

http://arxiv.org/abs/2401.12532
DAFA: Distance-Aware Fair Adversarial Training. (2%)
Hyungyu Lee; Saehyung Lee; Hyemi Jang; Junsung Park; Ho Bae; Sungroh Yoon

http://arxiv.org/abs/2401.12610
The twin peaks of learning neural networks. (2%)
Elizaveta Demyanenko; Christoph Feinauer; Enrico M. Malatesta; Luca Saglietti

http://arxiv.org/abs/2401.12461
Fast Adversarial Training against Textual Adversarial Attacks. (99%)
Yichen Yang; Xin Liu; Kun He

http://arxiv.org/abs/2401.11902
A Training-Free Defense Framework for Robust Learned Image Compression. (74%)
Myungseo Song; Jinyoung Choi; Bohyung Han

http://arxiv.org/abs/2401.11857
Adversarial speech for voice privacy protection from Personalized Speech generation. (73%)
Shihao Chen; Liping Chen; Jie Zhang; KongAik Lee; Zhenhua Ling; Lirong Dai

http://arxiv.org/abs/2401.12055
NEUROSEC: FPGA-Based Neuromorphic Audio Security. (13%)
Murat Isik; Hiruna Vishwamith; Yusuf Sur; Kayode Inadagbo; I. Can Dikmen

http://arxiv.org/abs/2401.11723
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them. (13%)
Chao Liu; Boxi Chen; Wei Shao; Chris Zhang; Kelvin Wong; Yi Zhang

http://arxiv.org/abs/2401.12014
Robustness to distribution shifts of compressed networks for edge devices. (8%)
Lulan Shen; Ali Edalati; Brett Meyer; Warren Gross; James J. Clark

http://arxiv.org/abs/2401.12192
Text Embedding Inversion Security for Multilingual Language Models. (2%)
Yiyi Chen; Heather Lent; Johannes Bjerva

http://arxiv.org/abs/2401.12129
Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy. (1%)
Will LeVine; Benjamin Pikus; Jacob Phillips; Berk Norman; Fernando Amat Gil; Sean Hendryx

http://arxiv.org/abs/2401.11543
How Robust Are Energy-Based Models Trained With Equilibrium Propagation? (99%)
Siddharth Mansingh; Michal Kucer; Garrett Kenyon; Juston Moore; Michael Teti

http://arxiv.org/abs/2401.12261
Analyzing the Quality Attributes of AI Vision Models in Open Repositories Under Adversarial Attacks. (56%)
Zerui Wang; Yan Liu

http://arxiv.org/abs/2401.11406
Adversarial Augmentation Training Makes Action Recognition Models More Robust to Realistic Video Distribution Shifts. (11%)
Kiyoon Kim; Shreyank N Gowda; Panagiotis Eustratiadis; Antreas Antoniou; Robert B Fisher

http://arxiv.org/abs/2401.11618
Efficient local linearity regularization to overcome catastrophic overfitting. (8%)
Elias Abad Rocamora; Fanghui Liu; Grigorios G. Chrysos; Pablo M. Olmos; Volkan Cevher

http://arxiv.org/abs/2401.11224
Susceptibility of Adversarial Attack on Medical Image Segmentation Models. (99%)
Zhongxuan Wang; Leo Xu

http://arxiv.org/abs/2401.11373
Finding a Needle in the Adversarial Haystack: A Targeted Paraphrasing Approach For Uncovering Edge Cases with Minimal Distribution Distortion. (96%)
Aly M. Kassem; Sherif Saad

http://arxiv.org/abs/2401.11126
CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications. (80%)
Hangsheng Zhang; Jiqiang Liu; Jinsong Dong

http://arxiv.org/abs/2401.11170
Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images. (33%)
Kuofeng Gao; Yang Bai; Jindong Gu; Shu-Tao Xia; Philip Torr; Zhifeng Li; Wei Liu

http://arxiv.org/abs/2401.10586
PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks. (99%)
Ping Guo; Zhiyuan Yang; Xi Lin; Qingchuan Zhao; Qingfu Zhang

http://arxiv.org/abs/2401.10691
Explainable and Transferable Adversarial Attack for ML-Based Network Intrusion Detectors. (99%)
Hangsheng Zhang; Dongqi Han; Yinlong Liu; Zhiliang Wang; Jiyan Sun; Shangyuan Zhuang; Jiqiang Liu; Jinsong Dong

http://arxiv.org/abs/2401.12236
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness. (98%)
Yifan Hao; Tong Zhang

http://arxiv.org/abs/2401.10657
FIMBA: Evaluating the Robustness of AI in Genomics via Feature Importance Adversarial Attacks. (56%)
Heorhii Skovorodnikov; Hoda Alkhzaimi

http://arxiv.org/abs/2401.10590
Adversarially Robust Signed Graph Contrastive Learning from Balance Augmentation. (10%)
Jialong Zhou; Xing Ai; Yuni Lai; Kai Zhou

http://arxiv.org/abs/2401.12242
BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models. (3%)
Zhen Xiang; Fengqing Jiang; Zidi Xiong; Bhaskar Ramasubramanian; Radha Poovendran; Bo Li

http://arxiv.org/abs/2401.11035
Image Safeguarding: Reasoning with Conditional Vision Language Model and Obfuscating Unsafe Content Counterfactually. (1%)
Mazal Bethany; Brandon Wherry; Nishant Vishwamitra; Peyman Najafirad

http://arxiv.org/abs/2401.09945
HGAttack: Transferable Heterogeneous Graph Adversarial Attack. (99%)
He Zhao; Zhiwei Zeng; Yongwei Wang; Deheng Ye; Chunyan Miao

http://arxiv.org/abs/2401.09740
Hijacking Attacks against Neural Networks by Analyzing Training Data. (99%)
Yunjie Ge; Qian Wang; Huayang Huang; Qi Li; Cong Wang; Chao Shen; Lingchen Zhao; Peipei Jiang; Zheng Fang; Shenyi Zhang

http://arxiv.org/abs/2401.10111
Marrying Adapters and Mixup to Efficiently Enhance the Adversarial Robustness of Pre-Trained Language Models for Text Classification. (98%)
Tuc Nguyen; Thai Le

http://arxiv.org/abs/2401.10313
Hacking Predictors Means Hacking Cars: Using Sensitivity Analysis to Identify Trajectory Prediction Vulnerabilities for Autonomous Driving Security. (92%)
Marsalis Gibson; David Babazadeh; Claire Tomlin; Shankar Sastry

http://arxiv.org/abs/2401.10405
Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation. (80%)
Janvi Thakkar; Giulio Zizzo; Sergio Maffeis

http://arxiv.org/abs/2401.10447
Investigating Training Strategies and Model Robustness of Low-Rank Adaptation for Language Modeling in Speech Recognition. (15%)
Yu Yu; Chao-Han Huck Yang; Tuan Dinh; Sungho Ryu; Jari Kolehmainen; Roger Ren; Denis Filimonov; Prashanth G. Shivakumar; Ankur Gandhe; Ariya Rastow; Jia Xu; Ivan Bulyko; Andreas Stolcke

http://arxiv.org/abs/2401.10091
Power in Numbers: Robust reading comprehension by finetuning with four adversarial sentences per example. (13%)
Ariel Marcus

http://arxiv.org/abs/2401.10090
Cross-Modality Perturbation Synergy Attack for Person Re-identification. (2%)
Yunpeng Gong; Zhun Zhong; Zhiming Luo; Yansong Qu; Rongrong Ji; Min Jiang

http://arxiv.org/abs/2401.10375
Vulnerabilities of Foundation Model Integrated Federated Learning Under Adversarial Threats. (2%)
Chen Wu; Xi Li; Jiaqi Wang

http://arxiv.org/abs/2401.10446
Large Language Models are Efficient Learners of Noise-Robust Speech Recognition. (1%)
Yuchen Hu; Chen Chen; Chao-Han Huck Yang; Ruizhe Li; Chao Zhang; Pin-Yu Chen; EnSiong Chng

http://arxiv.org/abs/2401.09574
Towards Scalable and Robust Model Versioning. (93%)
Wenxin Ding; Arjun Nitin Bhagoji; Ben Y. Zhao; Haitao Zheng

http://arxiv.org/abs/2401.09673
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack. (93%)
Zhongliang Guo; Junhao Dong; Yifei Qian; Kaixuan Wang; Weiye Li; Ziheng Guo; Yuheng Wang; Yanli Li; Ognjen Arandjelović; Lei Fang

http://arxiv.org/abs/2401.08984
A GAN-based data poisoning framework against anomaly detection in vertical federated learning. (3%)
Xiaolin Chen; Daoguang Zan; Wei Li; Bei Guan; Yongji Wang

http://arxiv.org/abs/2401.09191
An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification. (3%)
Nicolas Garcia Trillos; Matt Jacobs; Jakwang Kim; Matthew Werenski

http://arxiv.org/abs/2401.09624
MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks. (2%)
Giovanni Pasqualino; Luca Guarnera; Alessandro Ortis; Sebastiano Battiato

http://arxiv.org/abs/2401.09395
Caught in the Quicksand of Reasoning, Far from AGI Summit: Evaluating LLMs' Mathematical and Coding Competency through Ontology-guided Interventions. (1%)
Pengfei Hong; Deepanway Ghosal; Navonil Majumder; Somak Aditya; Rada Mihalcea; Soujanya Poria

http://arxiv.org/abs/2401.08998
Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization. (1%)
Yoonhwa Jung; Ikhyun Cho; Shun-Hsiang Hsu; Julia Hockenmaier

http://arxiv.org/abs/2401.08725
Revealing Vulnerabilities in Stable Diffusion via Targeted Attacks. (99%)
Chenyu Zhang; Lanjun Wang; Anan Liu

http://arxiv.org/abs/2401.08734
Bag of Tricks to Boost Adversarial Transferability. (99%)
Zeliang Zhang; Rongyi Zhu; Wei Yao; Xiaosen Wang; Chenliang Xu

http://arxiv.org/abs/2401.08255
A Generative Adversarial Attack for Multilingual Text Classifiers. (99%)
Tom Roth; Inigo Jauregi Unanue; Alsharif Abuadbba; Massimo Piccardi

http://arxiv.org/abs/2401.08903
PPR: Enhancing Dodging Attacks while Maintaining Impersonation Attacks on Face Recognition Systems. (99%)
Fengfan Zhou; Heifei Ling

http://arxiv.org/abs/2401.08863
Robust Localization of Key Fob Using Channel Impulse Response of Ultra Wide Band Sensors for Keyless Entry Systems. (92%)
Abhiram Kolli; Filippo Casamassima; Horst Possegger; Horst Bischof

http://arxiv.org/abs/2401.08865
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images. (87%)
Nicholas Konz; Maciej A. Mazurowski

http://arxiv.org/abs/2401.08925
RandOhm: Mitigating Impedance Side-channel Attacks using Randomized Circuit Configurations. (9%)
Saleh Khalaj Monfared; Domenic Forte; Shahin Tajik

http://arxiv.org/abs/2401.08216
Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning. (8%)
Yu Jiang; Jiyuan Shen; Ziyao Liu; Chee Wei Tan; Kwok-Yan Lam

http://arxiv.org/abs/2401.09495
IPR-NeRF: Ownership Verification meets Neural Radiance Field. (3%)
Win Kent Ong; Kam Woh Ng; Chee Seng Chan; Yi Zhe Song; Tao Xiang

http://arxiv.org/abs/2401.08141
IoTWarden: A Deep Reinforcement Learning Based Real-time Defense System to Mitigate Trigger-action IoT Attacks. (1%)
Md Morshed Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, USA Alam; Israt Department of Computer Science, University of Memphis, Memphis, USA Jahan; Weichao Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, USA Wang

http://arxiv.org/abs/2401.07991
Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes. (99%)
Shayan Mohajer Hamidi; Linfeng Ye

http://arxiv.org/abs/2401.07867
Authorship Obfuscation in Multilingual Machine-Generated Text Detection. (12%)
Dominik Macko; Robert Moro; Adaku Uchendu; Ivan Srba; Jason Samuel Lucas; Michiharu Yamashita; Nafis Irtiza Tripto; Dongwon Lee; Jakub Simko; Maria Bielikova

http://arxiv.org/abs/2401.07261
LookAhead: Preventing DeFi Attacks via Unveiling Adversarial Contracts. (80%)
Shoupeng Ren; Tianyu Tu; Jian Liu; Di Wu; Kui Ren

http://arxiv.org/abs/2401.07205
Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models. (70%)
Shiming Wang; Zhe Ji; Liyao Xiang; Hao Zhang; Xinbing Wang; Chenghu Zhou; Bo Li

http://arxiv.org/abs/2401.07087
Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability. (99%)
Junxi Chen; Junhao Dong; Xiaohua Xie

http://arxiv.org/abs/2401.07188
Left-right Discrepancy for Adversarial Attack on Stereo Networks. (98%)
Pengfei Wang; Xiaofei Hui; Beijia Lu; Nimrod Lilith; Jun Liu; Sameer Alam

http://arxiv.org/abs/2401.06637
Adversarial Examples are Misaligned in Diffusion Model Manifolds. (98%)
Peter Lorenz; Ricard Durall; Janis Keuper

http://arxiv.org/abs/2401.06373
How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs. (2%)
Yi Zeng; Hongpeng Lin; Jingwen Zhang; Diyi Yang; Ruoxi Jia; Weiyan Shi

http://arxiv.org/abs/2401.06548
Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning. (1%)
Chenyang Wang; Junjun Jiang; Xingyu Hu; Xianming Liu; Xiangyang Ji

http://arxiv.org/abs/2401.06916
An Analytical Framework for Modeling and Synthesizing Malicious Attacks on ACC Vehicles. (1%)
Shian Wang

http://arxiv.org/abs/2401.06561
Intention Analysis Makes LLMs A Good Jailbreak Defender. (1%)
Yuqi Zhang; Liang Ding; Lefei Zhang; Dacheng Tao

http://arxiv.org/abs/2401.06031
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model. (99%)
Zhiyu Zhu; Huaming Chen; Xinyi Wang; Jiayu Zhang; Zhibo Jin; Kim-Kwang Raymond Choo; Jun Shen; Dong Yuan

http://arxiv.org/abs/2401.05949
Universal Vulnerabilities in Large Language Models: In-context Learning Backdoor Attacks. (61%)
Shuai Zhao; Meihuizi Jia; Luu Anh Tuan; Jinming Wen

http://arxiv.org/abs/2401.06824
Open the Pandora's Box of LLMs: Jailbreaking LLMs through Representation Engineering. (22%)
Tianlong Li; Shihan Dou; Wenhao Liu; Muling Wu; Changze Lv; Xiaoqing Zheng; Xuanjing Huang

http://arxiv.org/abs/2401.06030
Can We Trust the Unlabeled Target Data? Towards Backdoor Attack and Defense on Model Adaptation. (8%)
Lijun Sheng; Jian Liang; Ran He; Zilei Wang; Tieniu Tan

http://arxiv.org/abs/2401.06122
Manipulating Feature Visualizations with Gradient Slingshots. (3%)
Dilyara Bareeva; Marina M. -C. Höhne; Alexander Warnecke; Lukas Pirch; Klaus-Robert Müller; Konrad Rieck; Kirill Bykov

http://arxiv.org/abs/2401.05998
Combating Adversarial Attacks with Multi-Agent Debate. (3%)
Steffi Chern; Zhen Fan; Andy Liu

http://arxiv.org/abs/2401.05217
Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Method. (83%)
Chenxi Yang; Yujia Liu; Dingquan Li; Tingting Jiang

http://arxiv.org/abs/2401.05561
TrustLLM: Trustworthiness in Large Language Models. (75%)
Lichao Sun; Yue Huang; Haoran Wang; Siyuan Wu; Qihui Zhang; Chujie Gao; Yixin Huang; Wenhan Lyu; Yixuan Zhang; Xiner Li; Zhengliang Liu; Yixin Liu; Yijue Wang; Zhikun Zhang; Bhavya Kailkhura; Caiming Xiong; Chaowei Xiao; Chunyuan Li; Eric Xing; Furong Huang; Hao Liu; Heng Ji; Hongyi Wang; Huan Zhang; Huaxiu Yao; Manolis Kellis; Marinka Zitnik; Meng Jiang; Mohit Bansal; James Zou; Jian Pei; Jian Liu; Jianfeng Gao; Jiawei Han; Jieyu Zhao; Jiliang Tang; Jindong Wang; John Mitchell; Kai Shu; Kaidi Xu; Kai-Wei Chang; Lifang He; Lifu Huang; Michael Backes; Neil Zhenqiang Gong; Philip S. Yu; Pin-Yu Chen; Quanquan Gu; Ran Xu; Rex Ying; Shuiwang Ji; Suman Jana; Tianlong Chen; Tianming Liu; Tianyi Zhou; Willian Wang; Xiang Li; Xiangliang Zhang; Xiao Wang; Xing Xie; Xun Chen; Xuyu Wang; Yan Liu; Yanfang Ye; Yinzhi Cao; Yong Chen; Yue Zhao

http://arxiv.org/abs/2401.05569
SENet: Visual Detection of Online Social Engineering Attack Campaigns. (4%)
Irfan Ozen; Karthika Subramani; Phani Vadrevu; Roberto Perdisci

http://arxiv.org/abs/2401.05566
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training. (2%)
Evan Hubinger; Carson Denison; Jesse Mu; Mike Lambert; Meg Tong; Monte MacDiarmid; Tamera Lanham; Daniel M. Ziegler; Tim Maxwell; Newton Cheng; Adam Jermyn; Amanda Askell; Ansh Radhakrishnan; Cem Anil; David Duvenaud; Deep Ganguli; Fazl Barez; Jack Clark; Kamal Ndousse; Kshitij Sachan; Michael Sellitto; Mrinank Sharma; Nova DasSarma; Roger Grosse; Shauna Kravec; Yuntao Bai; Zachary Witten; Marina Favaro; Jan Brauner; Holden Karnofsky; Paul Christiano; Samuel R. Bowman; Logan Graham; Jared Kaplan; Sören Mindermann; Ryan Greenblatt; Buck Shlegeris; Nicholas Schiefer; Ethan Perez

http://arxiv.org/abs/2401.05458
CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidance. (1%)
Dongyu Zhang; Ruofan Hu; Elke Rundensteiner

http://arxiv.org/abs/2401.05562
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning. (1%)
Zhangchen Xu; Fengqing Jiang; Luyao Niu; Jinyuan Jia; Radha Poovendran

http://arxiv.org/abs/2401.04958
FBSDetector: Fake Base Station and Multi Step Attack Detection in Cellular Networks using Machine Learning. (1%)
Kazi Samin Mubasshir; Imtiaz Karim; Elisa Bertino

http://arxiv.org/abs/2401.04727
Revisiting Adversarial Training at Scale. (26%)
Zeyu Wang; Xianhang Li; Hongru Zhu; Cihang Xie

http://arxiv.org/abs/2401.04364
SoK: Facial Deepfake Detectors. (11%)
Binh M. Le; Jiwon Kim; Shahroz Tariq; Kristen Moore; Alsharif Abuadbba; Simon S. Woo

http://arxiv.org/abs/2401.04647
Advancing Ante-Hoc Explainable Models through Generative Adversarial Networks. (3%)
Tanmay Garg; Deepika Vemuri; Vineeth N Balasubramanian

http://arxiv.org/abs/2401.04350
Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness. (99%)
Sibo Wang; Jie Zhang; Zheng Yuan; Shiguang Shan

http://arxiv.org/abs/2402.00035
Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing. (54%)
Yizhak Elboher; Raya Elsaleh; Omri Isac; Mélanie Ducoffe; Audrey Galametz; Guillaume Povéda; Ryma Boumazouza; Noémie Cohen; Guy Katz

http://arxiv.org/abs/2401.04331
Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study. (45%)
Qiyu Kang; Kai Zhao; Yang Song; Yihang Xie; Yanan Zhao; Sijie Wang; Rui She; Wee Peng Tay

http://arxiv.org/abs/2401.03685
Logits Poisoning Attack in Federated Distillation. (12%)
Yuhan Tang; Zhiyuan Wu; Bo Gao; Tian Wen; Yuwei Wang; Sheng Sun

http://arxiv.org/abs/2401.04191
Dense Hopfield Networks in the Teacher-Student Setting. (1%)
Robin Thériault; Daniele Tantari

http://arxiv.org/abs/2401.03582
Invisible Reflections: Leveraging Infrared Laser Reflections to Target Traffic Sign Perception. (87%)
Takami Sato; Sri Hrushikesh Varma Bhupathiraju; Michael Clifford; Takeshi Sugawara; Qi Alfred Chen; Sara Rampazzi

http://arxiv.org/abs/2401.03488
Data-Driven Subsampling in the Presence of an Adversarial Actor. (86%)
Abu Shafin Mohammad Mahdee Jameel; Ahmed P. Mohamed; Jinho Yi; Aly El Gamal; Akshay Malhotra

http://arxiv.org/abs/2401.03514
ROIC-DM: Robust Text Inference and Classification via Diffusion Model. (33%)
Shilong Yuan; Wei Yuan; Hongzhi Yin; Tieke He

http://arxiv.org/abs/2401.03156
Data-Dependent Stability Analysis of Adversarial Training. (98%)
Yihan Wang; Shuang Liu; Xiao-Shan Gao

http://arxiv.org/abs/2401.03215
End-to-End Anti-Backdoor Learning on Images and Time Series. (61%)
Yujing Jiang; Xingjun Ma; Sarah Monazam Erfani; Yige Li; James Bailey

http://arxiv.org/abs/2401.03115
Transferable Learned Image Compression-Resistant Adversarial Perturbations. (99%)
Yang Sui; Zhuohang Li; Ding Ding; Xiang Pan; Xiaozhong Xu; Shan Liu; Zhenzhong Chen

http://arxiv.org/abs/2401.02727
Enhancing targeted transferability via feature space fine-tuning. (98%)
Hui Zeng; Biwei Chen; Anjie Peng

http://arxiv.org/abs/2401.02718
Calibration Attack: A Framework For Adversarial Attacks Targeting Calibration. (68%)
Stephen Obadinma; Xiaodan Zhu; Hongyu Guo

http://arxiv.org/abs/2401.02663
A backdoor attack against link prediction tasks with graph neural networks. (38%)
Jiazhu Dai; Haoyu Sun

http://arxiv.org/abs/2401.05432
TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks. (1%)
Khondoker Murad Hossain; Tim Oates

http://arxiv.org/abs/2401.02906
MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance. (1%)
Renjie Pi; Tianyang Han; Yueqi Xie; Rui Pan; Qing Lian; Hanze Dong; Jipeng Zhang; Tong Zhang

http://arxiv.org/abs/2401.02565
Vulnerabilities Unveiled: Adversarially Attacking a Multimodal Vision Langauge Model for Pathology Imaging. (99%)
Jai Prakash Veerla; Poojitha Thota; Partha Sai Guttikonda; Shirin Nilizadeh; Jacob M. Luber

http://arxiv.org/abs/2401.02633
A Random Ensemble of Encrypted models for Enhancing Robustness against Adversarial Examples. (99%)
Ryota Iijima; Sayaka Shiota; Hitoshi Kiya

http://arxiv.org/abs/2401.02615
AdvSQLi: Generating Adversarial SQL Injections against Real-world WAF-as-a-service. (95%)
Zhenqing Qu; Xiang Ling; Ting Wang; Xiang Chen; Shouling Ji; Chunming Wu

http://arxiv.org/abs/2401.02342
Evasive Hardware Trojan through Adversarial Power Trace. (92%)
Behnam Omidi; Khaled N. Khasawneh; Ihsen Alouani

http://arxiv.org/abs/2401.02600
Object-oriented backdoor attack against image captioning. (76%)
Meiling Li; Nan Zhong; Xinpeng Zhang; Zhenxing Qian; Sheng Li

http://arxiv.org/abs/2401.02283
DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace. (2%)
Guy Katz; Natan Levy; Idan Refaeli; Raz Yerushalmi

http://arxiv.org/abs/2401.02306
Secure Control of Connected and Automated Vehicles Using Trust-Aware Robust Event-Triggered Control Barrier Functions. (2%)
H M Sabbir Ahmad; Ehsan Sabouni; Akua Dickson; Wei Xiao; Christos G. Cassandras; Wenchao Li

http://arxiv.org/abs/2401.01750
Towards Robust Semantic Segmentation against Patch-based Attack via Attention Refinement. (92%)
Zheng Yuan; Jie Zhang; Yude Wang; Shiguang Shan; Xilin Chen

http://arxiv.org/abs/2401.02031
Spy-Watermark: Robust Invisible Watermarking for Backdoor Attack. (62%)
Ruofei Wang; Renjie Wan; Zongyu Guo; Qing Guo; Rui Huang

http://arxiv.org/abs/2401.01752
FullLoRA-AT: Efficiently Boosting the Robustness of Pretrained Vision Transformers. (33%)
Zheng Yuan; Jie Zhang; Shiguang Shan

http://arxiv.org/abs/2401.01963
Integrated Cyber-Physical Resiliency for Power Grids under IoT-Enabled Dynamic Botnet Attacks. (22%)
Yuhan Zhao; Juntao Chen; Quanyan Zhu

http://arxiv.org/abs/2401.01575
Enhancing Generalization of Invisible Facial Privacy Cloak via Gradient Accumulation. (1%)
Xuannan Liu; Yaoyao Zhong; Weihong Deng; Hongzhi Shi; Xingchen Cui; Yunfeng Yin; Dongchao Wen

http://arxiv.org/abs/2401.01199
JMA: a General Algorithm to Craft Nearly Optimal Targeted Adversarial Example. (99%)
Benedetta Tondi; Wei Guo; Mauro Barni

http://arxiv.org/abs/2401.01102
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing. (92%)
Zhe Kong; Wentian Zhang; Tao Wang; Kaihao Zhang; Yuexiang Li; Xiaoying Tang; Wenhan Luo

http://arxiv.org/abs/2402.03317
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization. (64%)
Xixu Hu; Runkai Zheng; Jindong Wang; Cheuk Hang Leung; Qi Wu; Xing Xie

http://arxiv.org/abs/2401.01394
Unveiling the Stealthy Threat: Analyzing Slow Drift GPS Spoofing Attacks for Autonomous Vehicles in Urban Environments and Enabling the Resilience. (10%)
Sagar Dasgupta; Abdullah Ahmed; Mizanur Rahman; Thejesh N. Bandi

http://arxiv.org/abs/2401.01085
Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control. (4%)
Ka-Ho Chow; Wenqi Wei; Lei Yu

http://arxiv.org/abs/2401.01531
Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing. (2%)
Yalin E. Sagduyu; Tugba Erpek; Aylin Yener; Sennur Ulukus

http://arxiv.org/abs/2401.00996
Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment. (12%)
Jie Zhu; Leye Wang; Xiao Han; Anmin Liu; Tao Xie

http://arxiv.org/abs/2401.00994
Detection and Defense Against Prominent Attacks on Preconditioned LLM-Integrated Virtual Assistants. (8%)
Chun Fai Chan; Daniel Wankit Yip; Aysan Esmradi

http://arxiv.org/abs/2401.00991
A Novel Evaluation Framework for Assessing Resilience Against Prompt Injection Attacks in Large Language Models. (2%)
Daniel Wankit Yip; Aysan Esmradi; Chun Fai Chan

http://arxiv.org/abs/2401.14232
AR-GAN: Generative Adversarial Network-Based Defense Method Against Adversarial Attacks on the Traffic Sign Classification System of Autonomous Vehicles. (99%)
M Sabbir Salek; Abdullah Al Mamun; Mashrur Chowdhury

http://arxiv.org/abs/2401.01377
Does Few-shot Learning Suffer from Backdoor Attacks? (98%)
Xinwei Liu; Xiaojun Jia; Jindong Gu; Yuan Xun; Siyuan Liang; Xiaochun Cao

http://arxiv.org/abs/2401.00414
Is It Possible to Backdoor Face Forgery Detection with Natural Triggers? (68%)
Xiaoxuan Han; Songlin Yang; Wei Wang; Ziwen He; Jing Dong

http://arxiv.org/abs/2401.00334
Explainability-Driven Leaf Disease Classification using Adversarial Training and Knowledge Distillation. (84%)
Sebastian-Vasile Echim; Iulian-Marius Tăiatu; Dumitru-Clementin Cercel; Florin Pop

http://arxiv.org/abs/2401.00151
CamPro: Camera-based Anti-Facial Recognition. (81%)
Wenjun Zhu; Yuan Sun; Jiani Liu; Yushi Cheng; Xiaoyu Ji; Wenyuan Xu

http://arxiv.org/abs/2401.00148
TPatch: A Triggered Physical Adversarial Patch. (76%)
Wenjun Zhu; Xiaoyu Ji; Yushi Cheng; Shibo Zhang; Wenyuan Xu

http://arxiv.org/abs/2401.00163
A clean-label graph backdoor attack method in node classification task. (9%)
Xiaogang Xing; Ming Xu; Yujing Bai; Dongdong Yang

http://arxiv.org/abs/2312.17673
Jatmo: Prompt Injection Defense by Task-Specific Finetuning. (54%)
Julien Piet; Maha Alrashed; Chawin Sitawarin; Sizhe Chen; Zeming Wei; Elizabeth Sun; Basel Alomair; David Wagner

http://arxiv.org/abs/2312.17591
Towards Faithful Explanations for Text Classification with Robustness Improvement and Explanation Guided Training. (9%)
Dongfang Li; Baotian Hu; Qingcai Chen; Shan He

http://arxiv.org/abs/2312.16880
Adversarial Attacks on Image Classification Models: Analysis and Defense. (99%)
Jaydip Sen; Abhiraj Sen; Ananda Chatterjee

http://arxiv.org/abs/2312.16979
BlackboxBench: A Comprehensive Benchmark of Black-box Adversarial Attacks. (99%)
Meixi Zheng; Xuanchen Yan; Zihao Zhu; Hongrui Chen; Baoyuan Wu

http://arxiv.org/abs/2312.16957
Attack Tree Analysis for Adversarial Evasion Attacks. (99%)
Yuki Yamaguchi; Toshiaki Aoki

http://arxiv.org/abs/2312.17356
Can you See me? On the Visibility of NOPs against Android Malware Detectors. (98%)
Diego Soi; Davide Maiorca; Giorgio Giacinto; Harel Berger

http://arxiv.org/abs/2312.17431
MVPatch: More Vivid Patch for Adversarial Camouflaged Attacks on Object Detectors in the Physical World. (98%)
Zheng Zhou; Hongbo Zhao; Ju Liu; Qiaosheng Zhang; Liwei Geng; Shuchang Lyu; Wenquan Feng

http://arxiv.org/abs/2312.17301
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation. (92%)
Dibaloke Chanda; Saba Heidari Gheshlaghi; Nasim Yahya Soltani

http://arxiv.org/abs/2312.16907
DOEPatch: Dynamically Optimized Ensemble Model for Adversarial Patches Generation. (83%)
Wenyi Tan; Yang Li; Chenxing Zhao; Zhunga Liu; Quan Pan

http://arxiv.org/abs/2312.17164
Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution. (64%)
Yalin E. Sagduyu; Tugba Erpek; Yi Shi

http://arxiv.org/abs/2312.17220
Timeliness: A New Design Metric and a New Attack Surface. (1%)
Priyanka Kaswan; Sennur Ulukus

http://arxiv.org/abs/2312.16715
Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning. (98%)
Yalin E. Sagduyu; Tugba Erpek

http://arxiv.org/abs/2312.16451
Domain Generalization with Vital Phase Augmentation. (3%)
Ingyun Lee; Wooju Lee; Hyun Myung

http://arxiv.org/abs/2312.16156
From Text to Multimodal: A Comprehensive Survey of Adversarial Example Generation in Question Answering Systems. (92%)
Gulsum Yigit; Mehmet Fatih Amasyali

http://arxiv.org/abs/2312.16401
Natural Adversarial Patch Generation Method Based on Latent Diffusion Model. (76%)
Xianyi Chen; Fazhan Liu; Dong Jiang; Kai Yan

http://arxiv.org/abs/2312.16339
Universal Pyramid Adversarial Training for Improved ViT Performance. (5%)
Ping-yeh Chiang; Yipin Zhou; Omid Poursaeed; Satya Narayan Shukla; Ashish Shah; Tom Goldstein; Ser-Nam Lim

http://arxiv.org/abs/2312.16019
Robust Survival Analysis with Adversarial Regularization. (4%)
Michael Potter; Stefano Maxenti; Michael Everett

http://arxiv.org/abs/2312.15617
GanFinger: GAN-Based Fingerprint Generation for Deep Neural Network Ownership Verification. (96%)
Huali Ren; Anli Yan; Xiaojun Ren; Pei-Gen Ye; Chong-zhi Gao; Zhili Zhou; Jin Li

http://arxiv.org/abs/2312.15826
Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion. (84%)
Lijian Chen; Wei Yuan; Tong Chen; Guanhua Ye; Quoc Viet Hung Nguyen; Hongzhi Yin

http://arxiv.org/abs/2312.15867
Punctuation Matters! Stealthy Backdoor Attack for Language Models. (11%)
Xuan Sheng; Zhicheng Li; Zhaoyang Han; Xiangmao Chang; Piji Li

http://arxiv.org/abs/2312.15228
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It. (10%)
Federico Siciliano; Luca Maiano; Lorenzo Papa; Federica Baccin; Irene Amerini; Fabrizio Silvestri

http://arxiv.org/abs/2312.15172
Pre-trained Trojan Attacks for Visual Recognition. (1%)
Aishan Liu; Xinwei Zhang; Yisong Xiao; Yuguang Zhou; Siyuan Liang; Jiakai Wang; Xianglong Liu; Xiaochun Cao; Dacheng Tao

http://arxiv.org/abs/2312.14677
MEAOD: Model Extraction Attack against Object Detectors. (83%)
Zeyu Li; Chenghui Shi; Yuwen Pu; Xuhong Zhang; Yu Li; Jinbao Li; Shouling Ji

http://arxiv.org/abs/2312.14440
Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks. (82%)
Haz Sameen Shahgir; Xianghao Kong; Greg Ver Steeg; Yue Dong

http://arxiv.org/abs/2312.14820
Understanding the Regularity of Self-Attention with Optimal Transport. (31%)
Valérie Castin; Pierre Ablin; Gabriel Peyré

http://arxiv.org/abs/2312.14461
Attacking Byzantine Robust Aggregation in High Dimensions. (22%)
Sarthak Choudhary; Aashish Kolluri; Prateek Saxena

http://arxiv.org/abs/2312.15036
SODA: Protecting Proprietary Information in On-Device Machine Learning Models. (4%)
Akanksha Atrey; Ritwik Sinha; Saayan Mitra; Prashant Shenoy

http://arxiv.org/abs/2312.15103
Energy-based learning algorithms for analog computing: a comparative study. (2%)
Benjamin Scellier; Maxence Ernoult; Jack Kendall; Suhas Kumar

http://arxiv.org/abs/2312.15088
Adaptive Domain Inference Attack. (1%)
Yuechun Gu; Keke Chen

http://arxiv.org/abs/2312.14218
AutoAugment Input Transformation for Highly Transferable Targeted Attacks. (99%)
Haobo Lu; Xin Liu; Kun He

http://arxiv.org/abs/2312.13628
Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples. (98%)
Ruichu Cai; Yuxuan Zhu; Jie Qiao; Zefeng Liang; Furui Liu; Zhifeng Hao

http://arxiv.org/abs/2312.14260
Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience. (86%)
Janvi Thakkar; Giulio Zizzo; Sergio Maffeis

http://arxiv.org/abs/2312.14217
Adversarial Infrared Curves: An Attack on Infrared Pedestrian Detectors in the Physical World. (74%)
Chengyin Hu; Weiwen Shi

http://arxiv.org/abs/2312.14302
Exploiting Novel GPT-4 APIs. (8%)
Kellin Pelrine; Mohammad Taufeeque; Michał Zając; Euan McLean; Adam Gleave

http://arxiv.org/abs/2312.12768
Mutual-modality Adversarial Attack with Semantic Perturbation. (99%)
Jingwen Ye; Ruonan Yu; Songhua Liu; Xinchao Wang

http://arxiv.org/abs/2312.13118
LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate. (99%)
Tao Wu; Tie Luo; Donald C. Wunsch

http://arxiv.org/abs/2312.13435
Adversarial Markov Games: On Adaptive Decision-Based Attacks and Defenses. (98%)
Ilias Tsingenopoulos; Vera Rimmer; Davy Preuveneers; Fabio Pierazzi; Lorenzo Cavallaro; Wouter Joosen

http://arxiv.org/abs/2312.14197
Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models. (98%)
Jingwei Yi; Yueqi Xie; Bin Zhu; Emre Kiciman; Guangzhong Sun; Xing Xie; Fangzhao Wu

http://arxiv.org/abs/2312.12904
PGN: A perturbation generation network against deep reinforcement learning. (96%)
Xiangjuan Li; Feifan Li; Yang Li; Quan Pan

http://arxiv.org/abs/2312.13575
ARBiBench: Benchmarking Adversarial Robustness of Binarized Neural Networks. (96%)
Peng Zhao; Jiehua Zhang; Bowen Peng; Longguang Wang; YingMei Wei; Yu Liu; Li Liu

http://arxiv.org/abs/2312.13131
Scaling Compute Is Not All You Need for Adversarial Robustness. (93%)
Edoardo Debenedetti; Zishen Wan; Maksym Andriushchenko; Vikash Sehwag; Kshitij Bhardwaj; Bhavya Kailkhura

http://arxiv.org/abs/2312.13027
Doubly Perturbed Task Free Continual Learning. (9%)
Byung Hyun Lee; Min-hwan Oh; Se Young Chun

http://arxiv.org/abs/2312.14973
Interactive Visualization of Time-Varying Flow Fields Using Particle Tracing Neural Networks. (1%)
Mengjiao Han; Jixian Li; Sudhanshu Sane; Shubham Gupta; Bei Wang; Steve Petruzza; Chris R. Johnson

http://arxiv.org/abs/2312.12556
Tensor Train Decomposition for Adversarial Attacks on Computer Vision Models. (96%)
Andrei Chertkov; Ivan Oseledets

http://arxiv.org/abs/2312.12608
Trust, But Verify: A Survey of Randomized Smoothing Techniques. (78%)
Anupriya Kumari; Devansh Bhardwaj; Sukrit Jindal; Sarthak Gupta

http://arxiv.org/abs/2312.12484
SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable Masks. (73%)
Peishen Yan; Hao Wang; Tao Song; Yang Hua; Ruhui Ma; Ningxin Hu; Mohammad R. Haghighat; Haibing Guan

http://arxiv.org/abs/2312.12724
Progressive Poisoned Data Isolation for Training-time Backdoor Defense. (61%)
Yiming Chen; Haiwei Wu; Jiantao Zhou

http://arxiv.org/abs/2312.11954
Adversarial AutoMixup. (11%)
Huafeng Qin; Xin Jin; Yun Jiang; Mounim A. El-Yacoubi; Xinbo Gao

http://arxiv.org/abs/2312.12102
I-CEE: Tailoring Explanations of Image Classifications Models to User Expertise. (1%)
Yao Rong; Peizhu Qian; Vaibhav Unhelkar; Enkelejda Kasneci

http://arxiv.org/abs/2312.11805
Gemini: A Family of Highly Capable Multimodal Models. (99%)
Team Gemini; Rohan Anil; Sebastian Borgeaud; Yonghui Wu; Jean-Baptiste Alayrac; Jiahui Yu; Radu Soricut; Johan Schalkwyk; Andrew M. Dai; Anja Hauth; Katie Millican; David Silver; Slav Petrov; Melvin Johnson; Ioannis Antonoglou; Julian Schrittwieser; Amelia Glaese; Jilin Chen; Emily Pitler; Timothy Lillicrap; Angeliki Lazaridou; Orhan Firat; James Molloy; Michael Isard; Paul R. Barham; Tom Hennigan; Benjamin Lee; Fabio Viola; Malcolm Reynolds; Yuanzhong Xu; Ryan Doherty; Eli Collins; Clemens Meyer; Eliza Rutherford; Erica Moreira; Kareem Ayoub; Megha Goel; George Tucker; Enrique Piqueras; Maxim Krikun; Iain Barr; Nikolay Savinov; Ivo Danihelka; Becca Roelofs; Anaïs White; Anders Andreassen; Glehn Tamara von; Lakshman Yagati; Mehran Kazemi; Lucas Gonzalez; Misha Khalman; Jakub Sygnowski; Alexandre Frechette; Charlotte Smith; Laura Culp; Lev Proleev; Yi Luan; Xi Chen; James Lottes; Nathan Schucher; Federico Lebron; Alban Rrustemi; Natalie Clay; Phil Crone; Tomas Kocisky; Jeffrey Zhao; Bartek Perz; Dian Yu; Heidi Howard; Adam Bloniarz; Jack W. Rae; Han Lu; Laurent Sifre; Marcello Maggioni; Fred Alcober; Dan Garrette; Megan Barnes; Shantanu Thakoor; Jacob Austin; Gabriel Barth-Maron; William Wong; Rishabh Joshi; Rahma Chaabouni; Deeni Fatiha; Arun Ahuja; Ruibo Liu; Yunxuan Li; Sarah Cogan; Jeremy Chen; Chao Jia; Chenjie Gu; Qiao Zhang; Jordan Grimstad; Ale Jakse Hartman; Martin Chadwick; Gaurav Singh Tomar; Xavier Garcia; Evan Senter; Emanuel Taropa; Thanumalayan Sankaranarayana Pillai; Jacob Devlin; Michael Laskin; Diego de Las Casas; Dasha Valter; Connie Tao; Lorenzo Blanco; Adrià Puigdomènech Badia; David Reitter; Mianna Chen; Jenny Brennan; Clara Rivera; Sergey Brin; Shariq Iqbal; Gabriela Surita; Jane Labanowski; Abhi Rao; Stephanie Winkler; Emilio Parisotto; Yiming Gu; Kate Olszewska; Yujing Zhang; Ravi Addanki; Antoine Miech; Annie Louis; Laurent El Shafey; Denis Teplyashin; Geoff Brown; Elliot Catt; Nithya Attaluri; Jan Balaguer; Jackie Xiang; Pidong Wang; Zoe Ashwood; Anton Briukhov; Albert Webson; Sanjay Ganapathy; Smit Sanghavi; Ajay Kannan; Ming-Wei Chang; Axel Stjerngren; Josip Djolonga; Yuting Sun; Ankur Bapna; Matthew Aitchison; Pedram Pejman; Henryk Michalewski; Tianhe Yu; Cindy Wang; Juliette Love; Junwhan Ahn; Dawn Bloxwich; Kehang Han; Peter Humphreys; Thibault Sellam; James Bradbury; Varun Godbole; Sina Samangooei; Bogdan Damoc; Alex Kaskasoli; Sébastien M. R. Arnold; Vijay Vasudevan; Shubham Agrawal; Jason Riesa; Dmitry Lepikhin; Richard Tanburn; Srivatsan Srinivasan; Hyeontaek Lim; Sarah Hodkinson; Pranav Shyam; Johan Ferret; Steven Hand; Ankush Garg; Tom Le Paine; Jian Li; Yujia Li; Minh Giang; Alexander Neitz; Zaheer Abbas; Sarah York; Machel Reid; Elizabeth Cole; Aakanksha Chowdhery; Dipanjan Das; Dominika Rogozińska; Vitaly Nikolaev; Pablo Sprechmann; Zachary Nado; Lukas Zilka; Flavien Prost; Luheng He; Marianne Monteiro; Gaurav Mishra; Chris Welty; Josh Newlan; Dawei Jia; Miltiadis Allamanis; Clara Huiyi Hu; Liedekerke Raoul de; Justin Gilmer; Carl Saroufim; Shruti Rijhwani; Shaobo Hou; Disha Shrivastava; Anirudh Baddepudi; Alex Goldin; Adnan Ozturel; Albin Cassirer; Yunhan Xu; Daniel Sohn; Devendra Sachan; Reinald Kim Amplayo; Craig Swanson; Dessie Petrova; Shashi Narayan; Arthur Guez; Siddhartha Brahma; Jessica Landon; Miteyan Patel; Ruizhe Zhao; Kevin Villela; Luyu Wang; Wenhao Jia; Matthew Rahtz; Mai Giménez; Legg Yeung; Hanzhao Lin; James Keeling; Petko Georgiev; Diana Mincu; Boxi Wu; Salem Haykal; Rachel Saputro; Kiran Vodrahalli; James Qin; Zeynep Cankara; Abhanshu Sharma; Nick Fernando; Will Hawkins; Behnam Neyshabur; Solomon Kim; Adrian Hutter; Priyanka Agrawal; Alex Castro-Ros; George van den Driessche; Tao Wang; Fan Yang; Shuo-yiin Chang; Paul Komarek; Ross McIlroy; Mario Lučić; Guodong Zhang; Wael Farhan; Michael Sharman; Paul Natsev; Paul Michel; Yong Cheng; Yamini Bansal; Siyuan Qiao; Kris Cao; Siamak Shakeri; Christina Butterfield; Justin Chung; Paul Kishan Rubenstein; Shivani Agrawal; Arthur Mensch; Kedar Soparkar; Karel Lenc; Timothy Chung; Aedan Pope; Loren Maggiore; Jackie Kay; Priya Jhakra; Shibo Wang; Joshua Maynez; Mary Phuong; Taylor Tobin; Andrea Tacchetti; Maja Trebacz; Kevin Robinson; Yash Katariya; Sebastian Riedel; Paige Bailey; Kefan Xiao; Nimesh Ghelani; Lora Aroyo; Ambrose Slone; Neil Houlsby; Xuehan Xiong; Zhen Yang; Elena Gribovskaya; Jonas Adler; Mateo Wirth; Lisa Lee; Music Li; Thais Kagohara; Jay Pavagadhi; Sophie Bridgers; Anna Bortsova; Sanjay Ghemawat; Zafarali Ahmed; Tianqi Liu; Richard Powell; Vijay Bolina; Mariko Iinuma; Polina Zablotskaia; James Besley; Da-Woon Chung; Timothy Dozat; Ramona Comanescu; Xiance Si; Jeremy Greer; Guolong Su; Martin Polacek; Raphaël Lopez Kaufman; Simon Tokumine; Hexiang Hu; Elena Buchatskaya; Yingjie Miao; Mohamed Elhawaty; Aditya Siddhant; Nenad Tomasev; Jinwei Xing; Christina Greer; Helen Miller; Shereen Ashraf; Aurko Roy; Zizhao Zhang; Ada Ma; Angelos Filos; Milos Besta; Rory Blevins; Ted Klimenko; Chih-Kuan Yeh; Soravit Changpinyo; Jiaqi Mu; Oscar Chang; Mantas Pajarskas; Carrie Muir; Vered Cohen; Charline Le Lan; Krishna Haridasan; Amit Marathe; Steven Hansen; Sholto Douglas; Rajkumar Samuel; Mingqiu Wang; Sophia Austin; Chang Lan; Jiepu Jiang; Justin Chiu; Jaime Alonso Lorenzo; Lars Lowe Sjösund; Sébastien Cevey; Zach Gleicher; Thi Avrahami; Anudhyan Boral; Hansa Srinivasan; Vittorio Selo; Rhys May; Konstantinos Aisopos; Léonard Hussenot; Livio Baldini Soares; Kate Baumli; Michael B. Chang; Adrià Recasens; Ben Caine; Alexander Pritzel; Filip Pavetic; Fabio Pardo; Anita Gergely; Justin Frye; Vinay Ramasesh; Dan Horgan; Kartikeya Badola; Nora Kassner; Subhrajit Roy; Ethan Dyer; Víctor Campos; Alex Tomala; Yunhao Tang; Dalia El Badawy; Elspeth White; Basil Mustafa; Oran Lang; Abhishek Jindal; Sharad Vikram; Zhitao Gong; Sergi Caelles; Ross Hemsley; Gregory Thornton; Fangxiaoyu Feng; Wojciech Stokowiec; Ce Zheng; Phoebe Thacker; Çağlar Ünlü; Zhishuai Zhang; Mohammad Saleh; James Svensson; Max Bileschi; Piyush Patil; Ankesh Anand; Roman Ring; Katerina Tsihlas; Arpi Vezer; Marco Selvi; Toby Shevlane; Mikel Rodriguez; Tom Kwiatkowski; Samira Daruki; Keran Rong; Allan Dafoe; Nicholas FitzGerald; Keren Gu-Lemberg; Mina Khan; Lisa Anne Hendricks; Marie Pellat; Vladimir Feinberg; James Cobon-Kerr; Tara Sainath; Maribeth Rauh; Sayed Hadi Hashemi; Richard Ives; Yana Hasson; YaGuang Li; Eric Noland; Yuan Cao; Nathan Byrd; Le Hou; Qingze Wang; Thibault Sottiaux; Michela Paganini; Jean-Baptiste Lespiau; Alexandre Moufarek; Samer Hassan; Kaushik Shivakumar; Amersfoort Joost van; Amol Mandhane; Pratik Joshi; Anirudh Goyal; Matthew Tung; Andrew Brock; Hannah Sheahan; Vedant Misra; Cheng Li; Nemanja Rakićević; Mostafa Dehghani; Fangyu Liu; Sid Mittal; Junhyuk Oh; Seb Noury; Eren Sezener; Fantine Huot; Matthew Lamm; Cao Nicola De; Charlie Chen; Gamaleldin Elsayed; Ed Chi; Mahdis Mahdieh; Ian Tenney; Nan Hua; Ivan Petrychenko; Patrick Kane; Dylan Scandinaro; Rishub Jain; Jonathan Uesato; Romina Datta; Adam Sadovsky; Oskar Bunyan; Dominik Rabiej; Shimu Wu; John Zhang; Gautam Vasudevan; Edouard Leurent; Mahmoud Alnahlawi; Ionut Georgescu; Nan Wei; Ivy Zheng; Betty Chan; Pam G Rabinovitch; Piotr Stanczyk; Ye Zhang; David Steiner; Subhajit Naskar; Michael Azzam; Matthew Johnson; Adam Paszke; Chung-Cheng Chiu; Jaume Sanchez Elias; Afroz Mohiuddin; Faizan Muhammad; Jin Miao; Andrew Lee; Nino Vieillard; Sahitya Potluri; Jane Park; Elnaz Davoodi; Jiageng Zhang; Jeff Stanway; Drew Garmon; Abhijit Karmarkar; Zhe Dong; Jong Lee; Aviral Kumar; Luowei Zhou; Jonathan Evens; William Isaac; Zhe Chen; Johnson Jia; Anselm Levskaya; Zhenkai Zhu; Chris Gorgolewski; Peter Grabowski; Yu Mao; Alberto Magni; Kaisheng Yao; Javier Snaider; Norman Casagrande; Paul Suganthan; Evan Palmer; Geoffrey Irving; Edward Loper; Manaal Faruqui; Isha Arkatkar; Nanxin Chen; Izhak Shafran; Michael Fink; Alfonso Castaño; Irene Giannoumis; Wooyeol Kim; Mikołaj Rybiński; Ashwin Sreevatsa; Jennifer Prendki; David Soergel; Adrian Goedeckemeyer; Willi Gierke; Mohsen Jafari; Meenu Gaba; Jeremy Wiesner; Diana Gage Wright; Yawen Wei; Harsha Vashisht; Yana Kulizhskaya; Jay Hoover; Maigo Le; Lu Li; Chimezie Iwuanyanwu; Lu Liu; Kevin Ramirez; Andrey Khorlin; Albert Cui; Tian LIN; Marin Georgiev; Marcus Wu; Ricardo Aguilar; Keith Pallo; Abhishek Chakladar; Alena Repina; Xihui Wu; der Weide Tom van; Priya Ponnapalli; Caroline Kaplan; Jiri Simsa; Shuangfeng Li; Olivier Dousse; Fan Yang; Jeff Piper; Nathan Ie; Minnie Lui; Rama Pasumarthi; Nathan Lintz; Anitha Vijayakumar; Lam Nguyen Thiet; Daniel Andor; Pedro Valenzuela; Cosmin Paduraru; Daiyi Peng; Katherine Lee; Shuyuan Zhang; Somer Greene; Duc Dung Nguyen; Paula Kurylowicz; Sarmishta Velury; Sebastian Krause; Cassidy Hardin; Lucas Dixon; Lili Janzer; Kiam Choo; Ziqiang Feng; Biao Zhang; Achintya Singhal; Tejasi Latkar; Mingyang Zhang; Quoc Le; Elena Allica Abellan; Dayou Du; Dan McKinnon; Natasha Antropova; Tolga Bolukbasi; Orgad Keller; David Reid; Daniel Finchelstein; Maria Abi Raad; Remi Crocker; Peter Hawkins; Robert Dadashi; Colin Gaffney; Sid Lall; Ken Franko; Egor Filonov; Anna Bulanova; Rémi Leblond; Vikas Yadav; Shirley Chung; Harry Askham; Luis C. Cobo; Kelvin Xu; Felix Fischer; Jun Xu; Christina Sorokin; Chris Alberti; Chu-Cheng Lin; Colin Evans; Hao Zhou; Alek Dimitriev; Hannah Forbes; Dylan Banarse; Zora Tung; Jeremiah Liu; Mark Omernick; Colton Bishop; Chintu Kumar; Rachel Sterneck; Ryan Foley; Rohan Jain; Swaroop Mishra; Jiawei Xia; Taylor Bos; Geoffrey Cideron; Ehsan Amid; Francesco Piccinno; Xingyu Wang; Praseem Banzal; Petru Gurita; Hila Noga; Premal Shah; Daniel J. Mankowitz; Alex Polozov; Nate Kushman; Victoria Krakovna; Sasha Brown; MohammadHossein Bateni; Dennis Duan; Vlad Firoiu; Meghana Thotakuri; Tom Natan; Anhad Mohananey; Matthieu Geist; Sidharth Mudgal; Sertan Girgin; Hui Li; Jiayu Ye; Ofir Roval; Reiko Tojo; Michael Kwong; James Lee-Thorp; Christopher Yew; Quan Yuan; Sumit Bagri; Danila Sinopalnikov; Sabela Ramos; John Mellor; Abhishek Sharma; Aliaksei Severyn; Jonathan Lai; Kathy Wu; Heng-Tze Cheng; David Miller; Nicolas Sonnerat; Denis Vnukov; Rory Greig; Jennifer Beattie; Emily Caveness; Libin Bai; Julian Eisenschlos; Alex Korchemniy; Tomy Tsai; Mimi Jasarevic; Weize Kong; Phuong Dao; Zeyu Zheng; Frederick Liu; Fan Yang; Rui Zhu; Mark Geller; Tian Huey Teh; Jason Sanmiya; Evgeny Gladchenko; Nejc Trdin; Andrei Sozanschi; Daniel Toyama; Evan Rosen; Sasan Tavakkol; Linting Xue; Chen Elkind; Oliver Woodman; John Carpenter; George Papamakarios; Rupert Kemp; Sushant Kafle; Tanya Grunina; Rishika Sinha; Alice Talbert; Abhimanyu Goyal; Diane Wu; Denese Owusu-Afriyie; Cosmo Du; Chloe Thornton; Jordi Pont-Tuset; Pradyumna Narayana; Jing Li; Sabaer Fatehi; John Wieting; Omar Ajmeri; Benigno Uria; Tao Zhu; Yeongil Ko; Laura Knight; Amélie Héliou; Ning Niu; Shane Gu; Chenxi Pang; Dustin Tran; Yeqing Li; Nir Levine; Ariel Stolovich; Norbert Kalb; Rebeca Santamaria-Fernandez; Sonam Goenka; Wenny Yustalim; Robin Strudel; Ali Elqursh; Balaji Lakshminarayanan; Charlie Deck; Shyam Upadhyay; Hyo Lee; Mike Dusenberry; Zonglin Li; Xuezhi Wang; Kyle Levin; Raphael Hoffmann; Dan Holtmann-Rice; Olivier Bachem; Summer Yue; Sho Arora; Eric Malmi; Daniil Mirylenka; Qijun Tan; Christy Koh; Soheil Hassas Yeganeh; Siim Põder; Steven Zheng; Francesco Pongetti; Mukarram Tariq; Yanhua Sun; Lucian Ionita; Mojtaba Seyedhosseini; Pouya Tafti; Ragha Kotikalapudi; Zhiyu Liu; Anmol Gulati; Jasmine Liu; Xinyu Ye; Bart Chrzaszcz; Lily Wang; Nikhil Sethi; Tianrun Li; Ben Brown; Shreya Singh; Wei Fan; Aaron Parisi; Joe Stanton; Chenkai Kuang; Vinod Koverkathu; Christopher A. Choquette-Choo; Yunjie Li; TJ Lu; Abe Ittycheriah; Prakash Shroff; Pei Sun; Mani Varadarajan; Sanaz Bahargam; Rob Willoughby; David Gaddy; Ishita Dasgupta; Guillaume Desjardins; Marco Cornero; Brona Robenek; Bhavishya Mittal; Ben Albrecht; Ashish Shenoy; Fedor Moiseev; Henrik Jacobsson; Alireza Ghaffarkhah; Morgane Rivière; Alanna Walton; Clément Crepy; Alicia Parrish; Yuan Liu; Zongwei Zhou; Clement Farabet; Carey Radebaugh; Praveen Srinivasan; der Salm Claudia van; Andreas Fidjeland; Salvatore Scellato; Eri Latorre-Chimoto; Hanna Klimczak-Plucińska; David Bridson; Cesare Dario de; Tom Hudson; Piermaria Mendolicchio; Lexi Walker; Alex Morris; Ivo Penchev; Matthew Mauger; Alexey Guseynov; Alison Reid; Seth Odoom; Lucia Loher; Victor Cotruta; Madhavi Yenugula; Dominik Grewe; Anastasia Petrushkina; Tom Duerig; Antonio Sanchez; Steve Yadlowsky; Amy Shen; Amir Globerson; Adam Kurzrok; Lynette Webb; Sahil Dua; Dong Li; Preethi Lahoti; Surya Bhupatiraju; Dan Hurt; Haroon Qureshi; Ananth Agarwal; Tomer Shani; Matan Eyal; Anuj Khare; Shreyas Rammohan Belle; Lei Wang; Chetan Tekur; Mihir Sanjay Kale; Jinliang Wei; Ruoxin Sang; Brennan Saeta; Tyler Liechty; Yi Sun; Yao Zhao; Stephan Lee; Pandu Nayak; Doug Fritz; Manish Reddy Vuyyuru; John Aslanides; Nidhi Vyas; Martin Wicke; Xiao Ma; Taylan Bilal; Evgenii Eltyshev; Daniel Balle; Nina Martin; Hardie Cate; James Manyika; Keyvan Amiri; Yelin Kim; Xi Xiong; Kai Kang; Florian Luisier; Nilesh Tripuraneni; David Madras; Mandy Guo; Austin Waters; Oliver Wang; Joshua Ainslie; Jason Baldridge; Han Zhang; Garima Pruthi; Jakob Bauer; Feng Yang; Riham Mansour; Jason Gelman; Yang Xu; George Polovets; Ji Liu; Honglong Cai; Warren Chen; XiangHai Sheng; Emily Xue; Sherjil Ozair; Adams Yu; Christof Angermueller; Xiaowei Li; Weiren Wang; Julia Wiesinger; Emmanouil Koukoumidis; Yuan Tian; Anand Iyer; Madhu Gurumurthy; Mark Goldenson; Parashar Shah; MK Blake; Hongkun Yu; Anthony Urbanowicz; Jennimaria Palomaki; Chrisantha Fernando; Kevin Brooks; Ken Durden; Harsh Mehta; Nikola Momchev; Elahe Rahimtoroghi; Maria Georgaki; Amit Raul; Sebastian Ruder; Morgan Redshaw; Jinhyuk Lee; Komal Jalan; Dinghua Li; Ginger Perng; Blake Hechtman; Parker Schuh; Milad Nasr; Mia Chen; Kieran Milan; Vladimir Mikulik; Trevor Strohman; Juliana Franco; Tim Green; Demis Hassabis; Koray Kavukcuoglu; Jeffrey Dean; Oriol Vinyals

http://arxiv.org/abs/2312.11285
Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent Diffusion Model. (99%)
Decheng Liu; Xijun Wang; Chunlei Peng; Nannan Wang; Ruiming Hu; Xinbo Gao

http://arxiv.org/abs/2312.11309
The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data Augmentations. (99%)
Zebin Yun; Achi-Or Weingarten; Eyal Ronen; Mahmood Sharif

http://arxiv.org/abs/2312.11057
DataElixir: Purifying Poisoned Dataset to Mitigate Backdoor Attacks via Diffusion Models. (16%)
Jiachen Zhou; Peizhuo Lv; Yibing Lan; Guozhu Meng; Kai Chen; Hualong Ma

http://arxiv.org/abs/2312.10982
A Comprehensive Survey of Attack Techniques, Implementation, and Mitigation Strategies in Large Language Models. (10%)
Aysan Esmradi; Daniel Wankit Yip; Chun Fai Chan

http://arxiv.org/abs/2312.11571
Model Stealing Attack against Recommender System. (10%)
Zhihao Zhu; Rui Fan; Chenwang Wu; Yi Yang; Defu Lian; Enhong Chen

http://arxiv.org/abs/2312.10943
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity. (4%)
Zhihao Zhu; Chenwang Wu; Rui Fan; Yi Yang; Defu Lian; Enhong Chen

http://arxiv.org/abs/2312.11026
MISA: Unveiling the Vulnerabilities in Split Federated Learning. (1%)
Wei Wan; Yuxuan Ning; Shengshan Hu; Lulu Xue; Minghui Li; Leo Yu Zhang; Hai Jin

http://arxiv.org/abs/2312.11094
A Survey of Side-Channel Attacks in Context of Cache -- Taxonomies, Analysis and Mitigation. (1%)
Ankit Pulkit; Smita Naval; Vijay Laxmi

http://arxiv.org/abs/2312.10657
UltraClean: A Simple Framework to Train Robust Neural Networks against Backdoor Attacks. (98%)
Bingyin Zhao; Yingjie Lao

http://arxiv.org/abs/2312.10911
The Pros and Cons of Adversarial Robustness. (92%)
Yacine Izza; Joao Marques-Silva

http://arxiv.org/abs/2312.10766
A Mutation-Based Method for Multi-Modal Jailbreaking Attack Detection. (80%)
Xiaoyu Zhang; Cen Zhang; Tianlin Li; Yihao Huang; Xiaojun Jia; Xiaofei Xie; Yang Liu; Chao Shen

http://arxiv.org/abs/2312.10903
Robust Node Representation Learning via Graph Variational Diffusion Networks. (11%)
Jun Zhuang; Mohammad Al Hasan

http://arxiv.org/abs/2312.11550
A Study on Transferability of Deep Learning Models for Network Intrusion Detection. (4%)
Shreya Ghosh; Abu Shafin Mohammad Mahdee Jameel; Aly El Gamal

http://arxiv.org/abs/2312.10329
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off. (99%)
Yu-An Liu; Ruqing Zhang; Mingkun Zhang; Wei Chen; Rijke Maarten de; Jiafeng Guo; Xueqi Cheng

http://arxiv.org/abs/2312.10534
Rethinking Robustness of Model Attributions. (80%)
Sandesh Kamath; Sankalp Mittal; Amit Deshpande; Vineeth N Balasubramanian

http://arxiv.org/abs/2312.10578
SAME: Sample Reconstruction Against Model Extraction Attacks. (13%)
Yi Xie; Jie Zhang; Shiqian Zhao; Tianwei Zhang; Xiaofeng Chen

http://arxiv.org/abs/2312.10508
TrojFair: Trojan Fairness Attacks. (8%)
Mengxin Zheng; Jiaqi Xue; Yi Sheng; Lei Yang; Qian Lou; Lei Jiang

http://arxiv.org/abs/2312.10529
Transformers in Unsupervised Structure-from-Motion. (3%)
Hemang Chawla; Arnav Varma; Elahe Arani; Bahram Zonooz

http://arxiv.org/abs/2312.10467
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning. (2%)
Mengxin Zheng; Jiaqi Xue; Xun Chen; YanShan Wang; Qian Lou; Lei Jiang

http://arxiv.org/abs/2312.09935
LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfer. (99%)
Yuxin Cao; Ziyu Zhao; Xi Xiao; Derui Wang; Minhui Xue; Jin Lu

http://arxiv.org/abs/2312.09554
Embodied Adversarial Attack: A Dynamic Robust Physical Attack in Autonomous Driving. (99%)
Yitong Sun; Yao Huang; Xingxing Wei

http://arxiv.org/abs/2312.09558
Towards Transferable Targeted 3D Adversarial Attack in the Physical World. (99%)
Yao Huang; Yinpeng Dong; Shouwei Ruan; Xiao Yang; Hang Su; Xingxing Wei

http://arxiv.org/abs/2312.09636
A Malware Classification Survey on Adversarial Attacks and Defences. (98%)
Mahesh Datta Sai Ponnuru; Likhitha Amasala; Tanu Sree Bhimavarapu; Guna Chaitanya Garikipati

http://arxiv.org/abs/2312.09665
FlowMur: A Stealthy and Practical Audio Backdoor Attack with Limited Knowledge. (76%)
Jiahe Lan; Jie Wang; Baochen Yan; Zheng Yan; Elisa Bertino

http://arxiv.org/abs/2312.10132
Closing the Gap: Achieving Better Accuracy-Robustness Tradeoffs Against Query-Based Attacks. (74%)
Pascal Zimmer; Sébastien Andreina; Giorgia Azzurra Marson; Ghassan Karame

http://arxiv.org/abs/2312.09821
Fragility, Robustness and Antifragility in Deep Learning. (67%)
Chandresh Pravin; Ivan Martino; Giuseppe Nicosia; Varun Ojha

http://arxiv.org/abs/2312.09748
Verification-Friendly Deep Neural Networks. (56%)
Anahita Baninajjar; Ahmed Rezine; Amir Aminifar

http://arxiv.org/abs/2312.09669
Silent Guardian: Protecting Text from Malicious Exploitation by Large Language Models. (8%)
Jiawei Zhao; Kejiang Chen; Xiaojian Yuan; Yuang Qi; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2312.08675
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection. (99%)
Xiangtao Meng; Li Wang; Shanqing Guo; Lei Ju; Qingchuan Zhao

http://arxiv.org/abs/2312.09481
Continual Adversarial Defense. (95%)
Qian Wang; Yaoyao Liu; Hefei Ling; Yingwei Li; Qihao Liu; Ping Li; Jiazhong Chen; Alan Yuille; Ning Yu

http://arxiv.org/abs/2312.09520
SlowTrack: Increasing the Latency of Camera-based Perception in Autonomous Driving Using Adversarial Examples. (92%)
Chen Ma; Ningfei Wang; Qi Alfred Chen; Chao Shen

http://arxiv.org/abs/2312.09057
On the Difficulty of Defending Contrastive Learning against Backdoor Attacks. (84%)
Changjiang Li; Ren Pang; Bochuan Cao; Zhaohan Xi; Jinghui Chen; Shouling Ji; Ting Wang

http://arxiv.org/abs/2312.08898
Detection and Defense of Unlearnable Examples. (81%)
Yifan Zhu; Lijia Yu; Xiao-Shan Gao

http://arxiv.org/abs/2312.08751
Improve Robustness of Reinforcement Learning against Observation Perturbations via $l_\infty$ Lipschitz Policy Networks. (81%)
Buqing Nie; Jingtian Ji; Yangqing Fu; Yue Gao

http://arxiv.org/abs/2312.09533
Adversarial Robustness on Image Classification with $k$-means. (81%)
Rollin Omari; Junae Kim; Paul Montague

http://arxiv.org/abs/2312.08667
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey. (76%)
Yichen Wan; Youyang Qu; Wei Ni; Yong Xiang; Longxiang Gao; Ekram Hossain

http://arxiv.org/abs/2312.09027
DRAM-Locker: A General-Purpose DRAM Protection Mechanism against Adversarial DNN Weight Attacks. (45%)
Ranyang Zhou; Sabbir Ahmed; Arman Roohi; Adnan Siraj Rakin; Shaahin Angizi

http://arxiv.org/abs/2312.09494
No-Skim: Towards Efficiency Robustness Evaluation on Skimming-based Language Models. (45%)
Shengyao Zhang; Mi Zhang; Xudong Pan; Min Yang

http://arxiv.org/abs/2312.08793
Forbidden Facts: An Investigation of Competing Objectives in Llama-2. (45%)
Tony T. Wang; Miles Wang; Kaivalya Hariharan; Nir Shavit

http://arxiv.org/abs/2312.09078
Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret. (13%)
Adam Żychowski; Andrew Perrault; Jacek Mańdziuk

http://arxiv.org/abs/2312.09020
Exploring Transferability for Randomized Smoothing. (5%)
Kai Qiu; Huishuai Zhang; Zhirong Wu; Stephen Lin

http://arxiv.org/abs/2312.09148
Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting. (1%)
Anthony Chen; Huanrui Yang; Yulu Gan; Denis A Gudovskiy; Zhen Dong; Haofan Wang; Tomoyuki Okuno; Yohei Nakata; Shanghang Zhang; Kurt Keutzer

http://arxiv.org/abs/2312.08890
Defenses in Adversarial Machine Learning: A Survey. (99%)
Baoyuan Wu; Shaokui Wei; Mingli Zhu; Meixi Zheng; Zihao Zhu; Mingda Zhang; Hongrui Chen; Danni Yuan; Li Liu; Qingshan Liu

http://arxiv.org/abs/2312.07961
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification. (99%)
Xiaojun Xue; Chunxia Zhang; Tianxiang Xu; Zhendong Niu

http://arxiv.org/abs/2312.08193
Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection. (98%)
Samrat Mukherjee; Dibyanayan Bandyopadhyay; Baban Gain; Asif Ekbal

http://arxiv.org/abs/2312.08651
Towards Inductive Robustness: Distilling and Fostering Wave-induced Resonance in Transductive GCNs Against Graph Adversarial Attacks. (83%)
Ao Liu; Wenshan Li; Tao Li; Beibei Li; Hanyuan Huang; Pan Zhou

http://arxiv.org/abs/2312.08622
Scalable Ensemble-based Detection Method against Adversarial Attacks for speaker verification. (64%)
Haibin Wu; Heng-Cheng Kuo; Yu Tsao; Hung-yi Lee

http://arxiv.org/abs/2312.07991
Accelerating the Global Aggregation of Local Explanations. (47%)
Alon Mor; Yonatan Belinkov; Benny Kimelfeld

http://arxiv.org/abs/2312.07955
Erasing Self-Supervised Learning Backdoor by Cluster Activation Masking. (13%)
Shengsheng Qian; Yifei Wang; Dizhan Xue; Shengjie Zhang; Huaiwen Zhang; Changsheng Xu

http://arxiv.org/abs/2312.08143
Efficient Representation of the Activation Space in Deep Neural Networks. (11%)
Tanya Akumu; Celia Cintas; Girmaw Abebe Tadesse; Adebayo Oshingbesan; Skyler Speakman; Edward III McFowland

http://arxiv.org/abs/2312.08303
Efficient Toxic Content Detection by Bootstrapping and Distilling Large Language Models. (1%)
Jiang Zhang; Qiong Wu; Yiming Xu; Cheng Cao; Zheng Du; Konstantinos Psounis

http://arxiv.org/abs/2312.07821
Radio Signal Classification by Adversarially Robust Quantum Machine Learning. (99%)
Yanqiu Wu; Eromanga Adermann; Chandra Thapa; Seyit Camtepe; Hajime Suzuki; Muhammad Usman

http://arxiv.org/abs/2312.07258
SSTA: Salient Spatially Transformed Attack. (99%)
Renyang Liu; Wei Zhou; Sixin Wu; Jun Zhao; Kwok-Yan Lam

http://arxiv.org/abs/2312.07245
DTA: Distribution Transform-based Attack for Query-Limited Scenario. (99%)
Renyang Liu; Wei Zhou; Xin Jin; Song Gao; Yuanyu Wang; Ruxin Wang

http://arxiv.org/abs/2312.08877
May the Noise be with you: Adversarial Training without Adversarial Examples. (98%)
Ayoub Arous; Andres F Lopez-Lopera; Nael Abu-Ghazaleh; Ihsen Alouani

http://arxiv.org/abs/2312.07364
Collapse-Oriented Adversarial Training with Triplet Decoupling for Robust Image Retrieval. (98%)
Qiwei Tian; Chenhao Lin; Qian Li; Zhengyu Zhao; Chao Shen

http://arxiv.org/abs/2312.07067
Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training. (98%)
Qian Li; Yuxiao Hu; Yinpeng Dong; Dongxiao Zhang; Yuntian Chen

http://arxiv.org/abs/2312.11510
QuadAttack: A Quadratic Programming Approach to Ordered Top-K Attacks. (97%)
Thomas Paniagua; Ryan Grainger; Tianfu Wu

http://arxiv.org/abs/2312.06991
Attacking the Loop: Adversarial Attacks on Graph-based Loop Closure Detection. (92%)
Jonathan J. Y. Kim; Martin Urschler; Patricia J. Riddle; Jorg S. Wicker

http://arxiv.org/abs/2312.07392
ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning. (86%)
Xiangyu Yin; Sihao Wu; Jiaxu Liu; Meng Fang; Xingyu Zhao; Xiaowei Huang; Wenjie Ruan

http://arxiv.org/abs/2312.07784
Robust MRI Reconstruction by Smoothed Unrolling (SMUG). (82%)
Shijun Liang; Van Hoang Minh Nguyen; Jinghan Jia; Ismail Alkhouri; Sijia Liu; Saiprasad Ravishankar

http://arxiv.org/abs/2312.07158
Cost Aware Untargeted Poisoning Attack against Graph Neural Networks,. (70%)
Yuwei Han; Yuni Lai; Yulin Zhu; Kai Zhou

http://arxiv.org/abs/2312.07022
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning. (47%)
Hiroya Kato; Kento Hasegawa; Seira Hidano; Kazuhide Fukushima

http://arxiv.org/abs/2312.07876
Causality Analysis for Evaluating the Security of Large Language Models. (22%)
Wei Zhao; Zhe Li; Jun Sun

http://arxiv.org/abs/2312.07865
SimAC: A Simple Anti-Customization Method against Text-to-Image Synthesis of Diffusion Models. (13%)
Feifei Wang; Zhentao Tan; Tianyi Wei; Yue Wu; Qidong Huang

http://arxiv.org/abs/2312.07130
Divide-and-Conquer Attack: Harnessing the Power of LLM to Bypass Safety Filters of Text-to-Image Models. (8%)
Yimo Deng; Huangxun Chen

http://arxiv.org/abs/2312.07389
Eroding Trust In Aerial Imagery: Comprehensive Analysis and Evaluation Of Adversarial Attacks In Geospatial Systems. (5%)
Michael Lanier; Aayush Dhakal; Zhexiao Xiong; Arthur Li; Nathan Jacobs; Yevgeniy Vorobeychik

http://arxiv.org/abs/2312.07870
Securing Graph Neural Networks in MLaaS: A Comprehensive Realization of Query-based Integrity Verification. (2%)
Bang Wu; Xingliang Yuan; Shuo Wang; Qi Li; Minhui Xue; Shirui Pan

http://arxiv.org/abs/2312.07709
Majority is Not Required: A Rational Analysis of the Private Double-Spend Attack from a Sub-Majority Adversary. (1%)
Yanni Georghiades; Rajesh Mishra; Karl Kreder; Sriram Vishwanath

http://arxiv.org/abs/2312.06199
Towards Transferable Adversarial Attacks with Centralized Perturbation. (99%)
Shangbo Wu; Yu-an Tan; Yajie Wang; Ruinan Ma; Wencong Ma; Yuanzhang Li

http://arxiv.org/abs/2312.06423
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks. (98%)
Yuyang Zhou; Guang Cheng; Zongyao Chen; Shui Yu

http://arxiv.org/abs/2312.06568
Sparse but Strong: Crafting Adversarially Robust Graph Lottery Tickets. (83%)
Subhajit Dutta Chowdhury; Zhiyu Ni; Qingyuan Peng; Souvik Kundu; Pierluigi Nuzzo

http://arxiv.org/abs/2312.06436
Reward Certification for Policy Smoothed Reinforcement Learning. (78%)
Ronghui Mu; Leandro Soriano Marcolino; Tianle Zhang; Yanghao Zhang; Xiaowei Huang; Wenjie Ruan

http://arxiv.org/abs/2312.06230
Activation Gradient based Poisoned Sample Detection Against Backdoor Attacks. (31%)
Danni Yuan; Shaokui Wei; Mingda Zhang; Li Liu; Baoyuan Wu

http://arxiv.org/abs/2312.06227
Poisoned ChatGPT Finds Work for Idle Hands: Exploring Developers' Coding Practices with Insecure Suggestions from Poisoned AI Models. (22%)
Sanghak Oh; Kiho Lee; Seonhye Park; Doowon Kim; Hyoungshick Kim

http://arxiv.org/abs/2312.06564
Promoting Counterfactual Robustness through Diversity. (13%)
Francesco Leofante; Nico Potyka

http://arxiv.org/abs/2401.08634
Resilient Path Planning for UAVs in Data Collection under Adversarial Attacks. (10%)
Xueyuan Wang; M. Cenk Gursoy

http://arxiv.org/abs/2312.06163
Adversarial Camera Patch: An Effective and Robust Physical-World Attack on Object Detectors. (1%)
Kalibinuer Tiliwalidi

http://arxiv.org/abs/2312.06557
Robust Graph Neural Network based on Graph Denoising. (1%)
Victor M. Tenorio; Samuel Rey; Antonio G. Marques

http://arxiv.org/abs/2312.05924
Data-Free Hard-Label Robustness Stealing Attack. (86%)
Xiaojian Yuan; Kejiang Chen; Wen Huang; Jie Zhang; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2312.06010
A Practical Survey on Emerging Threats from AI-driven Voice Attacks: How Vulnerable are Commercial Voice Control Systems? (76%)
Yuanda Wang; Qiben Yan; Nikolay Ivanov; Xun Chen

http://arxiv.org/abs/2312.06077
An Ambiguity Measure for Recognizing the Unknowns in Deep Learning. (12%)
Roozbeh Yousefzadeh

http://arxiv.org/abs/2312.06056
METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities. (2%)
Sangwon Hyun; Mingyu Guo; M. Ali Babar

http://arxiv.org/abs/2312.05502
Poisoning $\times$ Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks. (99%)
Ege Erdogan; Simon Geisler; Stephan Günnemann

http://arxiv.org/abs/2312.05508
Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation. (98%)
Shiji Zhao; Xizhe Wang; Xingxing Wei

http://arxiv.org/abs/2312.06701
Dynamic Adversarial Attacks on Autonomous Driving Systems. (98%)
Amirhosein Chahe; Chenan Wang; Abhishek Jeyapratap; Kaidi Xu; Lifeng Zhou

http://arxiv.org/abs/2312.05716
Initialization Matters for Adversarial Transfer Learning. (76%)
Andong Hua; Jindong Gu; Zhiyu Xue; Nicholas Carlini; Eric Wong; Yao Qin

http://arxiv.org/abs/2312.04879
HC-Ref: Hierarchical Constrained Refinement for Robust Adversarial Training of GNNs. (99%)
Xiaobing Pei; Haoran Yang; Gang Shen

http://arxiv.org/abs/2312.04913
SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation. (99%)
Bangyan He; Xiaojun Jia; Siyuan Liang; Tianrui Lou; Yang Liu; Xiaochun Cao

http://arxiv.org/abs/2312.04960
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness. (99%)
Xiaoyun Xu; Shujian Yu; Jingzheng Wu; Stjepan Picek

http://arxiv.org/abs/2312.04902
BELT: Old-School Backdoor Attacks can Evade the State-of-the-Art Defense with Backdoor Exclusivity Lifting. (96%)
Huming Qiu; Junjie Sun; Mi Zhang; Xudong Pan; Min Yang

http://arxiv.org/abs/2312.06627
An adversarial attack approach for eXplainable AI evaluation on deepfake detection models. (38%)
Balachandar Gowrisankar; Vrizlynn L. L. Thing

http://arxiv.org/abs/2312.11500
A Red Teaming Framework for Securing AI in Maritime Autonomous Systems. (3%)
Mathew J. Walter; Aaron Barrett; Kimberly Tam

http://arxiv.org/abs/2312.04893
Annotation-Free Group Robustness via Loss-Based Resampling. (2%)
Mahdi Ghaznavi; Hesam Asadollahzadeh; HamidReza Yaghoubi Araghi; Fahimeh Hosseini Noohdani; Mohammad Hossein Rohban; Mahdieh Soleymani Baghshah

http://arxiv.org/abs/2312.04828
HuRef: HUman-REadable Fingerprint for Large Language Models. (1%)
Boyi Zeng; Chenghu Zhou; Xinbing Wang; Zhouhan Lin

http://arxiv.org/abs/2312.04802
MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model. (99%)
Kaiyu Song; Hanjiang Lai

http://arxiv.org/abs/2312.04403
OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization. (99%)
Dongchen Han; Xiaojun Jia; Yang Bai; Jindong Gu; Yang Liu; Xiaochun Cao

http://arxiv.org/abs/2312.04692
Diffence: Fencing Membership Privacy With Diffusion Models. (97%)
Yuefeng Peng; Ali Naseh; Amir Houmansadr

http://arxiv.org/abs/2312.04432
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning. (70%)
Hossein Fereidooni; Alessandro Pegoraro; Phillip Rieger; Alexandra Dmitrienko; Ahmad-Reza Sadeghi

http://arxiv.org/abs/2312.04748
Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks. (64%)
Shuli Jiang; Swanand Ravindra Kadhe; Yi Zhou; Ling Cai; Nathalie Baracaldo

http://arxiv.org/abs/2312.04730
DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions. (15%)
Fangzhou Wu; Xiaogeng Liu; Chaowei Xiao

http://arxiv.org/abs/2312.04035
Defense against ML-based Power Side-channel Attacks on DNN Accelerators with Adversarial Attacks. (98%)
Xiaobei Yan; Chip Hong Chang; Tianwei Zhang

http://arxiv.org/abs/2312.03520
Defense Against Adversarial Attacks using Convolutional Auto-Encoders. (97%)
Shreyasi Mandal

http://arxiv.org/abs/2312.03979
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks. (88%)
Yuni Lai; Yulin Zhu; Bailin Pan; Kai Zhou

http://arxiv.org/abs/2312.04032
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training. (54%)
Jaehyung Kim; Yuning Mao; Rui Hou; Hanchao Yu; Davis Liang; Pascale Fung; Qifan Wang; Fuli Feng; Lifu Huang; Madian Khabsa

http://arxiv.org/abs/2312.03410
Detecting Voice Cloning Attacks via Timbre Watermarking. (13%)
Chang Liu; Jie Zhang; Tianwei Zhang; Xi Yang; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2312.03419
Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models. (11%)
Sze Jue Yang; Chinh D. La; Quang H. Nguyen; Eugene Bagdasaryan; Kok-Seng Wong; Anh Tuan Tran; Chee Seng Chan; Khoa D. Doan

http://arxiv.org/abs/2312.03853
Dr. Jekyll and Mr. Hyde: Two Faces of LLMs. (4%)
Matteo Gioele Collu; Tom Janssen-Groesbeek; Stefanos Koffas; Mauro Conti; Stjepan Picek

http://arxiv.org/abs/2312.03991
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operator. (2%)
Xiao-Yin Liu; Xiao-Hu Zhou; Guo-Tao Li; Hao Li; Mei-Jiang Gui; Tian-Yu Xiang; De-Xing Huang; Zeng-Guang Hou

http://arxiv.org/abs/2312.03030
Generating Visually Realistic Adversarial Patch. (99%)
Xiaosen Wang; Kunyu Wang

http://arxiv.org/abs/2312.03245
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System. (99%)
Xinwei Yuan; Shu Han; Wei Huang; Hongliang Ye; Xianglong Kong; Fan Zhang

http://arxiv.org/abs/2312.02912
Realistic Scatterer Based Adversarial Attacks on SAR Image Classifiers. (99%)
Tian Ye; Rajgopal Kannan; Viktor Prasanna; Carl Busart; Lance Kaplan

http://arxiv.org/abs/2312.03085
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection. (99%)
Xiaohu Lu; Hayder Radha

http://arxiv.org/abs/2312.03289
Class Incremental Learning for Adversarial Robustness. (98%)
Seungju Cho; Hongsin Lee; Changick Kim

http://arxiv.org/abs/2312.02708
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More. (89%)
Jan Schuchardt; Yan Scholten; Stephan Günnemann

http://arxiv.org/abs/2312.03777
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks. (69%)
Xuanimng Cui; Alejandro Aparcedo; Young Kyun Jang; Ser-Nam Lim

http://arxiv.org/abs/2312.02780
Scaling Laws for Adversarial Attacks on Language Model Activations. (50%)
Stanislav Fort

http://arxiv.org/abs/2312.03286
Indirect Gradient Matching for Adversarial Robust Distillation. (13%)
Hongsin Lee; Seungju Cho; Changick Kim

http://arxiv.org/abs/2312.02673
Robust Backdoor Detection for Deep Learning via Topological Evolution Dynamics. (3%)
Xiaoxing Mo; Yechao Zhang; Leo Yu Zhang; Wei Luo; Nan Sun; Shengshan Hu; Shang Gao; Yang Xiang

http://arxiv.org/abs/2312.02614
Prompt Optimization via Adversarial In-Context Learning. (3%)
Xuan Long Do; Yiran Zhao; Hannah Brown; Yuxi Xie; James Xu Zhao; Nancy F. Chen; Kenji Kawaguchi; Michael Qizhe Xie; Junxian He

http://arxiv.org/abs/2312.03252
Privacy-Preserving Task-Oriented Semantic Communications Against Model Inversion Attacks. (2%)
Yanhu Wang; Shuaishuai Guo; Yiqin Deng; Haixia Zhang; Yuguang Fang

http://arxiv.org/abs/2312.02546
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning. (2%)
Zhuo Huang; Chang Liu; Yinpeng Dong; Hang Su; Shibao Zheng; Tongliang Liu

http://arxiv.org/abs/2312.01679
Adversarial Medical Image with Hierarchical Feature Hiding. (99%)
Qingsong Yao; Zecheng He; Yuexiang Li; Yi Lin; Kai Ma; Yefeng Zheng; S. Kevin Zhou

http://arxiv.org/abs/2312.01886
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models. (99%)
Xunguang Wang; Zhenlan Ji; Pingchuan Ma; Zongjie Li; Shuai Wang

http://arxiv.org/abs/2312.02237
Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness. (98%)
Guanlin Li; Naishan Zheng; Man Zhou; Jie Zhang; Tianwei Zhang

http://arxiv.org/abs/2312.01789
Two-stage optimized unified adversarial patch for attacking visible-infrared cross-modal detectors in the physical world. (12%)
Chengyin Hu; Weiwen Shi

http://arxiv.org/abs/2312.02400
Auto DP-SGD: Dual Improvements of Privacy and Accuracy via Automatic Clipping Threshold and Noise Multiplier Estimation. (1%)
Sai Venkatesh Chilukoti; Md Imran Hossen; Liqun Shan; Vijay Srinivas Tida; Xiai Hei

http://arxiv.org/abs/2312.02147
Rejuvenating image-GPT as Strong Visual Representation Learners. (1%)
Sucheng Ren; Zeyu Wang; Hongru Zhu; Junfei Xiao; Alan Yuille; Cihang Xie

http://arxiv.org/abs/2312.02220
QuantAttack: Exploiting Dynamic Quantization to Attack Vision Transformers. (99%)
Amit Baras; Alon Zolfi; Yuval Elovici; Asaf Shabtai

http://arxiv.org/abs/2312.01585
OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection. (61%)
Haoyu Jiang; Haiyang Yu; Nan Li; Ping Yi

http://arxiv.org/abs/2312.01330
Evaluating the Security of Satellite Systems. (16%)
Roy Peled; Eran Aizikovich; Edan Habler; Yuval Elovici; Asaf Shabtai

http://arxiv.org/abs/2312.01468
Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving. (13%)
Bo Yang; Xiaoyu Ji; Xiaoyu Ji; Xiaoyu Ji; Xiaoyu Ji

http://arxiv.org/abs/2312.04584
Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger. (2%)
Yiming Li; Mingyan Zhu; Junfeng Guo; Tao Wei; Shu-Tao Xia; Zhan Qin

http://arxiv.org/abs/2312.02207
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation. (99%)
Xiaojun Jia; Jindong Gu; Yihao Huang; Simeng Qin; Qing Guo; Yang Liu; Xiaochun Cao

http://arxiv.org/abs/2312.01260
Rethinking PGD Attack: Is Sign Function Necessary? (98%)
Junjie Yang; Tianlong Chen; Xuxi Chen; Zhangyang Wang; Yingbin Liang

http://arxiv.org/abs/2312.01045
PROFL: A Privacy-Preserving Federated Learning Method with Stringent Defense Against Poisoning Attacks. (61%)
Yisheng Zhong; Li-Ping Wang

http://arxiv.org/abs/2312.01281
Mendata: A Framework to Purify Manipulated Training Data. (2%)
Zonghao Huang; Neil Gong; Michael K. Reiter

http://arxiv.org/abs/2312.00508
PyraTrans: Learning Attention-Enriched Multi-Scale Pyramid Network from Pre-Trained Transformers for Effective Malicious URL Detection. (69%)
Ruitong Liu; Yanbin Wang; Zhenhao Guo; Haitao Xu; Zhan Qin; Wenrui Ma; Fan Zhang

http://arxiv.org/abs/2312.00942
Survey of Security Issues in Memristor-based Machine Learning Accelerators for RF Analysis. (22%)
William Lillis; Max Cohen Hoffing; Wayne Burleson

http://arxiv.org/abs/2312.00987
Deep Generative Attacks and Countermeasures for Data-Driven Offline Signature Verification. (10%)
An Ngo; MinhPhuong Cao; Rajesh Kumar

http://arxiv.org/abs/2312.00359
Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training. (1%)
Yefan Zhou; Tianyu Pang; Keqin Liu; Charles H. Martin; Michael W. Mahoney; Yaoqing Yang

http://arxiv.org/abs/2312.00741
Crystal: Enhancing Blockchain Mining Transparency with Quorum Certificate. (1%)
Jianyu Niu; Fangyu Gai; Runchao Han; Ren Zhang; Yinqian Zhang; Chen Feng

http://arxiv.org/abs/2312.00105
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training. (98%)
Saurabh Farkya; Aswin Raghavan; Avi Ziskind

http://arxiv.org/abs/2311.18820
Adversarial Attacks and Defenses for Wireless Signal Classifiers using CDI-aware GANs. (98%)
Sujata Sinha; Alkan Soysal

http://arxiv.org/abs/2312.00157
Universal Backdoor Attacks. (97%)
Benjamin Schneider; Nils Lukas; Florian Kerschbaum

http://arxiv.org/abs/2312.00173
Fool the Hydra: Adversarial Attacks against Multi-view Object Detection Systems. (97%)
Bilel Tarchoun; Quazi Mishkatul Alam; Nael Abu-Ghazaleh; Ihsen Alouani

http://arxiv.org/abs/2311.18403
Corrupting Convolution-based Unlearnable Datasets with Pixel-based Image Transformations. (88%)
Xianlong Wang; Shengshan Hu; Minghui Li; Zhifei Yu; Ziqi Zhou; Leo Yu Zhang; Hai Jin

http://arxiv.org/abs/2312.00198
Optimal Attack and Defense for Reinforcement Learning. (76%)
Jeremy McMahan; Young Wu; Xiaojin Zhu; Qiaomin Xie

http://arxiv.org/abs/2312.00084
Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion? (74%)
Zhengyue Zhao; Jinhao Duan; Kaidi Xu; Chenan Wang; Rui Zhangp Zidong Dup Qi Guo; Xing Hu

http://arxiv.org/abs/2311.18495
Improving Adversarial Transferability via Model Alignment. (68%)
Avery Ma; Amir-massoud Farahmand; Yangchen Pan; Philip Torr; Jindong Gu

http://arxiv.org/abs/2311.18498
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach. (62%)
Kai Li; Jingjing Zheng; Xin Yuan; Wei Ni; Ozgur B. Akan; H. Vincent Poor

http://arxiv.org/abs/2312.00273
Mark My Words: Analyzing and Evaluating Language Model Watermarks. (2%)
Julien Piet; Chawin Sitawarin; Vivian Fang; Norman Mu; David Wagner

http://arxiv.org/abs/2311.17400
Improving the Robustness of Transformer-based Large Language Models with Dynamic Attention. (98%)
Lujia Shen; Yuwen Pu; Shouling Ji; Changjiang Li; Xuhong Zhang; Chunpeng Ge; Ting Wang

http://arxiv.org/abs/2311.17434
Group-wise Sparse and Explainable Adversarial Attacks. (96%)
Shpresim Sadiku; Moritz Wagner; Sebastian Pokutta

http://arxiv.org/abs/2311.17458
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses. (88%)
David Winderl; Nicola Franco; Jeanette Miriam Lorenz

http://arxiv.org/abs/2311.17853
On the Adversarial Robustness of Graph Contrastive Learning Methods. (83%)
Filippo Guerranti; Zinuo Yi; Anna Starovoit; Rafiq Kamel; Simon Geisler; Stephan Günnemann

http://arxiv.org/abs/2311.17608
Adversarial Robust Memory-Based Continual Learner. (81%)
Xiaoyue Mi; Fan Tang; Zonghan Yang; Danding Wang; Juan Cao; Peng Li; Yang Liu

http://arxiv.org/abs/2311.17983
Improving Faithfulness for Vision Transformers. (80%)
Lijie Hu; Yixin Liu; Ninghao Liu; Mengdi Huai; Lichao Sun; Di Wang

http://arxiv.org/abs/2311.17429
TARGET: Template-Transferable Backdoor Attack Against Prompt-based NLP Models via GPT4. (68%)
Zihao Tan; Qingliang Chen; Yongjian Huang; Chen Liang

http://arxiv.org/abs/2311.17607
Topology-Preserving Adversarial Training. (10%)
Xiaoyue Mi; Fan Tang; Yepeng Weng; Danding Wang; Juan Cao; Sheng Tang; Peng Li; Yang Liu

http://arxiv.org/abs/2311.17600
Query-Relevant Images Jailbreak Large Multi-Modal Models. (9%)
Xin Liu; Yichen Zhu; Yunshi Lan; Chao Yang; Yu Qiao

http://arxiv.org/abs/2311.17833
Analyzing and Explaining Image Classifiers via Diffusion Guidance. (8%)
Maximilian Augustin; Yannic Neuhaus; Matthias Hein

http://arxiv.org/abs/2311.18244
Poisoning Attacks Against Contrastive Recommender Systems. (2%)
Zongwei Wang; Junliang Yu; Min Gao; Hongzhi Yin; Bin Cui; Shazia Sadiq

http://arxiv.org/abs/2311.17722
SenTest: Evaluating Robustness of Sentence Encoders. (2%)
Tanmay Chavan; Shantanu Patankar; Aditya Kane; Omkar Gokhale; Geetanjali Kale; Raviraj Joshi

http://arxiv.org/abs/2311.17583
CLIPC8: Face liveness detection algorithm based on image-text pairs and contrastive learning. (1%)
Xu Liu; Shu Zhou; Yurong Song; Wenzhe Luo; Xin Zhang

http://arxiv.org/abs/2311.17391
Unveiling the Implicit Toxicity in Large Language Models. (1%)
Jiaxin Wen; Pei Ke; Hao Sun; Zhexin Zhang; Chengfei Li; Jinfeng Bai; Minlie Huang

http://arxiv.org/abs/2311.17128
Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks. (99%)
Lucas Beerens; Desmond J. Higham

http://arxiv.org/abs/2311.17332
NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields. (99%)
Xiaoliang Liu; Furao Shen; Feng Han; Jian Zhao; Changhai Nie

http://arxiv.org/abs/2311.16577
Efficient Key-Based Adversarial Defense for ImageNet by Using Pre-trained Model. (98%)
AprilPyone MaungMaung; Isao Echizen; Hitoshi Kiya

http://arxiv.org/abs/2311.17339
RADAP: A Robust and Adaptive Defense Against Diverse Adversarial Patches on Face Recognition. (92%)
Xiaoliang Liu; Furao Shen; Jian Zhao; Changhai Nie

http://arxiv.org/abs/2401.05338
STR-Cert: Robustness Certification for Deep Text Recognition on Deep Learning Pipelines and Vision Transformers. (26%)
Daqian Shao; Lukas Fesser; Marta Kwiatkowska

http://arxiv.org/abs/2311.16833
1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness. (13%)
Bernd Prach; Fabio Brau; Giorgio Buttazzo; Christoph H. Lampert

http://arxiv.org/abs/2311.17035
Scalable Extraction of Training Data from (Production) Language Models. (10%)
Milad Nasr; Nicholas Carlini; Jonathan Hayase; Matthew Jagielski; A. Feder Cooper; Daphne Ippolito; Christopher A. Choquette-Choo; Eric Wallace; Florian Tramèr; Katherine Lee

http://arxiv.org/abs/2311.16661
Cooperative Abnormal Node Detection with Adversary Resistance: A Probabilistic Approach. (10%)
Yingying Huangfu; Tian Bai

http://arxiv.org/abs/2311.16526
On robust overfitting: adversarial training induced distribution matters. (1%)
Runzhi Tian; Yongyi Mao

http://arxiv.org/abs/2311.16681
Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations. (1%)
Maximilian Dreyer; Reduan Achtibat; Wojciech Samek; Sebastian Lapuschkin

http://arxiv.org/abs/2311.17138
Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now. (1%)
Ayush Sarkar; Hanlin Mai; Amitabh Mahapatra; Svetlana Lazebnik; D. A. Forsyth; Anand Bhattad

http://arxiv.org/abs/2311.16478
RetouchUAA: Unconstrained Adversarial Attack via Image Retouching. (99%)
Mengda Xie; Yiling He; Meie Fang

http://arxiv.org/abs/2311.15994
Adversaral Doodles: Interpretable and Human-drawable Attacks Provide Describable Insights. (99%)
Ryoya Nara; Yusuke Matsui

http://arxiv.org/abs/2311.17087
Rethinking Mixup for Improving the Adversarial Transferability. (98%)
Xiaosen Wang; Zeyuan Yin

http://arxiv.org/abs/2311.15551
Instruct2Attack: Language-Guided Semantic Adversarial Attacks. (98%)
Jiang Liu; Chen Wei; Yuxiang Guo; Heng Yu; Alan Yuille; Soheil Feizi; Chun Pong Lau; Rama Chellappa

http://arxiv.org/abs/2311.16445
CLAP: Contrastive Learning with Augmented Prompts for Robustness on Pretrained Vision-Language Models. (95%)
Yichao Cai; Yuhang Liu; Zhen Zhang; Javen Qinfeng Shi

http://arxiv.org/abs/2311.16065
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective. (50%)
Xianghua Xie; Chen Hu; Hanchi Ren; Jingjing Deng

http://arxiv.org/abs/2311.16460
Threshold Breaker: Can Counter-Based RowHammer Prevention Mechanisms Truly Safeguard DRAM? (31%)
Ranyang Zhou; Jacqueline Liu; Sabbir Ahmed; Nakul Kochar; Adnan Siraj Rakin; Shaahin Angizi

http://arxiv.org/abs/2312.00050
Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift. (31%)
Shengwei An; Sheng-Yen Chou; Kaiyuan Zhang; Qiuling Xu; Guanhong Tao; Guangyu Shen; Siyuan Cheng; Shiqing Ma; Pin-Yu Chen; Tsung-Yi Ho; Xiangyu Zhang

http://arxiv.org/abs/2311.15894
Distributed Attacks over Federated Reinforcement Learning-enabled Cell Sleep Control. (22%)
Han Zhang; Hao Zhou; Medhat Elsayed; Majid Bavand; Raimundas Gaigalas; Yigit Ozcan; Melike Erol-Kantarci

http://arxiv.org/abs/2311.16383
"Do Users fall for Real Adversarial Phishing?" Investigating the Human response to Evasive Webpages. (15%)
Ajka Draganovic; Savino Dambra; Javier Aldana Iuit; Kevin Roundy; Giovanni Apruzzese

http://arxiv.org/abs/2311.16101
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs. (12%)
Haoqin Tu; Chenhang Cui; Zijun Wang; Yiyang Zhou; Bingchen Zhao; Junlin Han; Wangchunshu Zhou; Huaxiu Yao; Cihang Xie

http://arxiv.org/abs/2311.15999
Microarchitectural Security of AWS Firecracker VMM for Serverless Cloud Platforms. (1%)
Zane Worcester Polytechnic Institute Weissman; Thore University of Lübeck Tiemann; Thomas University of Lübeck Eisenbarth; Berk Worcester Polytechnic Institute Sunar

http://arxiv.org/abs/2311.15339
Adversarial Purification of Information Masking. (99%)
Sitong Liu; Zhichao Lian; Shuangquan Zhang; Liang Xiao

http://arxiv.org/abs/2311.15356
Having Second Thoughts? Let's hear it. (56%)
Jung H. Lee; Sujith Vijayan

http://arxiv.org/abs/2311.16194
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP. (13%)
Jiawang Bai; Kuofeng Gao; Shaobo Min; Shu-Tao Xia; Zhifeng Li; Wei Liu

http://arxiv.org/abs/2311.15373
Confidence Is All You Need for MI Attacks. (2%)
Abhishek Sinha; Himanshi Tibrewal; Mansi Gupta; Nikhar Waghela; Shivank Garg

http://arxiv.org/abs/2311.15165
Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off. (26%)
Yatong Bai; Brendon G. Anderson; Somayeh Sojoudi

http://arxiv.org/abs/2311.14934
Robust Graph Neural Networks via Unbiased Aggregation. (10%)
Ruiqi Feng; Zhichao Hou; Tyler Derr; Xiaorui Liu

http://arxiv.org/abs/2311.14948
Effective Backdoor Mitigation Depends on the Pre-training Objective. (10%)
Sahil Verma; Gantavya Bhatt; Avi Schwarzschild; Soumye Singhal; Arnav Mohanty Das; Chirag Shah; John P Dickerson; Jeff Bilmes

http://arxiv.org/abs/2311.14772
Trainwreck: A damaging adversarial attack on image classifiers. (99%)
Jan Zahálka

http://arxiv.org/abs/2311.14450
Segment (Almost) Nothing: Prompt-Agnostic Adversarial Attacks on Segmentation Models. (96%)
Francesco Croce; Matthias Hein

http://arxiv.org/abs/2311.14455
Universal Jailbreak Backdoors from Poisoned Human Feedback. (1%)
Javier Rando; Florian Tramèr

http://arxiv.org/abs/2311.14005
When Side-Channel Attacks Break the Black-Box Property of Embedded Artificial Intelligence. (99%)
Benoit Coqueret; Mathieu Carbone; Olivier Sentieys; Gabriel Zaid

http://arxiv.org/abs/2311.13841
Adversarial defense based on distribution transfer. (99%)
Jiahao Chen; Diqun Yan; Li Dong

http://arxiv.org/abs/2311.14227
Robust and Interpretable COVID-19 Diagnosis on Chest X-ray Images using Adversarial Training. (68%)
Karina Yang; Alexis Bennett; Dominique Duncan

http://arxiv.org/abs/2312.00041
Presentation Attack Detection using Convolutional Neural Networks and Local Binary Patterns. (1%)
Justin Spencer; Deborah Lawrence; Prosenjit Chatterjee; Kaushik Roy; Albert Esterline; Jung-Hee Kim

http://arxiv.org/abs/2311.13233
A Survey of Adversarial CAPTCHAs on its History, Classification and Generation. (99%)
Zisheng Xu; Qiao Yan; F. Richard Yu; Victor C. M. Leung

http://arxiv.org/abs/2311.13445
Transfer Attacks and Defenses for Large Language Models on Coding Tasks. (99%)
Chi Zhang; Zifan Wang; Ravi Mangal; Matt Fredrikson; Limin Jia; Corina Pasareanu

http://arxiv.org/abs/2311.13656
Panda or not Panda? Understanding Adversarial Attacks with Interactive Visualization. (98%)
Yuzhe You; Jarvis Tse; Jian Zhao

http://arxiv.org/abs/2311.13244
Hard Label Black Box Node Injection Attack on Graph Neural Networks. (93%)
Yu Zhou; Zihao Dong; Guofeng Zhang; Jingchen Tang

http://arxiv.org/abs/2311.13744
Security and Privacy Challenges in Deep Learning Models. (74%)
Gopichandh Golla

http://arxiv.org/abs/2311.13713
A Somewhat Robust Image Watermark against Diffusion-based Editing Models. (50%)
Mingtian Tan; Tianhao Wang; Somesh Jha

http://arxiv.org/abs/2311.13739
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning. (2%)
Tre' R. Jeter; Truc Nguyen; Raed Alharbi; My T. Thai

http://arxiv.org/abs/2311.12981
SD-NAE: Generating Natural Adversarial Examples with Stable Diffusion. (96%)
Yueqian Lin; Jingyang Zhang; Yiran Chen; Hai Li

http://arxiv.org/abs/2311.13091
Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise. (96%)
Yixin Liu; Kaidi Xu; Xun Chen; Lichao Sun

http://arxiv.org/abs/2311.12914
Attention Deficit is Ordered! Fooling Deformable Vision Transformers with Collaborative Adversarial Patches. (75%)
Quazi Mishkatul Alam; Bilel Tarchoun; Ihsen Alouani; Nael Abu-Ghazaleh

http://arxiv.org/abs/2311.12722
Attacking Motion Planners Using Adversarial Perception Errors. (69%)
Jonathan Sadeghi; Nicholas A. Lord; John Redford; Romain Mueller

http://arxiv.org/abs/2311.13127
Toward Robust Imperceptible Perturbation against Unauthorized Text-to-image Diffusion-based Synthesis. (62%)
Yixin Liu; Chenrui Fan; Yutong Dai; Xun Chen; Pan Zhou; Lichao Sun

http://arxiv.org/abs/2311.12773
Iris Presentation Attack: Assessing the Impact of Combining Vanadium Dioxide Films with Artificial Eyes. (1%)
Darshika Jauhari; Renu Sharma; Cunjian Chen; Nelson Sepulveda; Arun Ross

http://arxiv.org/abs/2311.12084
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches. (99%)
Nandish Chattopadhyay; Amira Guesmi; Muhammad Abdullah Hanif; Bassem Ouni; Muhammad Shafique

http://arxiv.org/abs/2311.12211
DefensiveDR: Defending against Adversarial Patches using Dimensionality Reduction. (99%)
Nandish Chattopadhyay; Amira Guesmi; Muhammad Abdullah Hanif; Bassem Ouni; Muhammad Shafique

http://arxiv.org/abs/2311.11861
Generating Valid and Natural Adversarial Examples with Large Language Models. (99%)
Zimu Wang; Wei Wang; Qi Chen; Qiufeng Wang; Anh Nguyen

http://arxiv.org/abs/2311.11753
AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems. (99%)
Sai Amrit Patnaik; Shivali Chansoriya; Anil K. Jain; Anoop M. Namboodiri

http://arxiv.org/abs/2311.11796
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems. (50%)
Guangjing Wang; Ce Zhou; Yuanda Wang; Bocheng Chen; Hanqing Guo; Qiben Yan

http://arxiv.org/abs/2311.11544
Understanding Variation in Subpopulation Susceptibility to Poisoning Attacks. (15%)
Evan Rose; Fnu Suya; David Evans

http://arxiv.org/abs/2311.11871
Training robust and generalizable quantum models. (10%)
Julian Berberich; Daniel Fink; Daniel Pranjić; Christian Tutschku; Christian Holm

http://arxiv.org/abs/2311.11995
BrainWash: A Poisoning Attack to Forget in Continual Learning. (4%)
Ali Abbasi; Parsa Nooralinejad; Hamed Pirsiavash; Soheil Kolouri

http://arxiv.org/abs/2311.11261
Adversarial Prompt Tuning for Vision-Language Models. (98%)
Jiaming Zhang; Xingjun Ma; Xin Wang; Lingyu Qiu; Jiaqi Wang; Yu-Gang Jiang; Jitao Sang

http://arxiv.org/abs/2311.11509
Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information. (78%)
Zhengmian Hu; Gang Wu; Saayan Mitra; Ruiyi Zhang; Tong Sun; Heng Huang; Viswanathan Swaminathan

http://arxiv.org/abs/2311.12075
BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning. (69%)
Siyuan Liang; Mingli Zhu; Aishan Liu; Baoyuan Wu; Xiaochun Cao; Ee-Chien Chang

http://arxiv.org/abs/2311.12066
EditShield: Protecting Unauthorized Image Editing by Instruction-guided Diffusion Models. (10%)
Ruoxi Chen; Haibo Jin; Jinyin Chen; Lichao Sun

http://arxiv.org/abs/2311.12051
Boost Adversarial Transferability by Uniform Scale and Mix Mask Method. (99%)
Tao Wang; Zijian Ying; Qianmu Li; zhichao Lian

http://arxiv.org/abs/2311.11017
Improving Adversarial Transferability by Stable Diffusion. (99%)
Jiayang Liu; Siyu Zhu; Siyuan Liang; Jie Zhang; Han Fang; Weiming Zhang; Ee-Chien Chang

http://arxiv.org/abs/2311.11191
Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications. (92%)
Giulio Rossolini; Alessandro Biondi; Giorgio Buttazzo

http://arxiv.org/abs/2311.11225
TextGuard: Provable Defense against Backdoor Attacks on Text Classification. (82%)
Hengzhi Pei; Jinyuan Jia; Wenbo Guo; Bo Li; Dawn Song

http://arxiv.org/abs/2311.11206
Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies. (1%)
Feng Wang; M. Cenk Gursoy; Senem Velipasalar

http://arxiv.org/abs/2311.10366
Breaking Temporal Consistency: Generating Video Universal Adversarial Perturbations Using Image Models. (97%)
Hee-Seon Kim; Minji Son; Minbeom Kim; Myung-Joon Kwon; Changick Kim

http://arxiv.org/abs/2311.10919
PACOL: Poisoning Attacks Against Continual Learners. (93%)
Huayu Li; Gregory Ditzler

http://arxiv.org/abs/2311.10389
Two-Factor Authentication Approach Based on Behavior Patterns for Defeating Puppet Attacks. (1%)
Wenhao Wang; Guyue Li; Zhiming Chu; Haobo Li; Daniele Faccio

http://arxiv.org/abs/2311.09790
Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting. (99%)
Romain Ilbert; Thai V. Hoang; Zonghua Zhang; Themis Palpanas

http://arxiv.org/abs/2311.09948
Hijacking Large Language Models via Adversarial In-Context Learning. (75%)
Yao Qiang; Xiangyu Zhou; Dongxiao Zhu

http://arxiv.org/abs/2311.09827
Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking. (54%)
Nan Xu; Fei Wang; Ben Zhou; Bang Zheng Li; Chaowei Xiao; Muhao Chen

http://arxiv.org/abs/2311.09763
Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations. (38%)
Wenjie Mo; Jiashu Xu; Qin Liu; Jiongxiao Wang; Jun Yan; Chaowei Xiao; Muhao Chen

http://arxiv.org/abs/2311.09641
On the Exploitability of Reinforcement Learning with Human Feedback for Large Language Models. (13%)
Jiongxiao Wang; Junlin Wu; Muhao Chen; Yevgeniy Vorobeychik; Chaowei Xiao

http://arxiv.org/abs/2311.10177
Towards Improving Robustness Against Common Corruptions using Mixture of Class Specific Experts. (2%)
Shashank Kotyan; Danilo Vasconcellos Vargas

http://arxiv.org/abs/2311.16169
Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities. (2%)
Avishree Khare; Saikat Dutta; Ziyang Li; Alaia Solko-Breslin; Rajeev Alur; Mayur Naik

http://arxiv.org/abs/2312.00029
Bergeron: Combating Adversarial Attacks through a Conscience-Based Alignment Framework. (2%)
Matthew Pisano; Peter Ly; Abraham Sanders; Bingsheng Yao; Dakuo Wang; Tomek Strzalkowski; Mei Si

http://arxiv.org/abs/2311.09994
Towards more Practical Threat Models in Artificial Intelligence Security. (2%)
Kathrin Grosse; Lukas Bieringer; Tarek Richard Besold; Alexandre Alahi

http://arxiv.org/abs/2311.10197
You Cannot Escape Me: Detecting Evasions of SIEM Rules in Enterprise Networks. (1%)
Rafael Uetz; Marco Herzog; Louis Hackländer; Simon Schwarz; Martin Henze

http://arxiv.org/abs/2311.09127
Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts. (99%)
Yuanwei Wu; Xiang Li; Yixin Liu; Pan Zhou; Lichao Sun

http://arxiv.org/abs/2311.09433
Backdoor Activation Attack: Attack Large Language Models using Activation Steering for Safety-Alignment. (74%)
Haoran Wang; Kai Shu

http://arxiv.org/abs/2311.09024
Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing. (64%)
A K Iowa State University Nirala; A New York University Joshi; C New York University Hegde; S Iowa State University Sarkar

http://arxiv.org/abs/2311.09266
Adversarially Robust Spiking Neural Networks Through Conversion. (61%)
Ozan Özdenizci; Robert Legenstein

http://arxiv.org/abs/2311.09447
How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities. (16%)
Lingbo Mo; Boshi Wang; Muhao Chen; Huan Sun

http://arxiv.org/abs/2311.09096
Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization. (15%)
Zhexin Zhang; Junxiao Yang; Pei Ke; Minlie Huang

http://arxiv.org/abs/2311.09355
Privacy Threats in Stable Diffusion Models. (13%)
Thomas Cilloni; Charles Fleming; Charles Walter

http://arxiv.org/abs/2311.09489
MirrorNet: A TEE-Friendly Framework for Secure On-device DNN Inference. (2%)
Ziyu Liu; Yukui Luo; Shijin Duan; Tong Zhou; Xiaolin Xu

http://arxiv.org/abs/2311.09473
JAB: Joint Adversarial Prompting and Belief Augmentation. (1%)
Ninareh Mehrabi; Palash Goyal; Anil Ramakrishna; Jwala Dhamala; Shalini Ghosh; Richard Zemel; Kai-Wei Chang; Aram Galstyan; Rahul Gupta

http://arxiv.org/abs/2311.09428
Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models. (1%)
Yueqing Liang; Lu Cheng; Ali Payani; Kai Shu

http://arxiv.org/abs/2311.07928
Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learning. (99%)
Shashank Kotyan; Danilo Vasconcellos Vargas

http://arxiv.org/abs/2311.08539
Physical Adversarial Examples for Multi-Camera Systems. (99%)
Ana Răduţoiu; Jan-Philipp Schulze; Philip Sperl; Konstantin Böttinger

http://arxiv.org/abs/2311.08598
DALA: A Distribution-Aware LoRA-Based Adversarial Attack against Language Models. (99%)
Yibo Wang; Xiangjue Dong; James Caverlee; Philip S. Yu

http://arxiv.org/abs/2311.08265
On The Relationship Between Universal Adversarial Attacks And Sparse Representations. (98%)
Dana Weitzner; Raja Giryes

http://arxiv.org/abs/2311.08268
A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily. (62%)
Peng Ding; Jun Kuang; Dan Ma; Xuezhi Cao; Yunsen Xian; Jiajun Chen; Shujian Huang

http://arxiv.org/abs/2311.08662
Multi-Set Inoculation: Assessing Model Robustness Across Multiple Challenge Sets. (13%)
Vatsal Gupta; Pranshu Pandya; Tushar Kataria; Vivek Gupta; Dan Roth

http://arxiv.org/abs/2311.09253
The Perception-Robustness Tradeoff in Deterministic Image Restoration. (1%)
Guy Ohayon; Tomer Michaeli; Michael Elad

http://arxiv.org/abs/2311.07110
Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models. (99%)
Yuanbin Cheng; Koji Yamashita; Jim Follum; Nanpeng Yu

http://arxiv.org/abs/2311.07780
Parrot-Trained Adversarial Examples: Pushing the Practicality of Black-Box Audio Attacks against Speaker Recognition Models. (99%)
Rui Duan; Zhe Qu; Leah Ding; Yao Liu; Zhuo Lu

http://arxiv.org/abs/2311.07553
An Extensive Study on Adversarial Attack against Pre-trained Models of Code. (99%)
Xiaohu Du; Ming Wen; Zichao Wei; Shangwen Wang; Hai Jin

http://arxiv.org/abs/2311.07127
Untargeted Black-box Attacks for Social Recommendations. (96%)
Wenqi Fan; Shijie Wang; Xiao-yong Wei; Xiaowei Mei; Qing Li

http://arxiv.org/abs/2311.07444
On the Robustness of Neural Collapse and the Neural Collapse of Robustness. (80%)
Jingtong Su; Ya Shi Zhang; Nikolaos Tsilivis; Julia Kempe

http://arxiv.org/abs/2311.07550
Tabdoor: Backdoor Vulnerabilities in Transformer-based Neural Networks for Tabular Data. (70%)
Bart Pleiter; Behrad Tajalli; Stefanos Koffas; Gorka Abad; Jing Xu; Martha Larson; Stjepan Picek

http://arxiv.org/abs/2311.06771
Learning Globally Optimized Language Structure via Adversarial Training. (83%)
Xuwang Yin

http://arxiv.org/abs/2311.06942
Contractive Systems Improve Graph Neural Networks Against Adversarial Attacks. (70%)
Moshe Eliasof; Davide Murari; Ferdia Sherry; Carola-Bibiane Schönlieb

http://arxiv.org/abs/2311.06973
Analytical Verification of Deep Neural Network Performance for Time-Synchronized Distribution System State Estimation. (5%)
Behrouz Azimian; Shiva Moshtagh; Anamitra Pal; Shanshan Ma

http://arxiv.org/abs/2311.06855
DialMAT: Dialogue-Enabled Transformer with Moment-Based Adversarial Training. (1%)
Kanta Kaneda; Ryosuke Korekata; Yuiga Wada; Shunya Nagashima; Motonari Kambara; Yui Iioka; Haruka Matsuo; Yuto Imai; Takayuki Nishimura; Komei Sugiura

http://arxiv.org/abs/2311.06423
Flatness-aware Adversarial Attack. (99%)
Mingyuan Fan; Xiaodan Li; Cen Chen; Yinggui Wang

http://arxiv.org/abs/2311.05992
Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous. (99%)
Ziwei Wang; Nabil Aouf; Jose Pizarro; Christophe Honvault

http://arxiv.org/abs/2311.06122
Fight Fire with Fire: Combating Adversarial Patch Attacks using Pattern-randomized Defensive Patches. (98%)
Jianan Feng; Jiachun Li; Changqing Miao; Jianjun Huang; Wei You; Wenchang Shi; Bin Liang

http://arxiv.org/abs/2311.05935
Resilient and constrained consensus against adversarial attacks: A distributed MPC framework. (84%)
Henglai Wei; Kunwu Zhang; Hui Zhang; Yang Shi

http://arxiv.org/abs/2311.06361
CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization. (1%)
Danish Gufran; Sudeep Pasricha

http://arxiv.org/abs/2311.06062
Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration. (1%)
Wenjie Fu; Huandong Wang; Chen Gao; Guanghua Liu; Yong Li; Tao Jiang

http://arxiv.org/abs/2311.05316
ABIGX: A Unified Framework for eXplainable Fault Detection and Classification. (68%)
Yue Zhuo; Jinchuan Qian; Zhihuan Song; Zhiqiang Ge

http://arxiv.org/abs/2311.05826
Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios. (50%)
Yanli Li; Huaming Chen; Wei Bao; Zhengmeng Xu; Dong Yuan

http://arxiv.org/abs/2311.05808
Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction. (15%)
Shanghao Shi; Ning Wang; Yang Xiao; Chaoyu Zhang; Yi Shi; Y. Thomas Hou; Wenjing Lou

http://arxiv.org/abs/2311.05608
FigStep: Jailbreaking Large Vision-language Models via Typographic Visual Prompts. (1%)
Yichen Gong; Delong Ran; Jinyuan Liu; Conglei Wang; Tianshuo Cong; Anyu Wang; Sisi Duan; Xiaoyun Wang

http://arxiv.org/abs/2311.05168
FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment. (1%)
Qinghua Lin; Zuoyong Li; Kun Zeng; Haoyi Fan; Wei Li; Xiaoguang Zhou

http://arxiv.org/abs/2311.04503
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data. (99%)
Thibault Simonetto; Salah Ghamizi; Antoine Desjardins; Maxime Cordy; Yves Le Traon

http://arxiv.org/abs/2311.04588
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection. (70%)
Akshit Jindal; Vikram Goyal; Saket Anand; Chetan Arora

http://arxiv.org/abs/2311.07587
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? (61%)
C. Daniel Freeman; Laura Culp; Aaron Parisi; Maxwell L Bileschi; Gamaleldin F Elsayed; Alex Rizkowsky; Isabelle Simpson; Alex Alemi; Azade Nova; Ben Adlam; Bernd Bohnet; Gaurav Mishra; Hanie Sedghi; Igor Mordatch; Izzeddin Gur; Jaehoon Lee; JD Co-Reyes; Jeffrey Pennington; Kelvin Xu; Kevin Swersky; Kshiteej Mahajan; Lechao Xiao; Rosanne Liu; Simon Kornblith; Noah Constant; Peter J. Liu; Roman Novak; Yundi Qian; Noah Fiedel; Jascha Sohl-Dickstein

http://arxiv.org/abs/2311.05143
SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training. (10%)
Rui Xu; Wenkang Qin; Peixiang Huang; Haowang; Lin Luo

http://arxiv.org/abs/2311.04815
Domain Adaptive Object Detection via Balancing Between Self-Training and Adversarial Learning. (1%)
Muhammad Akhtar Munir; Muhammad Haris Khan; M. Saquib Sarfraz; Mohsen Ali

http://arxiv.org/abs/2311.05144
Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System. (1%)
Xiangguo Sun; Hong Cheng; Hang Dong; Bo Qiao; Si Qin; Qingwei Lin

http://arxiv.org/abs/2311.04124
Unveiling Safety Vulnerabilities of Large Language Models. (61%)
George Kour; Marcel Zalmanovici; Naama Zwerdling; Esther Goldbraich; Ora Nova Fandina; Ateret Anaby-Tavor; Orna Raz; Eitan Farchi

http://arxiv.org/abs/2311.03865
When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers through Membership Inference Attacks. (10%)
Huan Tian; Guangsheng Zhang; Bo Liu; Tianqing Zhu; Ming Ding; Wanlei Zhou

http://arxiv.org/abs/2311.16153
Identifying and Mitigating Vulnerabilities in LLM-Integrated Applications. (2%)
Fengqing Jiang; Zhangchen Xu; Luyao Niu; Boxin Wang; Jinyuan Jia; Bo Li; Radha Poovendran

http://arxiv.org/abs/2311.03809
SoK: Security Below the OS -- A Security Analysis of UEFI. (1%)
Priyanka Prakash Surve; Oleg Brodt; Mark Yampolskiy; Yuval Elovici; Asaf Shabtai

http://arxiv.org/abs/2311.04076
Do LLMs exhibit human-like response biases? A case study in survey design. (1%)
Lindia Tjuatja; Valerie Chen; Sherry Tongshuang Wu; Ameet Talwalkar; Graham Neubig

http://arxiv.org/abs/2311.03566
Measuring Adversarial Datasets. (92%)
Yuanchen Bai; Raoyi Huang; Vijay Viswanathan; Tzu-Sheng Kuo; Tongshuang Wu

http://arxiv.org/abs/2311.04235
Can LLMs Follow Simple Rules? (68%)
Norman Mu; Sarah Chen; Zifan Wang; Sizhe Chen; David Karamardian; Lulwa Aljeraisy; Basel Alomair; Dan Hendrycks; David Wagner

http://arxiv.org/abs/2311.03172
Preserving Privacy in GANs Against Membership Inference Attack. (33%)
Mohammadhadi Shateri; Francisco Messina; Fabrice Labeau; Pablo Piantanida

http://arxiv.org/abs/2311.03570
Cal-DETR: Calibrated Detection Transformer. (4%)
Muhammad Akhtar Munir; Salman Khan; Muhammad Haris Khan; Mohsen Ali; Fahad Shahbaz Khan

http://arxiv.org/abs/2311.02757
ELEGANT: Certified Defense on the Fairness of Graph Neural Networks. (10%)
Yushun Dong; Binchi Zhang; Hanghang Tong; Jundong Li

http://arxiv.org/abs/2311.02373
From Trojan Horses to Castle Walls: Unveiling Bilateral Backdoor Effects in Diffusion Models. (22%)
Zhuoshi Pan; Yuguang Yao; Gaowen Liu; Bingquan Shen; H. Vicky Zhao; Ramana Rao Kompella; Sijia Liu

http://arxiv.org/abs/2311.01873
Efficient Black-Box Adversarial Attacks on Neural Text Detectors. (22%)
Vitalii Fishchuk; Daniel Braun

http://arxiv.org/abs/2311.02147
The Alignment Problem in Context. (2%)
Raphaël Millière

http://arxiv.org/abs/2311.01478
Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms. (99%)
Aakriti Shah

http://arxiv.org/abs/2311.01323
Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly. (99%)
Qizhang Li; Yiwen Guo; Wangmeng Zuo; Hao Chen

http://arxiv.org/abs/2311.01011
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game. (93%)
Sam Toyer; Olivia Watkins; Ethan Adrian Mendes; Justin Svegliato; Luke Bailey; Tiffany Wang; Isaac Ong; Karim Elmaaroufi; Pieter Abbeel; Trevor Darrell; Alan Ritter; Stuart Russell

http://arxiv.org/abs/2311.01356
On the Lipschitz constant of random neural networks. (92%)
Paul Geuchen; Thomas Heindl; Dominik Stöger; Felix Voigtlaender

http://arxiv.org/abs/2311.01696
Universal Perturbation-based Secret Key-Controlled Data Hiding. (80%)
Donghua Wang; Wen Yao; Tingsong Jiang; Xiaoqian Chen

http://arxiv.org/abs/2311.01441
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models. (76%)
Andy Zhou; Jindong Wang; Yu-Xiong Wang; Haohan Wang

http://arxiv.org/abs/2311.01563
Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction. (13%)
Abhijith Sharma; Phil Munz; Apurva Narayan

http://arxiv.org/abs/2311.01642
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula. (12%)
Aryaman Reddi; Maximilian Tölle; Jan Peters; Georgia Chalvatzaki; Carlo D'Eramo

http://arxiv.org/abs/2311.01570
Sequential Subset Matching for Dataset Distillation. (1%)
Jiawei Du; Qin Shi; Joey Tianyi Zhou

http://arxiv.org/abs/2311.01500
E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification. (1%)
Sneh Pandya; Purvik Patel; Franc O; Jonathan Blazek

http://arxiv.org/abs/2311.01357
Robust Identity Perceptual Watermark Against Deepfake Face Swapping. (1%)
Tianyi Wang; Mengxiao Huang; Harry Cheng; Bin Ma; Yinglong Wang

http://arxiv.org/abs/2311.00428
NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks. (99%)
Seokil Ham; Jungwuk Park; Dong-Jun Han; Jaekyun Moon

http://arxiv.org/abs/2311.01473
Adversarial Examples in the Physical World: A Survey. (98%)
Jiakai Wang; Donghua Wang; Jin Hu; Siyang Wu; Tingsong Jiang; Wen Yao; Aishan Liu; Xianglong Liu

http://arxiv.org/abs/2311.00859
Optimal Cost Constrained Adversarial Attacks For Multiple Agent Systems. (80%)
Ziqing Lu; Guanlin Liu; Lifeng Cai; Weiyu Xu

http://arxiv.org/abs/2311.00441
Improving Robustness for Vision Transformer with a Simple Dynamic Scanning Augmentation. (76%)
Shashank Kotyan; Danilo Vasconcellos Vargas

http://arxiv.org/abs/2311.00919
MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training. (75%)
Jiacheng Li; Ninghui Li; Bruno Ribeiro

http://arxiv.org/abs/2311.00508
Robustness Tests for Automatic Machine Translation Metrics with Adversarial Attacks. (1%)
Yichen Huang; Timothy Baldwin

http://arxiv.org/abs/2311.00400
Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss. (1%)
Rafael Henrique Vareto; Yu Linghu; Terrance E. Boult; William Robson Schwartz; Manuel Günther

http://arxiv.org/abs/2310.20469
Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning. (99%)
Haoyu Liu; Alec F. Diallo; Paul Patras

http://arxiv.org/abs/2311.00172
Robust Safety Classifier for Large Language Models: Adversarial Prompt Shield. (99%)
Jinhwa Kim; Ali Derakhshan; Ian G. Harris

http://arxiv.org/abs/2310.20175
LFAA: Crafting Transferable Targeted Adversarial Examples with Low-Frequency Perturbations. (99%)
Kunyu Wang; Juluan Shi; Wenxuan Wang

http://arxiv.org/abs/2311.00207
Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems. (98%)
Jung-Woo Chang; Ke Sun; Nasimeh Heydaribeni; Seira Hidano; Xinyu Zhang; Farinaz Koushanfar

http://arxiv.org/abs/2310.20162
Is Robustness Transferable across Languages in Multilingual Neural Machine Translation? (26%)
Leiyu Pan; Supryadi; Deyi Xiong

http://arxiv.org/abs/2310.20649
Dynamic Batch Norm Statistics Update for Natural Robustness. (22%)
Shahbaz Rezaei; Mohammad Sadegh Norouzzadeh

http://arxiv.org/abs/2310.20199
In Search of Lost Online Test-time Adaptation: A Survey. (1%)
Zixin Wang; Yadan Luo; Liang Zheng; Zhuoxiao Chen; Sen Wang; Zi Huang

http://arxiv.org/abs/2310.19342
Label-Only Model Inversion Attacks via Knowledge Transfer. (83%)
Ngoc-Bao Nguyen; Keshigeyan Chandrasegaran; Milad Abdollahzadeh; Ngai-Man Cheung

http://arxiv.org/abs/2310.19889
Exploring Geometry of Blind Spots in Vision Models. (83%)
Sriram Balasubramanian; Gaurang Sriramanan; Vinu Sankar Sadasivan; Soheil Feizi

http://arxiv.org/abs/2310.19737
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats. (74%)
Leo Schwinn; David Dobre; Stephan Günnemann; Gauthier Gidel

http://arxiv.org/abs/2310.19410
Generated Distributions Are All You Need for Membership Inference Attacks Against Generative Models. (61%)
Minxing Zhang; Ning Yu; Rui Wen; Michael Backes; Yang Zhang

http://arxiv.org/abs/2310.19391
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness. (33%)
Ahmad-Reza Ehyaei; Golnoosh Farnadi; Samira Samadi

http://arxiv.org/abs/2310.19733
Differentially Private Reward Estimation with Preference Feedback. (16%)
Sayak Ray Chowdhury; Xingyu Zhou; Nagarajan Natarajan

http://arxiv.org/abs/2310.19439
Asymmetric Diffusion Based Channel-Adaptive Secure Wireless Semantic Communications. (10%)
Xintian Ren; Jun Wu; Hansong Xu; Qianqian Pan

http://arxiv.org/abs/2310.19304
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection. (1%)
Swanand Ravindra Kadhe; Heiko Ludwig; Nathalie Baracaldo; Alan King; Yi Zhou; Keith Houck; Ambrish Rawat; Mark Purcell; Naoise Holohan; Mikio Takeuchi; Ryo Kawahara; Nir Drucker; Hayim Shaul; Eyal Kushnir; Omri Soceanu

http://arxiv.org/abs/2310.18975
Blacksmith: Fast Adversarial Training of Vision Transformers via a Mixture of Single-step and Multi-step Methods. (99%)
Mahdi Salmani; Alireza Dehghanpour Farashah; Mohammad Azizmalayeri; Mahdi Amiri; Navid Eslami; Mohammad Taghi Manzuri; Mohammad Hossein Rohban

http://arxiv.org/abs/2310.19038
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors. (98%)
Han Liu; Xingshuo Huang; Xiaotong Zhang; Qimai Li; Fenglong Ma; Wei Wang; Hongyang Chen; Hong Yu; Xianchao Zhang

http://arxiv.org/abs/2310.19152
BERT Lost Patience Won't Be Robust to Adversarial Slowdown. (98%)
Zachary Coalson; Gabriel Ritter; Rakesh Bobba; Sanghyun Hong

http://arxiv.org/abs/2310.18936
Adversarial Examples Are Not Real Features. (98%)
Ang Li; Yifei Wang; Yiwen Guo; Yisen Wang

http://arxiv.org/abs/2310.19248
IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI. (82%)
Bochuan Cao; Changjiang Li; Ting Wang; Jinyuan Jia; Bo Li; Jinghui Chen

http://arxiv.org/abs/2310.19156
Poisoning Retrieval Corpora by Injecting Adversarial Passages. (68%)
Zexuan Zhong; Ziqing Huang; Alexander Wettig; Danqi Chen

http://arxiv.org/abs/2310.18933
Label Poisoning is All You Need. (54%)
Rishi D. Jha; Jonathan Hayase; Sewoong Oh

http://arxiv.org/abs/2310.19177
Robustifying Language Models with Test-Time Adaptation. (47%)
Noah Thomas McDermott; Junfeng Yang; Chengzhi Mao

http://arxiv.org/abs/2310.18987
Path Analysis for Effective Fault Localization in Deep Neural Networks. (1%)
Soroush Hashemifar; Saeed Parsa; Akram Kalaee

http://arxiv.org/abs/2310.19181
From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude. (1%)
Sayak Saha Roy; Poojitha Thota; Krishna Vamsi Naragam; Shirin Nilizadeh

http://arxiv.org/abs/2310.18587
Assessing and Improving Syntactic Adversarial Robustness of Pre-trained Models for Code Translation. (92%)
Guang Yang; Yu Zhou; Xiangyu Zhang; Xiang Chen; Tingting Han; Taolue Chen

http://arxiv.org/abs/2310.18626
Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness. (86%)
Soumyendu Sarkar; Ashwin Ramesh Babu; Sajad Mousavi; Zachariah Carmichael; Vineet Gundecha; Sahand Ghorbanpour; Ricardo Luna; Gutierrez Antonio Guillen; Avisek Naug

http://arxiv.org/abs/2310.18762
Purify++: Improving Diffusion-Purification with Advanced Diffusion Models and Control of Randomness. (61%)
Boya Zhang; Weijian Luo; Zhihua Zhang

http://arxiv.org/abs/2310.18603
Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers. (47%)
Wencong You; Zayd Hammoudeh; Daniel Lowd

http://arxiv.org/abs/2310.18606
Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation. (8%)
Kunlin Cai; Jinghuai Zhang; Will Shand; Zhiqing Hong; Guang Wang; Desheng Zhang; Jianfeng Chi; Yuan Tian

http://arxiv.org/abs/2311.16124
DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification. (99%)
Mintong Kang; Dawn Song; Bo Li

http://arxiv.org/abs/2310.18477
Understanding and Improving Ensemble Adversarial Defense. (99%)
Yian Deng; Tingting Mu

http://arxiv.org/abs/2310.18274
LipSim: A Provably Robust Perceptual Similarity Metric. (45%)
Sara Ghazanfari; Alexandre Araujo; Prashanth Krishnamurthy; Farshad Khorrami; Siddharth Garg

http://arxiv.org/abs/2310.18155
Elevating Code-mixed Text Handling through Auditory Information of Words. (5%)
Mamta; Zishan Ahmad; Asif Ekbal

http://arxiv.org/abs/2310.17951
Understanding Parameter Saliency via Extreme Value Theory. (1%)
Shuo Wang; Issei Sato

http://arxiv.org/abs/2311.03373
Unscrambling the Rectification of Adversarial Attacks Transferability across Computer Networks. (99%)
Ehsan Nowroozi; Samaneh Ghelichkhani; Imran Haider; Ali Dehghantanha

http://arxiv.org/abs/2310.17626
A Survey on Transferability of Adversarial Examples across Deep Neural Networks. (99%)
Jindong Gu; Xiaojun Jia; Jorge Pau de; Wenqain Yu; Xinwei Liu; Avery Ma; Yuan Xun; Anjun Hu; Ashkan Khakzar; Zhijiang Li; Xiaochun Cao; Philip Torr

http://arxiv.org/abs/2310.17645
Defending Against Transfer Attacks From Public Models. (99%)
Chawin Sitawarin; Jaewon Chang; David Huang; Wesson Altoyan; David Wagner

http://arxiv.org/abs/2310.17436
Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation. (93%)
Kira Maag; Asja Fischer

http://arxiv.org/abs/2310.17403
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow. (93%)
Erik Scheurer; Jenny Schmalfuss; Alexander Lis; Andrés Bruhn

http://arxiv.org/abs/2310.17498
CBD: A Certified Backdoor Detector Based on Local Dominant Probability. (76%)
Zhen Xiang; Zidi Xiong; Bo Li

http://arxiv.org/abs/2310.17534
SoK: Pitfalls in Evaluating Black-Box Attacks. (76%)
Fnu Suya; Anshuman Suri; Tingwei Zhang; Jingtao Hong; Yuan Tian; David Evans

http://arxiv.org/abs/2310.17559
Instability of computer vision models is a necessary result of the task itself. (26%)
Oliver Turnbull; George Cevora

http://arxiv.org/abs/2310.17588
PAC-tuning:Fine-tuning Pretrained Language Models with PAC-driven Perturbed Gradient Descent. (1%)
Guangliang Liu; Zhiyu Xue; Xitong Zhang; Kristen Marie Johnson; Rongrong Wang

http://arxiv.org/abs/2310.17584
A minimax optimal control approach for robust neural ODEs. (1%)
Cristina Cipriani; Alessandro Scagliotti; Tobias Wöhrer

http://arxiv.org/abs/2310.16955
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks. (93%)
Aradhana Sinha; Ananth Balashankar; Ahmad Beirami; Thi Avrahami; Jilin Chen; Alex Beutel

http://arxiv.org/abs/2310.16999
Trust, but Verify: Robust Image Segmentation using Deep Learning. (54%)
Fahim Ahmed Zaman; Xiaodong Wu; Weiyu Xu; Milan Sonka; Raghuraman Mudumbai

http://arxiv.org/abs/2310.16540
Dual Defense: Adversarial, Traceable, and Invisible Robust Watermarking against Face Swapping. (26%)
Yunming Zhang; Dengpan Ye; Caiyun Xie; Long Tang; Chuanxi Chen; Ziyi Liu; Jiacheng Deng

http://arxiv.org/abs/2310.16613
On the Proactive Generation of Unsafe Images From Text-To-Image Models Using Benign Prompts. (22%)
Yixin Wu; Ning Yu; Michael Backes; Yun Shen; Yang Zhang

http://arxiv.org/abs/2310.16919
Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs. (12%)
Jianwei Fei; Zhihua Xia; Benedetta Tondi; Mauro Barni

http://arxiv.org/abs/2310.16779
Multi-scale Diffusion Denoised Smoothing. (1%)
Jongheon Jeong; Jinwoo Shin

http://arxiv.org/abs/2310.16838
SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation. (1%)
Qianxu Wang; Haotong Zhang; Congyue Deng; Yang You; Hao Dong; Yixin Zhu; Leonidas Guibas

http://arxiv.org/abs/2311.12857
Adversarial sample generation and training using geometric masks for accurate and resilient license plate character recognition. (99%)
Bishal Shrestha; Griwan Khakurel; Kritika Simkhada; Badri Adhikari

http://arxiv.org/abs/2311.12858
RAEDiff: Denoising Diffusion Probabilistic Models Based Reversible Adversarial Examples Self-Generation and Self-Recovery. (92%)
Fan Xing; Xiaoyi Zhou; Xuefeng Fan; Zhuo Tian; Yan Zhao

http://arxiv.org/abs/2310.16335
Defense Against Model Extraction Attacks on Recommender Systems. (92%)
Sixiao Zhang; Hongzhi Yin; Hongxu Chen; Cheng Long

http://arxiv.org/abs/2310.16061
Segue: Side-information Guided Generative Unlearnable Examples for Facial Privacy Protection in Real World. (89%)
Zhiling Zhang; Jie Zhang; Kui Zhang; Wenbo Zhou; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2310.16221
Hierarchical Randomized Smoothing. (75%)
Yan Scholten; Jan Schuchardt; Aleksandar Bojchevski; Stephan Günnemann

http://arxiv.org/abs/2310.15656
Momentum Gradient-based Untargeted Attack on Hypergraph Neural Networks. (73%)
Yang Chen; Stjepan Picek; Zhonglin Ye; Zhaoyang Wang; Haixing Zhao

http://arxiv.org/abs/2310.16332
Corrupting Neuron Explanations of Deep Visual Features. (41%)
Divyansh Srivastava; Tuomas Oikarinen; Tsui-Wei Weng

http://arxiv.org/abs/2310.18360
Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut Triggers. (10%)
Mosh Levy; Shauli Ravfogel; Yoav Goldberg

http://arxiv.org/abs/2310.15654
A Survey on Detection of LLMs-Generated Content. (1%)
Xianjun Yang; Liangming Pan; Xuandong Zhao; Haifeng Chen; Linda Petzold; William Yang Wang; Wei Cheng

http://arxiv.org/abs/2310.15991
White-box Compiler Fuzzing Empowered by Large Language Models. (1%)
Chenyuan Yang; Yinlin Deng; Runyu Lu; Jiayi Yao; Jiawei Liu; Reyhaneh Jabbarvand; Lingming Zhang

http://arxiv.org/abs/2310.16263
Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation. (1%)
Jiexin Wang; Liuwen Cao; Xitong Luo; Zhiping Zhou; Jiayuan Xie; Adam Jatowt; Yi Cai

http://arxiv.org/abs/2310.14637
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval. (99%)
Xu Yuan; Zheng Zhang; Xunguang Wang; Lin Wu

http://arxiv.org/abs/2310.14561
F$^2$AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns. (99%)
Yaguan Qian; Chenyu Zhao; Zhaoquan Gu; Bin Wang; Shouling Ji; Wei Wang; Boyang Zhou; Pan Zhou

http://arxiv.org/abs/2310.15140
AutoDAN: Automatic and Interpretable Adversarial Attacks on Large Language Models. (98%)
Sicheng Zhu; Ruiyi Zhang; Bang An; Gang Wu; Joe Barrow; Zichao Wang; Furong Huang; Ani Nenkova; Tong Sun

http://arxiv.org/abs/2310.15444
Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks. (98%)
Xiaojun Jia; Jianshu Li; Jindong Gu; Yang Bai; Xiaochun Cao

http://arxiv.org/abs/2310.15085
On the Detection of Image-Scaling Attacks in Machine Learning. (15%)
Erwin Quiring; Andreas Müller; Konrad Rieck

http://arxiv.org/abs/2310.15171
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions. (1%)
Lingdong Kong; Shaoyuan Xie; Hanjiang Hu; Lai Xing Ng; Benoit R. Cottereau; Wei Tsang Ooi

http://arxiv.org/abs/2310.14270
Diffusion-Based Adversarial Purification for Speaker Verification. (99%)
Yibo Bai; Xiao-Lei Zhang

http://arxiv.org/abs/2310.14265
CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability. (99%)
Minxuan Lv; Chengwei Dai; Kun Li; Wei Zhou; Songlin Hu

http://arxiv.org/abs/2311.16118
Imperceptible CMOS camera dazzle for adversarial attacks on deep neural networks. (92%)
Zvi Stein; Adrian Stern

http://arxiv.org/abs/2310.14504
ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency. (26%)
Minkyoung Cho; Yulong Cao; Zixiang Zhou; Z. Morley Mao

http://arxiv.org/abs/2310.14480
Attention-Enhancing Backdoor Attacks Against BERT-based Models. (13%)
Weimin Lyu; Songzhu Zheng; Lu Pang; Haibin Ling; Chao Chen

http://arxiv.org/abs/2310.14369
MoPe: Model Perturbation-based Privacy Attacks on Language Models. (9%)
Marvin Li; Jason Wang; Jeffrey Wang; Seth Neel

http://arxiv.org/abs/2401.01896
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification. (1%)
Zhibo Zhang; Pengfei Li; Ahmed Y. Al Hammadi; Fusen Guo; Ernesto Damiani; Chan Yeob Yeun

http://arxiv.org/abs/2310.13950
Adversarial Image Generation by Spatial Transformation in Perceptual Colorspaces. (99%)
Ayberk Aydin; Alptekin Temizel

http://arxiv.org/abs/2310.14045
Training Image Derivatives: Increased Accuracy and Universal Robustness. (5%)
Vsevolod I. Avrutskiy

http://arxiv.org/abs/2310.13321
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting. (99%)
Zecheng Tang; Kaifeng Qi; Juntao Li; Min Zhang

http://arxiv.org/abs/2310.13345
An LLM can Fool Itself: A Prompt-Based Adversarial Attack. (99%)
Xilie Xu; Keyi Kong; Ning Liu; Lizhen Cui; Di Wang; Jingfeng Zhang; Mohan Kankanhalli

http://arxiv.org/abs/2310.13828
Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models. (61%)
Shawn Shan; Wenxin Ding; Josephine Passananti; Haitao Zheng; Ben Y. Zhao

http://arxiv.org/abs/2310.13893
The Hidden Adversarial Vulnerabilities of Medical Federated Learning. (45%)
Erfan Darzi; Florian Dubost; Nanna. M. Sijtsema; Ooijen P. M. A van

http://arxiv.org/abs/2310.13822
Adversarial Attacks on Fairness of Graph Neural Networks. (26%)
Binchi Zhang; Yushun Dong; Chen Chen; Yada Zhu; Minnan Luo; Jundong Li

http://arxiv.org/abs/2310.13424
FLTracer: Accurate Poisoning Attack Provenance in Federated Learning. (26%)
Xinyu Zhang; Qingyu Liu; Zhongjie Ba; Yuan Hong; Tianhang Zheng; Feng Lin; Li Lu; Kui Ren

http://arxiv.org/abs/2311.03369
Can We Trust the Similarity Measurement in Federated Learning? (15%)
Zhilin Wang; Qin Hu; Xukai Zou

http://arxiv.org/abs/2310.13782
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images. (4%)
Logan Frank; Jim Davis

http://arxiv.org/abs/2310.13894
VOICE-ZEUS: Impersonating Zoom's E2EE-Protected Static Media and Textual Communications via Simple Voice Manipulations. (4%)
Mashari Alatawi; Nitesh Saxena

http://arxiv.org/abs/2310.12708
Generating Robust Adversarial Examples against Online Social Networks (OSNs). (98%)
Jun Liu; Jiantao Zhou; Haiwei Wu; Weiwei Sun; Jinyu Tian

http://arxiv.org/abs/2310.12707
Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples. (98%)
Jun Liu; Jiantao Zhou; Jinyu Tian; Weiwei Sun

http://arxiv.org/abs/2310.12713
Learn from the Past: A Proxy based Adversarial Defense Framework to Boost Robustness. (98%)
Yaohua Liu; Jiaxin Gao; Zhu Liu; Xianghao Jiao; Xin Fan; Risheng Liu

http://arxiv.org/abs/2310.12793
OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift. (97%)
Lin Li; Yifei Wang; Chawin Sitawarin; Michael Spratling

http://arxiv.org/abs/2310.12516
Automatic Hallucination Assessment for Aligned Large Language Models via Transferable Adversarial Attacks. (97%)
Xiaodong Yu; Hao Cheng; Xiaodong Liu; Dan Roth; Jianfeng Gao

http://arxiv.org/abs/2310.13076
PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses. (97%)
Chong Xiang; Tong Wu; Sihui Dai; Jonathan Petit; Suman Jana; Prateek Mittal

http://arxiv.org/abs/2310.12815
Prompt Injection Attacks and Defenses in LLM-Integrated Applications. (47%)
Yupei Liu; Yuqi Jia; Runpeng Geng; Jinyuan Jia; Neil Zhenqiang Gong

http://arxiv.org/abs/2310.12505
Attack Prompt Generation for Red Teaming and Defending Large Language Models. (15%)
Boyi Deng; Wenjie Wang; Fuli Feng; Yang Deng; Qifan Wang; Xiangnan He

http://arxiv.org/abs/2310.12665
SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models. (5%)
Boyang Zhang; Zheng Li; Ziqing Yang; Xinlei He; Michael Backes; Mario Fritz; Yang Zhang

http://arxiv.org/abs/2310.13061
To grok or not to grok: Disentangling generalization and memorization on corrupted algorithmic datasets. (1%)
Darshil Doshi; Aritra Das; Tianyu He; Andrey Gromov

http://arxiv.org/abs/2310.13252
Detecting Shared Data Manipulation in Distributed Optimization Algorithms. (1%)
Mohannad Alkhraijah; Rachel Harris; Samuel Litchfield; David Huggins; Daniel K. Molzahn

http://arxiv.org/abs/2310.13191
Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models. (1%)
Jianwei Li; Qi Lei; Wei Cheng; Dongkuan Xu

http://arxiv.org/abs/2310.12017
Exploring Decision-based Black-box Attacks on Face Forgery Detection. (99%)
Zhaoyu Chen; Bo Li; Kaixun Jiang; Shuang Wu; Shouhong Ding; Wenqiang Zhang

http://arxiv.org/abs/2310.12431
Segment Anything Meets Universal Adversarial Perturbation. (99%)
Dongshen Han; Sheng Zheng; Chaoning Zhang

http://arxiv.org/abs/2310.11890
IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks. (99%)
Yue Cao; Tianlin Li; Xiaofeng Cao; Ivor Tsang; Yang Liu; Qing Guo

http://arxiv.org/abs/2310.11850
Revisiting Transferable Adversarial Image Examples: Attack Categorization, Evaluation Guidelines, and New Insights. (99%)
Zhengyu Zhao; Hanwei Zhang; Renjue Li; Ronan Sicre; Laurent Amsaleg; Michael Backes; Qi Li; Chao Shen

http://arxiv.org/abs/2310.13019
Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class Manipulation Using DeepFool Algorithm. (99%)
S. M. Fazle Rabby Labib; Joyanta Jyoti Mondal; Meem Arafat Manab

http://arxiv.org/abs/2310.11901
Malicious Agent Detection for Robust Multi-Agent Collaborative Perception. (87%)
Yangheng Zhao; Zhen Xiang; Sheng Yin; Xianghe Pang; Siheng Chen; Yanfeng Wang

http://arxiv.org/abs/2310.12063
Black-Box Training Data Identification in GANs via Detector Networks. (82%)
Lukman Olagoke; Salil Vadhan; Seth Neel

http://arxiv.org/abs/2310.11789
Adversarial Training for Physics-Informed Neural Networks. (81%)
Yao Li; Shengzhu Shi; Zhichang Guo; Boying Wu

http://arxiv.org/abs/2310.12243
REVAMP: Automated Simulations of Adversarial Attacks on Arbitrary Objects in Realistic Scenes. (80%)
Matthew Hull; Zijie J. Wang; Duen Horng Chau

http://arxiv.org/abs/2310.11970
Quantifying Privacy Risks of Prompts in Visual Prompt Learning. (76%)
Yixin Wu; Rui Wen; Michael Backes; Pascal Berrang; Mathias Humbert; Yun Shen; Yang Zhang

http://arxiv.org/abs/2310.11868
To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now. (47%)
Yimeng Zhang; Jinghan Jia; Xin Chen; Aochuan Chen; Yihua Zhang; Jiancheng Liu; Ke Ding; Sijia Liu

http://arxiv.org/abs/2310.12432
CAT: Closed-loop Adversarial Training for Safe End-to-End Driving. (2%)
Linrui Zhang; Zhenghao Peng; Quanyi Li; Bolei Zhou

http://arxiv.org/abs/2310.12214
PrivInfer: Privacy-Preserving Inference for Black-box Large Language Model. (1%)
Meng Tong; Kejiang Chen; Yuang Qi; Jie Zhang; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2310.11597
The Efficacy of Transformer-based Adversarial Attacks in Security Domains. (99%)
Kunyang Li; Kyle Domico; Jean-Charles Noirot Ferrand; Patrick McDaniel

http://arxiv.org/abs/2310.11594
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning. (93%)
Taejin Kim; Jiarui Li; Shubhranshu Singh; Nikhil Madaan; Carlee Joe-Wong

http://arxiv.org/abs/2310.11595
WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks. (15%)
Jun Xia; Zhihao Yue; Yingbo Zhou; Zhiwei Ling; Xian Wei; Mingsong Chen

http://arxiv.org/abs/2310.11105
Generalizability of CNN Architectures for Face Morph Presentation Attack. (1%)
Sherko R. HmaSalah; Aras Asaad

http://arxiv.org/abs/2310.10844
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks. (98%)
Erfan Shayegani; Md Abdullah Al Mamun; Yu Fu; Pedram Zaree; Yue Dong; Nael Abu-Ghazaleh

http://arxiv.org/abs/2310.10807
Regularization properties of adversarially-trained linear regression. (92%)
Antônio H. Ribeiro; Dave Zachariah; Francis Bach; Thomas B. Schön

http://arxiv.org/abs/2310.10744
Fast Adversarial Label-Flipping Attack on Tabular Data. (84%)
Xinglong Chang; Gillian Dobbie; Jörg Wicker

http://arxiv.org/abs/2310.10126
A Non-monotonic Smooth Activation Function. (83%)
Koushik Biswas; Meghana Karri; Ulaş Bağcı

http://arxiv.org/abs/2310.10610
Quantifying Assistive Robustness Via the Natural-Adversarial Frontier. (68%)
Jerry Zhi-Yang He; Zackory Erickson; Daniel S. Brown; Anca D. Dragan

http://arxiv.org/abs/2310.10124
A Comprehensive Study of Privacy Risks in Curriculum Learning. (67%)
Joann Qiongna Chen; Xinlei He; Zheng Li; Yang Zhang; Zhou Li

http://arxiv.org/abs/2310.10427
DANAA: Towards transferable attacks with double adversarial neuron attribution. (26%)
Zhibo Jin; Zhiyu Zhu; Xinyi Wang; Jiayu Zhang; Jun Shen; Huaming Chen

http://arxiv.org/abs/2310.10780
Demystifying Poisoning Backdoor Attacks from a Statistical Perspective. (9%)
Ganghua Wang; Xun Xian; Jayanth Srinivasa; Ashish Kundu; Xuan Bi; Mingyi Hong; Jie Ding

http://arxiv.org/abs/2310.10077
Prompt Packer: Deceiving LLMs through Compositional Instruction with Hidden Attacks. (4%)
Shuyu Jiang; Xingshu Chen; Rui Tang

http://arxiv.org/abs/2310.10810
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms. (3%)
Alexander Bukharin; Yan Li; Yue Yu; Qingru Zhang; Zhehui Chen; Simiao Zuo; Chao Zhang; Songan Zhang; Tuo Zhao

http://arxiv.org/abs/2310.10483
Passive Inference Attacks on Split Learning via Adversarial Regularization. (3%)
Xiaochen Zhu; Xinjian Luo; Yuncheng Wu; Yangfan Jiang; Xiaokui Xiao; Beng Chin Ooi

http://arxiv.org/abs/2310.10490
On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data. (2%)
Rongjun Qin; Guixiang Zhang; Yang Tang

http://arxiv.org/abs/2310.10090
Orthogonal Uncertainty Representation of Data Manifold for Robust Long-Tailed Learning. (1%)
Yanbiao Ma; Licheng Jiao; Fang Liu; Shuyuan Yang; Xu Liu; Lingling Li

http://arxiv.org/abs/2310.10865
Will the Prince Get True Love's Kiss? On the Model Sensitivity to Gender Perturbation over Fairytale Texts. (1%)
Christina Chance; Da Yin; Dakuo Wang; Kai-Wei Chang

http://arxiv.org/abs/2310.09891
Towards Deep Learning Models Resistant to Transfer-based Adversarial Attacks via Data-centric Robust Learning. (99%)
Yulong Yang; Chenhao Lin; Xiang Ji; Qiwei Tian; Qian Li; Hongshan Yang; Zhibo Wang; Chao Shen

http://arxiv.org/abs/2310.09792
SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack. (99%)
Renyang Liu; Jinhong Zhang; Kwok-Yan Lam; Jun Zhao; Wei Zhou

http://arxiv.org/abs/2310.09795
AFLOW: Developing Adversarial Examples under Extremely Noise-limited Settings. (99%)
Renyang Liu; Jinhong Zhang; Haoran Li; Jin Zhang; Yuanyu Wang; Wei Zhou

http://arxiv.org/abs/2310.10010
Black-box Targeted Adversarial Attack on Segment Anything (SAM). (99%)
Sheng Zheng; Chaoning Zhang; Xinhong Hao

http://arxiv.org/abs/2310.10036
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization. (97%)
Long Zhuo; Shenghai Luo; Shunquan Tan; Han Chen; Bin Li; Jiwu Huang

http://arxiv.org/abs/2310.09744
Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks. (61%)
Ziqiang Li; Pengfei Xia; Hong Sun; Yueqi Zeng; Wei Zhang; Bin Li

http://arxiv.org/abs/2310.10012
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models? (9%)
Yu-Lin Tsai; Chia-Yi Hsu; Chulin Xie; Chih-Hsun Lin; Jia-You Chen; Bo Li; Pin-Yu Chen; Chia-Mu Yu; Chun-Ying Huang

http://arxiv.org/abs/2310.09827
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning. (3%)
Tianyuan Zou; Zixuan Gu; Yu He; Hideaki Takahashi; Yang Liu; Ya-Qin Zhang

http://arxiv.org/abs/2310.09652
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries. (98%)
Wenjie Lv; Zhen Wang; Yitao Zheng; Zhehua Zhong; Qi Xuan; Tianyi Chen

http://arxiv.org/abs/2310.09361
Is Certifying $\ell_p$ Robustness Still Worthwhile? (99%)
Ravi Mangal; Klas Leino; Zifan Wang; Kai Hu; Weicheng Yu; Corina Pasareanu; Anupam Datta; Matt Fredrikson

http://arxiv.org/abs/2310.09266
User Inference Attacks on Large Language Models. (41%)
Nikhil Kandpal; Krishna Pillutla; Alina Oprea; Peter Kairouz; Christopher A. Choquette-Choo; Zheng Xu

http://arxiv.org/abs/2310.08847
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting. (1%)
Runqi Lin; Chaojian Yu; Bo Han; Tongliang Liu

http://arxiv.org/abs/2310.08073
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks. (99%)
Giorgio Piras; Maura Pintor; Ambra Demontis; Battista Biggio

http://arxiv.org/abs/2310.08292
Concealed Electronic Countermeasures of Radar Signal with Adversarial Examples. (93%)
Ruinan Ma; Canjie Zhu; Mingfeng Lu; Yunjie Li; Yu-an Tan; Ruibin Zhang; Ran Tao

http://arxiv.org/abs/2310.08808
Attacks Meet Interpretability (AmI) Evaluation and Findings. (92%)
Qian Ma; Ziping Ye; Shagufta Mehnaz

http://arxiv.org/abs/2310.08177
Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization. (68%)
Giuseppe Floris; Raffaele Mura; Luca Scionis; Giorgio Piras; Maura Pintor; Ambra Demontis; Battista Biggio

http://arxiv.org/abs/2310.08681
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks. (64%)
Erfan Darzi; Nanna M. Sijtsema; Ooijen P. M. A van

http://arxiv.org/abs/2310.08732
Provably Robust Cost-Sensitive Learning via Randomized Smoothing. (45%)
Yuan Xin; Michael Backes; Xiao Zhang

http://arxiv.org/abs/2310.08571
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. (31%)
Jan Dubiński; Stanisław Pawlak; Franziska Boenisch; Tomasz Trzciński; Adam Dziedzic

http://arxiv.org/abs/2310.08097
Sentinel: An Aggregation Function to Secure Decentralized Federated Learning. (11%)
Chao Feng; Alberto Huertas Celdran; Janosch Baltensperger; Enrique Tomas Matınez Bertran; Gerome Bovet; Burkhard Stiller

http://arxiv.org/abs/2310.08772
Investigating the Robustness and Properties of Detection Transformers (DETR) Toward Difficult Images. (9%)
Zhao Ning Zou; Yuhang Zhang; Robert Wijaya

http://arxiv.org/abs/2310.08320
Defending Our Privacy With Backdoors. (9%)
Dominik Hintersdorf; Lukas Struppek; Daniel Neider; Kristian Kersting

http://arxiv.org/abs/2310.08708
Polynomial Time Cryptanalytic Extraction of Neural Network Models. (3%)
Adi Shamir; Isaac Canales-Martinez; Anna Hambitzer; Jorge Chavez-Saab; Francisco Rodrigez-Henriquez; Nitin Satpute

http://arxiv.org/abs/2310.08040
SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection. (1%)
Xiaoyang Song; Wenbo Sun; Maher Nouiehed; Raed Al Kontar; Judy Jin

http://arxiv.org/abs/2310.08537
XAI Benchmark for Visual Explanation. (1%)
Yifei Zhang; Siyi Gu; James Song; Bo Pan; Liang Zhao

http://arxiv.org/abs/2310.08419
Jailbreaking Black Box Large Language Models in Twenty Queries. (1%)
Patrick Chao; Alexander Robey; Edgar Dobriban; Hamed Hassani; George J. Pappas; Eric Wong

http://arxiv.org/abs/2310.08739
Voyager: MTD-Based Aggregation Protocol for Mitigating Poisoning Attacks on DFL. (1%)
Chao Feng; Alberto Huertas Celdran; Michael Vuong; Gerome Bovet; Burkhard Stiller

http://arxiv.org/abs/2310.07492
Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models. (99%)
Renyang Liu; Wei Zhou; Tianwei Zhang; Kangjie Chen; Jun Zhao; Kwok-Yan Lam

http://arxiv.org/abs/2310.07780
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. (89%)
Linbo Liu; Trong Nghia Hoang; Lam M. Nguyen; Tsui-Wei Weng

http://arxiv.org/abs/2310.07325
An Adversarial Example for Direct Logit Attribution: Memory Management in gelu-4l. (13%)
James Dao; Yeu-Tong Lao; Can Rager; Jett Janiak

http://arxiv.org/abs/2310.07632
Prompt Backdoors in Visual Prompt Learning. (11%)
Hai Huang; Zhengyu Zhao; Michael Backes; Yun Shen; Yang Zhang

http://arxiv.org/abs/2310.08015
Why Train More? Effective and Efficient Membership Inference via Memorization. (10%)
Jihye Choi; Shruti Tople; Varun Chandrasekaran; Somesh Jha

http://arxiv.org/abs/2310.07958
Towards Causal Deep Learning for Vulnerability Detection. (4%)
Md Mahbubur Rahman; Ira Ceka; Chengzhi Mao; Saikat Chakraborty; Baishakhi Ray; Wei Le

http://arxiv.org/abs/2310.07745
Deep Reinforcement Learning for Autonomous Cyber Operations: A Survey. (3%)
Gregory Palmer; Chris Parry; Daniel J. B. Harrold; Chris Willis

http://arxiv.org/abs/2310.06468
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks. (99%)
Yang Wang; Bo Dong; Ke Xu; Haiyin Piao; Yufei Ding; Baocai Yin; Xin Yang

http://arxiv.org/abs/2310.07159
My Brother Helps Me: Node Injection Based Adversarial Attack on Social Bot Detection. (98%)
Lanjun Wang; Xinran Qiao; Yanwei Xie; Weizhi Nie; Yongdong Zhang; Anan Liu

http://arxiv.org/abs/2310.06396
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach. (83%)
Kai Zhao; Qiyu Kang; Yang Song; Rui She; Sijie Wang; Wee Peng Tay

http://arxiv.org/abs/2310.06956
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help. (76%)
Charles Dawson; Chuchu Fan

http://arxiv.org/abs/2310.07152
No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML. (62%)
Ziqi Zhang; Chen Gong; Yifeng Cai; Yuanyuan Yuan; Bingyan Liu; Ding Li; Yao Guo; Xiangqun Chen

http://arxiv.org/abs/2310.06958
Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks. (47%)
Anastasia Antsiferova; Khaled Abud; Aleksandr Gushchin; Ekaterina Shumitskaya; Sergey Lavrushkin; Dmitriy Vatolin

http://arxiv.org/abs/2310.07100
GraphCloak: Safeguarding Task-specific Knowledge within Graph-structured Data from Unauthorized Exploitation. (22%)
Yixin Liu; Chenrui Fan; Xun Chen; Pan Zhou; Lichao Sun

http://arxiv.org/abs/2310.06668
Latent Diffusion Counterfactual Explanations. (5%)
Karim Farid; Simon Schrodi; Max Argus; Thomas Brox

http://arxiv.org/abs/2310.06588
FTFT: efficient and robust Fine-Tuning by transFerring Training dynamics. (2%)
Yupei Du; Albert Gatt; Dong Nguyen

http://arxiv.org/abs/2310.07084
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE. (2%)
Marius Arvinte; Cory Cornelius; Jason Martin; Nageen Himayat

http://arxiv.org/abs/2310.06387
Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations. (1%)
Zeming Wei; Yifei Wang; Yisen Wang

http://arxiv.org/abs/2310.06182
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization. (92%)
Jiancong Xiao; Ruoyu Sun; Zhi- Quan Luo

http://arxiv.org/abs/2310.14942
Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand. (22%)
Junfeng Guo; Yiming Li; Lixu Wang; Shu-Tao Xia; Heng Huang; Cong Liu; Bo Li

http://arxiv.org/abs/2310.06112
Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach. (5%)
Shaopeng Fu; Di Wang

http://arxiv.org/abs/2310.06227
Exploring adversarial attacks in federated learning for medical imaging. (2%)
Erfan Darzi; Florian Dubost; N. M. Sijtsema; Ooijen P. M. A van

http://arxiv.org/abs/2310.05354
An Initial Investigation of Neural Replay Simulator for Over-the-Air Adversarial Perturbations to Automatic Speaker Verification. (99%)
Jiaqi Li; Li Wang; Liumeng Xue; Lei Wang; Zhizheng Wu

http://arxiv.org/abs/2310.05369
AdvSV: An Over-the-Air Adversarial Attack Dataset for Speaker Verification. (96%)
Li Wang; Jiaqi Li; Yuhao Luo; Jiahao Zheng; Lei Wang; Hao Li; Ke Xu; Chengfang Fang; Jie Shi; Zhizheng Wu

http://arxiv.org/abs/2310.05057
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models. (26%)
Yifan Jiang; Filip Ilievski; Kaixin Ma; Zhivar Sourati

http://arxiv.org/abs/2310.04687
Improving Adversarial Attacks on Latent Diffusion Model. (99%)
Boyang Zheng; Chumeng Liang; Xiaoyu Wu; Yan Liu

http://arxiv.org/abs/2310.04780
IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers. (76%)
Zhenglin Huang; Xiaoan Bao; Na Zhang; Qingqi Zhang; Xiaomei Tu; Biao Wu; Xi Yang

http://arxiv.org/abs/2310.04655
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models. (98%)
Ziyi Yin; Muchao Ye; Tianrong Zhang; Tianyu Du; Jinguo Zhu; Han Liu; Jinghui Chen; Ting Wang; Fenglong Ma

http://arxiv.org/abs/2310.04055
Kick Bad Guys Out! Zero-Knowledge-Proof-Based Anomaly Detection in Federated Learning. (84%)
Shanshan Han; Wenxuan Wu; Baturalp Buyukates; Weizhao Jin; Qifan Zhang; Yuhang Yao; Salman Avestimehr; Chaoyang He

http://arxiv.org/abs/2310.03707
OMG-ATTACK: Self-Supervised On-Manifold Generation of Transferable Evasion Attacks. (99%)
Ofir Bar Tal; Adi Haviv; Amit H. Bermano

http://arxiv.org/abs/2310.03334
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System. (99%)
Khushnaseeb Roshan; Aasim Zafar; Sheikh Burhan Ul Haque

http://arxiv.org/abs/2310.03358
Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria. (99%)
Nuoyan Zhou; Nannan Wang; Decheng Liu; Dawei Zhou; Xinbo Gao

http://arxiv.org/abs/2310.03349
An Integrated Algorithm for Robust and Imperceptible Audio Adversarial Examples. (98%)
Armin Ettenhofer; Jan-Philipp Schulze; Karla Pizzi

http://arxiv.org/abs/2310.03614
Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally. (98%)
Shawqi Al-Maliki; Adnan Qayyum; Hassan Ali; Mohamed Abdallah; Junaid Qadir; Dinh Thai Hoang; Dusit Niyato; Ala Al-Fuqaha

http://arxiv.org/abs/2310.03684
SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks. (92%)
Alexander Robey; Eric Wong; Hamed Hassani; George J. Pappas

http://arxiv.org/abs/2310.03578
Targeted Adversarial Attacks on Generalizable Neural Radiance Fields. (56%)
Andras Horvath; Csaba M. Jozsa

http://arxiv.org/abs/2310.03664
Certification of Deep Learning Models for Medical Image Segmentation. (15%)
Othmane Laousy; Alexandre Araujo; Guillaume Chassagnon; Nikos Paragios; Marie-Pierre Revel; Maria Vakalopoulou

http://arxiv.org/abs/2310.03312
Certifiably Robust Graph Contrastive Learning. (5%)
Minhua Lin; Teng Xiao; Enyan Dai; Xiang Zhang; Suhang Wang

http://arxiv.org/abs/2310.03518
Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations. (2%)
Jiachi Liu; Liwen Wang; Guanting Dong; Xiaoshuai Song; Zechen Wang; Zhengyang Wang; Shanglin Lei; Jinzheng Zhao; Keqing He; Bo Xiao; Weiran Xu

http://arxiv.org/abs/2310.02997
Optimizing Key-Selection for Face-based One-Time Biometrics via Morphing. (98%)
Daile Osorio-Roig; Mahdi Ghafourian; Christian Rathgeb; Ruben Vera-Rodriguez; Christoph Busch; Julian Fierrez

http://arxiv.org/abs/2310.03185
Misusing Tools in Large Language Models With Visual Adversarial Examples. (97%)
Xiaohan Fu; Zihan Wang; Shuheng Li; Rajesh K. Gupta; Niloofar Mireshghallah; Taylor Berg-Kirkpatrick; Earlence Fernandes

http://arxiv.org/abs/2310.03285
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations. (82%)
Ahmed Abusnaina; Yizhen Wang; Sunpreet Arora; Ke Wang; Mihai Christodorescu; David Mohaisen

http://arxiv.org/abs/2310.03166
Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors. (81%)
Biagio Montaruli; Luca Demetrio; Maura Pintor; Luca Compagna; Davide Balzarotti; Battista Biggio

http://arxiv.org/abs/2310.03125
Shielding the Unseen: Privacy Protection through Poisoning NeRF with Spatial Deformation. (10%)
Yihan Wu; Brandon Y. Feng; Heng Huang

http://arxiv.org/abs/2310.02480
Splitting the Difference on Adversarial Training. (99%)
Matan Levi; Aryeh Kontorovich

http://arxiv.org/abs/2310.02025
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training. (97%)
Aochuan Chen; Yimeng Zhang; Jinghan Jia; James Diffenderfer; Jiancheng Liu; Konstantinos Parasyris; Yihua Zhang; Zheng Zhang; Bhavya Kailkhura; Sijia Liu

http://arxiv.org/abs/2310.02544
SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers. (86%)
KL Navaneet; Soroush Abbasi Koohpayegani; Essam Sleiman; Hamed Pirsiavash

http://arxiv.org/abs/2310.01875
Towards Stable Backdoor Purification through Feature Shift Tuning. (83%)
Rui Min; Zeyu Qin; Li Shen; Minhao Cheng

http://arxiv.org/abs/2310.02417
Jailbreaker in Jail: Moving Target Defense for Large Language Models. (73%)
Bocheng Chen; Advait Paliwal; Qiben Yan

http://arxiv.org/abs/2310.04451
AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models. (56%)
Xiaogeng Liu; Nan Xu; Muhao Chen; Chaowei Xiao

http://arxiv.org/abs/2310.01959
Beyond Labeling Oracles: What does it mean to steal ML models? (47%)
Avital Shafran; Ilia Shumailov; Murat A. Erdogdu; Nicolas Papernot

http://arxiv.org/abs/2310.02237
Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness. (13%)
Yanzhao Wu; Ka-Ho Chow; Wenqi Wei; Ling Liu

http://arxiv.org/abs/2310.02113
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks. (11%)
Jorge Castillo; Phillip Rieger; Hossein Fereidooni; Qian Chen; Ahmad Sadeghi

http://arxiv.org/abs/2310.01818
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework. (3%)
Xilie Xu; Jingfeng Zhang; Mohan Kankanhalli

http://arxiv.org/abs/2310.01452
Fooling the Textual Fooler via Randomizing Latent Representations. (99%)
Duy C. Hoang; Quang H. Nguyen; Saurav Manchanda; MinLong Peng; Kok-Seng Wong; Khoa D. Doan

http://arxiv.org/abs/2310.01537
Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things. (92%)
Xianjian Xie; Xiaochen Xian; Dan Li; Andi Wang

http://arxiv.org/abs/2310.04445
LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model. (87%)
Muhammad Ahmed Shah; Roshan Sharma; Hira Dhamyal; Raphael Olivier; Ankit Shah; Joseph Konan; Dareen Alharthi; Hazim T Bukhari; Massa Baali; Soham Deshmukh; Michael Kuhlmann; Bhiksha Raj; Rita Singh

http://arxiv.org/abs/2310.01469
LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples. (87%)
Jia-Yu Yao; Kun-Peng Ning; Zhen-Hui Liu; Mu-Nan Ning; Li Yuan

http://arxiv.org/abs/2310.01166
Gotcha! This Model Uses My Code! Evaluating Membership Leakage Risks in Code Models. (13%)
Zhou Yang; Zhipeng Zhao; Chenyu Wang; Jieke Shi; Dongsum Kim; Donggyun Han; David Lo

http://arxiv.org/abs/2311.12832
Toward effective protection against diffusion based mimicry through score distillation. (3%)
Haotian Xue; Chumeng Liang; Xiaoyu Wu; Yongxin Chen

http://arxiv.org/abs/2310.01651
Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations. (1%)
Yongshuo Zong; Tingyang Yu; Bingchen Zhao; Ruchika Chavhan; Timothy Hospedales

http://arxiv.org/abs/2310.00633
A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attacks. (99%)
Yanjie Li; Bin Xie; Songtao Guo; Yuanyuan Yang; Bin Xiao

http://arxiv.org/abs/2310.00761
Counterfactual Image Generation for adversarially robust and interpretable Classifiers. (96%)
Rafael Bischof; Florian Scheidegger; Michael A. Kraus; A. Cristiano I. Malossi

http://arxiv.org/abs/2310.00607
On the Onset of Robust Overfitting in Adversarial Training. (64%)
Chaojian Yu; Xiaolong Shi; Jun Yu; Bo Han; Tongliang Liu

http://arxiv.org/abs/2310.00616
Understanding Adversarial Transferability in Federated Learning. (64%)
Yijiang Li; Ying Gao; Haohan Wang

http://arxiv.org/abs/2310.00626
GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning. (61%)
Qiannan Wang; Changchun Yin; Zhe Liu; Liming Fang; Run Wang; Chenhao Lin

http://arxiv.org/abs/2310.00648
Fewer is More: Trojan Attacks on Parameter-Efficient Fine-Tuning. (9%)
Lauren Hong; Ting Wang

http://arxiv.org/abs/2310.00847
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data? (1%)
Atsuyuki Miyai; Qing Yu; Go Irie; Kiyoharu Aizawa

http://arxiv.org/abs/2310.00710
How well does LLM generate security tests? (1%)
Ying Daphne Zhang; Wenjia Daphne Song; Zhengjie Daphne Ji; Daphne Danfeng; Yao; Na Meng

http://arxiv.org/abs/2310.00567
Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks. (99%)
Quang H. Nguyen; Yingjie Lao; Tung Pham; Kok-Seng Wong; Khoa D. Doan

http://arxiv.org/abs/2310.00438
Human-Producible Adversarial Examples. (98%)
David Khachaturov; Yue Gao; Ilia Shumailov; Robert Mullins; Ross Anderson; Kassem Fawaz

http://arxiv.org/abs/2310.00503
Black-box Attacks on Image Activity Prediction and its Natural Language Explanations. (98%)
Alina Elena Baia; Valentina Poggioni; Andrea Cavallaro

http://arxiv.org/abs/2310.00542
Horizontal Class Backdoor to Deep Learning. (84%)
Hua Ma; Shang Wang; Yansong Gao

http://arxiv.org/abs/2310.00416
Refutation of Shapley Values for XAI -- Additional Evidence. (8%)
Xuanxiang Huang; Joao Marques-Silva

http://arxiv.org/abs/2310.00076
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks. (99%)
Mehrdad Saberi; Vinu Sankar Sadasivan; Keivan Rezaei; Aounon Kumar; Atoosa Chegini; Wenxiao Wang; Soheil Feizi

http://arxiv.org/abs/2309.17348
Efficient Biologically Plausible Adversarial Training. (98%)
Matilde Tristany Farinha; Thomas Ortner; Giorgia Dellaferrera; Benjamin Grewe; Angeliki Pantazi

http://arxiv.org/abs/2309.17410
Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks. (96%)
Vaidehi Patil; Peter Hase; Mohit Bansal

http://arxiv.org/abs/2309.17048
On Continuity of Robust and Accurate Classifiers. (93%)
Ramin Barati; Reza Safabakhsh; Mohammad Rahmati

http://arxiv.org/abs/2309.17401
Adversarial Machine Learning in Latent Representations of Neural Networks. (93%)
Milin Zhang; Mohammad Abdi; Francesco Restuccia

http://arxiv.org/abs/2310.00116
Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization. (92%)
Mahyar Fazlyab; Taha Entesari; Aniket Roy; Rama Chellappa

http://arxiv.org/abs/2309.17278
Toward Robust Recommendation via Real-time Vicinal Defense. (82%)
Yichang Xu; Chenwang Wu; Defu Lian

http://arxiv.org/abs/2310.00070
Adversarial Explainability: Utilizing Explainable Machine Learning in Bypassing IoT Botnet Detection Systems. (31%)
Mohammed M. Alani; Atefeh Mashatan; Ali Miri

http://arxiv.org/abs/2310.00108
Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study. (13%)
Myeongseob Ko; Ming Jin; Chenguang Wang; Ruoxi Jia

http://arxiv.org/abs/2309.17301
Distributed Resilient Control of DC Microgrids Under Generally Unbounded FDI Attacks. (1%)
Yichao Wang; Mohamadamin Rajabinezhad; Omar A. Beg; Shan Zuo

http://arxiv.org/abs/2310.00222
Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning. (1%)
Hongsheng Hu; Xuyun Zhang; Zoran Salcic; Lichao Sun; Kim-Kwang Raymond Choo; Gillian Dobbie

http://arxiv.org/abs/2309.16878
Investigating Human-Identifiable Features Hidden in Adversarial Perturbations. (98%)
Dennis Y. Menn; Tzu-hsun Feng; Sriram Vishwanath; Hung-yi Lee

http://arxiv.org/abs/2309.16207
Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks. (98%)
Huihui Gong; Minjing Dong; Siqi Ma; Seyit Camtepe; Surya Nepal; Chang Xu

http://arxiv.org/abs/2309.16487
Towards Poisoning Fair Representations. (70%)
Tianci Liu; Haoyu Wang; Feijie Wu; Hengtong Zhang; Pan Li; Lu Su; Jing Gao

http://arxiv.org/abs/2309.16452
On the Trade-offs between Adversarial Robustness and Actionable Explanations. (68%)
Satyapriya Krishna; Chirag Agarwal; Himabindu Lakkaraju

http://arxiv.org/abs/2309.16883
The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing. (56%)
Blaise Delattre; Alexandre Araujo; Quentin Barthélemy; Alexandre Allauzen

http://arxiv.org/abs/2309.16827
Post-Training Overfitting Mitigation in DNN Classifiers. (41%)
Hang Wang; David J. Miller; George Kesidis

http://arxiv.org/abs/2309.16952
Leveraging Optimization for Adaptive Attacks on Image Watermarks. (13%)
Nils Lukas; Abdulrahman Diaa; Lucas Fenaux; Florian Kerschbaum

http://arxiv.org/abs/2309.16172
Random and Safe Cache Architecture to Defeat Cache Timing Attacks. (9%)
Guangyuan Hu; Ruby B. Lee

http://arxiv.org/abs/2309.16631
Robust Offline Reinforcement Learning -- Certify the Confidence Interval. (4%)
Jiarui Yao; Simon Shaolei Du

http://arxiv.org/abs/2309.16314
A Primer on Bayesian Neural Networks: Review and Debates. (2%)
Julyan Arbel; Konstantinos Pitas; Mariia Vladimirova; Vincent Fortuin

http://arxiv.org/abs/2309.16096
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness. (95%)
Ambar Pal; Jeremias Sulam; René Vidal

http://arxiv.org/abs/2309.15519
Defending Against Physical Adversarial Patch Attacks on Infrared Human Detection. (95%)
Lukas Strack; Futa Waseda; Huy H. Nguyen; Yinqiang Zheng; Isao Echizen

http://arxiv.org/abs/2309.15669
On the Computational Entanglement of Distant Features in Adversarial Machine Learning. (92%)
YenLung Lai; Xingbo Dong; Zhe Jin

http://arxiv.org/abs/2309.15418
Automatic Feature Fairness in Recommendation via Adversaries. (33%)
Hengchang Hu; Yiming Cao; Zhankui He; Samson Tan; Min-Yen Kan

http://arxiv.org/abs/2310.07726
Warfare:Breaking the Watermark Protection of AI-Generated Content. (12%)
Guanlin Li; Yifei Chen; Jie Zhang; Jiwei Li; Shangwei Guo; Tianwei Zhang

http://arxiv.org/abs/2309.15770
Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural Rendering. (11%)
Yasasa Abeysirigoonawardena; Kevin Xie; Chuhan Chen; Salar Hosseini; Ruiting Chen; Ruiqi Wang; Florian Shkurti

http://arxiv.org/abs/2309.15687
Breaking On-Chip Communication Anonymity using Flow Correlation Attacks. (4%)
Hansika Weerasena; Prabhat Mishra

http://arxiv.org/abs/2310.06855
Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic Classification. (1%)
Mahmoud Nazzal; Nura Aljaafari; Ahmed Sawalmeh; Abdallah Khreishah; Muhammad Anan; Abdulelah Algosaibi; Mohammed Alnaeem; Adel Aldalbahi; Abdulaziz Alhumam; Conrado P. Vizcarra; Shadan Alhamed

http://arxiv.org/abs/2309.14700
Structure Invariant Transformation for better Adversarial Transferability. (99%)
Xiaosen Wang; Zeliang Zhang; Jianping Zhang

http://arxiv.org/abs/2309.15087
Privacy-preserving and Privacy-attacking Approaches for Speech and Audio -- A Survey. (16%)
Yuchen Liu; Apu Kapadia; Donald Williamson

http://arxiv.org/abs/2309.15386
Neural Stochastic Differential Equations for Robust and Explainable Analysis of Electromagnetic Unintended Radiated Emissions. (2%)
Sumit Kumar Jha; Susmit Jha; Rickard Ewetz; Alvaro Velasquez

http://arxiv.org/abs/2309.15224
Collaborative Watermarking for Adversarial Speech Synthesis. (1%)
Lauri Aalto University, Finland Juvela; Xin National Institute of Informatics, Japan Wang

http://arxiv.org/abs/2309.14585
DifAttack: Query-Efficient Black-Box Attack via Disentangled Feature Space. (99%)
Liu Jun; Zhou Jiantao; Zeng Jiandian; Jinyu Tian

http://arxiv.org/abs/2309.14615
Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents. (98%)
Foozhan Ataiefard; Hadi Hemmati

http://arxiv.org/abs/2309.14122
SurrogatePrompt: Bypassing the Safety Filter of Text-To-Image Models via Substitution. (1%)
Zhongjie Ba; Jieming Zhong; Jiachen Lei; Peng Cheng; Qinglong Wang; Zhan Qin; Zhibo Wang; Kui Ren

http://arxiv.org/abs/2309.13857
Adversarial Attacks on Video Object Segmentation with Hard Region Discovery. (99%)
Ping Li; Yu Zhang; Li Yuan; Jian Zhao; Xianghua Xu; Xiaoqin Zhang

http://arxiv.org/abs/2309.13609
Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks. (98%)
Ao-Xiang Zhang; Yu Ran; Weixuan Tang; Yuan-Gen Wang

http://arxiv.org/abs/2309.13841
On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors. (86%)
Trong-Nghia To; Danh Le Kim; Do Thi Thu Hien; Nghi Hoang Khoa; Hien Do Hoang; Phan The Duy; Van-Hau Pham

http://arxiv.org/abs/2310.03033
Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign Recognition. (80%)
Andreea Postovan; Mădălina Eraşcu

http://arxiv.org/abs/2309.13794
Projected Randomized Smoothing for Certified Adversarial Robustness. (76%)
Samuel Pfrommer; Brendon G. Anderson; Somayeh Sojoudi

http://arxiv.org/abs/2309.13763
Combining Two Adversarial Attacks Against Person Re-Identification Systems. (73%)
Eduardo de O. Andrade; Igor Garcia Ballhausen Sampaio; Joris Guérin; José Viterbo

http://arxiv.org/abs/2309.13579
Seeing Is Not Always Believing: Invisible Collision Attack and Defence on Pre-Trained Models. (2%)
Minghang Deng; Zhong Zhang; Junming Shao

http://arxiv.org/abs/2309.13256
Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks. (61%)
Zhaohan Xi; Tianyu Du; Changjiang Li; Ren Pang; Shouling Ji; Jinghui Chen; Fenglong Ma; Ting Wang

http://arxiv.org/abs/2309.13444
Moving Target Defense based Secured Network Slicing System in the O-RAN Architecture. (1%)
Mojdeh Karbalaee Motalleb; Chafika Benzaïd; Tarik Taleb; Vahid Shah-Mansouri

http://arxiv.org/abs/2309.13475
Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers. (1%)
Aryaman Gupta; Kaustav Chakraborty; Somil Bansal

http://arxiv.org/abs/2309.13245
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias. (99%)
Hao Cheng; Jinhao Duan; Hui Li; Lyutianyang Zhang; Jiahang Cao; Ping Wang; Jize Zhang; Kaidi Xu; Renjing Xu

http://arxiv.org/abs/2309.13190
Spatial-frequency channels, shape bias, and adversarial robustness. (69%)
Ajay Subramanian; Elena Sizikova; Najib J. Majaj; Denis G. Pelli

http://arxiv.org/abs/2309.12914
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks. (69%)
Heitor R. Guimarães; Arthur Pimentel; Anderson Avila; Tiago H. Falk

http://arxiv.org/abs/2309.13016
Understanding Deep Gradient Leakage via Inversion Influence Functions. (15%)
Haobo Zhang; Junyuan Hong; Yuyang Deng; Mehrdad Mahdavi; Jiayu Zhou

http://arxiv.org/abs/2309.13150
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations. (10%)
Hanjiang Hu; Zuxin Liu; Linyi Li; Jiacheng Zhu; Ding Zhao

http://arxiv.org/abs/2309.13038
Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception? (5%)
Xiaoxiao Sun; Nidham Gazagnadou; Vivek Sharma; Lingjuan Lyu; Hongdong Li; Liang Zheng

http://arxiv.org/abs/2309.13002
Expressive variational quantum circuits provide inherent privacy in federated learning. (1%)
Niraj Kumar; Jamie Heredge; Changhao Li; Shaltiel Eloul; Shree Hari Sureshbabu; Marco Pistoia

http://arxiv.org/abs/2309.12955
On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures. (1%)
Qingzhao Zhang; Shuowei Jin; Ruiyang Zhu; Jiachen Sun; Xumiao Zhang; Qi Alfred Chen; Z. Morley Mao

http://arxiv.org/abs/2309.12593
Improving Machine Learning Robustness via Adversarial Training. (99%)
Long Dang; Thushari Hapuarachchi; Kaiqi Xiong; Jing Lin

http://arxiv.org/abs/2309.11830
Goal-Oriented Prompt Attack and Safety Evaluation for LLMs. (69%)
Chengyuan Liu; Fubang Zhao; Lizhi Qing; Yangyang Kang; Changlong Sun; Kun Kuang; Fei Wu

http://arxiv.org/abs/2309.12481
HANS, are you clever? Clever Hans Effect Analysis of Neural Systems. (45%)
Leonardo Ranaldi; Fabio Massimo Zanzotto

http://arxiv.org/abs/2309.12263
On the Relationship between Skill Neurons and Robustness in Prompt Tuning. (12%)
Leon Ackermann; Xenia Ohmer

http://arxiv.org/abs/2309.11894
DeepTheft: Stealing DNN Model Architectures through Power Side Channel. (1%)
Yansong Gao; Huming Qiu; Zhi Zhang; Binghui Wang; Hua Ma; Alsharif Abuadbba; Minhui Xue; Anmin Fu; Surya Nepal

http://arxiv.org/abs/2309.11751
How Robust is Google's Bard to Adversarial Image Attacks? (99%)
Yinpeng Dong; Huanran Chen; Jiawei Chen; Zhengwei Fang; Xiao Yang; Yichi Zhang; Yu Tian; Hang Su; Jun Zhu

http://arxiv.org/abs/2309.11111
PRAT: PRofiling Adversarial aTtacks. (99%)
Rahul Ambati; Naveed Akhtar; Ajmal Mian; Yogesh Singh Rawat

http://arxiv.org/abs/2309.11196
When to Trust AI: Advances and Challenges for Certification of Neural Networks. (64%)
Marta Kwiatkowska; Xiyue Zhang

http://arxiv.org/abs/2309.11462
AudioFool: Fast, Universal and synchronization-free Cross-Domain Attack on Speech Recognition. (54%)
Mohamad Fakih; Rouwaida Kanj; Fadi Kurdahi; Mohammed E. Fouda

http://arxiv.org/abs/2309.11667
Understanding Pose and Appearance Disentanglement in 3D Human Pose Estimation. (54%)
Krishna Kanth Nakka; Mathieu Salzmann

http://arxiv.org/abs/2309.11053
Fed-LSAE: Thwarting Poisoning Attacks against Federated Cyber Threat Detection System via Autoencoder-based Latent Space Inspection. (5%)
Tran Duc Luong; Vuong Minh Tien; Nguyen Huu Quyen; Do Thi Thu Hien; Phan The Duy; Van-Hau Pham

http://arxiv.org/abs/2309.16577
Compilation as a Defense: Enhancing DL Model Attack Robustness via Tensor Optimization. (2%)
Stefan Trawicki; William Hackett; Lewis Birch; Neeraj Suri; Peter Garraghan

http://arxiv.org/abs/2309.10348
Language Guided Adversarial Purification. (99%)
Himanshu Singh; A V Subramanyam

http://arxiv.org/abs/2309.10916
What Learned Representations and Influence Functions Can Tell Us About Adversarial Examples. (99%)
Shakila Mahjabin Tonni; Mark Dras

http://arxiv.org/abs/2309.10586
Adversarial Attacks Against Uncertainty Quantification. (99%)
Emanuele Ledda; Daniele Angioni; Giorgio Piras; Giorgio Fumera; Battista Biggio; Fabio Roli

http://arxiv.org/abs/2309.10544
Model Leeching: An Extraction Attack Targeting LLMs. (76%)
Lewis Birch; William Hackett; Stefan Trawicki; Neeraj Suri; Peter Garraghan

http://arxiv.org/abs/2309.11022
Information Leakage from Data Updates in Machine Learning Models. (16%)
Tian Hui; Farhad Farokhi; Olga Ohrimenko

http://arxiv.org/abs/2309.10644
Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers. (16%)
Zhen Li; Ruqian Zhang; Deqing Zou; Ning Wang; Yating Li; Shouhuai Xu; Chen Chen; Hai Jin

http://arxiv.org/abs/2309.10607
SPFL: A Self-purified Federated Learning Method Against Poisoning Attacks. (12%)
Zizhen Liu; Weiyang He; Chip-Hong Chang; Jing Ye; Huawei Li; Xiaowei Li

http://arxiv.org/abs/2309.11005
It's Simplex! Disaggregating Measures to Improve Certified Robustness. (11%)
Andrew C. Cullen; Paul Montague; Shijie Liu; Sarah M. Erfani; Benjamin I. P. Rubinstein

http://arxiv.org/abs/2310.10664
Nebula: Self-Attention for Dynamic Malware Analysis. (5%)
Dmitrijs Trizna; Luca Demetrio; Battista Biggio; Fabio Roli

http://arxiv.org/abs/2310.07725
Extreme Image Transformations Facilitate Robust Latent Object Representations. (1%)
Girik Malik; Dakarai Crowder; Ennio Mingolla

http://arxiv.org/abs/2309.09480
Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization. (99%)
Huihui Gong; Minjing Dong; Siqi Ma; Seyit Camtepe; Surya Nepal; Chang Xu

http://arxiv.org/abs/2309.10243
Transferable Adversarial Attack on Image Tampering Localization. (99%)
Yuqi Wang; Gang Cao; Zijie Lou; Haochen Zhu

http://arxiv.org/abs/2309.10136
Efficient Low-Rank GNN Defense Against Structural Attacks. (96%)
Abdullah Alchihabi; Qing En; Yuhong Guo

http://arxiv.org/abs/2309.09928
Evaluating Adversarial Robustness with Expected Viable Performance. (45%)
Ryan McCoppin; Colin Dawson; Sean M. Kennedy; Leslie M. Blaha

http://arxiv.org/abs/2309.10058
Dual Student Networks for Data-Free Model Stealing. (26%)
James Beetham; Navid Kardan; Ajmal Mian; Mubarak Shah

http://arxiv.org/abs/2309.09700
Securing Fixed Neural Network Steganography. (5%)
Zicong Luo; Sheng Li; Guobiao Li; Zhenxing Qian; Xinpeng Zhang

http://arxiv.org/abs/2309.10253
GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts. (4%)
Jiahao Yu; Xingwei Lin; Zheng Yu; Xinyu Xing

http://arxiv.org/abs/2309.09586
Spoofing attack augmentation: can differently-trained attack models improve generalisation? (3%)
Wanying Ge; Xin Wang; Junichi Yamagishi; Massimiliano Todisco; Nicholas Evans

http://arxiv.org/abs/2309.09837
Frame-to-Utterance Convergence: A Spectra-Temporal Approach for Unified Spoofing Detection. (1%)
Awais Khan; Khalid Mahmood Malik; Shah Nawaz

http://arxiv.org/abs/2309.09464
Reducing Adversarial Training Cost with Gradient Approximation. (99%)
Huihui Gong; Shuo Yang; Siqi Ma; Seyit Camtepe; Surya Nepal; Chang Xu

http://arxiv.org/abs/2309.14348
Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM. (61%)
Bochuan Cao; Yuanpu Cao; Lu Lin; Jinghui Chen

http://arxiv.org/abs/2309.08999
Context-aware Adversarial Attack on Named Entity Recognition. (99%)
Shuguang Chen; Leonardo Neves; Thamar Solorio

http://arxiv.org/abs/2309.08945
Inverse classification with logistic and softmax classifiers: efficient optimization. (56%)
Miguel Á. Carreira-Perpiñán; Suryabhan Singh Hada

http://arxiv.org/abs/2309.08953
Robust Backdoor Attacks on Object Detection in Real World. (11%)
Yaguan Qian; Boyuan Ji; Shuke He; Shenhui Huang; Xiang Ling; Bin Wang; Wei Wang

http://arxiv.org/abs/2309.09123
Conditional Mutual Information Constrained Deep Learning for Classification. (5%)
En-Hui Yang; Shayan Mohajer Hamidi; Linfeng Ye; Renhao Tan; Beverly Yang

http://arxiv.org/abs/2309.08650
Adversarial Attacks on Tables with Entity Swap. (92%)
Aneta Koleva; Martin Ringsquandl; Volker Tresp

http://arxiv.org/abs/2309.08549
HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks. (87%)
Minh-Hao Van; Alycia N. Carey; Xintao Wu

http://arxiv.org/abs/2309.08825
Distributionally Robust Post-hoc Classifiers under Prior Shifts. (1%)
Jiaheng Wei; Harikrishna Narasimhan; Ehsan Amid; Wen-Sheng Chu; Yang Liu; Abhishek Kumar

http://arxiv.org/abs/2309.08230
A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services. (1%)
Hongsheng Hu; Shuo Wang; Jiamin Chang; Haonan Zhong; Ruoxi Sun; Shuang Hao; Haojin Zhu; Minhui Xue

http://arxiv.org/abs/2309.08058
Unleashing the Adversarial Facet of Software Debloating. (98%)
Do-Men Su; Mohannad Alhanahnah

http://arxiv.org/abs/2309.07983
SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems. (76%)
Guangke Chen; Yedi Zhang; Fu Song

http://arxiv.org/abs/2309.07808
What Matters to Enhance Traffic Rule Compliance of Imitation Learning for Automated Driving. (50%)
Hongkuan Zhou; Aifen Sui; Wei Cao; Zhenshan Bing

http://arxiv.org/abs/2311.16113
BAGEL: Backdoor Attacks against Federated Contrastive Learning. (16%)
Yao Huang; Kongyang Chen; Jiannong Cao; Jiaxing Shen; Shaowei Wang; Yun Peng; Weilong Peng; Kechao Cai

http://arxiv.org/abs/2309.07428
Physical Invisible Backdoor Based on Camera Imaging. (2%)
Yusheng Guo; Nan Zhong; Zhenxing Qian; Xinpeng Zhang

http://arxiv.org/abs/2309.07973
M3Dsynth: A dataset of medical 3D images with AI-generated local manipulations. (1%)
Giada Zingarini; Davide Cozzolino; Riccardo Corvi; Giovanni Poggi; Luisa Verdoliva

http://arxiv.org/abs/2309.07398
Semantic Adversarial Attacks via Diffusion Models. (99%)
Chenan Wang; Jinhao Duan; Chaowei Xiao; Edward Kim; Matthew Stamm; Kaidi Xu

http://arxiv.org/abs/2309.07106
Hardening RGB-D Object Recognition Systems against Adversarial Patch Attacks. (99%)
Yang Zheng; Luca Demetrio; Antonio Emanuele Cinà; Xiaoyi Feng; Zhaoqiang Xia; Xiaoyue Jiang; Ambra Demontis; Battista Biggio; Fabio Roli

http://arxiv.org/abs/2309.07197
Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments. (99%)
Simon Queyrut; Valerio Schiavoni; Pascal Felber

http://arxiv.org/abs/2309.06960
PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection. (99%)
Hanqing Guo; Guangjing Wang; Yuanda Wang; Bocheng Chen; Qiben Yan; Li Xiao

http://arxiv.org/abs/2309.07026
APICom: Automatic API Completion via Prompt Learning and Adversarial Training-based Data Augmentation. (92%)
Yafeng Gu; Yiheng Shen; Xiang Chen; Shaoyu Yang; Yiling Huang; Zhixiang Cao

http://arxiv.org/abs/2309.07124
RAIN: Your Language Models Can Align Themselves without Finetuning. (83%)
Yuhui Li; Fangyun Wei; Jinjing Zhao; Chao Zhang; Hongyang Zhang

http://arxiv.org/abs/2309.06978
Differentiable JPEG: The Devil is in the Details. (70%)
Christoph Reich; Biplob Debnath; Deep Patel; Srimat Chakradhar

http://arxiv.org/abs/2309.06724
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense. (41%)
Jianqiao Wangni

http://arxiv.org/abs/2309.06981
MASTERKEY: Practical Backdoor Attack Against Speaker Verification Systems. (38%)
Hanqing Guo; Xun Chen; Junfeng Guo; Li Xiao; Qiben Yan

http://arxiv.org/abs/2309.07415
Client-side Gradient Inversion Against Federated Learning from Poisoning. (22%)
Jiaheng Wei; Yanjun Zhang; Leo Yu Zhang; Chao Chen; Shirui Pan; Kok-Leong Ong; Jun Zhang; Yang Xiang

http://arxiv.org/abs/2309.06835
Safe Reinforcement Learning with Dual Robustness. (1%)
Zeyang Li; Chuxiong Hu; Yunan Wang; Yujie Yang; Shengbo Eben Li

http://arxiv.org/abs/2309.06359
Using Reed-Muller Codes for Classification with Rejection and Recovery. (99%)
Daniel University of Birmingham Fentham; David University of Oxford Parker; Mark University of Birmingham Ryan

http://arxiv.org/abs/2309.06166
Certified Robust Models with Slack Control and Large Lipschitz Constants. (98%)
Max Losch; David Stutz; Bernt Schiele; Mario Fritz

http://arxiv.org/abs/2309.06438
Exploring Non-additive Randomness on ViT against Query-Based Black-Box Attacks. (98%)
Jindong Gu; Fangyun Wei; Philip Torr; Han Hu

http://arxiv.org/abs/2309.06055
Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Review. (61%)
Pengzhou Cheng; Zongru Wu; Wei Du; Gongshen Liu

http://arxiv.org/abs/2309.05978
CToMP: A Cycle-task-oriented Memory Protection Scheme for Unmanned Systems. (8%)
Chengyan Ma; Ning Xi; Di Lu; Yebo Feng; Jianfeng Ma

http://arxiv.org/abs/2309.05950
Language Models as Black-Box Optimizers for Vision-Language Models. (4%)
Shihong Liu; Zhiqiu Lin; Samuel Yu; Ryan Lee; Tiffany Ling; Deepak Pathak; Deva Ramanan

http://arxiv.org/abs/2309.06223
Unveiling Signle-Bit-Flip Attacks on DNN Executables. (1%)
Yanzuo The Hong Kong University of Science and Technology Chen; Zhibo The Hong Kong University of Science and Technology Liu; Yuanyuan The Hong Kong University of Science and Technology Yuan; Sihang Huawei Technologies Hu; Tianxiang Huawei Technologies Li; Shuai The Hong Kong University of Science and Technology Wang

http://arxiv.org/abs/2309.05879
Generalized Attacks on Face Verification Systems. (88%)
Ehsan Nazari; Paula Branco; Guy-Vincent Jourdan

http://arxiv.org/abs/2309.05900
Adversarial Attacks Assessment of Salient Object Detection via Symbolic Learning. (76%)
Gustavo Olague; Roberto Pineda; Gerardo Ibarra-Vazquez; Matthieu Olague; Axel Martinez; Sambit Bakshi; Jonathan Vargas; Isnardo Reducindo

http://arxiv.org/abs/2310.10659
Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System. (68%)
Peixin Zhang; Jun Sun; Mingtian Tan; Xinyu Wang

http://arxiv.org/abs/2309.05610
Privacy Side Channels in Machine Learning Systems. (10%)
Edoardo Debenedetti; Giorgio Severi; Nicholas Carlini; Christopher A. Choquette-Choo; Matthew Jagielski; Milad Nasr; Eric Wallace; Florian Tramèr

http://arxiv.org/abs/2309.05809
Divergences in Color Perception between Deep Neural Networks and Humans. (4%)
Ethan O. Nadler; Elise Darragh-Ford; Bhargav Srinivasa Desikan; Christian Conaway; Mark Chu; Tasker Hull; Douglas Guilbeault

http://arxiv.org/abs/2309.05940
Catch You Everything Everywhere: Guarding Textual Inversion via Concept Watermarking. (1%)
Weitao Feng; Jiyan He; Jie Zhang; Tianwei Zhang; Wenbo Zhou; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2309.05516
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs. (1%)
Wenhua Cheng; Weiwei Zhang; Haihao Shen; Yiyang Cai; Xin He; Kaokao Lv

http://arxiv.org/abs/2309.05145
Outlier Robust Adversarial Training. (98%)
Shu Hu; Zhenhuan Yang; Xin Wang; Yiming Ying; Siwei Lyu

http://arxiv.org/abs/2309.05132
DAD++: Improved Data-free Test Time Adversarial Defense. (98%)
Gaurav Kumar Nayak; Inder Khatri; Shubham Randive; Ruchit Rawal; Anirban Chakraborty

http://arxiv.org/abs/2309.06527
Machine Translation Models Stand Strong in the Face of Adversarial Attacks. (86%)
Pavel Burnyshev; Elizaveta Kostenok; Alexey Zaytsev

http://arxiv.org/abs/2309.05075
Secure Set-Based State Estimation for Linear Systems under Adversarial Attacks on Sensors. (3%)
Muhammad Umar B. Niazi; Michelle S. Chong; Amr Alanwar; Karl H. Johansson

http://arxiv.org/abs/2309.04777
Towards Robust Model Watermark via Reducing Parametric Vulnerability. (8%)
Guanhao Gan; Yiming Li; Dongxian Wu; Shu-Tao Xia

http://arxiv.org/abs/2309.04884
RecAD: Towards A Unified Library for Recommender Attack and Defense. (1%)
Changsheng Wang; Jianbai Ye; Wenjie Wang; Chongming Gao; Fuli Feng; Xiangnan He

http://arxiv.org/abs/2309.04650
Exploring Robust Features for Improving Adversarial Robustness. (99%)
Hong Wang; Yuefan Deng; Shinjae Yoo; Yuewei Lin

http://arxiv.org/abs/2309.04386
ARRTOC: Adversarially Robust Real-Time Optimization and Control. (2%)
Akhil Ahmed; Rio-Chanona Ehecatl Antonio del; Mehmet Mercangoz

http://arxiv.org/abs/2309.06377
Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection. (1%)
Biswaraj Baral; Reek Majumdar; Bhavika Bhalgamiya; Taposh Dutta Roy

http://arxiv.org/abs/2309.04211
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse. (1%)
Edward A. Small; Jeffrey N. Clark; Christopher J. McWilliams; Kacper Sokol; Jeffrey Chan; Flora D. Salim; Raul Santos-Rodriguez

http://arxiv.org/abs/2309.03665
How adversarial attacks can disrupt seemingly stable accurate classifiers. (99%)
Oliver J. Sutton; Qinghua Zhou; Ivan Y. Tyukin; Alexander N. Gorban; Alexander Bastounis; Desmond J. Higham

http://arxiv.org/abs/2309.03844
Experimental Study of Adversarial Attacks on ML-based xApps in O-RAN. (99%)
Naveen Naik Sapavath; Brian Kim; Kaushik Chowdhury; Vijay K Shah

http://arxiv.org/abs/2309.03791
Adversarially Robust Deep Learning with Optimal-Transport-Regularized Divergences. (95%)
Jeremiah Birrell; Mohammadreza Ebrahimi

http://arxiv.org/abs/2309.03702
DiffDefense: Defending against Adversarial Attacks via Diffusion Models. (80%)
Hondamunige Prasanna Silva; Lorenzo Seidenari; Bimbo Alberto Del

http://arxiv.org/abs/2309.04036
One-to-Multiple Clean-Label Image Camouflage (OmClic) based Backdoor Attack on Deep Learning. (73%)
Guohong Wang; Hua Ma; Yansong Gao; Alsharif Abuadbba; Zhi Zhang; Wei Kang; Said F. Al-Sarawib; Gongxuan Zhang; Derek Abbott

http://arxiv.org/abs/2309.03648
Promoting Fairness in GNNs: A Characterization of Stability. (1%)
Yaning Jia; Chunhui Zhang

http://arxiv.org/abs/2309.02705
Certifying LLM Safety against Adversarial Prompting. (99%)
Aounon Kumar; Chirag Agarwal; Suraj Srinivas; Aaron Jiaxun Li; Soheil Feizi; Himabindu Lakkaraju

http://arxiv.org/abs/2309.02752
SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series. (84%)
Chang George Dong; Liangwei Nathan Zheng; Weitong Chen; Wei Emma Zhang; Lin Yue

http://arxiv.org/abs/2309.03437
Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy. (68%)
Zikai Zhang; Rui Hu

http://arxiv.org/abs/2309.03164
J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News. (38%)
Tharindu Kumarage; Amrita Bhattacharjee; Djordje Padejski; Kristy Roschke; Dan Gillmor; Scott Ruston; Huan Liu; Joshua Garland

http://arxiv.org/abs/2309.03466
MIRA: Cracking Black-box Watermarking on Deep Neural Networks via Model Inversion-based Removal Attacks. (22%)
Yifan Lu; Wenxuan Li; Mi Zhang; Xudong Pan; Min Yang

http://arxiv.org/abs/2309.03198
My Art My Choice: Adversarial Protection Against Unruly AI. (2%)
Anthony Rhodes; Ram Bhagat; Umur Aybars Ciftci; Ilke Demir

http://arxiv.org/abs/2310.10656
VeriDIP: Verifying Ownership of Deep Neural Networks through Privacy Leakage Fingerprints. (1%)
Aoting Hu; Zhigang Lu; Renjie Xie; Minhui Xue

http://arxiv.org/abs/2309.03004
A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness. (1%)
Ze Peng; Lei Qi; Yinghuan Shi; Yang Gao

http://arxiv.org/abs/2309.02159
The Adversarial Implications of Variable-Time Inference. (99%)
Dudi Biton; Aditi Misra; Efrat Levy; Jaidip Kotak; Ron Bitton; Roei Schuster; Nicolas Papernot; Yuval Elovici; Ben Nassi

http://arxiv.org/abs/2309.02528
Adaptive Adversarial Training Does Not Increase Recourse Costs. (92%)
Ian Hardy; Jayanth Yetukuri; Yang Liu

http://arxiv.org/abs/2309.02396
Black-Box Attacks against Signed Graph Analysis via Balance Poisoning. (87%)
Jialong Zhou; Yuni Lai; Jian Ren; Kai Zhou

http://arxiv.org/abs/2310.06845
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems. (83%)
Abhishek Moitra; Abhiroop Bhattacharjee; Youngeun Kim; Priyadarshini Panda

http://arxiv.org/abs/2309.02429
Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach. (4%)
Vimal K B; Saketh Bachu; Tanmay Garg; Niveditha Lakshmi Narasimhan; Raghavan Konuru; Vineeth N Balasubramanian

http://arxiv.org/abs/2309.02057
Robust Recommender System: A Survey and Future Directions. (2%)
Kaike Zhang; Qi Cao; Fei Sun; Yunfan Wu; Shuchang Tao; Huawei Shen; Xueqi Cheng

http://arxiv.org/abs/2309.02088
Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning. (1%)
Siyang Jiang; Rui Fang; Hsi-Wen Chen; Wei Ding; Ming-Syan Chen

http://arxiv.org/abs/2309.01620
Hindering Adversarial Attacks with Multiple Encrypted Patch Embeddings. (99%)
AprilPyone MaungMaung; Isao Echizen; Hitoshi Kiya

http://arxiv.org/abs/2309.01582
Improving Visual Quality and Transferability of Adversarial Attacks on Face Recognition Simultaneously with Adversarial Restoration. (99%)
Fengfan Zhou; Hefei Ling; Yuxuan Shi; Jiazhong Chen; Ping Li

http://arxiv.org/abs/2309.01351
Adv3D: Generating 3D Adversarial Examples in Driving Scenarios with NeRF. (99%)
Leheng Li; Qing Lian; Ying-Cong Chen

http://arxiv.org/abs/2309.01452
Toward Defensive Letter Design. (98%)
Rentaro Kataoka; Akisato Kimura; Seiichi Uchida

http://arxiv.org/abs/2309.01686
MathAttack: Attacking Large Language Models Towards Math Solving Ability. (97%)
Zihao Zhou; Qiufeng Wang; Mingyu Jin; Jie Yao; Jianan Ye; Wei Liu; Wei Wang; Xiaowei Huang; Kaizhu Huang

http://arxiv.org/abs/2309.01838
Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks. (93%)
Kacem Khaled; Mouna Dhaouadi; Magalhães Felipe Gohring de; Gabriela Nicolescu

http://arxiv.org/abs/2309.01866
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting. (92%)
Ping He; Yifan Xia; Xuhong Zhang; Shouling Ji

http://arxiv.org/abs/2309.01786
Safe and Robust Watermark Injection with a Single OoD Image. (8%)
Shuyang Yu; Junyuan Hong; Haobo Zhang; Haotao Wang; Zhangyang Wang; Jiayu Zhou

http://arxiv.org/abs/2309.01614
Dropout Attacks. (2%)
Andrew Yuan; Alina Oprea; Cheng Tan

http://arxiv.org/abs/2309.01850
Uncertainty in AI: Evaluating Deep Neural Networks on Out-of-Distribution Images. (2%)
Jamiu Idowu; Ahmed Almasoud

http://arxiv.org/abs/2310.05947
Robust and Efficient Interference Neural Networks for Defending Against Adversarial Attacks in ImageNet. (99%)
Yunuo Xiong; Shujuan Liu; Hongwei Xiong

http://arxiv.org/abs/2309.01104
Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake Detection. (98%)
Weijie Wang; Zhengyu Zhao; Nicu Sebe; Bruno Lepri

http://arxiv.org/abs/2309.01106
AdvMono3D: Advanced Monocular 3D Object Detection with Depth-Aware Robust Adversarial Training. (98%)
Xingyuan Li; Jinyuan Liu; Long Ma; Xin Fan; Risheng Liu

http://arxiv.org/abs/2309.01077
Robust Adversarial Defense by Tensor Factorization. (89%)
Manish Bhattarai; Mehmet Cagri Kaymak; Ryan Barron; Ben Nebgen; Kim Rasmussen; Boian Alexandrov

http://arxiv.org/abs/2309.01102
Dual Adversarial Resilience for Collaborating Robust Underwater Image Enhancement and Perception. (13%)
Zengxi Zhang; Zhiying Jiang; Zeru Shi; Jinyuan Liu; Risheng Liu

http://arxiv.org/abs/2309.00879
Towards Certified Probabilistic Robustness with High Accuracy. (98%)
Ruihan Zhang; Peixin Zhang; Jun Sun

http://arxiv.org/abs/2309.00929
Timbre-reserved Adversarial Attack in Speaker Identification. (98%)
Qing Wang; Jixun Yao; Li Zhang; Pengcheng Guo; Lei Xie

http://arxiv.org/abs/2309.00894
Regularly Truncated M-estimators for Learning with Noisy Labels. (1%)
Xiaobo Xia; Pengqian Lu; Chen Gong; Bo Han; Jun Yu; Jun Yu; Tongliang Liu

http://arxiv.org/abs/2309.00614
Baseline Defenses for Adversarial Attacks Against Aligned Language Models. (99%)
Neel Jain; Avi Schwarzschild; Yuxin Wen; Gowthami Somepalli; John Kirchenbauer; Ping-yeh Chiang; Micah Goldblum; Aniruddha Saha; Jonas Geiping; Tom Goldstein

http://arxiv.org/abs/2309.00543
Curating Naturally Adversarial Datasets for Trustworthy AI in Healthcare. (99%)
Sydney Pugh; Ivan Ruchkin; Insup Lee; James Weimer

http://arxiv.org/abs/2309.00771
Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models. (89%)
Changyu Liu; Yuling Jiao; Junhui Wang; Jian Huang

http://arxiv.org/abs/2309.00254
Why do universal adversarial attacks work on large language models?: Geometry might be the answer. (83%)
Varshini Subhash; Anna Bialas; Weiwei Pan; Finale Doshi-Velez

http://arxiv.org/abs/2309.00810
RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model. (1%)
Fengxiang Bie; Yibo Yang; Zhongzhu Zhou; Adam Ghanem; Minjia Zhang; Zhewei Yao; Xiaoxia Wu; Connor Holmes; Pareesa Golnari; David A. Clifton; Yuxiong He; Dacheng Tao; Shuaiwen Leon Song

http://arxiv.org/abs/2309.00733
Learned Visual Features to Textual Explanations. (1%)
Saeid Asgari Taghanaki; Aliasghar Khani; Amir Khasahmadi; Aditya Sanghi; Karl D. D. Willis; Ali Mahdavi-Amiri

http://arxiv.org/abs/2308.16454
Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff. (98%)
Satoshi Suzuki; Shin'ya Yamaguchi; Shoichiro Takeda; Sekitoshi Kanai; Naoki Makishima; Atsushi Ando; Ryo Masumura

http://arxiv.org/abs/2309.00236
Image Hijacking: Adversarial Images can Control Generative Models at Runtime. (98%)
Luke Bailey; Euan Ong; Stuart Russell; Scott Emmons

http://arxiv.org/abs/2308.16562
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning. (93%)
Maria Rigaki; Sebastian Garcia

http://arxiv.org/abs/2308.16703
Fault Injection and Safe-Error Attack for Extraction of Embedded Neural Network Models. (75%)
Kevin Hector; Pierre-Alain Moellic; Mathieu Dumont; Jean-Max Dutertre

http://arxiv.org/abs/2308.16684
Everyone Can Attack: Repurpose Lossy Compression as a Natural Backdoor Attack. (75%)
Sze Jue Yang; Quang Nguyen; Chee Seng Chan; Khoa D. Doan

http://arxiv.org/abs/2309.00127
FTA: Stealthy and Robust Backdoor Attack with Flexible Trigger on Federated Learning. (45%)
Yanqi Qiao; Congwen Chen; Rui Wang; Kaitai Liang

http://arxiv.org/abs/2309.03215
Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive Logic Programming Approach. (98%)
Zahra University of Surrey Chaghazardi; Saber University of Surrey Fallah; Alireza University of Surrey Tamaddoni-Nezhad

http://arxiv.org/abs/2308.16258
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs. (11%)
ShengYun Peng; Weilin Xu; Cory Cornelius; Matthew Hull; Kevin Li; Rahul Duggal; Mansi Phute; Jason Martin; Duen Horng Chau

http://arxiv.org/abs/2308.15663
Adaptive Attack Detection in Text Classification: Leveraging Space Exploration Features for Text Sentiment Classification. (99%)
Atefeh Mahdavi; Neda Keivandarian; Marco Carvalho

http://arxiv.org/abs/2308.15072
Advancing Adversarial Robustness Through Adversarial Logit Update. (99%)
Hao Xuan; Peican Zhu; Xingyu Li

http://arxiv.org/abs/2308.15344
Imperceptible Adversarial Attack on Deep Neural Networks from Image Boundary. (99%)
Fahad Alrasheedi; Xin Zhong

http://arxiv.org/abs/2308.15246
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation. (99%)
Sahar Sadrizadeh; Ljiljana Dolamic; Pascal Frossard

http://arxiv.org/abs/2308.15673
MDTD: A Multi Domain Trojan Detector for Deep Neural Networks. (97%)
Arezoo Rajabi; Surudhi Asokraj; Fengqing Jiang; Luyao Niu; Bhaskar Ramasubramanian; Jim Ritcey; Radha Poovendran

http://arxiv.org/abs/2308.15479
3D Adversarial Augmentations for Robust Out-of-Domain Predictions. (87%)
Alexander Lehner; Stefano Gasperini; Alvaro Marcos-Ramiro; Michael Schmidt; Nassir Navab; Benjamin Busam; Federico Tombari

http://arxiv.org/abs/2308.15614
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks. (84%)
Haoran Liu; Bokun Wang; Jianling Wang; Xiangjue Dong; Tianbao Yang; James Caverlee

http://arxiv.org/abs/2308.15736
Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review. (75%)
Zhenyong Zhang; Mengxiang Liu; Mingyang Sun; Ruilong Deng; Peng Cheng; Dusit Niyato; Mo-Yuen Chow; Jiming Chen

http://arxiv.org/abs/2308.15692
Intriguing Properties of Diffusion Models: A Large-Scale Dataset for Evaluating Natural Attack Capability in Text-to-Image Generative Models. (67%)
Takami Sato; Justin Yue; Nanze Chen; Ningfei Wang; Qi Alfred Chen

http://arxiv.org/abs/2308.15092
Can We Rely on AI? (50%)
Desmond J. Higham

http://arxiv.org/abs/2308.15141
Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images. (1%)
Tareen Dawood; Chen Chen; Baldeep S. Sidhua; Bram Ruijsink; Justin Goulda; Bradley Porter; Mark K. Elliott; Vishal Mehta; Christopher A. Rinaldi; Esther Puyol-Anton; Reza Razavi; Andrew P. King

http://arxiv.org/abs/2308.14597
Adversarial Attacks on Foundational Vision Models. (80%)
Nathan Inkawhich; Gwendolyn McDonald; Ryan Luley

http://arxiv.org/abs/2308.14333
DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing. (45%)
Jiawei Zhang; Zhongzhu Chen; Huan Zhang; Chaowei Xiao; Bo Li

http://arxiv.org/abs/2308.14840
Identifying and Mitigating the Security Risks of Generative AI. (45%)
Clark Barrett; Brad Boyd; Elie Burzstein; Nicholas Carlini; Brad Chen; Jihye Choi; Amrita Roy Chowdhury; Mihai Christodorescu; Anupam Datta; Soheil Feizi; Kathleen Fisher; Tatsunori Hashimoto; Dan Hendrycks; Somesh Jha; Daniel Kang; Florian Kerschbaum; Eric Mitchell; John Mitchell; Zulfikar Ramzan; Khawaja Shams; Dawn Song; Ankur Taly; Diyi Yang

http://arxiv.org/abs/2308.14550
ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events. (13%)
Aizaz Sharif; Dusica Marijan

http://arxiv.org/abs/2308.14553
Rep2wav: Noise Robust text-to-speech Using self-supervised representations. (1%)
Qiushi Zhu; Yu Gu; Rilin Chen; Chao Weng; Yuchen Hu; Lirong Dai; Jie Zhang

http://arxiv.org/abs/2308.14376
Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection? (1%)
Andrea Corsini; Shanchieh Jay Yang

http://arxiv.org/abs/2308.14256
FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content. (1%)
Yang Liu; Cheng Yu; Lei Shang; Yongyi He; Ziheng Wu; Xingjun Wang; Chao Xu; Haoyu Xie; Weida Wang; Yuze Zhao; Lin Zhu; Chen Cheng; Weitao Chen; Yuan Yao; Wenmeng Zhou; Jiaqi Xu; Qiang Wang; Yingda Chen; Xuansong Xie; Baigui Sun

http://arxiv.org/abs/2308.14132
Detecting Language Model Attacks with Perplexity. (1%)
Gabriel Alon; Michael Kamfonas

http://arxiv.org/abs/2308.12636
Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning. (99%)
Youze Wang; Wenbo Hu; Yinpeng Dong; Richang Hong

http://arxiv.org/abs/2308.12661
Don't Look into the Sun: Adversarial Solarization Attacks on Image Classifiers. (92%)
Paul Gavrikov; Janis Keuper

http://arxiv.org/abs/2308.12918
Evaluating the Vulnerabilities in ML systems in terms of adversarial attacks. (82%)
John Harshith; Mantej Singh Gill; Madhan Jothimani

http://arxiv.org/abs/2308.12857
Fast Adversarial Training with Smooth Convergence. (3%)
Mengnan Zhao; Lihe Zhang; Yuqiu Kong; Baocai Yin

http://arxiv.org/abs/2308.12770
WavMark: Watermarking for Audio Generation. (2%)
Guangyu Chen; Yu Wu; Shujie Liu; Tao Liu; Xiaoyong Du; Furu Wei

http://arxiv.org/abs/2308.12279
On-Manifold Projected Gradient Descent. (99%)
Aaron Mahler; Tyrus Berry; Tom Stephens; Harbir Antil; Michael Merritt; Jeanie Schreiber; Ioannis Kevrekidis

http://arxiv.org/abs/2308.12054
Sample Complexity of Robust Learning against Evasion Attacks. (98%)
Pascale Gourdeau

http://arxiv.org/abs/2308.12882
LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition. (92%)
Sayanton V. Dibbo; Juston S. Moore; Garrett T. Kenyon; Michael A. Teti

http://arxiv.org/abs/2308.12439
BaDExpert: Extracting Backdoor Functionality for Accurate Backdoor Input Detection. (74%)
Tinghao Xie; Xiangyu Qi; Ping He; Yiming Li; Jiachen T. Wang; Prateek Mittal

http://arxiv.org/abs/2308.12319
RemovalNet: DNN Fingerprint Removal Attacks. (69%)
Hongwei Yao; Zheng Li; Kunzhe Huang; Jian Lou; Zhan Qin; Kui Ren

http://arxiv.org/abs/2310.02164
Graph Unlearning: A Review. (2%)
Anwar Said; Tyler Derr; Mudassir Shabbir; Waseem Abbas; Xenofon Koutsoukos

http://arxiv.org/abs/2308.12065
Ensembling Uncertainty Measures to Improve Safety of Black-Box Classifiers. (1%)
Tommaso Zoppi; Andrea Ceccarelli; Andrea Bondavalli

http://arxiv.org/abs/2308.12141
Aparecium: Revealing Secrets from Physical Photographs. (1%)
Zhe Lei; Jie Zhang; Jingtao Li; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2308.11845
SEA: Shareable and Explainable Attribution for Query-based Black-box Attacks. (99%)
Yue Gao; Ilia Shumailov; Kassem Fawaz

http://arxiv.org/abs/2308.11754
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection. (99%)
Mahmoud Nazzal; Issa Khalil; Abdallah Khreishah; NhatHai Phan; Yao Ma

http://arxiv.org/abs/2308.11894
Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack. (98%)
Ningfei Wang; Yunpeng Luo; Takami Sato; Kaidi Xu; Qi Alfred Chen

http://arxiv.org/abs/2308.11333
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation. (86%)
Yanxin Yang; Ming Hu; Yue Cao; Jun Xia; Yihao Huang; Yang Liu; Mingsong Chen

http://arxiv.org/abs/2308.11443
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging. (76%)
Xiaojun Jia; Yuefeng Chen; Xiaofeng Mao; Ranjie Duan; Jindong Gu; Rong Zhang; Hui Xue; Xiaochun Cao

http://arxiv.org/abs/2308.11406
Designing an attack-defense game: how to increase robustness of financial transaction models via a competition. (75%)
Alexey Zaytsev; Alex Natekin; Evgeni Vorsin; Valerii Smirnov; Oleg Sidorshin; Alexander Senin; Alexander Dudin; Dmitry Berestnev

http://arxiv.org/abs/2308.11881
Adversarial Training Using Feedback Loops. (74%)
Ali Haisam Muhammad Rafid; Adrian Sandu

http://arxiv.org/abs/2308.11804
Adversarial Illusions in Multi-Modal Embeddings. (74%)
Tingwei Zhang; Rishi Jha; Eugene Bagdasaryan; Vitaly Shmatikov

http://arxiv.org/abs/2308.11284
LEAP: Efficient and Automated Test Method for NLP Software. (31%)
Mingxuan Xiao; Yan Xiao; Hai Dong; Shunhui Ji; Pengcheng Zhang

http://arxiv.org/abs/2308.11822
PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification. (16%)
Yizhen Institute for AI Industry Research Yuan; Rui Shanghai Jiao Tong University, Shanghai, China Kong; Shenghao Wuhan University, Wuhan, China Xie; Yuanchun Institute for AI Industry Research Shanghai AI Laboratory, Shanghai, China Li; Yunxin Institute for AI Industry Research Shanghai AI Laboratory, Shanghai, China Liu

http://arxiv.org/abs/2308.10601
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer. (99%)
Zhijin Ge; Fanhua Shang; Hongying Liu; Yuanyuan Liu; Liang Wan; Wei Feng; Xiaosen Wang

http://arxiv.org/abs/2308.10779
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs. (99%)
Dongjin Lee; Juho Lee; Kijung Shin

http://arxiv.org/abs/2308.10743
Enhancing Adversarial Attacks: The Similar Target Method. (99%)
Shuo Zhang; Ziruo Wang; Zikai Zhou; Huanran Chen

http://arxiv.org/abs/2308.11161
Adversarial Attacks on Code Models with Discriminative Graph Patterns. (96%)
Thanh-Dat Pick Nguyen; Yang Pick Zhou; Xuan Bach D. Pick Le; Pick Patanamon; Thongtanunam; David Lo

http://arxiv.org/abs/2308.11070
Temporal-Distributed Backdoor Attack Against Video Based Action Recognition. (88%)
Xi Li; Songhe Wang; Ruiquan Huang; Mahanth Gowda; George Kesidis

http://arxiv.org/abs/2308.10708
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models. (76%)
Preben M. Ness; Dusica Marijan; Sunanda Bose

http://arxiv.org/abs/2308.10467
Single-User Injection for Invisible Shilling Attack against Recommender Systems. (62%)
Chengzhi Huang; Hui Li

http://arxiv.org/abs/2308.10741
On the Adversarial Robustness of Multi-Modal Foundation Models. (4%)
Christian Schlarmann; Matthias Hein

http://arxiv.org/abs/2308.10888
Unlocking Accuracy and Fairness in Differentially Private Image Classification. (2%)
Leonard Berrada; Soham De; Judy Hanwen Shen; Jamie Hayes; Robert Stanforth; David Stutz; Pushmeet Kohli; Samuel L. Smith; Borja Balle

http://arxiv.org/abs/2308.10299
Boosting Adversarial Transferability by Block Shuffle and Rotation. (99%)
Kunyu Wang; Xuanran He; Wenxuan Wang; Xiaosen Wang

http://arxiv.org/abs/2308.10315
Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting. (96%)
Qidong Huang; Xiaoyi Dong; Dongdong Chen; Yinpeng Chen; Lu Yuan; Gang Hua; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2308.10373
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds. (96%)
Hejia Geng; Peng Li

http://arxiv.org/abs/2308.10201
Hiding Backdoors within Event Sequence Data via Poisoning Attacks. (95%)
Elizaveta Kovtun; Alina Ermilova; Dmitry Berestnev; Alexey Zaytsev

http://arxiv.org/abs/2308.13541
Adversarial Collaborative Filtering for Free. (61%)
Huiyuan Chen; Xiaoting Li; Vivian Lai; Chin-Chia Michael Yeh; Yujie Fan; Yan Zheng; Mahashweta Das; Hao Yang

http://arxiv.org/abs/2308.10438
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks. (1%)
Kaixin Xu; Zhe Wang; Xue Geng; Jie Lin; Min Wu; Xiaoli Li; Weisi Lin

http://arxiv.org/abs/2308.10335
A Study on Robustness and Reliability of Large Language Model Code Generation. (1%)
Li Zhong; Zilong Wang

http://arxiv.org/abs/2308.09958
A Comparison of Adversarial Learning Techniques for Malware Detection. (99%)
Pavla Louthánová; Matouš Kozák; Martin Jureček; Mark Stamp

http://arxiv.org/abs/2308.10110
Robust Mixture-of-Expert Training for Convolutional Neural Networks. (83%)
Yihua Zhang; Ruisi Cai; Tianlong Chen; Guanhua Zhang; Huan Zhang; Pin-Yu Chen; Shiyu Chang; Zhangyang Wang; Sijia Liu

http://arxiv.org/abs/2308.09861
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method. (99%)
Yu-An Liu; Ruqing Zhang; Jiafeng Guo; Rijke Maarten de; Wei Chen; Yixing Fan; Xueqi Cheng

http://arxiv.org/abs/2308.09392
Attacking logo-based phishing website detectors with adversarial perturbations. (99%)
Jehyun Lee; Zhe Xin; Melanie Ng Pei See; Kanav Sabharwal; Giovanni Apruzzese; Dinil Mon Divakaran

http://arxiv.org/abs/2308.09546
Compensating Removed Frequency Components: Thwarting Voice Spectrum Reduction Attacks. (92%)
Shu Wang; Kun Sun; Qi Li

http://arxiv.org/abs/2308.09487
DFB: A Data-Free, Low-Budget, and High-Efficacy Clean-Label Backdoor Attack. (54%)
Binhao Ma; Jiahui Wang; Dejun Wang; Bo Meng

http://arxiv.org/abs/2308.09850
Backdoor Mitigation by Correcting the Distribution of Neural Activations. (11%)
Xi Li; Zhen Xiang; David J. Miller; George Kesidis

http://arxiv.org/abs/2308.09381
On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box. (9%)
Yi Cai; Gerhard Wunder

http://arxiv.org/abs/2308.09318
Towards Attack-tolerant Federated Learning via Critical Parameter Analysis. (9%)
Sungwon Han; Sungwon Park; Fangzhao Wu; Sundong Kim; Bin Zhu; Xing Xie; Meeyoung Cha

http://arxiv.org/abs/2308.09448
Defending Label Inference Attacks in Split Learning under Regression Setting. (4%)
Haoze Qiu; Fei Zheng; Chaochao Chen; Xiaolin Zheng

http://arxiv.org/abs/2308.09810
An Image is Worth a Thousand Toxic Words: A Metamorphic Testing Framework for Content Moderation Software. (1%)
Wenxuan Wang; Jingyuan Huang; Jen-tse Huang; Chang Chen; Jiazhen Gu; Pinjia He; Michael R. Lyu

http://arxiv.org/abs/2308.09520
Proceedings of the 2nd International Workshop on Adaptive Cyber Defense. (1%)
Marco Carvalho; Damian Marriott; Mark Bilinski; Ahmad Ridley

http://arxiv.org/abs/2309.16706
AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery. (99%)
Jinyin Chen; Jie Ge; Shilian Zheng; Linhui Ye; Haibin Zheng; Weiguo Shen; Keqiang Yue; Xiaoniu Yang

http://arxiv.org/abs/2308.08906
Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing. (99%)
Daniel Gibert; Giulio Zizzo; Quan Le

http://arxiv.org/abs/2308.08925
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models. (98%)
Zhongliang Guo; Weiye Li; Yifei Qian; Ognjen Arandjelović; Lei Fang

http://arxiv.org/abs/2308.08938
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces. (16%)
Ahmad-Reza Ehyaei; Kiarash Mohammadi; Amir-Hossein Karimi; Samira Samadi; Golnoosh Farnadi

http://arxiv.org/abs/2308.09146
That Doesn't Go There: Attacks on Shared State in Multi-User Augmented Reality Applications. (10%)
Carter Slocum; Yicheng Zhang; Erfan Shayegani; Pedram Zaree; Nael Abu-Ghazaleh; Jiasi Chen

http://arxiv.org/abs/2308.10819
Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection. (10%)
Zekun Li; Baolin Peng; Pengcheng He; Xifeng Yan

http://arxiv.org/abs/2309.16710
General Lipschitz: Certified Robustness Against Resolvable Semantic Transformations via Transformation-Dependent Randomized Smoothing. (3%)
Dmitrii Korzh; Mikhail Pautov; Olga Tsymboi; Ivan Oseledets

http://arxiv.org/abs/2308.08160
Benchmarking Adversarial Robustness of Compressed Deep Learning Models. (81%)
Brijesh Vora; Kartik Patwari; Syed Mahbub Hafiz; Zubair Shafiq; Chen-Nee Chuah

http://arxiv.org/abs/2308.08505
Test-Time Poisoning Attacks Against Test-Time Adaptation Models. (73%)
Tianshuo Cong; Xinlei He; Yun Shen; Yang Zhang

http://arxiv.org/abs/2308.11521
Self-Deception: Reverse Penetrating the Semantic Firewall of Large Language Models. (67%)
Zhenhua Wang; Wei Xie; Kai Chen; Baosheng Wang; Zhiwen Gui; Enze Wang

http://arxiv.org/abs/2308.08709
Dynamic Neural Network is All You Need: Understanding the Robustness of Dynamic Mechanisms in Neural Networks. (61%)
Mirazul Haque; Wei Yang

http://arxiv.org/abs/2308.08173
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness. (33%)
Francesco Campi; Lukas Gosch; Tom Wollschläger; Yan Scholten; Stephan Günnemann

http://arxiv.org/abs/2308.07874
SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classification. (99%)
Raza Imam; Ibrahim Almakky; Salma Alrashdi; Baketah Alrashdi; Mohammad Yaqub

http://arxiv.org/abs/2308.07625
Backpropagation Path Search On Adversarial Transferability. (99%)
Zhuoer Xu; Zhangxuan Gu; Jianping Zhang; Shiwen Cui; Changhua Meng; Weiqiang Wang

http://arxiv.org/abs/2308.07673
A Review of Adversarial Attacks in Computer Vision. (99%)
Yutong Zhang; Yao Li; Yin Li; Zhichang Guo

http://arxiv.org/abs/2308.07847
Robustness Over Time: Understanding Adversarial Examples' Effectiveness on Longitudinal Versions of Large Language Models. (95%)
Yugeng Liu; Tianshuo Cong; Zhengyu Zhao; Michael Backes; Yun Shen; Yang Zhang

http://arxiv.org/abs/2308.07834
Simple and Efficient Partial Graph Adversarial Attack: A New Perspective. (93%)
Guanghui Zhu; Mengyu Chen; Chunfeng Yuan; Yihua Huang

http://arxiv.org/abs/2308.07546
3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack. (99%)
Yunbo Tao; Daizong Liu; Pan Zhou; Yulai Xie; Wei Du; Wei Hu

http://arxiv.org/abs/2308.07433
White-Box Adversarial Attacks on Deep Learning-Based Radio Frequency Fingerprint Identification. (99%)
Jie Ma; Junqing Zhang; Guanxiong Shen; Alan Marshall; Chip-Hong Chang

http://arxiv.org/abs/2308.07026
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning. (99%)
Ziqi Zhou; Shengshan Hu; Minghui Li; Hangtao Zhang; Yechao Zhang; Hai Jin

http://arxiv.org/abs/2308.07553
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks. (68%)
Shijie Liu; Andrew C. Cullen; Paul Montague; Sarah M. Erfani; Benjamin I. P. Rubinstein

http://arxiv.org/abs/2308.07308
LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked. (54%)
Alec Helbling; Mansi Phute; Matthew Hull; Duen Horng Chau

http://arxiv.org/abs/2308.07387
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks. (13%)
Indu Joshi; Priyank Upadhya; Gaurav Kumar Nayak; Peter Schüffler; Nassir Navab

http://arxiv.org/abs/2308.07009
ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion. (10%)
Naufal Suryanto; Yongsu Kim; Harashta Tatimma Larasati; Hyoeun Kang; Thi-Thu-Huong Le; Yoonyoung Hong; Hunmin Yang; Se-Yoon Oh; Howon Kim

http://arxiv.org/abs/2308.07156
SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation. (1%)
An Wang; Mobarakol Islam; Mengya Xu; Yang Zhang; Hongliang Ren

http://arxiv.org/abs/2308.06819
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection. (99%)
João Vitorino; Isabel Praça; Eva Maia

http://arxiv.org/abs/2308.06703
Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods. (45%)
Avery Ma; Yangchen Pan; Amir-massoud Farahmand

http://arxiv.org/abs/2308.06767
A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations. (1%)
Hongrong Cheng; Miao Zhang; Javen Qinfeng Shi

http://arxiv.org/abs/2308.06887
Robustified ANNs Reveal Wormholes Between Human Category Percepts. (1%)
Guy Gaziv; Michael J. Lee; James J. DiCarlo

http://arxiv.org/abs/2308.06795
Faithful to Whom? Questioning Interpretability Measures in NLP. (1%)
Evan Crothers; Herna Viktor; Nathalie Japkowicz

http://arxiv.org/abs/2308.06467
Not So Robust After All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks. (99%)
Roman Garaev; Bader Rasheed; Adil Khan

http://arxiv.org/abs/2308.07934
One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training. (13%)
Jianshuo Dong; Han Qiu; Yiming Li; Tianwei Zhang; Yuanjie Li; Zeqi Lai; Chao Zhang; Shu-Tao Xia

http://arxiv.org/abs/2308.06015
Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation. (98%)
Xuannan Liu; Yaoyao Zhong; Yuhang Zhang; Lixiong Qin; Weihong Deng

http://arxiv.org/abs/2308.06173
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook. (98%)
Amira Guesmi; Muhammad Abdullah Hanif; Bassem Ouni; Muhammed Shafique

http://arxiv.org/abs/2308.05983
Face Encryption via Frequency-Restricted Identity-Agnostic Attacks. (96%)
Xin Dong; Rui Wang; Siyuan Liang; Aishan Liu; Lihua Jing

http://arxiv.org/abs/2308.06405
White-box Membership Inference Attacks against Diffusion Models. (68%)
Yan Pang; Tianhao Wang; Xuhui Kang; Mengdi Huai; Yang Zhang

http://arxiv.org/abs/2308.06107
Test-Time Backdoor Defense via Detecting and Repairing. (10%)
Jiyang Guan; Jian Liang; Ran He

http://arxiv.org/abs/2308.06217
Continual Face Forgery Detection via Historical Distribution Preserving. (2%)
Ke Sun; Shen Chen; Taiping Yao; Xiaoshuai Sun; Shouhong Ding; Rongrong Ji

http://arxiv.org/abs/2308.05986
Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance. (1%)
Huiwen Xu; U Kang

http://arxiv.org/abs/2308.05681
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient. (99%)
Zhengzhi Lu; He Wang; Ziyi Chang; Guoan Yang; Hubert P. H. Shum

http://arxiv.org/abs/2308.05575
Symmetry Defense Against XGBoost Adversarial Perturbation Attacks. (96%)
Blerta Lindqvist

http://arxiv.org/abs/2308.05498
Complex Network Effects on the Robustness of Graph Convolutional Networks. (92%)
Benjamin A. Miller; Kevin Chan; Tina Eliassi-Rad

http://arxiv.org/abs/2308.05525
Critical Points ++: An Agile Point Cloud Importance Measure for Robust Classification, Adversarial Defense and Explainable AI. (80%)
Meir Yossef Levi; Guy Gilboa

http://arxiv.org/abs/2310.10789
State Machine Frameworks for Website Fingerprinting Defenses: Maybe Not. (61%)
Ethan Witwer

http://arxiv.org/abs/2308.05832
FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks. (45%)
Ehsanul Kabir; Zeyu Song; Md Rafi Ur Rashid; Shagufta Mehnaz

http://arxiv.org/abs/2308.08012
Comprehensive Analysis of Network Robustness Evaluation Based on Convolutional Neural Networks with Spatial Pyramid Pooling. (1%)
Wenjun Jiang; Tianlong Fan; Changhao Li; Chuanfu Zhang; Tao Zhang; Zong-fu Luo

http://arxiv.org/abs/2308.05320
Adv-Inpainting: Generating Natural and Transferable Adversarial Patch via Attention-guided Feature Fusion. (98%)
Yanjie Li; Mingxing Duan; Bin Xiao

http://arxiv.org/abs/2308.04964
Adversarial ModSecurity: Countering Adversarial SQL Injections with Robust Machine Learning. (93%)
Biagio Montaruli; Luca Demetrio; Andrea Valenza; Battista Biggio; Luca Compagna; Davide Balzarotti; Davide Ariu; Luca Piras

http://arxiv.org/abs/2308.04909
Adversarial Deep Reinforcement Learning for Cyber Security in Software Defined Networks. (81%)
Luke Borchjes; Clement Nyirenda; Louise Leenen

http://arxiv.org/abs/2308.05127
Data-Free Model Extraction Attacks in the Context of Object Detection. (41%)
Harshit Shah; Aravindhan G; Pavan Kulkarni; Yuvaraj Govidarajulu; Manojkumar Parmar

http://arxiv.org/abs/2308.04373
Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning. (99%)
Simon Queyrut; Yérom-David Bromberg; Valerio Schiavoni

http://arxiv.org/abs/2308.04077
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients. (81%)
Yao Shu; Xiaoqiang Lin; Zhongxiang Dai; Bryan Kian Hsiang Low

http://arxiv.org/abs/2308.04304
The Model Inversion Eavesdropping Attack in Semantic Communication Systems. (67%)
Yuhao Chen; Qianqian Yang; Zhiguo Shi; Jiming Chen

http://arxiv.org/abs/2308.04137
Comprehensive Assessment of the Performance of Deep Learning Classifiers Reveals a Surprising Lack of Robustness. (64%)
Michael W. Spratling

http://arxiv.org/abs/2308.04406
XGBD: Explanation-Guided Graph Backdoor Detection. (54%)
Zihan Guan; Mengnan Du; Ninghao Liu

http://arxiv.org/abs/2308.04617
Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time Detection. (50%)
Hang Wang; Zhen Xiang; David J. Miller; George Kesidis

http://arxiv.org/abs/2308.04179
Evil Operation: Breaking Speaker Recognition with PaddingBack. (31%)
Zhe Ye; Diqun Yan; Li Dong; Kailai Shen

http://arxiv.org/abs/2308.04466
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers. (15%)
Haomin Zhuang; Mingxian Yu; Hao Wang; Yang Hua; Jian Li; Xu Yuan

http://arxiv.org/abs/2308.03956
Fixed Inter-Neuron Covariability Induces Adversarial Robustness. (98%)
Muhammad Ahmed Shah; Bhiksha Raj

http://arxiv.org/abs/2308.03476
Exploring the Physical World Adversarial Robustness of Vehicle Detection. (98%)
Wei Jiang; Tianyuan Zhang; Shuangcheng Liu; Weiyu Ji; Zichao Zhang; Gang Xiao

http://arxiv.org/abs/2308.03979
PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation. (86%)
Zhu Liu; Jinyuan Liu; Benzhuang Zhang; Long Ma; Xin Fan; Risheng Liu

http://arxiv.org/abs/2308.03363
A reading survey on adversarial machine learning: Adversarial attacks and their understanding. (81%)
Shashank Kotyan

http://arxiv.org/abs/2308.03331
A Four-Pronged Defense Against Byzantine Attacks in Federated Learning. (54%)
Wei Wan; Shengshan Hu; Minghui Li; Jianrong Lu; Longling Zhang; Leo Yu Zhang; Hai Jin

http://arxiv.org/abs/2308.04018
Improving Performance of Semi-Supervised Learning by Adversarial Attacks. (11%)
Dongyoon Yang; Kunwoong Kim; Yongdai Kim

http://arxiv.org/abs/2308.03558
Mondrian: Prompt Abstraction Attack Against Large Language Models for Cheaper API Pricing. (10%)
Wai Man Si; Michael Backes; Yang Zhang

http://arxiv.org/abs/2308.03108
SAAM: Stealthy Adversarial Attack on Monoculor Depth Estimation. (99%)
Amira Guesmi; Muhammad Abdullah Hanif; Bassem Ouni; Muhammad Shafique

http://arxiv.org/abs/2308.03163
CGBA: Curvature-aware Geometric Black-box Attack. (99%)
Md Farhamdur Reza; Ali Rahmati; Tianfu Wu; Huaiyu Dai

http://arxiv.org/abs/2308.03258
APBench: A Unified Benchmark for Availability Poisoning Attacks and Defenses. (98%)
Tianrui Qin; Xitong Gao; Juanjuan Zhao; Kejiang Ye; Cheng-Zhong Xu

http://arxiv.org/abs/2308.03243
Unsupervised Adversarial Detection without Extra Model: Training Loss Should Change. (82%)
Chien Cheng Chyou; Hung-Ting Su; Winston H. Hsu

http://arxiv.org/abs/2308.03081
Using Overlapping Methods to Counter Adversaries in Community Detection. (50%)
Benjamin A. Miller; Kevin Chan; Tina Eliassi-Rad

http://arxiv.org/abs/2308.02897
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability. (99%)
Bin Chen; Jia-Li Yin; Shukai Chen; Bo-Hao Chen; Ximeng Liu

http://arxiv.org/abs/2308.02923
An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networks. (92%)
Aneeqa Ijaz; Waseem Raza; Hasan Farooq; Marvin Manalastas; Ali Imran

http://arxiv.org/abs/2308.02973
A Security and Usability Analysis of Local Attacks Against FIDO2. (1%)
Tarun Kumar Yadav; Kent Seamons

http://arxiv.org/abs/2308.02836
Approximating Positive Homogeneous Functions with Scale Invariant Neural Networks. (1%)
Stefan Bamberger; Reinhard Heckel; Felix Krahmer

http://arxiv.org/abs/2308.03792
Multi-attacks: Many images $+$ the same adversarial attack $\to$ many target labels. (99%)
Stanislav Fort

http://arxiv.org/abs/2308.02350
RobustMQ: Benchmarking Robustness of Quantized Models. (75%)
Yisong Xiao; Aishan Liu; Tianyuan Zhang; Haotong Qin; Jinyang Guo; Xianglong Liu

http://arxiv.org/abs/2308.02747
SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection. (67%)
Nasimeh Heydaribeni; Ruisi Zhang; Tara Javidi; Cristina Nita-Rotaru; Farinaz Koushanfar

http://arxiv.org/abs/2308.04451
Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks. (67%)
Domenico Cotroneo; Cristina Improta; Pietro Liguori; Roberto Natella

http://arxiv.org/abs/2308.02369
Universal Defensive Underpainting Patch: Making Your Text Invisible to Optical Character Recognition. (31%)
JiaCheng Deng; Li Dong; Jiahao Chen; Diqun Yan; Rangding Wang; Dengpan Ye; Lingchen Zhao; Jinyu Tian

http://arxiv.org/abs/2308.02465
BlindSage: Label Inference Attacks against Node-level Vertical Federated Graph Neural Networks. (9%)
Marco Arazzi; Mauro Conti; Stefanos Koffas; Marina Krcek; Antonino Nocera; Stjepan Picek; Jing Xu

http://arxiv.org/abs/2308.01823
Hard Adversarial Example Mining for Improving Robust Fairness. (99%)
Chenhao Lin; Xiang Ji; Yulong Yang; Qian Li; Chao Shen; Run Wang; Liming Fang

http://arxiv.org/abs/2308.01840
URET: Universal Robustness Evaluation Toolkit (for Evasion). (99%)
Kevin Eykholt; Taesung Lee; Douglas Schales; Jiyong Jang; Ian Molloy; Masha Zorin

http://arxiv.org/abs/2308.02116
AdvFAS: A robust face anti-spoofing framework against adversarial examples. (98%)
Jiawei Chen; Xiao Yang; Heng Yin; Mingzhi Ma; Bihui Chen; Jianteng Peng; Yandong Guo; Zhaoxia Yin; Hang Su

http://arxiv.org/abs/2308.01888
FROD: Robust Object Detection for Free. (67%)
Muhammad; Awais; Weiming; Zhuang; Lingjuan; Lyu; Sung-Ho; Bae

http://arxiv.org/abs/2308.02122
ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP. (33%)
Lu Yan; Zhuo Zhang; Guanhong Tao; Kaiyuan Zhang; Xuan Chen; Guangyu Shen; Xiangyu Zhang

http://arxiv.org/abs/2308.01990
From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application? (4%)
Rodrigo Pedro; Daniel Castro; Paulo Carreira; Nuno Santos

http://arxiv.org/abs/2308.01040
Inaudible Adversarial Perturbation: Manipulating the Recognition of User Speech in Real Time. (99%)
Xinfeng Li; Chen Yan; Xuancun Lu; Zihan Zeng; Xiaoyu Ji; Wenyuan Xu

http://arxiv.org/abs/2308.00958
Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks. (98%)
Jun Guo; Aishan Liu; Xingyu Zheng; Siyuan Liang; Yisong Xiao; Yichao Wu; Xianglong Liu

http://arxiv.org/abs/2308.01193
Mercury: An Automated Remote Side-channel Attack to Nvidia Deep Learning Accelerator. (16%)
Xiaobei Yan; Xiaoxuan Lou; Guowen Xu; Han Qiu; Shangwei Guo; Chip Hong Chang; Tianwei Zhang

http://arxiv.org/abs/2308.01311
TEASMA: A Practical Approach for the Test Assessment of Deep Neural Networks using Mutation Analysis. (2%)
Amin Abbasishahkoo; Mahboubeh Dadkhah; Lionel Briand; Dayi Lin

http://arxiv.org/abs/2308.01237
LSF-IDM: Automotive Intrusion Detection Model with Lightweight Attribution and Semantic Fusion. (1%)
Pengzhou Cheng; Lei Hua; Haobin Jiang; Mohammad Samie; Gongshen Liu

http://arxiv.org/abs/2308.00346
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness. (99%)
Ruoxi Qin; Linyuan Wang; Xuehui Du; Xingyuan Chen; Bin Yan

http://arxiv.org/abs/2308.02533
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning. (99%)
Kaijie Zhu; Jindong Wang; Xixu Hu; Xing Xie; Ge Yang

http://arxiv.org/abs/2308.00319
LimeAttack: Local Explainable Method for Textual Hard-Label Adversarial Attack. (99%)
Hai Zhu; Zhaoqing Yang; Weiwei Shang; Yuren Wu

http://arxiv.org/abs/2308.00311
Doubly Robust Instance-Reweighted Adversarial Training. (82%)
Daouda Sow; Sen Lin; Zhangyang Wang; Yingbin Liang

http://arxiv.org/abs/2308.00854
Training on Foveated Images Improves Robustness to Adversarial Attacks. (82%)
Muhammad A. Shah; Bhiksha Raj

http://arxiv.org/abs/2308.00344
Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches. (47%)
Pia Hanfeld; Khaled Wahba; Marina M. -C. Höhne; Michael Bussmann; Wolfgang Hönig

http://arxiv.org/abs/2308.00556
Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms. (22%)
Elvis Dohmatob; Meyer Scetbon

http://arxiv.org/abs/2308.02535
Learning to Generate Training Datasets for Robust Semantic Segmentation. (9%)
Marwane Hariat; Olivier Laurent; Rémi Kazmierczak; Shihao Zhang; Andrei Bursuc; Angela Yao; Gianni Franchi

http://arxiv.org/abs/2308.00313
Zero-Shot Learning by Harnessing Adversarial Samples. (1%)
Zhi Chen; Pengfei Zhang; Jingjing Li; Sen Wang; Zi Huang

http://arxiv.org/abs/2308.00918
A Novel Cross-Perturbation for Single Domain Generalization. (1%)
Dongjia Zhao; Lei Qi; Xiao Shi; Yinghuan Shi; Xin Geng

http://arxiv.org/abs/2308.00077
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks. (99%)
Khushnaseeb Roshan; Aasim Zafar; Shiekh Burhan Ul Haque

http://arxiv.org/abs/2307.16572
Transferable Attack for Semantic Segmentation. (99%)
Mengqi He; Jing Zhang; Zhaoyuan Yang; Mingyi He; Nick Barnes; Yuchao Dai

http://arxiv.org/abs/2307.16865
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models. (99%)
Weikang Yu; Yonghao Xu; Pedram Ghamisi

http://arxiv.org/abs/2307.16816
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection. (97%)
Xuanang Chen; Ben He; Le Sun; Yingfei Sun

http://arxiv.org/abs/2307.16630
Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks. (86%)
Xinyu Zhang; Hanbin Hong; Yuan Hong; Peng Huang; Binghui Wang; Zhongjie Ba; Kui Ren

http://arxiv.org/abs/2307.16489
BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative Models. (26%)
Jordan Vice; Naveed Akhtar; Richard Hartley; Ajmal Mian

http://arxiv.org/abs/2308.00165
Adversarially Robust Neural Legal Judgement Systems. (11%)
Rohit Raj; V Susheela Devi

http://arxiv.org/abs/2307.16888
Virtual Prompt Injection for Instruction-Tuned Large Language Models. (10%)
Jun Yan; Vikas Yadav; Shiyang Li; Lichang Chen; Zheng Tang; Hai Wang; Vijay Srinivasan; Xiang Ren; Hongxia Jin

http://arxiv.org/abs/2307.16609
Noisy Self-Training with Data Augmentations for Offensive and Hate Speech Detection Tasks. (1%)
João A. Leite; Carolina Scarton; Diego F. Silva

http://arxiv.org/abs/2307.16331
Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks. (99%)
Ashish Hooda; Neal Mangaokar; Ryan Feng; Kassem Fawaz; Somesh Jha; Atul Prakash

http://arxiv.org/abs/2307.16361
Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples. (99%)
Qiufan Ji; Lin Wang; Cong Shi; Shengshan Hu; Yingying Chen; Lichao Sun

http://arxiv.org/abs/2307.16360
Probabilistically robust conformal prediction. (91%)
Subhankar Ghosh; Yuanjie Shi; Taha Belkhouja; Yan Yan; Jana Doppa; Brian Jones

http://arxiv.org/abs/2307.16178
On Updating Static Output Feedback Controllers Under State-Space Perturbation. (1%)
MirSaleh Bahavarnia; Ahmad F. Taha

http://arxiv.org/abs/2307.15971
You Can Backdoor Personalized Federated Learning. (92%)
Tiandi Ye; Cen Chen; Yinggui Wang; Xiang Li; Ming Gao

http://arxiv.org/abs/2307.16099
On Neural Network approximation of ideal adversarial attack and convergence of adversarial training. (92%)
Rajdeep Haldar; Qifan Song

http://arxiv.org/abs/2307.15926
Exposing Hidden Attackers in Industrial Control Systems using Micro-distortions. (41%)
Suman Sourav; Binbin Chen

http://arxiv.org/abs/2307.15539
Beating Backdoor Attack at Its Own Game. (97%)
Min Liu; Alberto Sangiovanni-Vincentelli; Xiangyu Yue

http://arxiv.org/abs/2307.15677
Adversarial training for tabular data with attack propagation. (67%)
Tiago Leon Melo; João Bravo; Marco O. P. Sampaio; Paolo Romano; Hugo Ferreira; João Tiago Ascensão; Pedro Bizarro

http://arxiv.org/abs/2307.15853
Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators through Training with Right-Censored Gaussian Noise. (10%)
Zheyu Yan; Yifan Qin; Wujie Wen; Xiaobo Sharon Hu; Yiyu Shi

http://arxiv.org/abs/2307.15860
What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network. (4%)
Ziheng Huang; Boheng Li; Yan Cai; Run Wang; Shangwei Guo; Liming Fang; Jing Chen; Lina Wang

http://arxiv.org/abs/2307.15157
R-LPIPS: An Adversarially Robust Perceptual Similarity Metric. (99%)
Sara Ghazanfari; Siddharth Garg; Prashanth Krishnamurthy; Farshad Khorrami; Alexandre Araujo

http://arxiv.org/abs/2307.15043
Universal and Transferable Adversarial Attacks on Aligned Language Models. (99%)
Andy Zou; Zifan Wang; Nicholas Carlini; Milad Nasr; J. Zico Kolter; Matt Fredrikson

http://arxiv.org/abs/2309.00007
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-$k$ Multi-Label Learning. (99%)
Yuchen Sun; Qianqian Xu; Zitai Wang; Qingming Huang

http://arxiv.org/abs/2307.14692
Backdoor Attacks for In-Context Learning with Language Models. (97%)
Nikhil Kandpal; Matthew Jagielski; Florian Tramèr; Nicholas Carlini

http://arxiv.org/abs/2307.14751
FLARE: Fingerprinting Deep Reinforcement Learning Agents using Universal Adversarial Masks. (93%)
Buse G. A. Tekgul; N. Asokan

http://arxiv.org/abs/2307.14682
Unified Adversarial Patch for Visible-Infrared Cross-modal Attacks in the Physical World. (92%)
Xingxing Wei; Yao Huang; Yitong Sun; Jie Yu

http://arxiv.org/abs/2307.14917
NSA: Naturalistic Support Artifact to Boost Network Confidence. (62%)
Abhijith Sharma; Phil Munz; Apurva Narayan

http://arxiv.org/abs/2307.14757
SEV-Step: A Single-Stepping Framework for AMD-SEV. (3%)
Luca Wilke; Jan Wichelmann; Anja Rabich; Thomas Eisenbarth

http://arxiv.org/abs/2307.14657
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance. (1%)
Savino Dambra; Yufei Han; Simone Aonzo; Platon Kotzias; Antonino Vitale; Juan Caballero; Davide Balzarotti; Leyla Bilge

http://arxiv.org/abs/2307.15282
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization. (1%)
Chuyan Zhang; Yuncheng Yang; Hao Zheng; Yun Gu

http://arxiv.org/abs/2307.13985
Enhanced Security against Adversarial Examples Using a Random Ensemble of Encrypted Vision Transformer Models. (99%)
Ryota Iijima; Miki Tanaka; Sayaka Shiota; Hitoshi Kiya

http://arxiv.org/abs/2307.14061
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models. (99%)
Dong Lu; Zhiqiang Wang; Teng Wang; Weili Guan; Hongchang Gao; Feng Zheng

http://arxiv.org/abs/2307.14242
Defending Adversarial Patches via Joint Region Localizing and Inpainting. (99%)
Junwen Chen; Xingxing Wei

http://arxiv.org/abs/2307.14540
Lateral-Direction Localization Attack in High-Level Autonomous Driving: Domain-Specific Defense Opportunity via Lane Detection. (67%)
Junjie Shen; Yunpeng Luo; Ziwen Wan; Qi Alfred Chen

http://arxiv.org/abs/2307.14539
Plug and Pray: Exploiting off-the-shelf components of Multi-Modal Models. (33%)
Erfan Shayegani; Yue Dong; Nael Abu-Ghazaleh

http://arxiv.org/abs/2307.14387
Coupled-Space Attacks against Random-Walk-based Anomaly Detection. (11%)
Yuni Lai; Marcin Waniek; Liying Li; Jingwen Wu; Yulin Zhu; Tomasz P. Michalak; Talal Rahwan; Kai Zhou

http://arxiv.org/abs/2307.14593
FakeTracer: Proactively Defending Against Face-swap DeepFakes via Implanting Traces in Training. (5%)
Pu Sun; Honggang Qi; Yuezun Li; Siwei Lyu

http://arxiv.org/abs/2307.14057
Open Image Content Disarm And Reconstruction. (1%)
Eli Belkind; Ran Dubin; Amit Dvir

http://arxiv.org/abs/2307.13856
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix. (99%)
Shashank Agnihotri; Kanchana Vaishnavi Gandikota; Julia Grabinski; Paramanand Chandramouli; Margret Keuper

http://arxiv.org/abs/2307.13294
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation. (99%)
Junbin Fang; Canjian Jiang; You Jiang; Puxi Lin; Zhaojie Chen; Yujing Sun; Siu-Ming Yiu; Zoe L. Jiang

http://arxiv.org/abs/2307.13721
Foundational Models Defining a New Era in Vision: A Survey and Outlook. (10%)
Muhammad Awais; Muzammal Naseer; Salman Khan; Rao Muhammad Anwer; Hisham Cholakkal; Mubarak Shah; Ming-Hsuan Yang; Fahad Shahbaz Khan

http://arxiv.org/abs/2307.13885
Efficient Estimation of Average-Case Robustness for Multi-Class Classification. (10%)
Tessa Han; Suraj Srinivas; Himabindu Lakkaraju

http://arxiv.org/abs/2307.13131
Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbations. (99%)
Yi Han; Matthew Chan; Eric Wengrowski; Zhuohuan Li; Nils Ole Tippenhauer; Mani Srivastava; Saman Zonouz; Luis Garcia

http://arxiv.org/abs/2307.12520
Lost In Translation: Generating Adversarial Examples Robust to Round-Trip Translation. (99%)
Neel Bhandari; Pin-Yu Chen

http://arxiv.org/abs/2307.12872
Data-free Black-box Attack based on Diffusion Model. (62%)
Mingwen Shao; Lingzhuang Meng; Yuanjian Qiao; Lixu Zhang; Wangmeng Zuo

http://arxiv.org/abs/2307.13078
Adaptive Certified Training: Towards Better Accuracy-Robustness Tradeoffs. (56%)
Zhakshylyk Nurlanov; Frank R. Schmidt; Florian Bernard

http://arxiv.org/abs/2307.12679
An Estimator for the Sensitivity to Perturbations of Deep Neural Networks. (31%)
Naman Maheshwari; Nicholas Malaya; Scott Moe; Jaydeep P. Kulkarni; Sudhanva Gurumurthi

http://arxiv.org/abs/2307.13107
Cyber Deception against Zero-day Attacks: A Game Theoretic Approach. (12%)
Md Abu University of Texas at El Paso Sayed; Ahmed H. US Army Research Laboratory Anwar; Christopher University of Texas at El Paso Kiekintveld; Branislav Czech Technical University in Prague Bosansky; Charles US Army Research Laboratory Kamhoua

http://arxiv.org/abs/2307.13164
Malware Resistant Data Protection in Hyper-connected Networks: A survey. (10%)
Jannatul Ferdous; Rafiqul Islam; Maumita Bhattacharya; Md Zahidul Islam

http://arxiv.org/abs/2307.13165
Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations. (9%)
Filippo Betello; Federico Siciliano; Pushkar Mishra; Fabrizio Silvestri

http://arxiv.org/abs/2307.13152
Digital Twins for Moving Target Defense Validation in AC Microgrids. (1%)
Suman Rath; Subham Sahoo; Shamik Sengupta

http://arxiv.org/abs/2307.12903
Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Case. (1%)
Swastika Roy; Hatim Chergui; Christos Verikoukis

http://arxiv.org/abs/2307.12822
Learning Provably Robust Estimators for Inverse Problems via Jittering. (1%)
Anselm Krainovic; Mahdi Soltanolkotabi; Reinhard Heckel

http://arxiv.org/abs/2307.12499
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models. (99%)
Xuelong Dai; Kaisheng Liang; Bin Xiao

http://arxiv.org/abs/2307.12342
Towards Generic and Controllable Attacks Against Object Detection. (99%)
Guopeng Li; Yue Xu; Jian Ding; Gui-Song Xia

http://arxiv.org/abs/2307.12280
Downstream-agnostic Adversarial Examples. (99%)
Ziqi Zhou; Shengshan Hu; Ruizhi Zhao; Qian Wang; Leo Yu Zhang; Junhui Hou; Hai Jin

http://arxiv.org/abs/2307.12507
Gradient-Based Word Substitution for Obstinate Adversarial Examples Generation in Language Models. (98%)
Yimu Wang; Peng Shi; Hongyang Zhang

http://arxiv.org/abs/2307.12328
A First Look at On-device Models in iOS Apps. (84%)
Han Hu; Yujin Huang; Qiuyuan Chen; Terry Tue Zhuo; Chunyang Chen

http://arxiv.org/abs/2307.12498
Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training. (83%)
Gege Qi; Yuefeng Chen; Xiaofeng Mao; Xiaojun Jia; Ranjie Duan; Rong Zhang; Hui Xue

http://arxiv.org/abs/2307.12502
Cross Contrastive Feature Perturbation for Domain Generalization. (1%)
Chenming Li; Daoan Zhang; Wenjian Huang; Jianguo Zhang

http://arxiv.org/abs/2307.13643
Backdoor Attacks against Voice Recognition Systems: A Survey. (13%)
Baochen Yan; Jiahe Lan; Zheng Yan

http://arxiv.org/abs/2307.11906
Unveiling Vulnerabilities in Interpretable Deep Learning Systems with Query-Efficient Black-box Attacks. (99%)
Eldor Abdukhamidov; Mohammed Abuhamad; Simon S. Woo; Eric Chan-Tin; Tamer Abuhmed

http://arxiv.org/abs/2307.11672
Fast Adaptive Test-Time Defense with Robust Features. (98%)
Anurag Singh; Mahalakshmi Sabanayagam; Krikamol Muandet; Debarghya Ghoshdastidar

http://arxiv.org/abs/2307.11565
FMT: Removing Backdoor Feature Maps via Feature Map Testing in Deep Neural Networks. (81%)
Dong Huang; Qingwen Bu; Yahao Qing; Yichao Fu; Heming Cui

http://arxiv.org/abs/2307.11528
Improving Viewpoint Robustness for Visual Recognition via Adversarial Training. (80%)
Shouwei Ruan; Yinpeng Dong; Hang Su; Jianteng Peng; Ning Chen; Xingxing Wei

http://arxiv.org/abs/2307.11729
OUTFOX: LLM-generated Essay Detection through In-context Learning with Adversarially Generated Examples. (62%)
Ryuto Koike; Masahiro Kaneko; Naoaki Okazaki

http://arxiv.org/abs/2307.11823
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness. (26%)
Mehmet Kerim Yucel; Ramazan Gokberk Cinbis; Pinar Duygulu

http://arxiv.org/abs/2307.11730
Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense. (1%)
Enrique Tomás Martínez Beltrán; Pedro Miguel Sánchez Sánchez; Sergio López Bernal; Gérôme Bovet; Manuel Gil Pérez; Gregorio Martínez Pérez; Alberto Huertas Celdrán

http://arxiv.org/abs/2307.15008
A LLM Assisted Exploitation of AI-Guardian. (98%)
Nicholas Carlini

http://arxiv.org/abs/2307.11334
Improving Transferability of Adversarial Examples via Bayesian Attacks. (98%)
Qizhang Li; Yiwen Guo; Xiaochen Yang; Wangmeng Zuo; Hao Chen

http://arxiv.org/abs/2307.10788
Adversarial attacks for mixtures of classifiers. (54%)
Lucas Gnecco Heredia; Benjamin Negrevergne; Yann Chevaleyre

http://arxiv.org/abs/2307.10981
PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks. (33%)
Shiwei Ding; Lan Zhang; Miao Pan; Xiaoyong Yuan

http://arxiv.org/abs/2307.10586
A Holistic Assessment of the Reliability of Machine Learning Systems. (4%)
Anthony Corso; David Karamadian; Romeo Valentin; Mary Cooper; Mykel J. Kochenderfer

http://arxiv.org/abs/2307.11316
Making Pre-trained Language Models both Task-solvers and Self-calibrators. (2%)
Yangyi Chen; Xingyao Wang; Heng Ji

http://arxiv.org/abs/2307.10590
Boundary State Generation for Testing and Improvement of Autonomous Driving Systems. (1%)
Matteo Biagiola; Paolo Tonella

http://arxiv.org/abs/2307.10655
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency. (1%)
Jiawei Shao; Zijian Li; Wenqiang Sun; Tailin Zhou; Yuchang Sun; Lumin Liu; Zehong Lin; Yuyi Mao; Jun Zhang

http://arxiv.org/abs/2307.10487
Backdoor Attack against Object Detection with Clean Annotation. (93%)
Yize Cheng; Wenbin Hu; Minhao Cheng

http://arxiv.org/abs/2307.10562
Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples. (92%)
Shaokui Wei; Mingda Zhang; Hongyuan Zha; Baoyuan Wu

http://arxiv.org/abs/2307.10163
Rethinking Backdoor Attacks. (83%)
Alaa Khaddaj; Guillaume Leclerc; Aleksandar Makelov; Kristian Georgiev; Hadi Salman; Andrew Ilyas; Aleksander Madry

http://arxiv.org/abs/2307.09763
Towards Building More Robust Models with Frequency Bias. (81%)
Qingwen Bu; Dong Huang; Heming Cui

http://arxiv.org/abs/2307.09762
Reinforcing POD based model reduction techniques in reaction-diffusion complex networks using stochastic filtering and pattern recognition. (26%)
Abhishek Ajayakumar; Soumyendu Raha

http://arxiv.org/abs/2307.09375
CertPri: Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space. (67%)
Haibin Zheng; Jinyin Chen; Haibo Jin

http://arxiv.org/abs/2307.09048
FedDefender: Client-Side Attack-Tolerant Federated Learning. (50%)
Sungwon Park; Sungwon Han; Fangzhao Wu; Sundong Kim; Bin Zhu; Xing Xie; Meeyoung Cha

http://arxiv.org/abs/2307.09542
Can Neural Network Memorization Be Localized? (4%)
Pratyush Maini; Michael C. Mozer; Hanie Sedghi; Zachary C. Lipton; J. Zico Kolter; Chiyuan Zhang

http://arxiv.org/abs/2307.08327
Analyzing the Impact of Adversarial Examples on Explainable Machine Learning. (99%)
Prathyusha Devabhakthini; Sasmita Parida; Raj Mani Shukla; Suvendu Chandan Nayak

http://arxiv.org/abs/2307.08278
Adversarial Attacks on Traffic Sign Recognition: A Survey. (98%)
Svetlana Pavlitska; Nico Lambing; J. Marius Zöllner

http://arxiv.org/abs/2307.08955
Discretization-based ensemble model for robust learning in IoT. (87%)
Anahita Namvar; Chandra Thapa; Salil S. Kanhere

http://arxiv.org/abs/2307.08424
Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion Model. (83%)
Rongke Liu; Dong Wang; Yizhi Ren; Zhen Wang; Kaitian Guo; Qianqian Qin; Xiaolei Liu

http://arxiv.org/abs/2307.08939
Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems. (22%)
Xugui Zhou; Anqi Chen; Maxfield Kouzel; Haotian Ren; Morgan McCarty; Cristina Nita-Rotaru; Homa Alemzadeh

http://arxiv.org/abs/2307.08551
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization. (2%)
Akshay Mehra; Yunbei Zhang; Bhavya Kailkhura; Jihun Hamm

http://arxiv.org/abs/2307.10252
A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks. (1%)
Umara Noor; Sawera Shahid; Rimsha Kanwal; Zahid Rashid

http://arxiv.org/abs/2307.10235
Towards Viewpoint-Invariant Visual Recognition via Adversarial Training. (83%)
Shouwei Ruan; Yinpeng Dong; Hang Su; Jianteng Peng; Ning Chen; Xingxing Wei

http://arxiv.org/abs/2307.08208
Towards Stealthy Backdoor Attacks against Speech Recognition via Elements of Sound. (73%)
Hanbo Cai; Pengcheng Zhang; Hai Dong; Yan Xiao; Stefanos Koffas; Yiming Li

http://arxiv.org/abs/2307.08076
Diffusion to Confusion: Naturalistic Adversarial Patch Generation Based on Diffusion Model for Object Detector. (10%)
Shuo-Yen Lin; Ernie Chu; Che-Hsien Lin; Jun-Cheng Chen; Jia-Ching Wang

http://arxiv.org/abs/2307.08213
Lipschitz Continuous Algorithms for Covering Problems. (1%)
Soh Kumabe; Yuichi Yoshida

http://arxiv.org/abs/2307.07916
On the Robustness of Split Learning against Adversarial Attacks. (99%)
Mingyuan Fan; Cen Chen; Chengyu Wang; Wenmeng Zhou; Jun Huang

http://arxiv.org/abs/2307.07873
Why Does Little Robustness Help? Understanding and Improving Adversarial Transferability from Surrogate Training. (99%)
Yechao Zhang; Shengshan Hu; Leo Yu Zhang; Junyu Shi; Minghui Li; Xiaogeng Liu; Wei Wan; Hai Jin

http://arxiv.org/abs/2307.07859
Unified Adversarial Patch for Cross-modal Attacks in the Physical World. (92%)
Xingxing Wei; Yao Huang; Yitong Sun; Jie Yu

http://arxiv.org/abs/2307.08715
MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots. (2%)
Gelei Deng; Yi Liu; Yuekang Li; Kailong Wang; Ying Zhang; Zefeng Li; Haoyu Wang; Tianwei Zhang; Yang Liu

http://arxiv.org/abs/2307.07167
Vulnerability-Aware Instance Reweighting For Adversarial Training. (99%)
Olukorede Fakorede; Ashutosh Kumar Nirala; Modeste Atsague; Jin Tian

http://arxiv.org/abs/2307.07250
Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning. (99%)
Byung-Kwan Lee; Junho Kim; Yong Man Ro

http://arxiv.org/abs/2307.10209
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks. (99%)
Hafsa Bousbiat; Yassine Himeur; Abbes Amira; Wathiq Mansoor

http://arxiv.org/abs/2307.07653
RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World. (98%)
Donghua Wang; Wen Yao; Tingsong Jiang; Chao Li; Xiaoqian Chen

http://arxiv.org/abs/2307.07269
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation. (98%)
Asif Hanif; Muzammal Naseer; Salman Khan; Mubarak Shah; Fahad Shahbaz Khan

http://arxiv.org/abs/2307.10205
Alleviating the Effect of Data Imbalance on Adversarial Training. (92%)
Guanlin Li; Guowen Xu; Tianwei Zhang

http://arxiv.org/abs/2307.07457
Structured Pruning of Neural Networks for Constraints Learning. (76%)
Matteo Cacciola; Antonio Frangioni; Andrea Lodi

http://arxiv.org/abs/2307.07328
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy. (68%)
Zihao Zhu; Mingda Zhang; Shaokui Wei; Li Shen; Yanbo Fan; Baoyuan Wu

http://arxiv.org/abs/2307.07187
Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification. (31%)
Neng Dong; Liyan Zhang; Shuanglin Yan; Hao Tang; Jinhui Tang

http://arxiv.org/abs/2307.08596
Omnipotent Adversarial Training in the Wild. (9%)
Guanlin Li; Kangjie Chen; Yuan Xu; Han Qiu; Tianwei Zhang

http://arxiv.org/abs/2307.07171
Certified Robustness for Large Language Models with Self-Denoising. (5%)
Zhen Zhang; Guanhua Zhang; Bairu Hou; Wenqi Fan; Qing Li; Sijia Liu; Yang Zhang; Shiyu Chang

http://arxiv.org/abs/2307.06548
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations. (99%)
Jialiang Suna; Wen Yao; Tingsong Jianga; Xiaoqian Chena

http://arxiv.org/abs/2307.06608
Introducing Foundation Models as Surrogate Models: Advancing Towards More Practical Adversarial Attacks. (99%)
Jiaming Zhang; Jitao Sang; Qi Yi; Changsheng Xu

http://arxiv.org/abs/2307.06865
Effective Prompt Extraction from Language Models. (4%)
Yiming Zhang; Nicholas Carlini; Daphne Ippolito

http://arxiv.org/abs/2307.06966
Layer-wise Linear Mode Connectivity. (1%)
Linara Adilova; Maksym Andriushchenko; Michael Kamp; Asja Fischer; Martin Jaggi

http://arxiv.org/abs/2307.06796
Defeating Proactive Jammers Using Deep Reinforcement Learning for Resource-Constrained IoT Networks. (1%)
Abubakar Sani Ali; Shimaa Naser; Sami Muhaidat

http://arxiv.org/abs/2307.06695
Towards Traitor Tracing in Black-and-White-Box DNN Watermarking with Tardos-based Codes. (1%)
Elena Rodriguez-Lois; Fernando Perez-Gonzalez

http://arxiv.org/abs/2307.06484
Single-Class Target-Specific Attack against Interpretable Deep Learning Systems. (99%)
Eldor Abdukhamidov; Mohammed Abuhamad; George K. Thiruvathukal; Hyoungshick Kim; Tamer Abuhmed

http://arxiv.org/abs/2307.06496
Microbial Genetic Algorithm-based Black-box Attack against Interpretable Deep Learning Systems. (99%)
Eldor Abdukhamidov; Mohammed Abuhamad; Simon S. Woo; Eric Chan-Tin; Tamer Abuhmed

http://arxiv.org/abs/2307.06287
Rational Neural Network Controllers. (2%)
Matthew Newton; Antonis Papachristodoulou

http://arxiv.org/abs/2307.05946
A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models. (1%)
Agnimitra Sengupta; Sudeepta Mondal; Adway Das; S. Ilgin Guler

http://arxiv.org/abs/2307.06483
Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can! (1%)
Nathan TeBlunthuis; Valerie Hase; Chung-Hong Chan

http://arxiv.org/abs/2307.05095
ATWM: Defense against adversarial malware based on adversarial training. (99%)
Kun Li; Fan Zhang; Wei Guo

http://arxiv.org/abs/2307.05193
Membership Inference Attacks on DNNs using Adversarial Perturbations. (89%)
Hassan Ali; Adnan Qayyum; Ala Al-Fuqaha; Junaid Qadir

http://arxiv.org/abs/2307.05397
On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models. (10%)
Marija Ivanovska; Vitomir Štruc

http://arxiv.org/abs/2307.05772
Random-Set Convolutional Neural Network (RS-CNN) for Epistemic Deep Learning. (4%)
Shireen Kudukkil Manchingal; Muhammad Mubashar; Kaizheng Wang; Keivan Shariatmadar; Fabio Cuzzolin

http://arxiv.org/abs/2307.05422
Differential Analysis of Triggers and Benign Features for Black-Box DNN Backdoor Detection. (2%)
Hao Fu; Prashanth Krishnamurthy; Siddharth Garg; Farshad Khorrami

http://arxiv.org/abs/2307.05471
Scale Alone Does not Improve Mechanistic Interpretability in Vision Models. (1%)
Roland S. Zimmermann; Thomas Klein; Wieland Brendel

http://arxiv.org/abs/2307.05831
Memorization Through the Lens of Curvature of Loss Function Around Samples. (1%)
Isha Garg; Deepak Ravikumar; Kaushik Roy

http://arxiv.org/abs/2307.05842
The Butterfly Effect in Artificial Intelligence Systems: Implications for AI Bias and Fairness. (1%)
Emilio Ferrara

http://arxiv.org/abs/2307.04677
Practical Trustworthiness Model for DNN in Dedicated 6G Application. (33%)
Anouar Nechi; Ahmed Mahmoudi; Christoph Herold; Daniel Widmer; Thomas Kürner; Mladen Berekovic; Saleh Mulhem

http://arxiv.org/abs/2307.04596
Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology. (1%)
Guillaume Vray; Devavrat Tomar; Jean-Philippe Thiran; Behzad Bozorgtabar

http://arxiv.org/abs/2307.04099
GNP Attack: Transferable Adversarial Examples via Gradient Norm Penalty. (98%)
Tao Wu; Tie Luo; Donald C. Wunsch

http://arxiv.org/abs/2307.04333
Enhancing Adversarial Robustness via Score-Based Optimization. (98%)
Boya Zhang; Weijian Luo; Zhihua Zhang

http://arxiv.org/abs/2307.03903
Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification. (99%)
Huafeng Li; Le Xu; Yafei Zhang; Dapeng Tao; Zhengtao Yu

http://arxiv.org/abs/2307.04066
Random Position Adversarial Patch for Vision Transformers. (83%)
Mingzhen Shao

http://arxiv.org/abs/2307.04024
Robust Ranking Explanations. (38%)
Chao Chen; Chenghua Guo; Guixiang Ma; Ming Zeng; Xi Zhang; Sihong Xie

http://arxiv.org/abs/2307.03803
A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness. (81%)
Jovon Craig; Josh Andle; Theodore S. Nowak; Salimeh Yasaei Sekeh

http://arxiv.org/abs/2307.03798
Fooling Contrastive Language-Image Pre-trained Models with CLIPMasterPrints. (68%)
Matthias Freiberger; Peter Kun; Christian Igel; Anders Sundnes Løvlie; Sebastian Risi

http://arxiv.org/abs/2307.03694
Scalable Membership Inference Attacks via Quantile Regression. (33%)
Martin Bertran; Shuai Tang; Michael Kearns; Jamie Morgenstern; Aaron Roth; Zhiwei Steven Wu

http://arxiv.org/abs/2307.03838
RADAR: Robust AI-Text Detection via Adversarial Learning. (5%)
Xiaomeng Hu; Pin-Yu Chen; Tsung-Yi Ho

http://arxiv.org/abs/2307.12399
Generation of Time-Varying Impedance Attacks Against Haptic Shared Control Steering Systems. (1%)
Alireza Mohammadi; Hafiz Malik

http://arxiv.org/abs/2307.02828
Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks. (99%)
Xu Han; Anmin Liu; Chenxuan Yao; Yanbo Fan; Kun He

http://arxiv.org/abs/2307.02849
NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic. (92%)
Zi'ou Zheng; Xiaodan Zhu

http://arxiv.org/abs/2307.03217
Quantification of Uncertainty with Adversarial Models. (68%)
Kajetan Schweighofer; Lukas Aichberger; Mykyta Ielanskyi; Günter Klambauer; Sepp Hochreiter

http://arxiv.org/abs/2307.03305
A Vulnerability of Attribution Methods Using Pre-Softmax Scores. (41%)
Miguel Lerma; Mirtha Lucas

http://arxiv.org/abs/2307.02881
Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications. (8%)
Peter Tu; Zhaoyuan Yang; Richard Hartley; Zhiwei Xu; Jing Zhang; Yiwei Fu; Dylan Campbell; Jaskirat Singh; Tianyu Wang

http://arxiv.org/abs/2307.03132
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. (1%)
Pratyush Maini; Sachin Goyal; Zachary C. Lipton; J. Zico Kolter; Aditi Raghunathan

http://arxiv.org/abs/2307.02055
Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and their Impact. (98%)
Jaydip Sen; Subhasis Dasgupta

http://arxiv.org/abs/2307.02094
DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications. (69%)
Adam Ivankay; Mattia Rigotti; Pascal Frossard

http://arxiv.org/abs/2307.02347
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality. (67%)
Peter Lorenz; Ricard Durall; Janis Keuper

http://arxiv.org/abs/2307.02672
GIT: Detecting Uncertainty, Out-Of-Distribution and Adversarial Samples using Gradients and Invariance Transformations. (62%)
Julia Lust; Alexandru P. Condurache

http://arxiv.org/abs/2307.02569
Securing Cloud FPGAs Against Power Side-Channel Attacks: A Case Study on Iterative AES. (5%)
Nithyashankari Gummidipoondi JV Jayasankaran; Hao JV Guo; Satwik JV Patnaik; JV Jeyavijayan; Rajendran; Jiang Hu

http://arxiv.org/abs/2307.02202
On the Adversarial Robustness of Generative Autoencoders in the Latent Space. (3%)
Mingfei Lu; Badong Chen

http://arxiv.org/abs/2307.01488
SCAT: Robust Self-supervised Contrastive Learning via Adversarial Training for Text Classification. (99%)
Junjie Wu; Dit-Yan Yeung

http://arxiv.org/abs/2307.01520
LEAT: Towards Robust Deepfake Disruption in Real-World Scenarios via Latent Ensemble Attack. (83%)
Joonkyo Shim; Hyunsoo Yoon

http://arxiv.org/abs/2307.02500
Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability Connection. (68%)
Delyan Boychev

http://arxiv.org/abs/2307.01610
Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction. (45%)
Zitao Chen; Karthik Pattabiraman

http://arxiv.org/abs/2307.01778
Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling. (26%)
Zhanhao Hu; Wenda Chu; Xiaopei Zhu; Hui Zhang; Bo Zhang; Xiaolin Hu

http://arxiv.org/abs/2307.01565
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems. (13%)
Shuyi Wang; Guido Zuccon

http://arxiv.org/abs/2307.01570
Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction. (1%)
Vu-Duc Ngo; Tuan-Cuong Vuong; Luong Thien Van; Hung Tran

http://arxiv.org/abs/2307.01701
Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data. (1%)
Florent Guépin; Matthieu Meeus; Ana-Maria Cretu; Montjoye Yves-Alexandre de

http://arxiv.org/abs/2307.01292
Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems. (99%)
Debopam Georgia Institute of Technology Sanyal; Jui-Tse Georgia Institute of Technology Hung; Manav Georgia Institute of Technology Agrawal; Prahlad Georgia Institute of Technology Jasti; Shahab University of California, Riverside Nikkhoo; Somesh University of Wisconsin-Madison Jha; Tianhao University of Virginia Wang; Sibin George Washington University Mohan; Alexey Georgia Institute of Technology Tumanov

http://arxiv.org/abs/2307.10184
A Dual Stealthy Backdoor: From Both Spatial and Frequency Perspectives. (83%)
Yudong Gao; Honglong Chen; Peng Sun; Junjian Li; Anqing Zhang; Zhibo Wang

http://arxiv.org/abs/2307.03197
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks. (62%)
Aysha Thahsin Zahir Ismail; Raj Mani Shukla

http://arxiv.org/abs/2307.01073
What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners? (62%)
Fnu Suya; Xiao Zhang; Yuan Tian; David Evans

http://arxiv.org/abs/2307.01390
Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives. (45%)
Danele Lunghi; Alkis Simitsis; Olivier Caelen; Gianluca Bontempi

http://arxiv.org/abs/2307.00823
Analysis of Task Transferability in Large Pre-trained Classifiers. (13%)
Akshay Mehra; Yunbei Zhang; Jihun Hamm

http://arxiv.org/abs/2307.00907
Enhancing the Robustness of QMIX against State-adversarial Attacks. (4%)
Weiran Guo; Guanjun Liu; Ziyuan Zhou; Ling Wang; Jiacun Wang

http://arxiv.org/abs/2307.00934
Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration. (1%)
Kemal Oksuz; Tom Joy; Puneet K. Dokania

http://arxiv.org/abs/2307.00477
Query-Efficient Decision-based Black-Box Patch Attack. (99%)
Zhaoyu Chen; Bo Li; Shuang Wu; Shouhong Ding; Wenqiang Zhang

http://arxiv.org/abs/2307.01225
Interpretability and Transparency-Driven Detection and Transformation of Textual Adversarial Examples (IT-DT). (99%)
Bushra Sabir; M. Ali Babar; Sharif Abuadbba

http://arxiv.org/abs/2307.00691
From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy. (10%)
Maanak Gupta; CharanKumar Akiri; Kshitiz Aryal; Eli Parker; Lopamudra Praharaj

http://arxiv.org/abs/2307.00680
CLIMAX: An exploration of Classifier-Based Contrastive Explanations. (2%)
Praharsh Nanavati; Ranjitha Prasad

http://arxiv.org/abs/2307.00274
Common Knowledge Learning for Generating Transferable Adversarial Examples. (99%)
Ruijie Yang; Yuanfang Guo; Junfu Wang; Jiantao Zhou; Yunhong Wang

http://arxiv.org/abs/2307.00309
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey. (99%)
Hanieh Naderi; Ivan V. Bajić

http://arxiv.org/abs/2307.00421
Brightness-Restricted Adversarial Attack Patch. (75%)
Mingzhen Shao

http://arxiv.org/abs/2307.00356
Fedward: Flexible Federated Backdoor Defense Framework with Non-IID Data. (54%)
Zekai Chen; Fuyi Wang; Zhiwei Zheng; Ximeng Liu; Yujie Lin

http://arxiv.org/abs/2307.00368
Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training. (26%)
Dario Lazzaro; Antonio Emanuele Cinà; Maura Pintor; Ambra Demontis; Battista Biggio; Fabio Roli; Marcello Pelillo

http://arxiv.org/abs/2307.00280
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency. (13%)
Yan Wang; Yuhang Li; Ruihao Gong; Aishan Liu; Yanfei Wang; Jian Hu; Yongqiang Yao; Yunchen Zhang; Tianzi Xiao; Fengwei Yu; Xianglong Liu

http://arxiv.org/abs/2307.00310
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD. (10%)
Anvith Thudi; Hengrui Jia; Casey Meehan; Ilia Shumailov; Nicolas Papernot

http://arxiv.org/abs/2307.00384
CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis. (5%)
Abdallah Alshantti; Damiano Varagnolo; Adil Rasheed; Aria Rahmati; Frank Westad

http://arxiv.org/abs/2307.08672
FedDefender: Backdoor Attack Defense in Federated Learning. (2%)
Waris Virginia Tech Gill; Ali University of Minnesota Twin Cities Anwar; Muhammad Ali Virginia Tech Gulzar

http://arxiv.org/abs/2307.00268
Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learning. (1%)
Md Tamjid Hossain; Hung La

http://arxiv.org/abs/2306.17431
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection. (99%)
Huiming Sun; Lan Fu; Jinlong Li; Qing Guo; Zibo Meng; Tianyun Zhang; Yuewei Lin; Hongkai Yu

http://arxiv.org/abs/2306.17441
Efficient Backdoor Removal Through Natural Gradient Fine-tuning. (8%)
Nazmul Karim; Abdullah Al Arafat; Umar Khalid; Zhishan Guo; Naznin Rahnavard

http://arxiv.org/abs/2306.17606
Minimum-norm Sparse Perturbations for Opacity in Linear Systems. (1%)
Varkey M John; Vaibhav Katewa

http://arxiv.org/abs/2306.16979
Defending Black-box Classifiers by Bayesian Boundary Correction. (99%)
He Wang; Yunfeng Diao

http://arxiv.org/abs/2306.16738
Towards Optimal Randomized Strategies in Adversarial Example Game. (96%)
Jiahao Xie; Chao Zhang; Weijie Liu; Wensong Bai; Hui Qian

http://arxiv.org/abs/2306.16697
Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features. (13%)
Mingli Zhu; Shaokui Wei; Hongyuan Zha; Baoyuan Wu

http://arxiv.org/abs/2306.16869
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes. (1%)
Hao-Lun Sun; Lei Hsiung; Nandhini Chandramoorthy; Pin-Yu Chen; Tsung-Yi Ho

http://arxiv.org/abs/2306.16170
Mitigating the Accuracy-Robustness Trade-off via Multi-Teacher Adversarial Distillation. (99%)
Shiji Zhao; Xizhe Wang; Xingxing Wei

http://arxiv.org/abs/2306.15931
Boosting Adversarial Transferability with Learnable Patch-wise Masks. (99%)
Xingxing Wei; Shiji Zhao

http://arxiv.org/abs/2306.16050
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack. (99%)
Jie Ning; Yao Li; Zhichang Guo

http://arxiv.org/abs/2306.16614
Group-based Robustness: A General Framework for Customized Robustness in the Real World. (98%)
Weiran Lin; Keane Lucas; Neo Eyal; Lujo Bauer; Michael K. Reiter; Mahmood Sharif

http://arxiv.org/abs/2306.16131
Distributional Modeling for Location-Aware Adversarial Patches. (98%)
Xingxing Wei; Shouwei Ruan; Yinpeng Dong; Hang Su

http://arxiv.org/abs/2306.16022
Enrollment-stage Backdoor Attacks on Speaker Recognition Systems via Adversarial Ultrasound. (98%)
Xinfeng Li; Junning Ze; Chen Yan; Yushi Cheng; Xiaoyu Ji; Wenyuan Xu

http://arxiv.org/abs/2306.16581
Does Saliency-Based Training bring Robustness for Deep Neural Networks in Image Classification? (93%)
Ali Karkehabadi

http://arxiv.org/abs/2306.16415
On Practical Aspects of Aggregation Defenses against Data Poisoning Attacks. (50%)
Wenxiao Wang; Soheil Feizi

http://arxiv.org/abs/2306.17194
On the Exploitability of Instruction Tuning. (13%)
Manli Shu; Jiongxiao Wang; Chen Zhu; Jonas Geiping; Chaowei Xiao; Tom Goldstein

http://arxiv.org/abs/2306.15451
Advancing Adversarial Training by Injecting Booster Signal. (98%)
Hong Joo Lee; Youngjoon Yu; Yong Man Ro

http://arxiv.org/abs/2306.15755
IMPOSITION: Implicit Backdoor Attack through Scenario Injection. (96%)
Mozhgan Pourkeshavarz; Mohammad Sabokrou; Amir Rasouli

http://arxiv.org/abs/2306.15427
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions. (92%)
Lukas Gosch; Simon Geisler; Daniel Sturm; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann

http://arxiv.org/abs/2306.15457
Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning. (89%)
Hong Joo Lee; Yong Man Ro

http://arxiv.org/abs/2306.15363
Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferability. (87%)
Marco Alecci; Mauro Conti; Francesco Marchiori; Luca Martinelli; Luca Pajola

http://arxiv.org/abs/2306.15221
[Re] Double Sampling Randomized Smoothing. (69%)
Aryan Gupta; Sarthak Gupta; Abhay Kumar; Harsh Dugar

http://arxiv.org/abs/2306.15482
Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets. (68%)
Yimu Wang; Dinghuai Zhang; Yihan Wu; Heng Huang; Hongyang Zhang

http://arxiv.org/abs/2306.15248
Catch Me If You Can: A New Low-Rate DDoS Attack Strategy Disguised by Feint. (26%)
Tianyang Cai; Yuqi Li; Tao Jia; Leo Yu Zhang; Zheng Yang

http://arxiv.org/abs/2306.16526
Shilling Black-box Review-based Recommender Systems through Fake Review Generation. (1%)
Hung-Yun Chiang; Yi-Syuan Chen; Yun-Zhu Song; Hong-Han Shuai; Jason S. Chang

http://arxiv.org/abs/2306.15705
On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection. (99%)
Songyang Gao; Shihan Dou; Qi Zhang; Xuanjing Huang; Jin Ma; Ying Shan

http://arxiv.org/abs/2306.15447
Are aligned neural networks adversarially aligned? (99%)
Nicholas Carlini; Milad Nasr; Christopher A. Choquette-Choo; Matthew Jagielski; Irena Gao; Anas Awadalla; Pang Wei Koh; Daphne Ippolito; Katherine Lee; Florian Tramer; Ludwig Schmidt

http://arxiv.org/abs/2306.14609
The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning security. (92%)
Harriet Farlow; Matthew Garratt; Gavin Mount; Tim Lynar

http://arxiv.org/abs/2306.14640
3D-Aware Adversarial Makeup Generation for Facial Privacy Protection. (92%)
Yueming Lyu; Yue Jiang; Ziwen He; Bo Peng; Yunfan Liu; Jing Dong

http://arxiv.org/abs/2306.15044
Towards Sybil Resilience in Decentralized Learning. (80%)
Thomas Werthenbach; Johan Pouwelse

http://arxiv.org/abs/2306.14782
On the Resilience of Machine Learning-Based IDS for Automotive Networks. (78%)
Ivo Zenden; Han Wang; Alfonso Iacovazzi; Arash Vahidi; Rolf Blom; Shahid Raza

http://arxiv.org/abs/2306.15164
DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization. (75%)
Songyang Gao; Shihan Dou; Yan Liu; Xiao Wang; Qi Zhang; Zhongyu Wei; Jin Ma; Ying Shan

http://arxiv.org/abs/2306.14672
PWSHAP: A Path-Wise Explanation Model for Targeted Variables. (8%)
Lucile Ter-Minassian; Oscar Clivio; Karla Diaz-Ordaz; Robin J. Evans; Chris Holmes

http://arxiv.org/abs/2306.14262
A Spectral Perspective towards Understanding and Improving Adversarial Robustness. (99%)
Binxiao Huang; Rui Lin; Chaofan Tao; Ngai Wong

http://arxiv.org/abs/2306.14217
On Evaluating the Adversarial Robustness of Semantic Segmentation Models. (99%)
Levente Halmosi; Mark Jelasity

http://arxiv.org/abs/2306.14126
Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training. (98%)
Fan Liu; Weijia Zhang; Hao Liu

http://arxiv.org/abs/2306.14275
Enhancing Adversarial Training via Reweighting Optimization Trajectory. (97%)
Tianjin Huang; Shiwei Liu; Tianlong Chen; Meng Fang; Li Shen; Vlaod Menkovski; Lu Yin; Yulong Pei; Mykola Pechenizkiy

http://arxiv.org/abs/2306.14321
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations. (87%)
Yilun Zhao; Chen Zhao; Linyong Nan; Zhenting Qi; Wenlin Zhang; Xiangru Tang; Boyu Mi; Dragomir Radev

http://arxiv.org/abs/2306.14326
Computational Asymmetries in Robust Classification. (80%)
Samuele Marro; Michele Lombardi

http://arxiv.org/abs/2306.13965
Boosting Model Inversion Attacks with Adversarial Examples. (98%)
Shuai Zhou; Tianqing Zhu; Dayong Ye; Xin Yu; Wanlei Zhou

http://arxiv.org/abs/2306.14043
Machine Learning needs Better Randomness Standards: Randomised Smoothing and PRNG-based attacks. (98%)
Pranav Dahiya; Ilia Shumailov; Ross Anderson

http://arxiv.org/abs/2306.13854
Similarity Preserving Adversarial Graph Contrastive Learning. (96%)
Yeonjun In; Kanghoon Yoon; Chanyoung Park

http://arxiv.org/abs/2306.14040
Weighted Automata Extraction and Explanation of Recurrent Neural Networks for Natural Language Tasks. (70%)
Zeming Wei; Xiyue Zhang; Yihao Zhang; Meng Sun

http://arxiv.org/abs/2306.13587
Creating Valid Adversarial Examples of Malware. (99%)
Matouš Kozák; Martin Jureček; Mark Stamp; Troia Fabio Di

http://arxiv.org/abs/2306.13614
Adversarial Robustness Certification for Bayesian Neural Networks. (92%)
Matthew Wicker; Andrea Patane; Luca Laurenti; Marta Kwiatkowska

http://arxiv.org/abs/2306.13800
A First Order Meta Stackelberg Method for Robust Federated Learning. (10%)
Yunian Pan; Tao Li; Henger Li; Tianyi Xu; Zizhan Zheng; Quanyan Zhu

http://arxiv.org/abs/2306.13213
Visual Adversarial Examples Jailbreak Large Language Models. (99%)
Xiangyu Qi; Kaixuan Huang; Ashwinee Panda; Mengdi Wang; Prateek Mittal

http://arxiv.org/abs/2306.12688
Towards quantum enhanced adversarial robustness in machine learning. (99%)
Maxwell T. West; Shu-Lok Tsang; Jia S. Low; Charles D. Hill; Christopher Leckie; Lloyd C. L. Hollenberg; Sarah M. Erfani; Muhammad Usman

http://arxiv.org/abs/2306.12685
Rethinking the Backward Propagation for Adversarial Transferability. (99%)
Xiaosen Wang; Kangheng Tong; Kun He

http://arxiv.org/abs/2306.13091
Evading Forensic Classifiers with Attribute-Conditioned Adversarial Faces. (96%)
Fahad Shamshad; Koushik Srivatsan; Karthik Nandakumar

http://arxiv.org/abs/2306.13119
Adversarial Resilience in Sequential Prediction via Abstention. (93%)
Surbhi Goel; Steve Hanneke; Shay Moran; Abhishek Shetty

http://arxiv.org/abs/2306.13236
Document Image Cleaning using Budget-Aware Black-Box Approximation. (92%)
Ganesh Tata; Katyani Singh; Oeveren Eric Van; Nilanjan Ray

http://arxiv.org/abs/2306.13157
Anticipatory Thinking Challenges in Open Worlds: Risk Management. (81%)
Adam Amos-Binks; Dustin Dannenhauer; Leilani H. Gilpin

http://arxiv.org/abs/2306.12941
Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models. (75%)
Francesco Croce; Naman D Singh; Matthias Hein

http://arxiv.org/abs/2306.13273
A First Order Meta Stackelberg Method for Robust Federated Learning (Technical Report). (33%)
Henger Li; Tianyi Xu; Tao Li; Yunian Pan; Quanyan Zhu; Zizhan Zheng

http://arxiv.org/abs/2306.13033
Impacts and Risk of Generative AI Technology on Cyber Defense. (4%)
Subash Neupane; Ivan A. Fernandez; Sudip Mittal; Shahram Rahimi

http://arxiv.org/abs/2306.12161
Adversarial Attacks Neutralization via Data Set Randomization. (99%)
Mouna Rabhi; Pietro Roberto Di

http://arxiv.org/abs/2306.12111
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking. (92%)
Shaohui Mei; Jiawei Lian; Xiaofei Wang; Yuru Su; Mingyang Ma; Lap-Pui Chau

http://arxiv.org/abs/2306.12043
Sample Attackability in Natural Language Adversarial Attacks. (92%)
Vyas Raina; Mark Gales

http://arxiv.org/abs/2306.12610
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches. (76%)
Aniruddha Saha; Shuhua Yu; Arash Norouzzadeh; Wan-Yi Lin; Chaithanya Kumar Mummadi

http://arxiv.org/abs/2306.12608
DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum. (47%)
Xiaolan Gu; Ming Li; Li Xiong

http://arxiv.org/abs/2306.12517
FFCV: Accelerating Training by Removing Data Bottlenecks. (3%)
Guillaume Leclerc; Andrew Ilyas; Logan Engstrom; Sung Min Park; Hadi Salman; Aleksander Madry

http://arxiv.org/abs/2306.11322
Reversible Adversarial Examples with Beam Search Attack and Grayscale Invariance. (99%)
Haodong Zhang; Chi Man Pun; Xia Du

http://arxiv.org/abs/2306.11974
Universal adversarial perturbations for multiple classification tasks with quantum classifiers. (99%)
Yun-Zhong Qiu

http://arxiv.org/abs/2306.11990
Physics-constrained Attack against Convolution-based Human Motion Prediction. (99%)
Chengxu Duan; Zhicheng Zhang; Xiaoli Liu; Yonghao Dang; Jianqin Yin

http://arxiv.org/abs/2306.11338
FDInet: Protecting against DNN Model Extraction via Feature Distortion Index. (50%)
Hongwei Yao; Zheng Li; Haiqin Weng; Feng Xue; Kui Ren; Zhan Qin

http://arxiv.org/abs/2306.11698
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models. (33%)
Boxin Wang; Weixin Chen; Hengzhi Pei; Chulin Xie; Mintong Kang; Chenhui Zhang; Chejian Xu; Zidi Xiong; Ritik Dutta; Rylan Schaeffer; Sang T. Truong; Simran Arora; Mantas Mazeika; Dan Hendrycks; Zinan Lin; Yu Cheng; Sanmi Koyejo; Dawn Song; Bo Li

http://arxiv.org/abs/2306.11797
Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data. (3%)
Shreejit Jadhav; Mihir Shrivastava; Sanjit Mitra

http://arxiv.org/abs/2306.11291
Mitigating Speculation-based Attacks through Configurable Hardware/Software Co-design. (1%)
Ali Hajiabadi; Archit Agarwal; Andreas Diavastos; Trevor E. Carlson

http://arxiv.org/abs/2306.11925
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching. (1%)
Duy M. H. Nguyen; Hoang Nguyen; Nghiem T. Diep; Tan N. Pham; Tri Cao; Binh T. Nguyen; Paul Swoboda; Nhat Ho; Shadi Albarqouni; Pengtao Xie; Daniel Sonntag; Mathias Niepert

http://arxiv.org/abs/2306.11261
Comparative Evaluation of Recent Universal Adversarial Perturbations in Image Classification. (99%)
Juanjuan Weng; Zhiming Luo; Dazhen Lin; Shaozi Li

http://arxiv.org/abs/2306.11066
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding. (75%)
Venkata Prabhakara Sarath Nookala; Gaurav Verma; Subhabrata Mukherjee; Srijan Kumar

http://arxiv.org/abs/2306.11035
Adversarial Training Should Be Cast as a Non-Zero-Sum Game. (73%)
Alexander Robey; Fabian Latorre; George J. Pappas; Hamed Hassani; Volkan Cevher

http://arxiv.org/abs/2306.10963
Eigenpatches -- Adversarial Patches from Principal Components. (38%)
Jens Bayer; Stefan Becker; David Münch; Michael Arens

http://arxiv.org/abs/2306.10746
Practical and General Backdoor Attacks against Vertical Federated Learning. (13%)
Yuexin Xuan; Xiaojun Chen; Zhendong Zhao; Bisheng Tang; Ye Dong

http://arxiv.org/abs/2306.10742
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming. (5%)
Steven Adams; Andrea Patane; Morteza Lahijanian; Luca Laurenti

http://arxiv.org/abs/2306.10309
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses. (98%)
Mohamed Amine Ferrag; Othmane Friha; Burak Kantarci; Norbert Tihanyi; Lucas Cordeiro; Merouane Debbah; Djallel Hamouda; Muna Al-Hawawreh; Kim-Kwang Raymond Choo

http://arxiv.org/abs/2306.10426
Understanding Certified Training with Interval Bound Propagation. (38%)
Yuhao Mao; Mark Niklas Müller; Marc Fischer; Martin Vechev

http://arxiv.org/abs/2306.10392
GlyphNet: Homoglyph domains dataset and detection using attention-based Convolutional Neural Networks. (9%)
Akshat Gupta; Laxman Singh Tomar; Ridhima Garg

http://arxiv.org/abs/2306.10351
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network. (1%)
Fan Liu; Siqi Lai; Yansong Ning; Hao Liu

http://arxiv.org/abs/2306.09844
Wasserstein distributional robustness of neural networks. (99%)
Xingjian Bai; Guangyi He; Yifan Jiang; Jan Obloj

http://arxiv.org/abs/2306.09925
Query-Free Evasion Attacks Against Machine Learning-Based Malware Detectors with Generative Adversarial Networks. (99%)
Daniel Gibert; Jordi Planes; Quan Le; Giulio Zizzo

http://arxiv.org/abs/2306.09951
You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks. (98%)
Edward Raff; Michel Benaroch; Andrew L. Farris

http://arxiv.org/abs/2306.09949
Towards Better Certified Segmentation via Diffusion Models. (73%)
Othmane Laousy; Alexandre Araujo; Guillaume Chassagnon; Marie-Pierre Revel; Siddharth Garg; Farshad Khorrami; Maria Vakalopoulou

http://arxiv.org/abs/2306.09977
Adversarially robust clustering with optimality guarantees. (4%)
Soham Jana; Kun Yang; Sanjeev Kulkarni

http://arxiv.org/abs/2306.10008
CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search. (1%)
Fahad Shamshad; Muzammal Naseer; Karthik Nandakumar

http://arxiv.org/abs/2306.09124
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks in the Physical World. (99%)
Caixin Kang; Yinpeng Dong; Zhengyi Wang; Shouwei Ruan; Hang Su; Xingxing Wei

http://arxiv.org/abs/2306.13215
OVLA: Neural Network Ownership Verification using Latent Watermarks. (64%)
Feisi Fu; Wenchao Li

http://arxiv.org/abs/2306.13103
Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks. (62%)
Hongcheng Gao; Hao Zhang; Yinpeng Dong; Zhijie Deng

http://arxiv.org/abs/2306.09104
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation. (33%)
Zhanke Zhou; Chenyu Zhou; Xuan Li; Jiangchao Yao; Quanming Yao; Bo Han

http://arxiv.org/abs/2306.09278
Robustness Analysis on Foundational Segmentation Models. (11%)
Madeline Chantry Schiappa; Sachidanand VS; Yunhao Ge; Ondrej Miksik; Yogesh S. Rawat; Vibhav Vineet

http://arxiv.org/abs/2306.09442
Explore, Establish, Exploit: Red Teaming Language Models from Scratch. (1%)
Stephen Casper; Jason Lin; Joe Kwon; Gatlen Culp; Dylan Hadfield-Menell

http://arxiv.org/abs/2306.08929
Community Detection Attack against Collaborative Learning-based Recommender Systems. (1%)
Yacine Belal; Sonia Ben Mokhtar; Mohamed Maouche; Anthony Simonet-Boulogne

http://arxiv.org/abs/2306.09206
Concealing CAN Message Sequences to Prevent Schedule-based Bus-off Attacks. (1%)
Sunandan Adhikary; Ipsita Koley; Arkaprava Sain; Soumyadeep das; Shuvam Saha; Soumyajit Dey

http://arxiv.org/abs/2306.08565
Reliable Evaluation of Adversarial Transferability. (99%)
Wenqian Yu; Jindong Gu; Zhijiang Li; Philip Torr

http://arxiv.org/abs/2306.08492
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models. (99%)
Sahar Sadrizadeh; Clément Barbier; Ljiljana Dolamic; Pascal Frossard

http://arxiv.org/abs/2306.08422
X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail. (98%)
Omer Hofman; Amit Giloni; Yarin Hayun; Ikuya Morikawa; Toshiya Shimizu; Yuval Elovici; Asaf Shabtai

http://arxiv.org/abs/2306.08656
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. (98%)
Jiapeng Wu; Atiyeh Ashari Ghomi; David Glukhov; Jesse C. Cresswell; Franziska Boenisch; Nicolas Papernot

http://arxiv.org/abs/2306.08386
Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios. (83%)
Ziqiang Li; Hong Sun; Pengfei Xia; Heng Li; Beihao Xia; Yi Wu; Bin Li

http://arxiv.org/abs/2306.08604
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy. (75%)
Enyan Dai; Limeng Cui; Zhengyang Wang; Xianfeng Tang; Yinghan Wang; Monica Cheng; Bing Yin; Suhang Wang

http://arxiv.org/abs/2306.08257
On the Robustness of Latent Diffusion Models. (73%)
Jianping Zhang; Zhuoer Xu; Shiwen Cui; Changhua Meng; Weibin Wu; Michael R. Lyu

http://arxiv.org/abs/2306.08313
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks. (38%)
Ziqiang Li; Hong Sun; Pengfei Xia; Beihao Xia; Xue Rui; Wei Zhang; Qinglang Guo; Bin Li

http://arxiv.org/abs/2306.08751
Improving Selective Visual Question Answering by Learning from Your Peers. (1%)
Corentin Dancette; Spencer Whitehead; Rishabh Maheshwary; Ramakrishna Vedantam; Stefan Scherer; Xinlei Chen; Matthieu Cord; Marcus Rohrbach

http://arxiv.org/abs/2306.07723
Theoretical Foundations of Adversarially Robust Learning. (99%)
Omar Montasser

http://arxiv.org/abs/2306.07796
Finite Gaussian Neurons: Defending against adversarial attacks by making neural networks say "I don't know". (99%)
Felix Grezes

http://arxiv.org/abs/2306.07591
I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models. (99%)
Raz Lapid; Moshe Sipper

http://arxiv.org/abs/2306.07713
Robustness of SAM: Segment Anything Under Corruptions and Beyond. (98%)
Yu Qiao; Chaoning Zhang; Taegoo Kang; Donghun Kim; Chenshuang Zhang; Choong Seon Hong

http://arxiv.org/abs/2306.07768
Area is all you need: repeatable elements make stronger adversarial attacks. (98%)
Dillon Niederhut

http://arxiv.org/abs/2306.07655
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systems. (96%)
Michele Panariello; Wanying Ge; Hemlata Tak; Massimiliano Todisco; Nicholas Evans

http://arxiv.org/abs/2306.07613
Revisiting and Advancing Adversarial Training Through A Simple Baseline. (87%)
Hong Liu

http://arxiv.org/abs/2306.07754
Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis. (78%)
Yihan Ma; Zhengyu Zhao; Xinlei He; Zheng Li; Michael Backes; Yang Zhang

http://arxiv.org/abs/2306.08011
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios. (22%)
Haochen Mei; Gaolei Li; Jun Wu; Longfei Zheng

http://arxiv.org/abs/2306.08009
DHBE: Data-free Holistic Backdoor Erasing in Deep Neural Networks via Restricted Adversarial Distillation. (22%)
Zhicong Yan; Shenghong Li; Ruijie Zhao; Yuan Tian; Yuanyuan Zhao

http://arxiv.org/abs/2306.07883
Temporal Gradient Inversion Attacks with Robust Optimization. (8%)
Bowen Li; Hanlin Gu; Ruoxin Chen; Jie Li; Chentao Wu; Na Ruan; Xueming Si; Lixin Fan

http://arxiv.org/abs/2306.07685
Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats. (2%)
Gaolei Li; Yuanyuan Zhao; Wenqi Wei; Yuchen Liu

http://arxiv.org/abs/2306.07033
When Vision Fails: Text Attacks Against ViT and OCR. (99%)
Nicholas Boucher; Jenny Blessing; Ilia Shumailov; Ross Anderson; Nicolas Papernot

http://arxiv.org/abs/2306.07197
AROID: Improving Adversarial Robustness through Online Instance-wise Data Augmentation. (99%)
Lin Li; Jianing Qiu; Michael Spratling

http://arxiv.org/abs/2306.06995
How robust accuracy suffers from certified training with convex relaxations. (73%)
Bartolomeis Piersilvio De; Jacob Clarysse; Amartya Sanyal; Fanny Yang

http://arxiv.org/abs/2306.06909
Graph Agent Network: Empowering Nodes with Decentralized Communications Capabilities for Adversarial Resilience. (54%)
Ao Liu; Wenshan Li; Tao Li; Beibei Li; Hanyuan Huang; Guangquan Xu; Pan Zhou

http://arxiv.org/abs/2306.07178
Frequency-Based Vulnerability Analysis of Deep Learning Models against Image Corruptions. (13%)
Harshitha Machiraju; Michael H. Herzog; Pascal Frossard

http://arxiv.org/abs/2306.07462
On the Robustness of Removal-Based Feature Attributions. (11%)
Chris Lin; Ian Covert; Su-In Lee

http://arxiv.org/abs/2306.06874
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models. (1%)
Sheng-Yen Chou; Pin-Yu Chen; Tsung-Yi Ho

http://arxiv.org/abs/2306.07992
Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Framework. (99%)
Minglei Yin; Bin Liu; Neil Zhenqiang Gong; Xin Li

http://arxiv.org/abs/2306.06712
Neural Architecture Design and Robustness: A Dataset. (76%)
Steffen Jung; Jovita Lukasik; Margret Keuper

http://arxiv.org/abs/2306.06815
TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models. (68%)
Jiaqi Xue; Mengxin Zheng; Ting Hua; Yilin Shen; Yepeng Liu; Ladislau Boloni; Qian Lou

http://arxiv.org/abs/2306.06462
Boosting Adversarial Robustness using Feature Level Stochastic Smoothing. (92%)
Sravanti Addepalli; Samyak Jain; Gaurang Sriramanan; R. Venkatesh Babu

http://arxiv.org/abs/2306.06359
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations. (83%)
Yonggan Fu; Ye Yuan; Souvik Kundu; Shang Wu; Shunyao Zhang; Yingyan Lin

http://arxiv.org/abs/2306.06485
The Defense of Networked Targets in General Lotto games. (13%)
Adel Aghajan; Keith Paarporn; Jason R. Marden

http://arxiv.org/abs/2306.05873
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions. (84%)
Ezgi Korkmaz; Jonah Brown-Cohen

http://arxiv.org/abs/2306.05923
When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems. (70%)
Emad Efatinasab; Francesco Marchiori; Denis Donadel; Alessandro Brighente; Mauro Conti

http://arxiv.org/abs/2306.05952
Overcoming Adversarial Attacks for Human-in-the-Loop Applications. (45%)
Ryan McCoppin; Marla Kennedy; Platon Lukyanenko; Sean Kennedy

http://arxiv.org/abs/2306.05494
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning. (99%)
Mohamed el Shehaby; Ashraf Matrawy

http://arxiv.org/abs/2306.05225
Boosting Adversarial Transferability by Achieving Flat Local Maxima. (99%)
Zhijin Ge; Hongying Liu; Xiaosen Wang; Fanhua Shang; Yuanyuan Liu

http://arxiv.org/abs/2306.05659
COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models. (93%)
Zihao Tan; Qingliang Chen; Wenbin Zhu; Yongjian Huang

http://arxiv.org/abs/2306.05031
Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations. (83%)
Hyeonjeong Ha; Minseon Kim; Sung Ju Hwang

http://arxiv.org/abs/2306.04984
G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering. (62%)
Hao Yu; Chuan Ma; Meng Liu; Xinwang Liu; Zhe Liu; Ming Ding

http://arxiv.org/abs/2306.04971
A Melting Pot of Evolution and Learning. (41%)
Moshe Sipper; Achiya Elyasaf; Tomer Halperin; Zvika Haramaty; Raz Lapid; Eyal Segal; Itai Tzruia; Snir Vitrack Tamam

http://arxiv.org/abs/2306.04959
FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs. (13%)
Shanshan Han; Baturalp Buyukates; Zijian Hu; Han Jin; Weizhao Jin; Lichao Sun; Xiaoyang Wang; Chulin Xie; Kai Zhang; Qifan Zhang; Yuhui Zhang; Chaoyang He; Salman Avestimehr

http://arxiv.org/abs/2306.05208
PriSampler: Mitigating Property Inference of Diffusion Models. (13%)
Hailong Hu; Jun Pang

http://arxiv.org/abs/2306.05093
Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting. (12%)
Ana-Maria Cretu; Daniel Jones; Montjoye Yves-Alexandre de; Shruti Tople

http://arxiv.org/abs/2306.06136
Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents. (10%)
Ziyuan Zhou; Guanjun Liu

http://arxiv.org/abs/2306.04974
Conservative Prediction via Data-Driven Confidence Minimization. (8%)
Caroline Choi; Fahim Tajwar; Yoonho Lee; Huaxiu Yao; Ananya Kumar; Chelsea Finn

http://arxiv.org/abs/2306.05501
Robust Framework for Explanation Evaluation in Time Series Classification. (2%)
Thu Trang Nguyen; Thach Le Nguyen; Georgiana Ifrim

http://arxiv.org/abs/2306.05079
Enhancing Robustness of AI Offensive Code Generators via Data Augmentation. (2%)
Cristina Improta; Pietro Liguori; Roberto Natella; Bojan Cukic; Domenico Cotroneo

http://arxiv.org/abs/2306.04950
Open Set Relation Extraction via Unknown-Aware Training. (1%)
Jun Zhao; Xin Zhao; Wenyu Zhan; Qi Zhang; Tao Gui; Zhongyu Wei; Yunwen Chen; Xiang Gao; Xuanjing Huang

http://arxiv.org/abs/2306.04192
Extracting Cloud-based Model with Prior Knowledge. (99%)
Shiqian Zhao; Kangjie Chen; Meng Hao; Jian Zhang; Guowen Xu; Hongwei Li; Tianwei Zhang

http://arxiv.org/abs/2306.04874
Expanding Scope: Adapting English Adversarial Attacks to Chinese. (99%)
Hanyu Liu; Chengyuan Cai; Yanjun Qi

http://arxiv.org/abs/2306.04535
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts. (92%)
Xiangjue Dong; Yun He; Ziwei Zhu; James Caverlee

http://arxiv.org/abs/2306.04178
Optimal Transport Model Distributional Robustness. (83%)
Van-Anh Nguyen; Trung Le; Anh Tuan Bui; Thanh-Toan Do; Dinh Phung

http://arxiv.org/abs/2306.04528
PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts. (76%)
Kaijie Zhu; Jindong Wang; Jiaheng Zhou; Zichen Wang; Hao Chen; Yidong Wang; Linyi Yang; Wei Ye; Neil Zhenqiang Gong; Yue Zhang; Xing Xie

http://arxiv.org/abs/2306.04756
A Linearly Convergent GAN Inversion-based Algorithm for Reverse Engineering of Deceptions. (45%)
Darshan Thaker; Paris Giampouras; René Vidal

http://arxiv.org/abs/2306.04431
Faithful Knowledge Distillation. (41%)
Tom A. Lamb; Rudy Brunel; Krishnamurthy DJ Dvijotham; M. Pawan Kumar; Philip H. S. Torr; Francisco Eiras

http://arxiv.org/abs/2306.04581
Divide and Repair: Using Options to Improve Performance of Imitation Learning Against Adversarial Demonstrations. (16%)
Prithviraj Dasgupta

http://arxiv.org/abs/2306.04523
Can current NLI systems handle German word order? Investigating language model performance on a new German challenge set of minimal pairs. (15%)
Ines Reinig; Katja Markert

http://arxiv.org/abs/2306.04252
Adversarial Sample Detection Through Neural Network Transport Dynamics. (10%)
Skander Karkar; Patrick Gallinari; Alain Rakotomamonjy

http://arxiv.org/abs/2306.03430
Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of Filters. (99%)
Xingxing Wei; Shiji Zhao

http://arxiv.org/abs/2306.03600
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations. (97%)
Torsten University of Würzburg Krauß; Alexandra University of Würzburg Dmitrienko

http://arxiv.org/abs/2306.04064
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings. (93%)
Klim Kireev; Maksym Andriushchenko; Carmela Troncoso; Nicolas Flammarion

http://arxiv.org/abs/2306.06123
Adversarial attacks and defenses in explainable artificial intelligence: A survey. (64%)
Hubert Baniecki; Przemyslaw Biecek

http://arxiv.org/abs/2306.03726
Exploring Model Dynamics for Accumulative Poisoning Discovery. (62%)
Jianing Zhu; Xiawei Guo; Jiangchao Yao; Chao Du; Li He; Shuo Yuan; Tongliang Liu; Liang Wang; Bo Han

http://arxiv.org/abs/2306.04109
Membership inference attack with relative decision boundary distance. (33%)
JiaCheng Xu; ChengXiang Tan

http://arxiv.org/abs/2306.03779
Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex. (8%)
Drew Linsley; Ivan F. Rodriguez; Thomas Fel; Michael Arcaro; Saloni Sharma; Margaret Livingstone; Thomas Serre

http://arxiv.org/abs/2306.03528
Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses. (1%)
Jiawen Kang; Jiayi He; Hongyang Du; Zehui Xiong; Zhaohui Yang; Xumin Huang; Shengli Xie

http://arxiv.org/abs/2306.03229
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception. (99%)
Drew Linsley; Pinyuan Feng; Thibaut Boissin; Alekh Karkada Ashok; Thomas Fel; Stephanie Olaiya; Thomas Serre

http://arxiv.org/abs/2306.02895
Evading Black-box Classifiers Without Breaking Eggs. (99%)
Edoardo Debenedetti; Nicholas Carlini; Florian Tramèr

http://arxiv.org/abs/2306.02639
Evaluating robustness of support vector machines with the Lagrangian dual approach. (97%)
Yuting Liu; Hong Gu; Pan Qin

http://arxiv.org/abs/2306.03331
A Robust Likelihood Model for Novelty Detection. (93%)
Ranya Almohsen; Shivang Patel; Donald A. Adjeroh; Gianfranco Doretto

http://arxiv.org/abs/2306.02918
Adversarial Ink: Componentwise Backward Error Attacks on Deep Learning. (86%)
Lucas Beerens; Desmond J. Higham

http://arxiv.org/abs/2306.02618
Enhance Diffusion to Improve Robust Generalization. (76%)
Jianhui Sun; Sanchit Sinha; Aidong Zhang

http://arxiv.org/abs/2306.02980
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations. (68%)
Myeongjun Jang; Bodhisattwa Prasad Majumder; Julian McAuley; Thomas Lukasiewicz; Oana-Maria Camburu

http://arxiv.org/abs/2306.02583
Stable Diffusion is Unstable. (45%)
Chengbin Du; Yanxi Li; Zhongwei Qiu; Chang Xu

http://arxiv.org/abs/2306.02879
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization. (1%)
Yibing Liu; Chris Xing Tian; Haoliang Li; Lei Ma; Shiqi Wang

http://arxiv.org/abs/2306.03269
Security Knowledge-Guided Fuzzing of Deep Learning Libraries. (1%)
Nima Shiri Harzevili; Hung Viet Pham; Song Wang

http://arxiv.org/abs/2306.02775
Input-gradient space particle inference for neural network ensembles. (1%)
Trung Trinh; Markus Heinonen; Luigi Acerbi; Samuel Kaski

http://arxiv.org/abs/2306.02488
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy. (98%)
Xiaoting Li; Lingwei Chen; Dinghao Wu

http://arxiv.org/abs/2306.02482
Aerial Swarm Defense using Interception and Herding Strategies. (1%)
Vishnu S. Chipade; Dimitra Panagou

http://arxiv.org/abs/2306.02021
Towards Black-box Adversarial Example Detection: A Data Reconstruction-based Method. (99%)
Yifei Gao; Zhiyu Lin; Yunfan Yang; Jitao Sang

http://arxiv.org/abs/2306.02165
Learning to Defend by Attacking (and Vice-Versa): Transfer of Learning in Cybersecurity Games. (67%)
Tyler Malloy; Cleotilde Gonzalez

http://arxiv.org/abs/2306.02002
Can Directed Graph Neural Networks be Adversarially Robust? (56%)
Zhichao Hou; Xitong Zhang; Wei Wang; Charu C. Aggarwal; Xiaorui Liu

http://arxiv.org/abs/2306.02064
Flew Over Learning Trap: Learn Unlearnable Samples by Progressive Staged Training. (13%)
Pucheng Dang; Xing Hu; Kaidi Xu; Jinhao Duan; Di Huang; Husheng Han; Rui Zhang; Zidong Du; Qi Guo; Yunji Chen

http://arxiv.org/abs/2306.02080
Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models. (1%)
Shuo Chen; Jindong Gu; Zhen Han; Yunpu Ma; Philip Torr; Volker Tresp

http://arxiv.org/abs/2306.01271
Towards Understanding Clean Generalization and Robust Overfitting in Adversarial Training. (99%)
Binghui Li; Yuanzhi Li

http://arxiv.org/abs/2306.01429
A Closer Look at the Adversarial Robustness of Deep Equilibrium Models. (92%)
Zonghan Yang; Tianyu Pang; Yang Liu

http://arxiv.org/abs/2306.01400
Adaptive Attractors: A Defense Strategy against ML Adversarial Collusion Attacks. (83%)
Jiyi Zhang; Han Fang; Ee-Chien Chang

http://arxiv.org/abs/2306.01655
Poisoning Network Flow Classifiers. (61%)
Giorgio Severi; Simona Boboila; Alina Oprea; John Holodnak; Kendra Kratkiewicz; Jason Matterer

http://arxiv.org/abs/2306.01613
Hyperparameter Learning under Data Poisoning: Analysis of the Influence of Regularization via Multiobjective Bilevel Optimization. (54%)
Javier Carnerero-Cano; Luis Muñoz-González; Phillippa Spencer; Emil C. Lupu

http://arxiv.org/abs/2306.01485
Robust low-rank training via approximate orthonormal constraints. (22%)
Dayana Savostianova; Emanuele Zangrando; Gianluca Ceruti; Francesco Tudisco

http://arxiv.org/abs/2306.01505
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations. (13%)
Dou Hu; Yinan Bao; Lingwei Wei; Wei Zhou; Songlin Hu

http://arxiv.org/abs/2306.01435
Improving Adversarial Robustness of DEQs with Explicit Regulations Along the Neural Dynamics. (11%)
Zonghan Yang; Peng Li; Tianyu Pang; Yang Liu

http://arxiv.org/abs/2306.01342
Covert Communication Based on the Poisoning Attack in Federated Learning. (10%)
Junchuan Liang; Rong Wang

http://arxiv.org/abs/2306.01953
Invisible Image Watermarks Are Provably Removable Using Generative AI. (10%)
Xuandong Zhao; Kexun Zhang; Zihao Su; Saastha Vasan; Ilya Grishchenko; Christopher Kruegel; Giovanni Vigna; Yu-Xiang Wang; Lei Li

http://arxiv.org/abs/2306.01273
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of Transformations. (3%)
Hoang-Quoc Nguyen-Son; Seira Hidano; Kazuhide Fukushima; Shinsaku Kiyomoto; Isao Echizen

http://arxiv.org/abs/2306.01902
Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation. (2%)
Zhengyue Zhao; Jinhao Duan; Xing Hu; Kaidi Xu; Chenan Wang; Rui Zhang; Zidong Du; Qi Guo; Yunji Chen

http://arxiv.org/abs/2306.01697
MutateNN: Mutation Testing of Image Recognition Models Deployed on Hardware Accelerators. (1%)
Nikolaos Louloudakis; Perry Gibson; José Cano; Ajitha Rajan

http://arxiv.org/abs/2306.01364
Towards Robust GAN-generated Image Detection: a Multi-view Completion Representation. (1%)
Chi Liu; Tianqing Zhu; Sheng Shen; Wanlei Zhou

http://arxiv.org/abs/2306.01925
Improving the generalizability and robustness of large-scale traffic signal control. (1%)
Tianyu Shi; Francois-Xavier Devailly; Denis Larocque; Laurent Charlin

http://arxiv.org/abs/2306.01809
Adversarial Attack Based on Prediction-Correction. (99%)
Chen Wan; Fangjun Huang

http://arxiv.org/abs/2306.00353
Constructing Semantics-Aware Adversarial Examples with Probabilistic Perspective. (98%)
Andi Zhang; Damon Wischik

http://arxiv.org/abs/2306.01125
Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations. (96%)
Yang Sui; Zhuohang Li; Ding Ding; Xiang Pan; Xiaozhong Xu; Shan Liu; Zhenzhong Chen

http://arxiv.org/abs/2306.00974
Intriguing Properties of Text-guided Diffusion Models. (92%)
Qihao Liu; Adam Kortylewski; Yutong Bai; Song Bai; Alan Yuille

http://arxiv.org/abs/2306.00816
Robust Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers. (82%)
Ruotong Wang; Hongrui Chen; Zihao Zhu; Li Liu; Yong Zhang; Yanbo Fan; Baoyuan Wu

http://arxiv.org/abs/2306.01090
Improving the Robustness of Summarization Systems with Dual Augmentation. (76%)
Xiuying Chen; Guodong Long; Chongyang Tao; Mingzhe Li; Xin Gao; Chengqi Zhang; Xiangliang Zhang

http://arxiv.org/abs/2306.00687
Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review. (38%)
Mathias Lundteigen Mohus; Jinyue Li

http://arxiv.org/abs/2306.00578
Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk? (13%)
Iyiola E. Olatunji; Anmar Hizber; Oliver Sihlovec; Megha Khosla

http://arxiv.org/abs/2306.00349
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception. (13%)
Jiachen Sun; Haizhong Zheng; Qingzhao Zhang; Atul Prakash; Z. Morley Mao; Chaowei Xiao

http://arxiv.org/abs/2306.06112
ModelObfuscator: Obfuscating Model Information to Protect Deployed ML-based Systems. (4%)
Mingyi Zhou; Xiang Gao; Jing Wu; John Grundy; Xiao Chen; Chunyang Chen; Li Li

http://arxiv.org/abs/2305.19593
Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis. (98%)
Mst Shapna Akter; Hossain Shahriar; Iysa Iqbal; MD Hossain; M. A. Karim; Victor Clincy; Razvan Voicu

http://arxiv.org/abs/2306.00042
Graph-based methods coupled with specific distributional distances for adversarial attack detection. (98%)
Dwight Nwaigwe; Lucrezia Carboni; Martial Mermillod; Sophie Achard; Michel Dojat

http://arxiv.org/abs/2306.00314
Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach. (98%)
Mohammed Alkhowaiter; Hisham Kholidy; Mnassar Alyami; Abdulmajeed Alghamdi; Cliff Zou

http://arxiv.org/abs/2305.19607
Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems. (54%)
Ashim Gupta; Amrith Krishna

http://arxiv.org/abs/2305.20043
Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning. (26%)
Deniz Koyuncu; Alex Gittens; Bülent Yener; Moti Yung

http://arxiv.org/abs/2305.19713
Red Teaming Language Model Detectors with Language Models. (15%)
Zhouxing Shi; Yihan Wang; Fan Yin; Xiangning Chen; Kai-Wei Chang; Cho-Jui Hsieh

http://arxiv.org/abs/2305.19774
Ambiguity in solving imaging inverse problems with deep learning based operators. (1%)
Davide Evangelista; Elena Morotti; Elena Loli Piccolomini; James Nagy

http://arxiv.org/abs/2305.19020
Pseudo-Siamese Network based Timbre-reserved Black-box Adversarial Attack in Speaker Identification. (99%)
Qing Wang; Jixun Yao; Ziqian Wang; Pengcheng Guo; Lei Xie

http://arxiv.org/abs/2305.19330
Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation. (64%)
Josef Jon; Ondřej Bojar

http://arxiv.org/abs/2305.19101
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness. (56%)
Suraj Srinivas; Sebastian Bordt; Hima Lakkaraju

http://arxiv.org/abs/2305.19521
Incremental Randomized Smoothing Certification. (33%)
Shubham Ugare; Tarun Suresh; Debangshu Banerjee; Gagandeep Singh; Sasa Misailovic

http://arxiv.org/abs/2305.19083
Defense Against Shortest Path Attacks. (16%)
Benjamin A. Miller; Zohair Shafi; Wheeler Ruml; Yevgeniy Vorobeychik; Tina Eliassi-Rad; Scott Alfeld

http://arxiv.org/abs/2305.18933
A Multilingual Evaluation of NER Robustness to Adversarial Inputs. (15%)
Akshay Srinivasan; Sowmya Vajjala

http://arxiv.org/abs/2305.18779
It begins with a boundary: A geometric view on probabilistically robust learning. (8%)
Leon Bungert; Nicolás García Trillos; Matt Jacobs; Daniel McKenzie; Đorđe Nikolić; Qingsong Wang

http://arxiv.org/abs/2305.19218
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models. (2%)
Zichen Wang; Rishab Balasubramanian; Hui Yuan; Chenyu Song; Mengdi Wang; Huazheng Wang

http://arxiv.org/abs/2305.18840
Learning Perturbations to Explain Time Series Predictions. (1%)
Joseph Enguehard

http://arxiv.org/abs/2305.18503
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework. (99%)
Yangyi Chen; Hongcheng Gao; Ganqu Cui; Lifan Yuan; Dehan Kong; Hanlu Wu; Ning Shi; Bo Yuan; Longtao Huang; Hui Xue; Zhiyuan Liu; Maosong Sun; Heng Ji

http://arxiv.org/abs/2305.17939
Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action Recognition. (92%)
Nariki Tanaka; Hiroshi Kera; Kazuhiko Kawamoto

http://arxiv.org/abs/2305.18585
Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models. (92%)
Pranath Reddy Kumbam; Sohaib Uddin Syed; Prashanth Thamminedi; Suhas Harish; Ian Perera; Bonnie J. Dorr

http://arxiv.org/abs/2305.18651
UMD: Unsupervised Model Detection for X2X Backdoor Attacks. (81%)
Zhen Xiang; Zidi Xiong; Bo Li

http://arxiv.org/abs/2305.18462
Membership Inference Attacks against Language Models via Neighbourhood Comparison. (73%)
Justus Mattern; Fatemehsadat Mireshghallah; Zhijing Jin; Bernhard Schölkopf; Mrinmaya Sachan; Taylor Berg-Kirkpatrick

http://arxiv.org/abs/2306.05358
Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System. (41%)
Jiwei Guan; Lei Pan; Chen Wang; Shui Yu; Longxiang Gao; Xi Zheng

http://arxiv.org/abs/2306.00010
Trainable and Explainable Simplicial Map Neural Networks. (41%)
Eduardo Paluzo-Hidalgo; Miguel A. Gutiérrez-Naranjo; Rocio Gonzalez-Diaz

http://arxiv.org/abs/2305.18543
Robust Lipschitz Bandits to Adversarial Corruptions. (11%)
Yue Kang; Cho-Jui Hsieh; Thomas C. M. Lee

http://arxiv.org/abs/2305.18216
Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection. (8%)
Roman Kessler; Kiran Raja; Juan Tapia; Christoph Busch

http://arxiv.org/abs/2305.17688
Amplification trojan network: Attack deep neural networks by amplifying their inherent weakness. (99%)
Zhanhao Hu; Jun Zhu; Bo Zhang; Xiaolin Hu

http://arxiv.org/abs/2305.17868
NaturalFinger: Generating Natural Fingerprint with Generative Adversarial Networks. (92%)
Kang Yang; Kunhao Lai

http://arxiv.org/abs/2305.18384
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study. (41%)
Yiqi Zhong; Xianming Liu; Deming Zhai; Junjun Jiang; Xiangyang Ji

http://arxiv.org/abs/2305.17826
NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models. (38%)
Kai Mei; Zheng Li; Zhenting Wang; Yang Zhang; Shiqing Ma

http://arxiv.org/abs/2305.17667
Choose your Data Wisely: A Framework for Semantic Counterfactuals. (13%)
Edmund Dervakos; Konstantinos Thomas; Giorgos Filandrianos; Giorgos Stamou

http://arxiv.org/abs/2305.18377
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning. (5%)
Jingfeng Zhang; Bo Song; Haohan Wang; Bo Han; Tongliang Liu; Lei Liu; Masashi Sugiyama

http://arxiv.org/abs/2305.18440
Black-Box Anomaly Attribution. (1%)
Tsuyoshi Idé; Naoki Abe

http://arxiv.org/abs/2306.06071
Adversarial Attack On Yolov5 For Traffic And Road Sign Detection. (99%)
Sanyam Jain

http://arxiv.org/abs/2306.01762
Pre-trained transformer for adversarial purification. (99%)
Kai Wu; Yujian Betterest Li; Xiaoyu Zhang; Handing Wang; Jing Liu

http://arxiv.org/abs/2305.17528
Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection. (98%)
Nils Palumbo; Yang Guo; Xi Wu; Jiefeng Chen; Yingyu Liang; Somesh Jha

http://arxiv.org/abs/2305.17440
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making. (92%)
Xuanjie Fang; Sijie Cheng; Yang Liu; Wei Wang

http://arxiv.org/abs/2305.17438
On the Importance of Backbone to the Adversarial Robustness of Object Detectors. (83%)
Xiao Li; Hang Chen; Xiaolin Hu

http://arxiv.org/abs/2305.17380
No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions. (2%)
Tiancheng Jin; Junyan Liu; Chloé Rouyer; William Chang; Chen-Yu Wei; Haipeng Luo

http://arxiv.org/abs/2305.17421
FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition. (1%)
Marawan Elbatel; Robert Martí; Xiaomeng Li

http://arxiv.org/abs/2305.16934
On Evaluating Adversarial Robustness of Large Vision-Language Models. (99%)
Yunqing Zhao; Tianyu Pang; Chao Du; Xiao Yang; Chongxuan Li; Ngai-Man Cheung; Min Lin

http://arxiv.org/abs/2305.17000
Leveraging characteristics of the output probability distribution for identifying adversarial audio examples. (98%)
Matías P. Pizarro B.; Dorothea Kolossa; Asja Fischer

http://arxiv.org/abs/2305.17342
Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in Multi-Agent RL. (96%)
Xiangyu Liu; Souradip Chakraborty; Yanchao Sun; Furong Huang

http://arxiv.org/abs/2305.16998
A Tale of Two Approximations: Tightening Over-Approximation for DNN Robustness Verification via Under-Approximation. (45%)
Zhiyi Xue; Si Liu; Zhaodi Zhang; Yiting Wu; Min Zhang

http://arxiv.org/abs/2305.17071
Adversarial Attacks on Online Learning to Rank with Click Feedback. (38%)
Jinhang Zuo; Zhiyao Zhang; Zhiyong Wang; Shuai Li; Mohammad Hajiesmaili; Adam Wierman

http://arxiv.org/abs/2306.06075
DeepSeaNet: Improving Underwater Object Detection using EfficientDet. (2%)
Sanyam Jain

http://arxiv.org/abs/2305.16818
Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles. (1%)
H M Sabbir Ahmad; Ehsan Sabouni; Wei Xiao; Christos G. Cassandras; Wenchao Li

http://arxiv.org/abs/2305.16617
Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model. (1%)
Zhijie Deng; Hongcheng Gao; Yibo Miao; Hao Zhang

http://arxiv.org/abs/2305.15792
IDEA: Invariant Defense for Graph Adversarial Robustness. (99%)
Shuchang Tao; Qi Cao; Huawei Shen; Yunfan Wu; Bingbing Xu; Xueqi Cheng

http://arxiv.org/abs/2305.16444
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text. (98%)
Ashim Gupta; Carter Wood Blum; Temma Choji; Yingjie Fei; Shalin Shah; Alakananda Vempala; Vivek Srikumar

http://arxiv.org/abs/2305.16494
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability. (98%)
Haotian Xue; Alexandre Araujo; Bin Hu; Yongxin Chen

http://arxiv.org/abs/2305.15709
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation. (96%)
Xianghao Jiao; Yaohua Liu; Jiaxin Gao; Xinyuan Chu; Risheng Liu; Xin Fan

http://arxiv.org/abs/2306.06107
Adversarial Attacks on Leakage Detectors in Water Distribution Networks. (86%)
Paul Stahlhofen; André Artelt; Luca Hermes; Barbara Hammer

http://arxiv.org/abs/2306.06081
CARSO: Counter-Adversarial Recall of Synthetic Observations. (86%)
Emanuele Ballarin; Alessio Ansuini; Luca Bortolussi

http://arxiv.org/abs/2305.16220
On the Robustness of Segment Anything. (73%)
Yihao Huang; Yue Cao; Tianlin Li; Felix Juefei-Xu; Di Lin; Ivor W. Tsang; Yang Liu; Qing Guo

http://arxiv.org/abs/2305.16035
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score. (67%)
Shuhai Zhang; Feng Liu; Jiahao Yang; Yifan Yang; Changsheng Li; Bo Han; Mingkui Tan

http://arxiv.org/abs/2305.15698
Rethinking Diversity in Deep Neural Network Testing. (50%)
Zi Wang; Jihye Choi; Ke Wang; Somesh Jha

http://arxiv.org/abs/2305.16503
IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks. (13%)
Xuanli He; Jun Wang; Benjamin Rubinstein; Trevor Cohn

http://arxiv.org/abs/2305.16310
Securing Deep Generative Models with Universal Adversarial Signature. (2%)
Yu Zeng; Mo Zhou; Yuan Xue; Vishal M. Patel

http://arxiv.org/abs/2305.15775
Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace. (1%)
Jinyung Hong; Keun Hee Park; Theodore P. Pavlic

http://arxiv.org/abs/2305.15587
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks. (99%)
Salijona Dyrmishi; Salah Ghamizi; Maxime Cordy

http://arxiv.org/abs/2305.15241
Robust Classification via a Single Diffusion Model. (99%)
Huanran Chen; Yinpeng Dong; Zhengyi Wang; Xiao Yang; Chengqi Duan; Hang Su; Jun Zhu

http://arxiv.org/abs/2305.14846
Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup. (99%)
Junyoung Byun; Myung-Joon Kwon; Seungju Cho; Yoonji Kim; Changick Kim

http://arxiv.org/abs/2305.15563
Fantastic DNN Classifiers and How to Identify them without Data. (91%)
Nathaniel Dean; Dilip Sarkar

http://arxiv.org/abs/2305.14950
Adversarial Demonstration Attacks on Large Language Models. (88%)
Jiongxiao Wang; Zichen Liu; Keun Hee Park; Muhao Chen; Chaowei Xiao

http://arxiv.org/abs/2305.15203
Relating Implicit Bias and Adversarial Attacks through Intrinsic Dimension. (86%)
Lorenzo Basile; Nikos Karantzas; Alberto D'Onofrio; Luca Bortolussi; Alex Rodriguez; Fabio Anselmi

http://arxiv.org/abs/2305.14700
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness. (76%)
Ziuhi Wu; Haichang Gao; Bingqian Zhou; Ping Wang

http://arxiv.org/abs/2305.14876
Reconstructive Neuron Pruning for Backdoor Defense. (75%)
Yige Li; Xixiang Lyu; Xingjun Ma; Nodens Koren; Lingjuan Lyu; Bo Li; Yu-Gang Jiang

http://arxiv.org/abs/2305.15119
Another Dead End for Morphological Tags? Perturbed Inputs and Parsing. (74%)
Alberto Muñoz-Ortiz; David Vilares

http://arxiv.org/abs/2305.14710
Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models. (50%)
Jiashu Xu; Mingyu Derek Ma; Fei Wang; Chaowei Xiao; Muhao Chen

http://arxiv.org/abs/2305.14910
From Shortcuts to Triggers: Backdoor Defense with Denoised PoE. (47%)
Qin Liu; Fei Wang; Chaowei Xiao; Muhao Chen

http://arxiv.org/abs/2305.14763
Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models. (22%)
Natalie Shapira; Mosh Levy; Seyed Hossein Alavi; Xuhui Zhou; Yejin Choi; Yoav Goldberg; Maarten Sap; Vered Shwartz

http://arxiv.org/abs/2305.14984
Adversarial robustness of amortized Bayesian inference. (11%)
Manuel Glöckler; Michael Deistler; Jakob H. Macke

http://arxiv.org/abs/2305.14851
Sharpness-Aware Data Poisoning Attack. (10%)
Pengfei He; Han Xu; Jie Ren; Yingqian Cui; Hui Liu; Charu C. Aggarwal; Jiliang Tang

http://arxiv.org/abs/2305.15508
How to fix a broken confidence estimator: Evaluating post-hoc methods for selective classification with deep neural networks. (3%)
Luís Felipe P. Cattelan; Danilo Silva

http://arxiv.org/abs/2305.14902
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection. (1%)
Yuxia Wang; Jonibek Mansurov; Petar Ivanov; Jinyan Su; Artem Shelmanov; Akim Tsvigun; Chenxi Whitehouse; Osama Mohammed Afzal; Tarek Mahmoud; Toru Sasaki; Thomas Arnold; Alham Fikri Aji; Nizar Habash; Iryna Gurevych; Preslav Nakov

http://arxiv.org/abs/2305.15047
Ghostbuster: Detecting Text Ghostwritten by Large Language Models. (1%)
Vivek Verma; Eve Fleisig; Nicholas Tomlin; Dan Klein

http://arxiv.org/abs/2305.14188
The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attacks. (99%)
Iuri Frosio; Jan Kautz

http://arxiv.org/abs/2305.13678
Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning. (99%)
Minchan Kwon; Kangil Kim

http://arxiv.org/abs/2305.14097
QFA2SR: Query-Free Adversarial Transfer Attacks to Speaker Recognition Systems. (98%)
Guangke Chen; Yedi Zhang; Zhe Zhao; Fu Song

http://arxiv.org/abs/2305.13991
Expressive Losses for Verified Robustness via Convex Combinations. (95%)
Palma Alessandro De; Rudy Bunel; Krishnamurthy Dvijotham; M. Pawan Kumar; Robert Stanforth; Alessio Lomuscio

http://arxiv.org/abs/2305.14165
Impact of Light and Shadow on Robustness of Deep Neural Networks. (87%)
Chengyin Hu; Weiwen Shi; Chao Li; Jialiang Sun; Donghua Wang; Junqi Wu; Guijian Tang

http://arxiv.org/abs/2305.14695
A Causal View of Entity Bias in (Large) Language Models. (10%)
Fei Wang; Wenjie Mo; Yiwei Wang; Wenxuan Zhou; Muhao Chen

http://arxiv.org/abs/2305.12906
Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Space. (99%)
BoYang Zheng

http://arxiv.org/abs/2305.12825
Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation. (99%)
Kira Maag; Asja Fischer

http://arxiv.org/abs/2305.12770
FGAM:Fast Adversarial Malware Generation Method Based on Gradient Sign. (98%)
Kun Li; Fan Zhang; Wei Guo

http://arxiv.org/abs/2305.13548
Attribute-Guided Encryption with Facial Texture Masking. (98%)
Chun Pong Lau; Jiang Liu; Rama Chellappa

http://arxiv.org/abs/2305.13625
DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection. (98%)
Jiang Liu; Chun Pong Lau; Rama Chellappa

http://arxiv.org/abs/2305.12872
Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game. (93%)
Simin Li; Jun Guo; Jingqiao Xiu; Xini Yu; Jiakai Wang; Aishan Liu; Yaodong Yang; Xianglong Liu

http://arxiv.org/abs/2305.12863
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks. (88%)
Simin Li; Shuing Zhang; Gujun Chen; Dong Wang; Pu Feng; Jiakai Wang; Aishan Liu; Xin Yi; Xianglong Liu

http://arxiv.org/abs/2305.12859
Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors. (54%)
Pia Hanfeld; Marina M. -C. Höhne; Michael Bussmann; Wolfgang Hönig

http://arxiv.org/abs/2305.13508
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound Propagation. (50%)
Haitham Khedr; Yasser Shoukry

http://arxiv.org/abs/2305.12804
The defender's perspective on automatic speaker verification: An overview. (22%)
Haibin Wu; Jiawen Kang; Lingwei Meng; Helen Meng; Hung-yi Lee

http://arxiv.org/abs/2305.13584
Model Stealing Attack against Multi-Exit Networks. (10%)
Li Pan; Lv Peizhuo; Chen Kai; Cai Yuling; Xiang Fan; Zhang Shengzhi

http://arxiv.org/abs/2305.13651
Adversarial Defenses via Vector Quantization. (8%)
Zhiyi Dong; Yongyi Mao

http://arxiv.org/abs/2305.14384
Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models. (2%)
Alicia Parrish; Hannah Rose Kirk; Jessica Quaye; Charvi Rastogi; Max Bartolo; Oana Inel; Juan Ciro; Rafael Mosquera; Addison Howard; Will Cukierski; D. Sculley; Vijay Janapa Reddi; Lora Aroyo

http://arxiv.org/abs/2305.13257
Watermarking Classification Dataset for Copyright Protection. (1%)
Yixin Liu; Hongsheng Hu; Xun Chen; Xuyun Zhang; Lichao Sun

http://arxiv.org/abs/2305.13535
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals. (1%)
Ananth Balashankar; Xuezhi Wang; Yao Qin; Ben Packer; Nithum Thain; Jilin Chen; Ed H. Chi; Alex Beutel

http://arxiv.org/abs/2305.13520
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation. (1%)
Emirhan Kurtulus; Zichao Li; Yann Dauphin; Ekin Dogus Cubuk

http://arxiv.org/abs/2305.13605
Adaptive Face Recognition Using Adversarial Information Network. (1%)
Mei Wang; Weihong Deng

http://arxiv.org/abs/2305.12683
Mist: Towards Improved Adversarial Examples for Diffusion Models. (99%)
Chumeng Liang; Xiaoyu Wu

http://arxiv.org/abs/2305.12351
Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack. (81%)
Christopher Burger; Lingwei Chen; Thai Le

http://arxiv.org/abs/2305.12590
FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN Accelerators through Fault-Aware Quantization. (1%)
Muhammad Abdullah Hanif; Muhammad Shafique

http://arxiv.org/abs/2305.12228
Dynamic Transformers Provide a False Sense of Efficiency. (92%)
Yiming Chen; Simin Chen; Zexin Li; Wei Yang; Cong Liu; Robby T. Tan; Haizhou Li

http://arxiv.org/abs/2305.12118
Annealing Self-Distillation Rectification Improves Adversarial Training. (76%)
Yu-Yu Wu; Hung-Jui Wang; Shang-Tse Chen

http://arxiv.org/abs/2305.12100
Stability, Generalization and Privacy: Precise Analysis for Random and NTK Features. (8%)
Simone Bombari; Marco Mondelli

http://arxiv.org/abs/2305.12066
Multi-Task Models Adversarial Attacks. (98%)
Lijun Zhang; Xiao Liu; Kaleel Mahmood; Caiwen Ding; Hui Guan

http://arxiv.org/abs/2305.11618
DAP: A Dynamic Adversarial Patch for Evading Person Detectors. (92%)
Amira Guesmi; Ruitian Ding; Muhammad Abdullah Hanif; Ihsen Alouani; Muhammad Shafique

http://arxiv.org/abs/2305.11624
Efficient ConvBN Blocks for Transfer Learning and Beyond. (67%)
Kaichao You; Guo Qin; Anchang Bao; Meng Cao; Ping Huang; Jiulong Shan; Mingsheng Long

http://arxiv.org/abs/2305.11596
Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation. (8%)
Xuanli He; Qiongkai Xu; Jun Wang; Benjamin Rubinstein; Trevor Cohn

http://arxiv.org/abs/2305.11733
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment. (5%)
Mengke Li; Yiu-ming Cheung; Yang Lu

http://arxiv.org/abs/2305.12082
SneakyPrompt: Evaluating Robustness of Text-to-image Generative Models' Safety Filters. (4%)
Yuchen Yang; Bo Hui; Haolin Yuan; Neil Gong; Yinzhi Cao

http://arxiv.org/abs/2305.11602
Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing. (2%)
Yisong Xiao; Aishan Liu; Tianlin Li; Xianglong Liu

http://arxiv.org/abs/2305.11759
Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning. (1%)
Mustafa Safa Ozdayi; Charith Peris; Jack FitzGerald; Christophe Dupuy; Jimit Majmudar; Haidar Khan; Rahil Parikh; Rahul Gupta

http://arxiv.org/abs/2305.11039
Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation. (99%)
Soumyadeep Hore; Jalal Ghadermazi; Diwas Paudel; Ankit Shah; Tapas K. Das; Nathaniel D. Bastian

http://arxiv.org/abs/2305.10766
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend. (99%)
Chong Yu; Tao Chen; Zhongxue Gan

http://arxiv.org/abs/2305.10929
Architecture-agnostic Iterative Black-box Certified Defense against Adversarial Patches. (99%)
Di Yang; Yihao Huang; Qing Guo; Felix Juefei-Xu; Ming Hu; Yang Liu; Geguang Pu

http://arxiv.org/abs/2305.10856
Towards an Accurate and Secure Detector against Adversarial Perturbations. (99%)
Chao Wang; Shuren Qi; Zhiqiu Huang; Yushu Zhang; Xiaochun Cao

http://arxiv.org/abs/2305.11347
Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning. (99%)
Elise Bishoff; Charles Godfrey; Myles McKay; Eleanor Byler

http://arxiv.org/abs/2305.10862
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses. (98%)
Joana C. Costa; Tiago Roxo; Hugo Proença; Pedro R. M. Inácio

http://arxiv.org/abs/2305.10906
RobustFair: Adversarial Evaluation through Fairness Confusion Directed Gradient Search. (93%)
Xuran Li; Peng Wu; Kaixiang Dong; Zhen Zhang

http://arxiv.org/abs/2305.11132
Attacks on Online Learners: a Teacher-Student Analysis. (54%)
Riccardo Giuseppe Margiotta; Sebastian Goldt; Guido Sanguinetti

http://arxiv.org/abs/2305.11275
Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture. (47%)
Galen Pogoncheff; Jacob Granley; Michael Beyeler

http://arxiv.org/abs/2305.10701
Zero-Day Backdoor Attack against Text-to-Image Diffusion Models via Personalization. (2%)
Yihao Huang; Qing Guo; Felix Juefei-Xu

http://arxiv.org/abs/2305.10847
Large Language Models can be Guided to Evade AI-Generated Text Detection. (1%)
Ning Lu; Shengcai Liu; Rui He; Ke Tang

http://arxiv.org/abs/2305.10691
Re-thinking Data Availablity Attacks Against Deep Neural Networks. (1%)
Bin Fang; Bo Li; Shuang Wu; Ran Yi; Shouhong Ding; Lizhuang Ma

http://arxiv.org/abs/2305.11229
TrustSER: On the Trustworthiness of Fine-tuning Pre-trained Speech Embeddings For Speech Emotion Recognition. (1%)
Tiantian Feng; Rajat Hebbar; Shrikanth Narayanan

http://arxiv.org/abs/2305.10665
Content-based Unrestricted Adversarial Attack. (99%)
Zhaoyu Chen; Bo Li; Shuang Wu; Kaixun Jiang; Shouhong Ding; Wenqiang Zhang

http://arxiv.org/abs/2305.10388
Raising the Bar for Certified Adversarial Robustness with Diffusion Models. (95%)
Thomas Altstidl; David Dobre; Björn Eskofier; Gauthier Gidel; Leo Schwinn

http://arxiv.org/abs/2305.09956
The Adversarial Consistency of Surrogate Risks for Binary Classification. (10%)
Natalie Frank; Jonathan Niles-Weed

http://arxiv.org/abs/2305.10406
Variational Classification. (1%)
Shehzaad Dhuliawala; Mrinmaya Sachan; Carl Allen

http://arxiv.org/abs/2305.11186
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt. (1%)
Zhaozhuo Xu; Zirui Liu; Beidi Chen; Yuxin Tang; Jue Wang; Kaixiong Zhou; Xia Hu; Anshumali Shrivastava

http://arxiv.org/abs/2305.10403
PaLM 2 Technical Report. (1%)
Rohan Anil; Andrew M. Dai; Orhan Firat; Melvin Johnson; Dmitry Lepikhin; Alexandre Passos; Siamak Shakeri; Emanuel Taropa; Paige Bailey; Zhifeng Chen; Eric Chu; Jonathan H. Clark; Laurent El Shafey; Yanping Huang; Kathy Meier-Hellstern; Gaurav Mishra; Erica Moreira; Mark Omernick; Kevin Robinson; Sebastian Ruder; Yi Tay; Kefan Xiao; Yuanzhong Xu; Yujing Zhang; Gustavo Hernandez Abrego; Junwhan Ahn; Jacob Austin; Paul Barham; Jan Botha; James Bradbury; Siddhartha Brahma; Kevin Brooks; Michele Catasta; Yong Cheng; Colin Cherry; Christopher A. Choquette-Choo; Aakanksha Chowdhery; Clément Crepy; Shachi Dave; Mostafa Dehghani; Sunipa Dev; Jacob Devlin; Mark Díaz; Nan Du; Ethan Dyer; Vlad Feinberg; Fangxiaoyu Feng; Vlad Fienber; Markus Freitag; Xavier Garcia; Sebastian Gehrmann; Lucas Gonzalez; Guy Gur-Ari; Steven Hand; Hadi Hashemi; Le Hou; Joshua Howland; Andrea Hu; Jeffrey Hui; Jeremy Hurwitz; Michael Isard; Abe Ittycheriah; Matthew Jagielski; Wenhao Jia; Kathleen Kenealy; Maxim Krikun; Sneha Kudugunta; Chang Lan; Katherine Lee; Benjamin Lee; Eric Li; Music Li; Wei Li; YaGuang Li; Jian Li; Hyeontaek Lim; Hanzhao Lin; Zhongtao Liu; Frederick Liu; Marcello Maggioni; Aroma Mahendru; Joshua Maynez; Vedant Misra; Maysam Moussalem; Zachary Nado; John Nham; Eric Ni; Andrew Nystrom; Alicia Parrish; Marie Pellat; Martin Polacek; Alex Polozov; Reiner Pope; Siyuan Qiao; Emily Reif; Bryan Richter; Parker Riley; Alex Castro Ros; Aurko Roy; Brennan Saeta; Rajkumar Samuel; Renee Shelby; Ambrose Slone; Daniel Smilkov; David R. So; Daniel Sohn; Simon Tokumine; Dasha Valter; Vijay Vasudevan; Kiran Vodrahalli; Xuezhi Wang; Pidong Wang; Zirui Wang; Tao Wang; John Wieting; Yuhuai Wu; Kelvin Xu; Yunhan Xu; Linting Xue; Pengcheng Yin; Jiahui Yu; Qiao Zhang; Steven Zheng; Ce Zheng; Weikang Zhou; Denny Zhou; Slav Petrov; Yonghui Wu

http://arxiv.org/abs/2305.13208
Iterative Adversarial Attack on Image-guided Story Ending Generation. (99%)
Youze Wang; Wenbo Hu; Richang Hong

http://arxiv.org/abs/2305.09305
Releasing Inequality Phenomena in $L_{\infty}$-Adversarial Training via Input Gradient Distillation. (98%)
Junxi Chen; Junhao Dong; Xiaohua Xie

http://arxiv.org/abs/2305.09179
Ortho-ODE: Enhancing Robustness and of Neural ODEs against Adversarial Attacks. (54%)
Vishal Purohit

http://arxiv.org/abs/2305.09241
Unlearnable Examples Give a False Sense of Security: Piercing through Unexploitable Data with Learnable Examples. (50%)
Wan Jiang; Yunfeng Diao; He Wang; Jianxin Sun; Meng Wang; Richang Hong

http://arxiv.org/abs/2305.08840
Attacking Perceptual Similarity Metrics. (99%)
Abhijay Ghildyal; Feng Liu

http://arxiv.org/abs/2305.08439
Exploiting Frequency Spectrum of Adversarial Images for General Robustness. (96%)
Chun Yang Tan; Kazuhiko Kawamoto; Hiroshi Kera

http://arxiv.org/abs/2305.08960
Training Neural Networks without Backpropagation: A Deeper Dive into the Likelihood Ratio Method. (4%)
Jinyang Jiang; Zeliang Zhang; Chenliang Xu; Zhaofei Yu; Yijie Peng

http://arxiv.org/abs/2305.10235
Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility. (1%)
Wentao Ye; Mingfeng Ou; Tianyi Li; Yipeng chen; Xuetao Ma; Yifan Yanggong; Sai Wu; Jie Fu; Gang Chen; Haobo Wang; Junbo Zhao

http://arxiv.org/abs/2305.08192
Diffusion Models for Imperceptible and Transferable Adversarial Attack. (99%)
Jianqi Chen; Hao Chen; Keyan Chen; Yilan Zhang; Zhengxia Zou; Zhenwei Shi

http://arxiv.org/abs/2305.08076
Improving Defensive Distillation using Teacher Assistant. (96%)
Maniratnam Mandal; Suna Gao

http://arxiv.org/abs/2305.08183
Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures. (82%)
Wei Yuan; Shilong Yuan; Chaoqun Yang; Quoc Viet Hung Nguyen; Hongzhi Yin

http://arxiv.org/abs/2305.08883
Watermarking Text Generated by Black-Box Language Models. (9%)
Xi Yang; Kejiang Chen; Weiming Zhang; Chang Liu; Yuang Qi; Jie Zhang; Han Fang; Nenghai Yu

http://arxiv.org/abs/2305.08034
DNN-Defender: An in-DRAM Deep Neural Network Defense Mechanism for Adversarial Weight Attack. (86%)
Ranyang Zhou; Sabbir Ahmed; Adnan Siraj Rakin; Shaahin Angizi

http://arxiv.org/abs/2305.08031
On enhancing the robustness of Vision Transformers: Defensive Diffusion. (76%)
Raza Imam; Muhammad Huzaifa; Mohammed El-Amine Azz

http://arxiv.org/abs/2305.09684
Decision-based iterative fragile watermarking for model integrity verification. (50%)
Zhaoxia Yin; Heng Yin; Hang Su; Xinpeng Zhang; Zhenzhe Gao

http://arxiv.org/abs/2305.07308
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation. (73%)
Jialiang Sun; Wen Yao; Tingsong Jiang; Xiaoqian Chen

http://arxiv.org/abs/2305.09679
Adversarial Security and Differential Privacy in mmWave Beam Prediction in 6G networks. (68%)
Ghanta Sai Krishna; Kundrapu Supriya; Sanskar Singh; Sabur Baidya

http://arxiv.org/abs/2305.07687
Mastering Percolation-like Games with Deep Learning. (1%)
Michael M. Danziger; Omkar R. Gojala; Sean P. Cornelius

http://arxiv.org/abs/2305.06716
Distracting Downpour: Adversarial Weather Attacks for Motion Estimation. (74%)
Jenny Schmalfuss; Lukas Mehl; Andrés Bruhn

http://arxiv.org/abs/2306.06209
Backdoor Attack with Sparse and Invisible Trigger. (68%)
Yinghua Gao; Yiming Li; Xueluan Gong; Shu-Tao Xia; Qian Wang

http://arxiv.org/abs/2305.06947
Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting. (1%)
Joshua Smailes; Sebastian Kohler; Simon Birnbach; Martin Strohmeier; Ivan Martinovic

http://arxiv.org/abs/2305.05896
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search. (99%)
Jie Zhang; Wei Ma; Qiang Hu; Shangqing Liu; Xiaofei Xie; Yves Le Traon; Yang Liu

http://arxiv.org/abs/2305.06540
Inter-frame Accelerate Attack against Video Interpolation Models. (99%)
Junpei Liao; Zhikai Chen; Liang Yi; Wenyuan Yang; Baoyuan Wu; Xiaochun Cao

http://arxiv.org/abs/2305.06522
Randomized Smoothing with Masked Inference for Adversarially Robust Text Classifications. (98%)
Han Cheol Moon; Shafiq Joty; Ruochen Zhao; Megh Thakkar; Xu Chi

http://arxiv.org/abs/2305.09677
Stealthy Low-frequency Backdoor Attack against Deep Neural Networks. (80%)
Xinrui Liu; Yu-an Tan; Yajie Wang; Kefan Qiu; Yuanzhang Li

http://arxiv.org/abs/2305.10596
Towards Invisible Backdoor Attacks in the Frequency Domain against Deep Neural Networks. (75%)
Xinrui Liu; Yajie Wang; Yu-an Tan; Kefan Qiu; Yuanzhang Li

http://arxiv.org/abs/2305.06024
The Robustness of Computer Vision Models against Common Corruptions: a Survey. (50%)
Shunxin Wang; Raymond Veldhuis; Nicola Strisciuglio

http://arxiv.org/abs/2305.06422
An Empirical Study on the Robustness of the Segment Anything Model (SAM). (22%)
Yuqing Wang; Yun Zhao; Linda Petzold

http://arxiv.org/abs/2305.05909
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers. (12%)
Lei Yuan; Zi-Qian Zhang; Ke Xue; Hao Yin; Feng Chen; Cong Guan; Li-He Li; Chao Qian; Yang Yu

http://arxiv.org/abs/2305.05875
Quantization Aware Attack: Enhancing the Transferability of Adversarial Attacks across Target Models with Different Quantization Bitwidths. (99%)
Yulong Yang; Chenhao Lin; Qian Li; Chao Shen; Dawei Zhou; Nannan Wang; Tongliang Liu

http://arxiv.org/abs/2305.05253
Attack Named Entity Recognition by Entity Boundary Interference. (98%)
Yifei Yang; Hongqiu Wu; Hai Zhao

http://arxiv.org/abs/2305.05736
VSMask: Defending Against Voice Synthesis Attack via Real-Time Predictive Perturbation. (96%)
Yuanda Wang; Hanqing Guo; Guangjing Wang; Bocheng Chen; Qiben Yan

http://arxiv.org/abs/2305.05400
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions. (75%)
Georg Siedel; Weijia Shao; Silvia Vock; Andrey Morozov

http://arxiv.org/abs/2305.05392
On the Relation between Sharpness-Aware Minimization and Adversarial Robustness. (56%)
Zeming Wei; Jingyu Zhu; Yihao Zhang

http://arxiv.org/abs/2305.05499
Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model. (13%)
Farhin Farhad Riya; Shahinul Hoque; Md Saif Hassan Onim; Edward Michaud; Edmon Begoli; Jinyuan Stella Sun

http://arxiv.org/abs/2305.05355
Turning Privacy-preserving Mechanisms against Federated Learning. (9%)
Marco Arazzi; Mauro Conti; Antonino Nocera; Stjepan Picek

http://arxiv.org/abs/2305.05503
BadCS: A Backdoor Attack Framework for Code search. (8%)
Shiyi Qi; Yuanhang Yang; Shuzhzeng Gao; Cuiyun Gao; Zenglin Xu

http://arxiv.org/abs/2305.09674
Quantum Machine Learning for Malware Classification. (1%)
Grégoire Barrué; Tony Quertier

http://arxiv.org/abs/2305.04557
Toward Adversarial Training on Contextualized Language Representation. (93%)
Hongqiu Wu; Yongxiang Liu; Hanwen Shi; Hai Zhao; Min Zhang

http://arxiv.org/abs/2305.04746
Understanding Noise-Augmented Training for Randomized Smoothing. (64%)
Ambar Pal; Jeremias Sulam

http://arxiv.org/abs/2305.04574
TAPS: Connecting Certified and Adversarial Training. (41%)
Yuhao Mao; Mark Niklas Müller; Marc Fischer; Martin Vechev

http://arxiv.org/abs/2305.05391
Privacy-preserving Adversarial Facial Features. (22%)
Zhibo Wang; He Wang; Shuaifan Jin; Wenwen Zhang; Jiahui Hu; Yan Wang; Peng Sun; Wei Yuan; Kaixin Liu; Kui Ren

http://arxiv.org/abs/2305.05116
Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation. (1%)
Lei Yuan; Feng Chen; Zhongzhang Zhang; Yang Yu

http://arxiv.org/abs/2305.04436
Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization. (99%)
Zhaoxia Yin; Shaowei Zhu; Hang Su; Jianteng Peng; Wanli Lyu; Bin Luo

http://arxiv.org/abs/2305.09671
Pick your Poison: Undetectability versus Robustness in Data Poisoning Attacks against Deep Image Classification. (93%)
Nils Lukas; Florian Kerschbaum

http://arxiv.org/abs/2305.04067
The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples. (99%)
Heng Yang; Ke Li

http://arxiv.org/abs/2305.03963
Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps. (92%)
Ye Sang; Yujin Huang; Shuo Huang; Helei Cui

http://arxiv.org/abs/2305.03980
Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework. (38%)
Ruijia Wu; Yuhang Wang; Huafeng Shi; Zhipeng Yu; Yichao Wu; Ding Liang

http://arxiv.org/abs/2305.04175
Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning. (2%)
Shengfang Zhai; Yinpeng Dong; Qingni Shen; Shi Pu; Yuejian Fang; Hang Su

http://arxiv.org/abs/2305.03655
White-Box Multi-Objective Adversarial Attack on Dialogue Generation. (99%)
Yufei Li; Zexin Li; Yingfan Gao; Cong Liu

http://arxiv.org/abs/2305.03807
Evading Watermark based Detection of AI-Generated Content. (87%)
Zhengyuan Jiang; Jinghuai Zhang; Neil Zhenqiang Gong

http://arxiv.org/abs/2305.03626
Verifiable Learning for Robust Tree Ensembles. (15%)
Stefano Calzavara; Lorenzo Cazzaro; Giulio Ermanno Pibiri; Nicola Prezza

http://arxiv.org/abs/2305.03365
Repairing Deep Neural Networks Based on Behavior Imitation. (4%)
Zhen Liang; Taoran Wu; Changyuan Zhao; Wanwei Liu; Bai Xue; Wenjing Yang; Ji Wang

http://arxiv.org/abs/2305.02559
Madvex: Instrumentation-based Adversarial Attacks on Machine Learning Malware Detection. (99%)
Nils Loose; Felix Mächtle; Claudius Pott; Volodymyr Bezsmertnyi; Thomas Eisenbarth

http://arxiv.org/abs/2305.02605
IMAP: Intrinsically Motivated Adversarial Policy. (99%)
Xiang Zheng; Xingjun Ma; Shengjie Wang; Xinyu Wang; Chao Shen; Cong Wang

http://arxiv.org/abs/2305.02901
Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning. (78%)
Dayuan Chen; Jian Zhang; Yuqian Lv; Jinhuan Wang; Hongjie Ni; Shanqing Yu; Zhen Wang; Qi Xuan

http://arxiv.org/abs/2305.02855
Faulting original McEliece's implementations is possible: How to mitigate this risk? (2%)
Vincent Giraud; Guillaume Bouffard

http://arxiv.org/abs/2305.03173
New Adversarial Image Detection Based on Sentiment Analysis. (99%)
Yulong Wang; Tianxiang Li; Shenghong Li; Xin Yuan; Wei Ni

http://arxiv.org/abs/2305.02022
LearnDefend: Learning to Defend against Targeted Model-Poisoning Attacks on Federated Learning. (84%)
Kiran Purohit; Soumi Das; Sourangshu Bhattacharya; Santu Rana

http://arxiv.org/abs/2305.02394
Defending against Insertion-based Textual Backdoor Attacks via Attribution. (61%)
Jiazhao Li; Zhuofeng Wu; Wei Ping; Chaowei Xiao; V. G. Vinod Vydiswaran

http://arxiv.org/abs/2305.02383
On the Security Risks of Knowledge Graph Reasoning. (26%)
Zhaohan Xi; Tianyu Du; Changjiang Li; Ren Pang; Shouling Ji; Xiapu Luo; Xusheng Xiao; Fenglong Ma; Ting Wang

http://arxiv.org/abs/2305.02424
Backdoor Learning on Sequence to Sequence Models. (5%)
Lichang Chen; Minhao Cheng; Heng Huang

http://arxiv.org/abs/2305.02190
Rethinking Graph Lottery Tickets: Graph Sparsity Matters. (2%)
Bo Hui; Da Yan; Xiaolong Ma; Wei-Shinn Ku

http://arxiv.org/abs/2305.02423
PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer. (1%)
Lichang Chen; Heng Huang; Minhao Cheng

http://arxiv.org/abs/2305.01361
Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature. (99%)
Juanjuan Weng; Zhiming Luo; Dazhen Lin; Shaozi Li; Zhun Zhong

http://arxiv.org/abs/2305.01267
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning. (73%)
Wenqiang Sun; Sen Li; Yuchang Sun; Jun Zhang

http://arxiv.org/abs/2305.01860
Towards Imperceptible Document Manipulations against Neural Ranking Models. (67%)
Xuanang Chen; Ben He; Zheng Ye; Le Sun; Yingfei Sun

http://arxiv.org/abs/2305.01437
Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems. (50%)
Vyas Raina; Mark Gales

http://arxiv.org/abs/2305.01219
Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models. (8%)
Shuai Zhao; Jinming Wen; Luu Anh Tuan; Junbo Zhao; Jie Fu

http://arxiv.org/abs/2305.00866
Attack-SAM: Towards Evaluating Adversarial Robustness of Segment Anything Model. (99%)
Chenshuang Zhang; Chaoning Zhang; Taegoo Kang; Donghun Kim; Sung-Ho Bae; In So Kweon

http://arxiv.org/abs/2305.01074
Physical Adversarial Attacks for Surveillance: A Survey. (98%)
Kien Nguyen; Tharindu Fernando; Clinton Fookes; Sridha Sridharan

http://arxiv.org/abs/2305.00851
Revisiting Robustness in Graph Machine Learning. (98%)
Lukas Gosch; Daniel Sturm; Simon Geisler; Stephan Günnemann

http://arxiv.org/abs/2305.01139
Stratified Adversarial Robustness with Rejection. (96%)
Jiefeng Chen; Jayaram Raghuram; Jihye Choi; Xi Wu; Yingyu Liang; Somesh Jha

http://arxiv.org/abs/2305.00944
Poisoning Language Models During Instruction Tuning. (2%)
Alexander Wan; Eric Wallace; Sheng Shen; Dan Klein

http://arxiv.org/abs/2305.00399
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. (98%)
Jingfeng Zhang; Bo Song; Bo Han; Lei Liu; Gang Niu; Masashi Sugiyama

http://arxiv.org/abs/2305.00328
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection. (81%)
Thuy Dung Nguyen; Anh Duy Nguyen; Kok-Seng Wong; Huy Hieu Pham; Thanh Hung Nguyen; Phi Le Nguyen; Truong Thao Nguyen

http://arxiv.org/abs/2305.00374
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. (33%)
Xilie Xu; Jingfeng Zhang; Feng Liu; Masashi Sugiyama; Mohan Kankanhalli

http://arxiv.org/abs/2305.00011
Adversarial Representation Learning for Robust Privacy Preservation in Audio. (1%)
Shayan Gharib; Minh Tran; Diep Luong; Konstantinos Drossos; Tuomas Virtanen

http://arxiv.org/abs/2304.14867
Topic-oriented Adversarial Attacks against Black-box Neural Ranking Models. (99%)
Yu-An Liu; Ruqing Zhang; Jiafeng Guo; Rijke Maarten de; Wei Chen; Yixing Fan; Xueqi Cheng

http://arxiv.org/abs/2305.00075
On the existence of solutions to adversarial training in multiclass classification. (75%)
Nicolas Garcia Trillos; Matt Jacobs; Jakwang Kim

http://arxiv.org/abs/2304.14888
The Power of Typed Affine Decision Structures: A Case Study. (3%)
Gerrit Nolte; Maximilian Schlüter; Alnis Murtovi; Bernhard Steffen

http://arxiv.org/abs/2304.14717
faulTPM: Exposing AMD fTPMs' Deepest Secrets. (3%)
Hans Niklas Jacob; Christian Werling; Robert Buhren; Jean-Pierre Seifert

http://arxiv.org/abs/2304.14674
SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective. (1%)
An Wang; Mobarakol Islam; Mengya Xu; Yang Zhang; Hongliang Ren

http://arxiv.org/abs/2304.14483
Adversary Aware Continual Learning. (80%)
Muhammad Umer; Robi Polikar

http://arxiv.org/abs/2304.14614
Fusion is Not Enough: Single-Modal Attacks to Compromise Fusion Models in Autonomous Driving. (75%)
Zhiyuan Cheng; Hongjun Choi; James Liang; Shiwei Feng; Guanhong Tao; Dongfang Liu; Michael Zuzak; Xiangyu Zhang

http://arxiv.org/abs/2304.14031
Boosting Big Brother: Attacking Search Engines with Encodings. (68%)
Nicholas Boucher; Luca Pajola; Ilia Shumailov; Ross Anderson; Mauro Conti

http://arxiv.org/abs/2304.14475
ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger. (62%)
Jiazhao Li; Yijin Yang; Zhuofeng Wu; V. G. Vinod Vydiswaran; Chaowei Xiao

http://arxiv.org/abs/2304.14601
Improve Video Representation with Temporal Adversarial Augmentation. (26%)
Jinhao Duan; Quanfu Fan; Hao Cheng; Xiaoshuang Shi; Kaidi Xu

http://arxiv.org/abs/2304.14072
Origin Tracing and Detecting of LLMs. (1%)
Linyang Li; Pengyu Wang; Ke Ren; Tianxiang Sun; Xipeng Qiu

http://arxiv.org/abs/2304.14613
Deep Intellectual Property Protection: A Survey. (1%)
Yuchen Sun; Tianpeng Liu; Panhe Hu; Qing Liao; Shaojing Fu; Nenghai Yu; Deke Guo; Yongxiang Liu; Li Liu

http://arxiv.org/abs/2304.14540
Interactive Greybox Penetration Testing for Cloud Access Control using IAM Modeling and Deep Reinforcement Learning. (1%)
Yang Hu; Wenxi Wang; Sarfraz Khurshid; Mohit Tiwari

http://arxiv.org/abs/2304.13410
Improving Adversarial Transferability via Intermediate-level Perturbation Decay. (98%)
Qizhang Li; Yiwen Guo; Wangmeng Zuo; Hao Chen

http://arxiv.org/abs/2304.13919
Detection of Adversarial Physical Attacks in Time-Series Image Data. (92%)
Ramneet Kaur; Yiannis Kantaros; Wenwen Si; James Weimer; Insup Lee

http://arxiv.org/abs/2304.13360
Blockchain-based Federated Learning with SMPC Model Verification Against Poisoning Attack for Healthcare Systems. (13%)
Aditya Pribadi Kalapaaking; Ibrahim Khalil; Xun Yi

http://arxiv.org/abs/2304.12829
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks. (99%)
Ferheen Ayaz; Idris Zakariyya; José Cano; Sye Loong Keoh; Jeremy Singer; Danilo Pau; Mounia Kharbouche-Harrari

http://arxiv.org/abs/2304.13229
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization. (99%)
Anh Bui; Trung Le; He Zhao; Quan Tran; Paul Montague; Dinh Phung

http://arxiv.org/abs/2304.13255
SHIELD: Thwarting Code Authorship Attribution. (98%)
Mohammed Abuhamad; Changhun Jung; David Mohaisen; DaeHun Nyang

http://arxiv.org/abs/2304.12707
Lyapunov-Stable Deep Equilibrium Models. (82%)
Haoyu Chu; Shikui Wei; Ting Liu; Yao Zhao; Yuto Miyatake

http://arxiv.org/abs/2304.13104
LSTM-based Load Forecasting Robustness Against Noise Injection Attack in Microgrid. (1%)
Amirhossein Nazeri; Pierluigi Pisu

http://arxiv.org/abs/2304.12486
Evaluating Adversarial Robustness on Document Image Classification. (99%)
Timothée Fronteau; Arnaud Paran; Aymen Shabou

http://arxiv.org/abs/2304.12550
Combining Adversaries with Anti-adversaries in Training. (64%)
Xiaoling Zhou; Nan Yang; Ou Wu

http://arxiv.org/abs/2304.11823
Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization. (41%)
Mingli Zhu; Shaokui Wei; Li Shen; Yanbo Fan; Baoyuan Wu

http://arxiv.org/abs/2304.12540
Opinion Control under Adversarial Network Perturbation: A Stackelberg Game Approach. (10%)
Yuejiang Li; Zhanjiang Chen; H. Vicky Zhao

http://arxiv.org/abs/2304.11834
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning. (1%)
Yonggan Fu; Ye Yuan; Shang Wu; Jiayi Yuan; Yingyan Lin

http://arxiv.org/abs/2304.11579
StyLess: Boosting the Transferability of Adversarial Examples. (99%)
Kaisheng Liang; Bin Xiao

http://arxiv.org/abs/2304.11670
Evading DeepFake Detectors via Adversarial Statistical Consistency. (98%)
Yang Hou; Qing Guo; Yihao Huang; Xiaofei Xie; Lei Ma; Jianjun Zhao

http://arxiv.org/abs/2304.11359
Detecting Adversarial Faces Using Only Real Face Self-Perturbations. (98%)
Qian Wang; Yongqin Xian; Hefei Ling; Jinyuan Zhang; Xiaorui Lin; Ping Li; Jiazhong Chen; Ning Yu

http://arxiv.org/abs/2304.11432
Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration. (70%)
Peng Chen; Xin Du; Zhihui Lu; Hongfeng Chai

http://arxiv.org/abs/2304.10985
Launching a Robust Backdoor Attack under Capability Constrained Scenarios. (92%)
Ming Yi; Yixiao Xu; Kangyi Ding; Mingyong Yin; Xiaolei Liu

http://arxiv.org/abs/2304.10828
Individual Fairness in Bayesian Neural Networks. (69%)
Alice Doherty; Matthew Wicker; Luca Laurenti; Andrea Patane

http://arxiv.org/abs/2304.10783
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning. (64%)
Hangtao Zhang; Zeming Yao; Leo Yu Zhang; Shengshan Hu; Chao Chen; Alan Liew; Zhetao Li

http://arxiv.org/abs/2304.10755
Interpretable and Robust AI in EEG Systems: A Survey. (12%)
Xinliang Zhou; Chenyu Liu; Liming Zhai; Ziyu Jia; Cuntai Guan; Yang Liu

http://arxiv.org/abs/2304.11300
MAWSEO: Adversarial Wiki Search Poisoning for Illicit Online Promotion. (2%)
Zilong Lin; Zhengyi Li; Xiaojing Liao; XiaoFeng Wang; Xiaozhong Liu

http://arxiv.org/abs/2304.10088
Towards the Universal Defense for Query-Based Audio Adversarial Attacks. (99%)
Feng Guo; Zheng Sun; Yuxuan Chen; Lei Ju

http://arxiv.org/abs/2304.10136
Diversifying the High-level Features for better Adversarial Transferability. (99%)
Zhiyuan Wang; Zeliang Zhang; Siyuan Liang; Xiaosen Wang

http://arxiv.org/abs/2304.10558
Using Z3 for Formal Modeling and Verification of FNN Global Robustness. (98%)
Yihao Zhang; Zeming Wei; Xiyue Zhang; Meng Sun

http://arxiv.org/abs/2304.10446
Certified Adversarial Robustness Within Multiple Perturbation Bounds. (96%)
Soumalya Nandi; Sravanti Addepalli; Harsh Rangwani; R. Venkatesh Babu

http://arxiv.org/abs/2304.11043
Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training. (93%)
Jiezhu Cheng; Kaizhu Huang; Zibin Zheng

http://arxiv.org/abs/2304.10712
Adversarial Infrared Blocks: A Black-box Attack to Thermal Infrared Detectors at Multiple Angles in Physical World. (89%)
Chengyin Hu; Weiwen Shi; Tingsong Jiang; Wen Yao; Ling Tian; Xiaoqian Chen

http://arxiv.org/abs/2304.10218
An Analysis of the Completion Time of the BB84 Protocol. (22%)
Sounak Kar; Jean-Yves Le Boudec

http://arxiv.org/abs/2304.10679
A Plug-and-Play Defensive Perturbation for Copyright Protection of DNN-based Applications. (13%)
Donghua Wang; Wen Yao; Tingsong Jiang; Weien Zhou; Lang Lin; Xiaoqian Chen

http://arxiv.org/abs/2304.10622
Enhancing object detection robustness: A synthetic and natural perturbation approach. (12%)
Nilantha Premakumara; Brian Jalaian; Niranjan Suri; Hooman Samani

http://arxiv.org/abs/2304.10727
RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models. (8%)
Seulki Park; Daeho Um; Hajung Yoon; Sanghyuk Chun; Sangdoo Yun; Jin Young Choi

http://arxiv.org/abs/2304.10638
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning. (2%)
Manaar Alam; Hithem Lamri; Michail Maniatakos

http://arxiv.org/abs/2304.10127
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction. (1%)
Peng Cui; Dan Zhang; Zhijie Deng; Yinpeng Dong; Jun Zhu

http://arxiv.org/abs/2304.10029
Jedi: Entropy-based Localization and Removal of Adversarial Patches. (84%)
Bilel Tarchoun; Anouar Ben Khalifa; Mohamed Ali Mahjoub; Nael Abu-Ghazaleh; Ihsen Alouani

http://arxiv.org/abs/2304.09875
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models. (81%)
Zaitang Li; Pin-Yu Chen; Tsung-Yi Ho

http://arxiv.org/abs/2304.09515
Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks. (5%)
Yunlong Mao; Zexi Xin; Zhenyu Li; Jue Hong; Qingyou Yang; Sheng Zhong

http://arxiv.org/abs/2304.09446
Density-Insensitive Unsupervised Domain Adaption on 3D Object Detection. (1%)
Qianjiang Hu; Daizong Liu; Wei Hu

http://arxiv.org/abs/2304.09563
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training. (1%)
Hao Fei; Tat-Seng Chua; Chenliang Li; Donghong Ji; Meishan Zhang; Yafeng Ren

http://arxiv.org/abs/2304.11082
Fundamental Limitations of Alignment in Large Language Models. (1%)
Yotam Wolf; Noam Wies; Oshri Avnery; Yoav Levine; Amnon Shashua

http://arxiv.org/abs/2304.09403
Wavelets Beat Monkeys at Adversarial Robustness. (99%)
Jingtong Su; Julia Kempe

http://arxiv.org/abs/2304.08811
Towards the Transferable Audio Adversarial Attack via Ensemble Methods. (99%)
Feng Guo; Zheng Sun; Yuxuan Chen; Lei Ju

http://arxiv.org/abs/2304.08767
Masked Language Model Based Textual Adversarial Example Detection. (99%)
Xiaomei Zhang; Zhaoxi Zhang; Qi Zhong; Xufei Zheng; Yanjun Zhang; Shengshan Hu; Leo Yu Zhang

http://arxiv.org/abs/2304.08979
In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT. (80%)
Xinyue Shen; Zeyuan Chen; Michael Backes; Yang Zhang

http://arxiv.org/abs/2304.09218
Generative models improve fairness of medical classifiers under distribution shifts. (13%)
Ira Ktena; Olivia Wiles; Isabela Albuquerque; Sylvestre-Alvise Rebuffi; Ryutaro Tanno; Abhijit Guha Roy; Shekoofeh Azizi; Danielle Belgrave; Pushmeet Kohli; Alan Karthikesalingam; Taylan Cemgil; Sven Gowal

http://arxiv.org/abs/2304.08411
Evil from Within: Machine Learning Backdoors through Hardware Trojans. (15%)
Alexander Warnecke; Julian Speith; Jan-Niklas Möller; Konrad Rieck; Christof Paar

http://arxiv.org/abs/2304.08566
GrOVe: Ownership Verification of Graph Neural Networks using Embeddings. (13%)
Asim Waheed; Vasisht Duddu; N. Asokan

http://arxiv.org/abs/2304.10266
OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images. (1%)
Bingchen Zhao; Jiahao Wang; Wufei Ma; Artur Jesslen; Siwei Yang; Shaozuo Yu; Oliver Zendel; Christian Theobalt; Alan Yuille; Adam Kortylewski

http://arxiv.org/abs/2304.07822
A Random-patch based Defense Strategy Against Physical Attacks for Face Recognition Systems. (98%)
JiaHao Xie; Ye Luo; Jianwei Lu

http://arxiv.org/abs/2304.07980
RNN-Guard: Certified Robustness Against Multi-frame Attacks for Recurrent Neural Networks. (96%)
Yunruo Zhang; Tianyu Du; Shouling Ji; Peng Tang; Shanqing Guo

http://arxiv.org/abs/2304.07744
JoB-VS: Joint Brain-Vessel Segmentation in TOF-MRA Images. (15%)
Natalia Valderrama; Ioannis Pitsiorlas; Luisa Vargas; Pablo Arbeláez; Maria A. Zuluaga

http://arxiv.org/abs/2304.06919
Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense. (99%)
Jingyuan Wang; Yufan Wu; Mingxuan Li; Xin Lin; Junjie Wu; Chao Li

http://arxiv.org/abs/2304.07360
Combining Generators of Adversarial Malware Examples to Increase Evasion Rate. (99%)
Matouš Kozák; Martin Jureček

http://arxiv.org/abs/2304.07288
Cross-Entropy Loss Functions: Theoretical Analysis and Applications. (3%)
Anqi Mao; Mehryar Mohri; Yutao Zhong

http://arxiv.org/abs/2304.07134
Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice. (2%)
Andrea Gadotti; Florimond Houssiau; Meenatchi Sundaram Muthu Selva Annamalai; Montjoye Yves-Alexandre de

http://arxiv.org/abs/2304.06908
Generating Adversarial Examples with Better Transferability via Masking Unimportant Parameters of Surrogate Model. (99%)
Dingcheng Yang; Wenjian Yu; Zihao Xiao; Jiaqi Luo

http://arxiv.org/abs/2304.06430
Certified Zeroth-order Black-Box Defense with Robust UNet Denoiser. (96%)
Astha Verma; Siddhesh Bangar; A V Subramanyam; Naman Lal; Rajiv Ratn Shah; Shin'ichi Satoh

http://arxiv.org/abs/2304.06607
False Claims against Model Ownership Resolution. (93%)
Jian Liu; Rui Zhang; Sebastian Szyller; Kui Ren; N. Asokan

http://arxiv.org/abs/2304.06575
Adversarial Examples from Dimensional Invariance. (45%)
Benjamin L. Badger

http://arxiv.org/abs/2304.06326
Understanding Overfitting in Adversarial Training in Kernel Regression. (1%)
Teng Zhang; Kang Li

http://arxiv.org/abs/2304.06672
LSFSL: Leveraging Shape Information in Few-shot Learning. (1%)
Deepan Chakravarthi Padmanabhan; Shruthi Gowda; Elahe Arani; Bahram Zonooz

http://arxiv.org/abs/2304.05644
Generative Adversarial Networks-Driven Cyber Threat Intelligence Detection Framework for Securing Internet of Things. (92%)
Mohamed Amine Ferrag; Djallel Hamouda; Merouane Debbah; Leandros Maglaras; Abderrahmane Lakas

http://arxiv.org/abs/2304.06017
Exploiting Logic Locking for a Neural Trojan Attack on Machine Learning Accelerators. (1%)
Hongye Xu; Dongfang Liu; Cory Merkel; Michael Zuzak

http://arxiv.org/abs/2304.05135
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense. (99%)
Yue Cui; Syed Irfan Ali Meerza; Zhuohang Li; Luyang Liu; Jiaxin Zhang; Jian Liu

http://arxiv.org/abs/2304.05048
Simultaneous Adversarial Attacks On Multiple Face Recognition System Components. (98%)
Inderjeet Singh; Kazuya Kakizaki; Toshinori Araki

http://arxiv.org/abs/2304.05402
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations. (98%)
Tony Ma; Songze Li; Yisong Xiao; Shunchang Liu

http://arxiv.org/abs/2304.05098
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection. (92%)
Tianyuan Zhang; Yisong Xiao; Xiaoya Zhang; Hao Li; Lu Wang

http://arxiv.org/abs/2304.05561
On the Adversarial Inversion of Deep Biometric Representations. (67%)
Gioacchino Tangari; Shreesh Keskar; Hassan Jameel Asghar; Dali Kaafar

http://arxiv.org/abs/2304.05370
Overload: Latency Attacks on Object Detection for Edge Devices. (33%)
Erh-Chung Chen; Pin-Yu Chen; I-Hsin Chung; Che-rung Lee

http://arxiv.org/abs/2304.05492
Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training. (9%)
Juntao Tan; Shelby Heinecke; Zhiwei Liu; Yongjun Chen; Yongfeng Zhang; Huan Wang

http://arxiv.org/abs/2304.04386
Generating Adversarial Attacks in the Latent Space. (98%)
Nitish Shukla; Sudipta Banerjee

http://arxiv.org/abs/2304.04625
Reinforcement Learning-Based Black-Box Model Inversion Attacks. (67%)
Gyojin Han; Jaehyun Choi; Haeil Lee; Junmo Kim

http://arxiv.org/abs/2304.04512
Defense-Prefix for Preventing Typographic Attacks on CLIP. (16%)
Hiroki Azuma; Yusuke Matsui

http://arxiv.org/abs/2304.04846
Helix++: A platform for efficiently securing software. (1%)
Jack W. Davidson; Jason D. Hiser; Anh Nguyen-Tuong

http://arxiv.org/abs/2304.04343
Certifiable Black-Box Attack: Ensuring Provably Successful Attack for Adversarial Examples. (99%)
Hanbin Hong; Yuan Hong

http://arxiv.org/abs/2304.04168
Adversarially Robust Neural Architecture Search for Graph Neural Networks. (80%)
Beini Xie; Heng Chang; Ziwei Zhang; Xin Wang; Daixin Wang; Zhiqiang Zhang; Rex Ying; Wenwu Zhu

http://arxiv.org/abs/2304.04228
Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval. (68%)
Yanru Xiao; Cong Wang; Xing Gao

http://arxiv.org/abs/2304.03955
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack. (99%)
Dashan Gao; Yunce Zhao; Yinghua Yao; Zeqi Zhang; Bifei Mao; Xin Yao

http://arxiv.org/abs/2304.03973
RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks. (98%)
Alberto Marchisio; Marco Antonio De; Alessio Colucci; Maurizio Martina; Muhammad Shafique

http://arxiv.org/abs/2304.04033
Exploring the Connection between Robust and Generative Models. (67%)
Senad Beadini; Iacopo Masi

http://arxiv.org/abs/2304.03968
Benchmarking the Robustness of Quantized Models. (47%)
Yisong Xiao; Tianyuan Zhang; Shunchang Liu; Haotong Qin

http://arxiv.org/abs/2304.04023
Attack is Good Augmentation: Towards Skeleton-Contrastive Representation Learning. (13%)
Binqian Xu; Xiangbo Shu; Rui Yan; Guo-Sen Xie; Yixiao Ge; Mike Zheng Shou

http://arxiv.org/abs/2304.04077
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations. (4%)
Daniel Flores-Araiza; Francisco Lopez-Tiro; Jonathan El-Beze; Jacques Hubert; Miguel Gonzalez-Mendoza; Gilberto Ochoa-Ruiz; Christian Daul

http://arxiv.org/abs/2304.03977
EMP-SSL: Towards Self-Supervised Learning in One Training Epoch. (1%)
Shengbang Tong; Yubei Chen; Yi Ma; Yann Lecun

http://arxiv.org/abs/2304.03496
Architecture-Preserving Provable Repair of Deep Neural Networks. (1%)
Zhe Tao; Stephanie Nawas; Jacqueline Mitchell; Aditya V. Thakur

http://arxiv.org/abs/2304.03870
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. (1%)
Jiefeng Chen; Jinsung Yoon; Sayna Ebrahimi; Sercan Arik; Somesh Jha; Tomas Pfister

http://arxiv.org/abs/2304.02932
Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding. (45%)
Yuke Hu; Wei Liang; Ruofan Wu; Kai Xiao; Weiqiang Wang; Xiaochen Li; Jinfei Liu; Zhan Qin

http://arxiv.org/abs/2304.03054
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures. (45%)
Wei Yuan; Quoc Viet Hung Nguyen; Tieke He; Liang Chen; Hongzhi Yin

http://arxiv.org/abs/2304.03147
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions. (10%)
Jia-Hong Huang; Modar Alfadly; Bernard Ghanem; Marcel Worring

http://arxiv.org/abs/2304.03388
EZClone: Improving DNN Model Extraction Attack via Shape Distillation from GPU Execution Profiles. (4%)
Jonah O'Brien Weiss; Tiago Alves; Sandip Kundu

http://arxiv.org/abs/2304.03145
Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming. (2%)
Clemencia Siro; Tunde Oluwaseyi Ajayi

http://arxiv.org/abs/2304.03456
Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning. (1%)
Jae-Hun Lee; Doyoung Yoon; ByeongMoon Ji; Kyungyul Kim; Sangheum Hwang

http://arxiv.org/abs/2304.03370
Reliable Learning for Test-time Attacks and Distribution Shift. (1%)
Maria-Florina Balcan; Steve Hanneke; Rattana Pukdee; Dravyansh Sharma

http://arxiv.org/abs/2304.02963
Benchmarking Robustness to Text-Guided Corruptions. (1%)
Mohammadreza Mofayezi; Yasamin Medghalchi

http://arxiv.org/abs/2304.02693
A Certified Radius-Guided Attack Framework to Image Segmentation Models. (99%)
Wenjie Qu; Youqi Li; Binghui Wang

http://arxiv.org/abs/2304.02312
How to choose your best allies for a transferable attack? (99%)
Thibault Maho; Seyed-Mohsen Moosavi-Dezfooli; Teddy Furon

http://arxiv.org/abs/2304.02688
Going Further: Flatness at the Rescue of Early Stopping for Adversarial Example Transferability. (99%)
Martin Gubri; Maxime Cordy; Yves Le Traon

http://arxiv.org/abs/2304.02845
Robust Neural Architecture Search. (92%)
Xunyu Zhu; Jian Li; Yong Liu; Weiping Wang

http://arxiv.org/abs/2304.02497
Hyper-parameter Tuning for Adversarially Robust Models. (62%)
Pedro Mendes; Paolo Romano; David Garlan

http://arxiv.org/abs/2304.02234
JPEG Compressed Images Can Bypass Protections Against AI Editing. (15%)
Pedro Sandoval-Segura; Jonas Geiping; Tom Goldstein

http://arxiv.org/abs/2304.02782
FACE-AUDITOR: Data Auditing in Facial Recognition Systems. (1%)
Min Chen; Zhikun Zhang; Tianhao Wang; Michael Backes; Yang Zhang

http://arxiv.org/abs/2304.01826
CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks. (31%)
Vineel Nagisetty; Laura Graves; Guanting Pan; Piyush Jha; Vijay Ganesh

http://arxiv.org/abs/2304.01731
Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher. (1%)
Jiawei Shao; Fangzhao Wu; Jun Zhang

http://arxiv.org/abs/2304.02012
EGC: Image Generation and Classification via a Single Energy-Based Model. (1%)
Qiushan Guo; Chuofan Ma; Yi Jiang; Zehuan Yuan; Yizhou Yu; Ping Luo

http://arxiv.org/abs/2304.01482
Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning. (76%)
Ajinkya Tejankar; Maziar Sanjabi; Qifan Wang; Sinong Wang; Hamed Firooz; Hamed Pirsiavash; Liang Tan

http://arxiv.org/abs/2304.00813
Model-Agnostic Reachability Analysis on Deep Neural Networks. (75%)
Chi Zhang; Wenjie Ruan; Fu Wang; Peipei Xu; Geyong Min; Xiaowei Huang

http://arxiv.org/abs/2304.01441
NetFlick: Adversarial Flickering Attacks on Deep Learning Based Video Compression. (69%)
Jung-Woo Chang; Nojan Sheybani; Shehzeen Samarah Hussain; Mojan Javaheripi; Seira Hidano; Farinaz Koushanfar

http://arxiv.org/abs/2304.01142
Learning About Simulated Adversaries from Human Defenders using Interactive Cyber-Defense Games. (1%)
Baptiste Prebot; Yinuo Du; Cleotilde Gonzalez

http://arxiv.org/abs/2304.06724
GradMDM: Adversarial Attack on Dynamic Networks. (84%)
Jianhong Pan; Lin Geng Foo; Qichen Zheng; Zhipeng Fan; Hossein Rahmani; Qiuhong Ke; Jun Liu

http://arxiv.org/abs/2304.00436
Instance-level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space. (67%)
Yuwei Sun; Hideya Ochiai; Jun Sakuma

http://arxiv.org/abs/2304.00202
Improving Fast Adversarial Training with Prior-Guided Knowledge. (99%)
Xiaojun Jia; Yong Zhang; Xingxing Wei; Baoyuan Wu; Ke Ma; Jue Wang; Xiaochun Sr Cao

http://arxiv.org/abs/2304.00061
To be Robust and to be Fair: Aligning Fairness with Robustness. (93%)
Junyi Chai; Xiaoqian Wang

http://arxiv.org/abs/2303.17890
Fooling Polarization-based Vision using Locally Controllable Polarizing Projection. (91%)
Zhuoxiao Li; Zhihang Zhong; Shohei Nobuhara; Ko Nishino; Yinqiang Zheng

http://arxiv.org/abs/2303.17940
Per-Example Gradient Regularization Improves Learning Signals from Noisy Data. (3%)
Xuran Meng; Yuan Cao; Difan Zou

http://arxiv.org/abs/2304.00160
Secure Federated Learning against Model Poisoning Attacks via Client Filtering. (2%)
Duygu Nur Yaldiz; Tuo Zhang; Salman Avestimehr

http://arxiv.org/abs/2303.18232
DIME-FM: DIstilling Multimodal and Efficient Foundation Models. (1%)
Ximeng Sun; Pengchuan Zhang; Peizhao Zhang; Hardik Shah; Kate Saenko; Xide Xia

http://arxiv.org/abs/2304.00083
A Generative Framework for Low-Cost Result Validation of Outsourced Machine Learning Tasks. (1%)
Abhinav Kumar; Miguel A. Guirao Aguilera; Reza Tourani; Satyajayant Misra

http://arxiv.org/abs/2303.17255
Adversarial Attack and Defense for Dehazing Networks. (97%)
Jie Gui; Xiaofeng Cong; Chengwei Peng; Yuan Yan Tang; James Tin-Yau Kwok

http://arxiv.org/abs/2303.17720
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness. (96%)
Timothy Redgrave; Colton Crum

http://arxiv.org/abs/2303.17764
Towards Adversarially Robust Continual Learning. (95%)
Tao Bai; Chen Chen; Lingjuan Lyu; Jun Zhao; Bihan Wen

http://arxiv.org/abs/2303.17297
Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving. (81%)
Zijian Zhu; Yichi Zhang; Hai Chen; Yinpeng Dong; Shu Zhao; Wenbo Ding; Jiachen Zhong; Shibao Zheng

http://arxiv.org/abs/2303.17597
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions. (2%)
Lingdong Kong; Youquan Liu; Xin Li; Runnan Chen; Wenwei Zhang; Jiawei Ren; Liang Pan; Kai Chen; Ziwei Liu

http://arxiv.org/abs/2303.17658
Establishing baselines and introducing TernaryMixOE for fine-grained out-of-distribution detection. (1%)
Noah Fleischmann; Walter Bennette; Nathan Inkawhich

http://arxiv.org/abs/2303.17387
Explainable Intrusion Detection Systems Using Competitive Learning Techniques. (1%)
Jesse Ables; Thomas Kirby; Sudip Mittal; Ioana Banicescu; Shahram Rahimi; William Anderson; Maria Seale

http://arxiv.org/abs/2303.17351
Differential Area Analysis for Ransomware: Attacks, Countermeasures, and Limitations. (1%)
Marco Venturini; Francesco Freda; Emanuele Miotto; Alberto Giaretta; Mauro Conti

http://arxiv.org/abs/2303.16697
Latent Feature Relation Consistency for Adversarial Robustness. (99%)
Xingbin Liu; Huafeng Kuang; Hong Liu; Xianming Lin; Yongjian Wu; Rongrong Ji

http://arxiv.org/abs/2303.16861
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness. (99%)
Wei Wei; Jiahuan Zhou; Ying Wu

http://arxiv.org/abs/2303.16633
Targeted Adversarial Attacks on Wind Power Forecasts. (88%)
René Heinrich; Christoph Scholz; Stephan Vogt; Malte Lehna

http://arxiv.org/abs/2304.00010
Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias. (67%)
Zihan Liu; Yun Luo; Lirong Wu; Zicheng Liu; Stan Z. Li

http://arxiv.org/abs/2303.17096
ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing. (56%)
Xiaodan Li; Yuefeng Chen; Yao Zhu; Shuhui Wang; Rong Zhang; Hui Xue

http://arxiv.org/abs/2303.16690
Graph Neural Networks for Hardware Vulnerability Analysis -- Can you Trust your GNN? (16%)
Lilas Alrahis; Ozgur Sinanoglu

http://arxiv.org/abs/2303.17080
Mole Recruitment: Poisoning of Image Classifiers via Selective Batch Sampling. (10%)
Ethan Wisdom; Tejas Gokhale; Chaowei Xiao; Yezhou Yang

http://arxiv.org/abs/2303.17061
A Tensor-based Convolutional Neural Network for Small Dataset Classification. (2%)
Zhenhua Chen; David Crandall

http://arxiv.org/abs/2303.16866
ALUM: Adversarial Data Uncertainty Modeling from Latent Model Uncertainty Compensation. (1%)
Wei Wei; Jiahuan Zhou; Hongze Li; Ying Wu

http://arxiv.org/abs/2303.16378
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion. (99%)
Haomin Zhuang; Yihua Zhang; Sijia Liu

http://arxiv.org/abs/2303.15735
Improving the Transferability of Adversarial Samples by Path-Augmented Method. (99%)
Jianping Zhang; Jen-tse Huang; Wenxuan Wang; Yichen Li; Weibin Wu; Xiaosen Wang; Yuxin Su; Michael R. Lyu

http://arxiv.org/abs/2303.15818
Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition. (99%)
Xiao Yang; Chang Liu; Longlong Xu; Yikai Wang; Yinpeng Dong; Ning Chen; Hang Su; Jun Zhu

http://arxiv.org/abs/2303.15754
Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization. (98%)
Jianping Zhang; Yizhan Huang; Weibin Wu; Michael R. Lyu

http://arxiv.org/abs/2303.15901
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm. (98%)
Bakary Badjie; José Cecílio; António Casimiro

http://arxiv.org/abs/2303.15940
TransAudio: Towards the Transferable Adversarial Audio Attack via Learning Contextualized Perturbations. (98%)
Qi Gege; Yuefeng Chen; Xiaofeng Mao; Yao Zhu; Binyuan Hui; Xiaodan Li; Rong Zhang; Hui Xue

http://arxiv.org/abs/2303.16004
A Survey on Malware Detection with Graph Representation Learning. (41%)
Tristan Bilot; Nour El Madhoun; Khaldoun Al Agha; Anis Zouaoui

http://arxiv.org/abs/2303.16308
Provable Robustness for Streaming Models with a Sliding Window. (15%)
Aounon Kumar; Vinu Sankar Sadasivan; Soheil Feizi

http://arxiv.org/abs/2303.18136
Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids. (9%)
Carmelo Ardito; Yashar Deldjoo; Noia Tommaso Di; Sciascio Eugenio Di; Fatemeh Nazary; Giovanni Servedio

http://arxiv.org/abs/2303.15736
On the Use of Reinforcement Learning for Attacking and Defending Load Frequency Control. (3%)
Amr S. Mohamed; Deepa Kundur

http://arxiv.org/abs/2303.16031
A Universal Identity Backdoor Attack against Speaker Verification based on Siamese Network. (1%)
Haodong Zhao; Wei Du; Junjie Guo; Gongshen Liu

http://arxiv.org/abs/2303.15409
Classifier Robustness Enhancement Via Test-Time Transformation. (99%)
Tsachi Blau; Roy Ganz; Chaim Baskin; Michael Elad; Alex Bronstein

http://arxiv.org/abs/2303.15109
Improving the Transferability of Adversarial Examples via Direction Tuning. (99%)
Xiangyuan Yang; Jie Lin; Hanlin Zhang; Xinyu Yang; Peng Zhao

http://arxiv.org/abs/2303.15571
EMShepherd: Detecting Adversarial Samples via Side-channel Leakage. (99%)
Ruyi Ding; Cheng Gongye; Siyue Wang; Aidong Ding; Yunsi Fei

http://arxiv.org/abs/2303.15127
Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks. (97%)
Tianrui Qin; Xitong Gao; Juanjuan Zhao; Kejiang Ye; Cheng-Zhong Xu

http://arxiv.org/abs/2303.18191
Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency. (76%)
Xiaogeng Liu; Minghui Li; Haoyu Wang; Shengshan Hu; Dengpan Ye; Hai Jin; Libing Wu; Chaowei Xiao

http://arxiv.org/abs/2303.14922
CAT:Collaborative Adversarial Training. (69%)
Xingbin Liu; Huafeng Kuang; Xianming Lin; Yongjian Wu; Rongrong Ji

http://arxiv.org/abs/2303.14961
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection. (67%)
Nicola Franco; Daniel Korth; Jeanette Miriam Lorenz; Karsten Roscher; Stephan Guennemann

http://arxiv.org/abs/2303.15168
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation. (41%)
Yang Lu; Pinxin Qian; Gang Huang; Hanzi Wang

http://arxiv.org/abs/2303.15564
Mask and Restore: Blind Backdoor Defense at Test Time with Masked Autoencoder. (41%)
Tao Sun; Lu Pang; Chao Chen; Haibin Ling

http://arxiv.org/abs/2303.15533
Sequential training of GANs against GAN-classifiers reveals correlated "knowledge gaps" present among independently trained GAN instances. (41%)
Arkanath Pathak; Nicholas Dufour

http://arxiv.org/abs/2303.15433
Anti-DreamBooth: Protecting users from personalized text-to-image synthesis. (5%)
Le Thanh Van; Hao Phung; Thuan Hoang Nguyen; Quan Dao; Ngoc Tran; Anh Tran

http://arxiv.org/abs/2303.14822
MGTBench: Benchmarking Machine-Generated Text Detection. (61%)
Xinlei He; Xinyue Shen; Zeyuan Chen; Michael Backes; Yang Zhang

http://arxiv.org/abs/2303.18131
AdvCheck: Characterizing Adversarial Examples via Local Gradient Checking. (99%)
Ruoxi Chen; Haibo Jin; Jinyin Chen; Haibin Zheng

http://arxiv.org/abs/2303.14460
CFA: Class-wise Calibrated Fair Adversarial Training. (98%)
Zeming Wei; Yifei Wang; Yiwen Guo; Yisen Wang

http://arxiv.org/abs/2303.14601
PORE: Provably Robust Recommender Systems against Data Poisoning Attacks. (68%)
Jinyuan Jia; Yupei Liu; Yuepeng Hu; Neil Zhenqiang Gong

http://arxiv.org/abs/2303.14511
Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface. (12%)
Annika Stein

http://arxiv.org/abs/2303.13955
PIAT: Parameter Interpolation based Adversarial Training for Image Classification. (99%)
Kun He; Xin Liu; Yichen Yang; Zhou Qin; Weigao Wen; Hui Xue; John E. Hopcroft

http://arxiv.org/abs/2303.14173
How many dimensions are required to find an adversarial example? (99%)
Charles Godfrey; Henry Kvinge; Elise Bishoff; Myles Mckay; Davis Brown; Tim Doster; Eleanor Byler

http://arxiv.org/abs/2303.13887
Effective black box adversarial attack with handcrafted kernels. (99%)
Petr Dvořáček; Petr Hurtik; Petra Števuliáková

http://arxiv.org/abs/2303.14133
Adversarial Attack and Defense for Medical Image Analysis: Methods and Applications. (99%)
Junhao Dong; Junxi Chen; Xiaohua Xie; Jianhuang Lai; Hao Chen

http://arxiv.org/abs/2303.14077
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing. (99%)
Lin Li; Michael Spratling

http://arxiv.org/abs/2303.13846
Feature Separation and Recalibration for Adversarial Robustness. (98%)
Woo Jae Kim; Yoonki Cho; Junsik Jung; Sung-Eui Yoon

http://arxiv.org/abs/2303.13868
Physically Adversarial Infrared Patches with Learnable Shapes and Locations. (97%)
Wei Xingxing; Yu Jie; Huang Yao

http://arxiv.org/abs/2303.13813
Generalist: Decoupling Natural and Robust Generalization. (96%)
Hongjun Wang; Yisen Wang

http://arxiv.org/abs/2303.14304
Ensemble-based Blackbox Attacks on Dense Prediction. (86%)
Zikui Cai; Yaoteng Tan; M. Salman Asif

http://arxiv.org/abs/2303.14325
Backdoor Attacks with Input-unique Triggers in NLP. (54%)
Xukun Zhou; Jiwei Li; Tianwei Zhang; Lingjuan Lyu; Muqiao Yang; Jun He

http://arxiv.org/abs/2303.14009
PoisonedGNN: Backdoor Attack on Graph Neural Networks-based Hardware Security Systems. (22%)
Lilas Alrahis; Satwik Patnaik; Muhammad Abdullah Hanif; Muhammad Shafique; Ozgur Sinanoglu

http://arxiv.org/abs/2303.14096
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck. (5%)
Jongheon Jeong; Sihyun Yu; Hankook Lee; Jinwoo Shin

http://arxiv.org/abs/2303.14197
Optimal Smoothing Distribution Exploration for Backdoor Neutralization in Deep Learning-based Traffic Systems. (2%)
Yue Wang; Wending Li; Michail Maniatakos; Saif Eddin Jabari

http://arxiv.org/abs/2303.14186
TRAK: Attributing Model Behavior at Scale. (1%)
Sung Min Park; Kristian Georgiev; Andrew Ilyas; Guillaume Leclerc; Aleksander Madry

http://arxiv.org/abs/2303.13131
Watch Out for the Confusing Faces: Detecting Face Swapping with the Probability Distribution of Face Identification Models. (68%)
Yuxuan Duan; Xuhong Zhang; Chuer Yu; Zonghui Wang; Shouling Ji; Wenzhi Chen

http://arxiv.org/abs/2303.14193
Quadratic Graph Attention Network (Q-GAT) for Robust Construction of Gene Regulatory Networks. (50%)
Hui Zhang; Xuexin An; Qiang He; Yudong Yao; Feng-Lei Fan; Yueyang Teng

http://arxiv.org/abs/2303.13401
Optimization and Optimizers for Adversarial Robustness. (41%)
Hengyue Liang; Buyun Liang; Le Peng; Ying Cui; Tim Mitchell; Ju Sun

http://arxiv.org/abs/2303.13649
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection. (41%)
João Vitorino; Lourenço Rodrigues; Eva Maia; Isabel Praça; André Lourenço

http://arxiv.org/abs/2303.13408
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense. (15%)
Kalpesh Krishna; Yixiao Song; Marzena Karpinska; John Wieting; Mohit Iyyer

http://arxiv.org/abs/2303.13326
Decentralized Adversarial Training over Graphs. (13%)
Ying Cao; Elsa Rizk; Stefan Vlaski; Ali H. Sayed

http://arxiv.org/abs/2303.13211
Don't FREAK Out: A Frequency-Inspired Approach to Detecting Backdoor Poisoned Samples in DNNs. (8%)
Hasan Abed Al Kader Hammoud; Adel Bibi; Philip H. S. Torr; Bernard Ghanem

http://arxiv.org/abs/2303.13713
Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness. (1%)
Huajie Chen; Tianqing Zhu; Yuan Zhao; Bo Liu; Xin Yu; Wanlei Zhou

http://arxiv.org/abs/2303.13588
Efficient Symbolic Reasoning for Neural-Network Verification. (1%)
Zi Dj Wang; Somesh Dj Jha; Dj Krishnamurthy; Dvijotham

http://arxiv.org/abs/2303.12658
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval. (99%)
Xunguang Wang; Jiawang Bai; Xinyue Xu; Xiaomeng Li

http://arxiv.org/abs/2303.13010
Semantic Image Attack for Visual Model Diagnosis. (99%)
Jinqi Luo; Zhaoning Wang; Chen Henry Wu; Dong Huang; la Torre Fernando De

http://arxiv.org/abs/2303.12481
Revisiting DeepFool: generalization and improvement. (99%)
Alireza Abdollahpourrostam; Mahed Abroshan; Seyed-Mohsen Moosavi-Dezfooli

http://arxiv.org/abs/2303.12357
Wasserstein Adversarial Examples on Univariant Time Series Data. (99%)
Wenjie Wang; Li Xiong; Jian Lou

http://arxiv.org/abs/2303.12848
Test-time Defense against Adversarial Attacks: Detection and Reconstruction of Adversarial Examples via Masked Autoencoder. (99%)
Yun-Yun Tsai; Ju-Chin Chao; Albert Wen; Zhaoyuan Yang; Chengzhi Mao; Tapan Shah; Junfeng Yang

http://arxiv.org/abs/2303.12512
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition. (78%)
Zexin Li; Bangjie Yin; Taiping Yao; Juefeng Guo; Shouhong Ding; Simin Chen; Cong Liu

http://arxiv.org/abs/2303.12669
An Extended Study of Human-like Behavior under Adversarial Training. (76%)
Paul Gavrikov; Janis Keuper; Margret Keuper

http://arxiv.org/abs/2303.12363
Distribution-restrained Softmax Loss for the Model Robustness. (38%)
Hao Wang; Chen Li; Jinzhe Jiang; Xin Zhang; Yaqian Zhao; Weifeng Gong

http://arxiv.org/abs/2303.12993
Backdoor Defense via Adaptively Splitting Poisoned Dataset. (16%)
Kuofeng Gao; Yang Bai; Jindong Gu; Yong Yang; Shu-Tao Xia

http://arxiv.org/abs/2303.12397
Edge Deep Learning Model Protection via Neuron Authorization. (11%)
Jinyin Chen; Haibin Zheng; Tao Liu; Rongchang Li; Yao Cheng; Xuhong Zhang; Shouling Ji

http://arxiv.org/abs/2303.12249
State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems. (99%)
Junbin Fang; You Jiang; Canjian Jiang; Zoe L. Jiang; Siu-Ming Yiu; Chuanyi Liu

http://arxiv.org/abs/2303.11625
Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems. (99%)
Yao Zhu; Yuefeng Chen; Xiaodan Li; Rong Zhang; Xiang Tian; Bolun Zheng; Yaowu Chen

http://arxiv.org/abs/2303.11793
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense. (99%)
Binh M. Le; Shahroz Tariq; Simon S. Woo

http://arxiv.org/abs/2303.11917
Efficient Decision-based Black-box Patch Attacks on Video Recognition. (98%)
Kaixun Jiang; Zhaoyu Chen; Hao Huang; Jiafeng Wang; Dingkang Yang; Bo Li; Yan Wang; Wenqiang Zhang

http://arxiv.org/abs/2303.12175
Black-box Backdoor Defense via Zero-shot Image Purification. (86%)
Yucheng Shi; Mengnan Du; Xuansheng Wu; Zihan Guan; Jin Sun; Ninghao Liu

http://arxiv.org/abs/2303.11611
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation. (10%)
Yuzheng Wang; Zhaoyu Chen; Dingkang Yang; Pinxue Guo; Kaixun Jiang; Wenqiang Zhang; Lizhe Qi

http://arxiv.org/abs/2303.12054
Influencer Backdoor Attack on Semantic Segmentation. (10%)
Haoheng Lan; Jindong Gu; Philip Torr; Hengshuang Zhao

http://arxiv.org/abs/2303.12233
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation. (9%)
Joshua C. Zhao; Atul Sharma; Ahmed Roushdy Elkordy; Yahya H. Ezzeldin; Salman Avestimehr; Saurabh Bagchi

http://arxiv.org/abs/2303.11745
Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study. (1%)
Mohamed Amine Ferrag; Burak Kantarci; Lucas C. Cordeiro; Merouane Debbah; Kim-Kwang Raymond Choo

http://arxiv.org/abs/2303.11135
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization. (99%)
Ziquan Liu; Yi Xu; Xiangyang Ji; Antoni B. Chan

http://arxiv.org/abs/2303.11143
Adversarial Attacks against Binary Similarity Systems. (99%)
Gianluca Capozzi; Daniele Cono D'Elia; Luna Giuseppe Antonio Di; Leonardo Querzoni

http://arxiv.org/abs/2303.13372
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness. (99%)
Shoumik Saha; Wenxiao Wang; Yigitcan Kaya; Soheil Feizi; Tudor Dumitras

http://arxiv.org/abs/2303.10974
Translate your gibberish: black-box adversarial attack on machine translation systems. (83%)
Andrei Chertkov; Olga Tsymboi; Mikhail Pautov; Ivan Oseledets

http://arxiv.org/abs/2303.11376
GNN-Ensemble: Towards Random Decision Graph Neural Networks. (56%)
Wenqi Wei; Mu Qiao; Divyesh Jadav

http://arxiv.org/abs/2303.11040
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving. (41%)
Yinpeng Dong; Caixin Kang; Jinlai Zhang; Zijian Zhu; Yikai Wang; Xiao Yang; Hang Su; Xingxing Wei; Jun Zhu

http://arxiv.org/abs/2303.11470
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking. (9%)
Ruixiang Tang; Qizhang Feng; Ninghao Liu; Fan Yang; Xia Hu

http://arxiv.org/abs/2303.11066
Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data. (2%)
Yuhao Chen; Xin Tan; Borui Zhao; Zhaowei Chen; Renjie Song; Jiajun Liang; Xuequan Lu

http://arxiv.org/abs/2303.11242
Make Landscape Flatter in Differentially Private Federated Learning. (1%)
Yifan Shi; Yingqi Liu; Kang Wei; Li Shen; Xueqian Wang; Dacheng Tao

http://arxiv.org/abs/2303.11126
Robustifying Token Attention for Vision Transformers. (1%)
Yong Guo; David Stutz; Bernt Schiele

http://arxiv.org/abs/2303.10653
Randomized Adversarial Training via Taylor Expansion. (99%)
Gaojie Jin; Xinping Yi; Dengyu Wu; Ronghui Mu; Xiaowei Huang

http://arxiv.org/abs/2303.10594
AdaptGuard: Defending Against Universal Attacks for Model Adaptation. (82%)
Lijun Sheng; Jian Liang; Ran He; Zilei Wang; Tieniu Tan

http://arxiv.org/abs/2303.10430
NoisyHate: Benchmarking Content Moderation Machine Learning Models with Human-Written Perturbations Online. (98%)
Yiran Ye; Thai Le; Dongwon Lee

http://arxiv.org/abs/2303.10399
FedRight: An Effective Model Copyright Protection for Federated Learning. (96%)
Jinyin Chen; Mingjun Li; Mingjun Li; Haibin Zheng

http://arxiv.org/abs/2303.10078
Fuzziness-tuned: Improving the Transferability of Adversarial Examples. (99%)
Xiangyuan Yang; Jie Lin; Hanlin Zhang; Xinyu Yang; Peng Zhao

http://arxiv.org/abs/2303.09767
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness. (99%)
Peiyu Xiong; Michael Tegegn; Jaskeerat Singh Sarin; Shubhraneel Pal; Julia Rubin

http://arxiv.org/abs/2303.10225
Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified $\ell_p$ Attacks. (99%)
Ren Wang; Yuxuan Li; Sijia Liu

http://arxiv.org/abs/2303.10291
Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer. (83%)
Terence Jie Chua; Wenhan Yu; Jun Zhao

http://arxiv.org/abs/2303.11156
Can AI-Generated Text be Reliably Detected? (45%)
Vinu Sankar Sadasivan; Aounon Kumar; Sriram Balasubramanian; Wenxiao Wang; Soheil Feizi

http://arxiv.org/abs/2303.09962
Adversarial Counterfactual Visual Explanations. (31%)
Guillaume Jeanneret; Loïc Simon; Frédéric Jurie

http://arxiv.org/abs/2303.09858
MedLocker: A Transferable Adversarial Watermarking for Preventing Unauthorized Analysis of Medical Image Dataset. (16%)
Bangzheng Pu; Xingxing Wei; Shiji Zhao; Huazhu Fu

http://arxiv.org/abs/2303.10288
Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning. (9%)
Terence Jie Chua; Wenhan Yu; Jun Zhao

http://arxiv.org/abs/2303.09893
Moving Target Defense for Service-oriented Mission-critical Networks. (1%)
Doğanalp Ergenç; Florian Schneider; Peter Kling; Mathias Fischer

http://arxiv.org/abs/2303.09105
Rethinking Model Ensemble in Transfer-based Adversarial Attacks. (99%)
Huanran Chen; Yichi Zhang; Yinpeng Dong; Jun Zhu

http://arxiv.org/abs/2303.09289
Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations. (68%)
Lukas Struppek; Dominik Hintersdorf; Felix Friedrich; Manuel Brack; Patrick Schramowski; Kristian Kersting

http://arxiv.org/abs/2303.09495
Among Us: Adversarially Robust Collaborative Perception by Consensus. (67%)
Yiming Li; Qi Fang; Jiamu Bai; Siheng Chen; Felix Juefei-Xu; Chen Feng

http://arxiv.org/abs/2303.09731
Exorcising ''Wraith'': Protecting LiDAR-based Object Detector in Automated Driving System from Appearing Attacks. (50%)
Qifan Xiao; Xudong Pan; Yifan Lu; Mi Zhang; Jiarun Dai; Min Yang

http://arxiv.org/abs/2303.09732
Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation. (11%)
Yifan Yan; Xudong Pan; Mi Zhang; Min Yang

http://arxiv.org/abs/2303.08509
Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Information. (99%)
Heng Li; Zhang Cheng; Bang Wu; Liheng Yuan; Cuiying Gao; Wei Yuan; Xiapu Luo

http://arxiv.org/abs/2303.09051
Robust Evaluation of Diffusion-Based Adversarial Purification. (83%)
Minjong Lee; Dongwoo Kim

http://arxiv.org/abs/2303.09024
DeeBBAA: A benchmark Deep Black Box Adversarial Attack against Cyber-Physical Power Systems. (81%)
Arnab Bhattacharjee; Tapan K. Saha; Ashu Verma; Sukumar Mishra

http://arxiv.org/abs/2303.08500
The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models. (67%)
Hadi M. Dolatabadi; Sarah Erfani; Christopher Leckie

http://arxiv.org/abs/2303.08866
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models. (45%)
Ian E. Nielsen; Ravi P. Ramachandran; Nidhal Bouaynaya; Hassan M. Fathallah-Shaykh; Ghulam Rasool

http://arxiv.org/abs/2303.08944
Agnostic Multi-Robust Learning Using ERM. (12%)
Saba Ahmadi; Avrim Blum; Omar Montasser; Kevin Stangl

http://arxiv.org/abs/2303.08983
Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement. (1%)
Fartash Faghri; Hadi Pouransari; Sachin Mehta; Mehrdad Farajtabar; Ali Farhadi; Mohammad Rastegari; Oncel Tuzel

http://arxiv.org/abs/2303.08774
GPT-4 Technical Report. (1%)
Rai OpenAI; Josh Rai Achiam; Steven Rai Adler; Sandhini Rai Agarwal; Lama Rai Ahmad; Ilge Rai Akkaya; Florencia Leoni Rai Aleman; Diogo Rai Almeida; Janko Rai Altenschmidt; Sam Rai Altman; Shyamal Rai Anadkat; Red Rai Avila; Igor Rai Babuschkin; Suchir Rai Balaji; Valerie Rai Balcom; Paul Rai Baltescu; Haiming Rai Bao; Mohammad Rai Bavarian; Jeff Rai Belgum; Irwan Rai Bello; Jake Rai Berdine; Gabriel Rai Bernadett-Shapiro; Christopher Rai Berner; Lenny Rai Bogdonoff; Oleg Rai Boiko; Madelaine Rai Boyd; Anna-Luisa Rai Brakman; Greg Rai Brockman; Tim Rai Brooks; Miles Rai Brundage; Kevin Rai Button; Trevor Rai Cai; Rosie Rai Campbell; Andrew Rai Cann; Brittany Rai Carey; Chelsea Rai Carlson; Rory Rai Carmichael; Brooke Rai Chan; Che Rai Chang; Fotis Rai Chantzis; Derek Rai Chen; Sully Rai Chen; Ruby Rai Chen; Jason Rai Chen; Mark Rai Chen; Ben Rai Chess; Chester Rai Cho; Casey Rai Chu; Hyung Won Rai Chung; Dave Rai Cummings; Jeremiah Rai Currier; Yunxing Rai Dai; Cory Rai Decareaux; Thomas Rai Degry; Noah Rai Deutsch; Damien Rai Deville; Arka Rai Dhar; David Rai Dohan; Steve Rai Dowling; Sheila Rai Dunning; Adrien Rai Ecoffet; Atty Rai Eleti; Tyna Rai Eloundou; David Rai Farhi; Liam Rai Fedus; Niko Rai Felix; Simón Posada Rai Fishman; Juston Rai Forte; Isabella Rai Fulford; Leo Rai Gao; Elie Rai Georges; Christian Rai Gibson; Vik Rai Goel; Tarun Rai Gogineni; Gabriel Rai Goh; Rapha Rai Gontijo-Lopes; Jonathan Rai Gordon; Morgan Rai Grafstein; Scott Rai Gray; Ryan Rai Greene; Joshua Rai Gross; Shixiang Shane Rai Gu; Yufei Rai Guo; Chris Rai Hallacy; Jesse Rai Han; Jeff Rai Harris; Yuchen Rai He; Mike Rai Heaton; Johannes Rai Heidecke; Chris Rai Hesse; Alan Rai Hickey; Wade Rai Hickey; Peter Rai Hoeschele; Brandon Rai Houghton; Kenny Rai Hsu; Shengli Rai Hu; Xin Rai Hu; Joost Rai Huizinga; Shantanu Rai Jain; Shawn Rai Jain; Joanne Rai Jang; Angela Rai Jiang; Roger Rai Jiang; Haozhun Rai Jin; Denny Rai Jin; Shino Rai Jomoto; Billie Rai Jonn; Heewoo Rai Jun; Tomer Rai Kaftan; Łukasz Rai Kaiser; Ali Rai Kamali; Ingmar Rai Kanitscheider; Nitish Shirish Rai Keskar; Tabarak Rai Khan; Logan Rai Kilpatrick; Jong Wook Rai Kim; Christina Rai Kim; Yongjik Rai Kim; Jan Hendrik Rai Kirchner; Jamie Rai Kiros; Matt Rai Knight; Daniel Rai Kokotajlo; Łukasz Rai Kondraciuk; Andrew Rai Kondrich; Aris Rai Konstantinidis; Kyle Rai Kosic; Gretchen Rai Krueger; Vishal Rai Kuo; Michael Rai Lampe; Ikai Rai Lan; Teddy Rai Lee; Jan Rai Leike; Jade Rai Leung; Daniel Rai Levy; Chak Ming Rai Li; Rachel Rai Lim; Molly Rai Lin; Stephanie Rai Lin; Mateusz Rai Litwin; Theresa Rai Lopez; Ryan Rai Lowe; Patricia Rai Lue; Anna Rai Makanju; Kim Rai Malfacini; Sam Rai Manning; Todor Rai Markov; Yaniv Rai Markovski; Bianca Rai Martin; Katie Rai Mayer; Andrew Rai Mayne; Bob Rai McGrew; Scott Mayer Rai McKinney; Christine Rai McLeavey; Paul Rai McMillan; Jake Rai McNeil; David Rai Medina; Aalok Rai Mehta; Jacob Rai Menick; Luke Rai Metz; Andrey Rai Mishchenko; Pamela Rai Mishkin; Vinnie Rai Monaco; Evan Rai Morikawa; Daniel Rai Mossing; Tong Rai Mu; Mira Rai Murati; Oleg Rai Murk; David Rai Mély; Ashvin Rai Nair; Reiichiro Rai Nakano; Rajeev Rai Nayak; Arvind Rai Neelakantan; Richard Rai Ngo; Hyeonwoo Rai Noh; Long Rai Ouyang; Cullen Rai O'Keefe; Jakub Rai Pachocki; Alex Rai Paino; Joe Rai Palermo; Ashley Rai Pantuliano; Giambattista Rai Parascandolo; Joel Rai Parish; Emy Rai Parparita; Alex Rai Passos; Mikhail Rai Pavlov; Andrew Rai Peng; Adam Rai Perelman; Filipe de Avila Belbute Rai Peres; Michael Rai Petrov; Henrique Ponde de Oliveira Rai Pinto; Rai Michael; Pokorny; Michelle Pokrass; Vitchyr H. Pong; Tolly Powell; Alethea Power; Boris Power; Elizabeth Proehl; Raul Puri; Alec Radford; Jack Rae; Aditya Ramesh; Cameron Raymond; Francis Real; Kendra Rimbach; Carl Ross; Bob Rotsted; Henri Roussez; Nick Ryder; Mario Saltarelli; Ted Sanders; Shibani Santurkar; Girish Sastry; Heather Schmidt; David Schnurr; John Schulman; Daniel Selsam; Kyla Sheppard; Toki Sherbakov; Jessica Shieh; Sarah Shoker; Pranav Shyam; Szymon Sidor; Eric Sigler; Maddie Simens; Jordan Sitkin; Katarina Slama; Ian Sohl; Benjamin Sokolowsky; Yang Song; Natalie Staudacher; Felipe Petroski Such; Natalie Summers; Ilya Sutskever; Jie Tang; Nikolas Tezak; Madeleine B. Thompson; Phil Tillet; Amin Tootoonchian; Elizabeth Tseng; Preston Tuggle; Nick Turley; Jerry Tworek; Juan Felipe Cerón Uribe; Andrea Vallone; Arun Vijayvergiya; Chelsea Voss; Carroll Wainwright; Justin Jay Wang; Alvin Wang; Ben Wang; Jonathan Ward; Jason Wei; CJ Weinmann; Akila Welihinda; Peter Welinder; Jiayi Weng; Lilian Weng; Matt Wiethoff; Dave Willner; Clemens Winter; Samuel Wolrich; Hannah Wong; Lauren Workman; Sherwin Wu; Jeff Wu; Michael Wu; Kai Xiao; Tao Xu; Sarah Yoo; Kevin Yu; Qiming Yuan; Wojciech Zaremba; Rowan Zellers; Chong Zhang; Marvin Zhang; Shengjia Zhao; Tianhao Zheng; Juntang Zhuang; William Zhuk; Barret Zoph

http://arxiv.org/abs/2303.08032
Verifying the Robustness of Automatic Credibility Assessment. (99%)
Piotr Przybyła; Alexander Shvets; Horacio Saggion

http://arxiv.org/abs/2303.08171
Resilient Dynamic Average Consensus based on Trusted agents. (69%)
Shamik Bhattacharyya; Rachel Kalpana Kalaimani

http://arxiv.org/abs/2303.08289
Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations. (31%)
Olukorede Fakorede; Ashutosh Nirala; Modeste Atsague; Jin Tian

http://arxiv.org/abs/2303.07546
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review. (99%)
João Vitorino; Tiago Dias; Tiago Fonseca; Eva Maia; Isabel Praça

http://arxiv.org/abs/2303.07474
Can Adversarial Examples Be Parsed to Reveal Victim Model Information? (99%)
Yuguang Yao; Jiancheng Liu; Yifan Gong; Xiaoming Liu; Yanzhi Wang; Xue Lin; Sijia Liu

http://arxiv.org/abs/2303.07003
Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems. (99%)
Islam Debicha; Benjamin Cochez; Tayeb Kenaza; Thibault Debatty; Jean-Michel Dricot; Wim Mees

http://arxiv.org/abs/2303.12735
SMUG: Towards robust MRI reconstruction by smoothed unrolling. (98%)
Hui Li; Jinghan Jia; Shijun Liang; Yuguang Yao; Saiprasad Ravishankar; Sijia Liu

http://arxiv.org/abs/2303.07320
Model-tuning Via Prompts Makes NLP Models Adversarially Robust. (96%)
Mrigank Raman; Pratyush Maini; J. Zico Kolter; Zachary C. Lipton; Danish Pruthi

http://arxiv.org/abs/2303.06854
Robust Contrastive Language-Image Pretraining against Adversarial Attacks. (83%)
Wenhan Yang; Baharan Mirzasoleiman

http://arxiv.org/abs/2303.08581
Model Extraction Attacks on Split Federated Learning. (47%)
Jingtao Li; Adnan Siraj Rakin; Xing Chen; Li Yang; Zhezhi He; Deliang Fan; Chaitali Chakrabarti

http://arxiv.org/abs/2303.07543
WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminative Analysis. (1%)
Yiye Chen; Yunzhi Lin; Ruinian Xu; Patricio A. Vela

http://arxiv.org/abs/2303.06920
Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation. (1%)
Kira Maag; Tobias Riedlinger

http://arxiv.org/abs/2303.06664
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems. (99%)
Islam Debicha; Benjamin Cochez; Tayeb Kenaza; Thibault Debatty; Jean-Michel Dricot; Wim Mees

http://arxiv.org/abs/2303.06641
Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality. (99%)
Weiquan Liu; Shijun Zheng; Cheng Wang

http://arxiv.org/abs/2303.06746
DNN-Alias: Deep Neural Network Protection Against Side-Channel Attacks via Layer Balancing. (96%)
Mahya Morid Ahmadi; Lilas Alrahis; Ozgur Sinanoglu; Muhammad Shafique

http://arxiv.org/abs/2303.06601
Multi-metrics adaptively identifies backdoors in Federated learning. (92%)
Siquan Huang; Yijiang Li; Chong Chen; Leyu Shi; Ying Gao

http://arxiv.org/abs/2303.06837
Adversarial Attacks to Direct Data-driven Control for Destabilization. (91%)
Hampei Sasahara

http://arxiv.org/abs/2303.06818
Backdoor Defense via Deconfounded Representation Learning. (83%)
Zaixi Zhang; Qi Liu; Zhicai Wang; Zepu Lu; Qingyong Hu

http://arxiv.org/abs/2303.06652
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network. (76%)
Weiquan Liu; Minghao Liu; Shijun Zheng; Cheng Wang

http://arxiv.org/abs/2303.06808
Boosting Source Code Learning with Data Augmentation: An Empirical Study. (11%)
Zeming Dong; Qiang Hu; Yuejun Guo; Zhenya Zhang; Maxime Cordy; Mike Papadakis; Yves Le Traon; Jianjun Zhao

http://arxiv.org/abs/2303.06425
Improving the Robustness of Deep Convolutional Neural Networks Through Feature Learning. (99%)
Jin Ding; Jie-Chao Zhao; Yong-Zhi Sun; Ping Tan; Ji-En Ma; You-Tong Fang

http://arxiv.org/abs/2303.06486
SHIELD: An Adaptive and Lightweight Defense against the Remote Power Side-Channel Attacks on Multi-tenant FPGAs. (8%)
Mahya Morid Ahmadi; Faiq Khalid; Radha Vaidya; Florian Kriebel; Andreas Steininger; Muhammad Shafique

http://arxiv.org/abs/2303.06199
Turning Strengths into Weaknesses: A Certified Robustness Inspired Attack Framework against Graph Neural Networks. (99%)
Binghui Wang; Meng Pang; Yun Dong

http://arxiv.org/abs/2303.05719
Boosting Adversarial Attacks by Leveraging Decision Boundary Information. (99%)
Boheng Zeng; LianLi Gao; QiLong Zhang; ChaoQun Li; JingKuan Song; ShuaiQi Jing

http://arxiv.org/abs/2303.06302
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey. (99%)
Yulong Wang; Tong Sun; Shenghong Li; Xin Yuan; Wei Ni; Ekram Hossain; H. Vincent Poor

http://arxiv.org/abs/2303.06280
Investigating Stateful Defenses Against Black-Box Adversarial Examples. (99%)
Ryan Feng; Ashish Hooda; Neal Mangaokar; Kassem Fawaz; Somesh Jha; Atul Prakash

http://arxiv.org/abs/2303.05758
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems. (99%)
Aminul Huq; Weiyi Zhang; Xiaolin Hu

http://arxiv.org/abs/2303.06241
Do we need entire training data for adversarial training? (99%)
Vipul Gupta; Apurva Narayan

http://arxiv.org/abs/2303.05762
TrojDiff: Trojan Attacks on Diffusion Models with Diverse Targets. (61%)
Weixin Chen; Dawn Song; Bo Li

http://arxiv.org/abs/2303.05828
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection. (13%)
Nikolas Adaloglou; Felix Michels; Tim Kaiser; Markus Kollmann

http://arxiv.org/abs/2303.06151
NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks. (99%)
Wenkai Tan; Justus Renkhoff; Alvaro Velasquez; Ziyu Wang; Lusi Li; Jian Wang; Shuteng Niu; Fan Yang; Yongxin Liu; Houbing Song

http://arxiv.org/abs/2303.05575
Evaluating the Robustness of Conversational Recommender Systems by Adversarial Examples. (92%)
Ali Montazeralghaem; James Allan

http://arxiv.org/abs/2303.05072
Identification of Systematic Errors of Image Classifiers on Rare Subgroups. (83%)
Jan Hendrik Metzen; Robin Hutmacher; N. Grace Hua; Valentyn Boreiko; Dan Zhang

http://arxiv.org/abs/2303.05077
Learning the Legibility of Visual Text Perturbations. (78%)
Dev Seth; Rickard Stureborg; Danish Pruthi; Bhuwan Dhingra

http://arxiv.org/abs/2303.05246
Efficient Certified Training and Robustness Verification of Neural ODEs. (75%)
Mustafa Zeqiri; Mark Niklas Müller; Marc Fischer; Martin Vechev

http://arxiv.org/abs/2303.05699
Feature Unlearning for Pre-trained GANs and VAEs. (68%)
Saemi Moon; Seunghyuk Cho; Dongwoo Kim

http://arxiv.org/abs/2303.04502
Immune Defense: A Novel Adversarial Defense Mechanism for Preventing the Generation of Adversarial Examples. (99%)
Jinwei Wang; Hao Wu; Haihua Wang; Jiawei Zhang; Xiangyang Luo; Bin Ma

http://arxiv.org/abs/2303.04980
Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient Estimation. (99%)
Geunhyeok Yu; Minwoo Jeon; Hyoseok Hwang

http://arxiv.org/abs/2303.06032
Exploring Adversarial Attacks on Neural Networks: An Explainable Approach. (99%)
Justus Renkhoff; Wenkai Tan; Alvaro Velasquez; illiam Yichen Wang; Yongxin Liu; Jian Wang; Shuteng Niu; Lejla Begic Fazlic; Guido Dartmann; Houbing Song

http://arxiv.org/abs/2303.07199
BeamAttack: Generating High-quality Textual Adversarial Examples through Beam Search and Mixed Semantic Spaces. (99%)
Hai Zhu; Qingyang Zhao; Yuren Wu

http://arxiv.org/abs/2303.04878
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks. (3%)
Zohreh Aghababaeyan; Manel Abdellatif; Mahboubeh Dadkhah; Lionel Briand

http://arxiv.org/abs/2303.03680
Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration. (99%)
Juanjuan Weng; Zhiming Luo; Zhun Zhong; Shaozi Li; Nicu Sebe

http://arxiv.org/abs/2303.04238
Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors. (98%)
Raz Lapid; Moshe Sipper

http://arxiv.org/abs/2303.04183
Robustness-preserving Lifelong Learning via Dataset Condensation. (96%)
Jinghan Jia; Yihua Zhang; Dogyoon Song; Sijia Liu; Alfred Hero

http://arxiv.org/abs/2303.04278
CUDA: Convolution-based Unlearnable Datasets. (82%)
Vinu Sankar Sadasivan; Mahdi Soltanolkotabi; Soheil Feizi

http://arxiv.org/abs/2303.03700
EavesDroid: Eavesdropping User Behaviors via OS Side-Channels on Smartphones. (11%)
Quancheng Wang; Ming Tang; Jianming Fu

http://arxiv.org/abs/2303.04187
Stabilized training of joint energy-based models and their practical applications. (2%)
Martin Sustek; Samik Sadhu; Lukas Burget; Hynek Hermansky; Jesus Villalba; Laureano Moro-Velazquez; Najim Dehak

http://arxiv.org/abs/2303.03323
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning. (41%)
Hritik Bansal; Nishad Singhi; Yu Yang; Fan Yin; Aditya Grover; Kai-Wei Chang

http://arxiv.org/abs/2303.03446
Students Parrot Their Teachers: Membership Inference on Model Distillation. (31%)
Matthew Jagielski; Milad Nasr; Christopher Choquette-Choo; Katherine Lee; Nicholas Carlini

http://arxiv.org/abs/2303.03012
On the Feasibility of Specialized Ability Extracting for Large Language Code Models. (22%)
Zongjie Li; Chaozheng Wang; Pingchuan Ma; Chaowei Liu; Shuai Wang; Daoyuan Wu; Cuiyun Gao

http://arxiv.org/abs/2303.03169
A Unified Algebraic Perspective on Lipschitz Neural Networks. (15%)
Alexandre Araujo; Aaron Havens; Blaise Delattre; Alexandre Allauzen; Bin Hu

http://arxiv.org/abs/2303.03320
Learning to Backdoor Federated Learning. (15%)
Henger Li; Chen Wu; Sencun Zhu; Zizhan Zheng

http://arxiv.org/abs/2303.03470
Partial-Information, Longitudinal Cyber Attacks on LiDAR in Autonomous Vehicles. (10%)
R. Spencer Hallyburton; Qingzhao Zhang; Z. Morley Mao; Miroslav Pajic

http://arxiv.org/abs/2303.03372
ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning. (1%)
Animesh Basak Chowdhury; Lilas Alrahis; Luca Collini; Johann Knechtel; Ramesh Karri; Siddharth Garg; Ozgur Sinanoglu; Benjamin Tan

http://arxiv.org/abs/2303.02970
Rethinking Confidence Calibration for Failure Prediction. (1%)
Fei Zhu; Zhen Cheng; Xu-Yao Zhang; Cheng-Lin Liu

http://arxiv.org/abs/2303.02814
Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks. (99%)
Yiran Li; Junpeng Wang; Takanori Fujiwara; Kwan-Liu Ma

http://arxiv.org/abs/2303.02669
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models. (99%)
Hassan Ali; Muhammad Atif Butt; Fethi Filali; Ala Al-Fuqaha; Junaid Qadir

http://arxiv.org/abs/2303.02874
Adversarial Sampling for Fairness Testing in Deep Neural Network. (98%)
Tosin Ige; William Marfo; Justin Tonkinson; Sikiru Adewale; Bolanle Hafiz Matti

http://arxiv.org/abs/2303.02725
Local Environment Poisoning Attacks on Federated Reinforcement Learning. (12%)
Evelyn Ma; Rasoul Etesami

http://arxiv.org/abs/2303.02781
Robustness, Evaluation and Adaptation of Machine Learning Models in the Wild. (10%)
Vihari Piratla

http://arxiv.org/abs/2303.02601
Knowledge-Based Counterfactual Queries for Visual Question Answering. (3%)
Theodoti Stoikou; Maria Lymperaiou; Giorgos Stamou

http://arxiv.org/abs/2303.02322
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes. (68%)
Thomas Philippon; Christian Gagné

http://arxiv.org/abs/2303.01959
PointCert: Point Cloud Classification with Deterministic Certified Robustness Guarantees. (91%)
Jinghuai Zhang; Jinyuan Jia; Hongbin Liu; Neil Zhenqiang Gong

http://arxiv.org/abs/2303.02251
Certified Robust Neural Networks: Generalization and Corruption Resistance. (82%)
Amine Bennouna; Ryan Lucas; Parys Bart Van

http://arxiv.org/abs/2303.01734
AdvART: Adversarial Art for Camouflaged Object Detection Attacks. (75%)
Amira Guesmi; Ioan Marius Bilasco; Muhammad Shafique; Ihsen Alouani

http://arxiv.org/abs/2303.02213
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions. (47%)
Thuy Dung Nguyen; Tuan Nguyen; Phi Le Nguyen; Hieu H. Pham; Khoa Doan; Kok-Seng Wong

http://arxiv.org/abs/2303.02214
Adversarial Attacks on Machine Learning in Embedded and IoT Platforms. (38%)
Christian Westbrook; Sudeep Pasricha

http://arxiv.org/abs/2303.01870
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models. (33%)
Naman D Singh; Francesco Croce; Matthias Hein

http://arxiv.org/abs/2303.02112
Stealthy Perception-based Attacks on Unmanned Aerial Vehicles. (16%)
Amir Khazraei; Haocheng Meng; Miroslav Pajic

http://arxiv.org/abs/2303.02242
TrojText: Test-time Invisible Textual Trojan Insertion. (2%)
Qian Lou; Yepeng Liu; Bo Feng

http://arxiv.org/abs/2303.01507
Defending against Adversarial Audio via Diffusion Model. (99%)
Shutong Wu; Jiongxiao Wang; Wei Ping; Weili Nie; Chaowei Xiao

http://arxiv.org/abs/2303.01052
Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression. (99%)
Junho Kim. Byung-Kwan Lee; Yong Man Ro

http://arxiv.org/abs/2303.01338
AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems. (99%)
Amira Guesmi; Muhammad Abdullah Hanif; Muhammad Shafique

http://arxiv.org/abs/2303.01351
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation. (99%)
Amira Guesmi; Muhammad Abdullah Hanif; Ihsen Alouani; Muhammad Shafique

http://arxiv.org/abs/2303.01068
Targeted Adversarial Attacks against Neural Machine Translation. (98%)
Sahar Sadrizadeh; AmirHossein Dabiri Aghdam; Ljiljana Dolamic; Pascal Frossard

http://arxiv.org/abs/2303.01456
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. (93%)
Spencer Frei; Gal Vardi; Peter L. Bartlett; Nathan Srebro

http://arxiv.org/abs/2303.01538
Feature Perturbation Augmentation for Reliable Evaluation of Importance Estimators in Neural Networks. (10%)
Lennart Brocki; Neo Christopher Chung

http://arxiv.org/abs/2303.01041
D-Score: An Expert-Based Method for Assessing the Detectability of IoT-Related Cyber-Attacks. (3%)
Yair Meidan; Daniel Benatar; Ron Bitton; Dan Avraham; Asaf Shabtai

http://arxiv.org/abs/2303.01193
Interpretable System Identification and Long-term Prediction on Time-Series Data. (1%)
Xiaoyi Liu; Duxin Chen; Wenjia Wei; Xia Zhu; Wenwu Yu

http://arxiv.org/abs/2303.01469
Consistency Models. (1%)
Yang Song; Prafulla Dhariwal; Mark Chen; Ilya Sutskever

http://arxiv.org/abs/2303.01021
CADeSH: Collaborative Anomaly Detection for Smart Homes. (1%)
Yair Meidan; Dan Avraham; Hanan Libhaber; Asaf Shabtai

http://arxiv.org/abs/2303.01276
Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation. (1%)
Zicheng Wang; Zhen Zhao; Xiaoxia Xing; Dong Xu; Xiangyu Kong; Luping Zhou

http://arxiv.org/abs/2303.00284
To Make Yourself Invisible with Adversarial Semantic Contours. (99%)
Yichi Zhang; Zijian Zhu; Hang Su; Jun Zhu; Shibao Zheng; Yuan He; Hui Xue

http://arxiv.org/abs/2303.00783
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Data Manifolds. (98%)
Odelia Melamed; Gilad Yehudai; Gal Vardi

http://arxiv.org/abs/2303.01234
Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation Process. (97%)
Mingze Ni; Zhensu Sun; Wei Liu

http://arxiv.org/abs/2303.00340
A Practical Upper Bound for the Worst-Case Attribution Deviations. (70%)
Fan Wang; Adams Wai-Kin Kong

http://arxiv.org/abs/2303.00250
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. (54%)
Jianing Zhu; Jiangchao Yao; Tongliang Liu; Quanming Yao; Jianliang Xu; Bo Han

http://arxiv.org/abs/2303.01243
Poster: Sponge ML Model Attacks of Mobile Apps. (8%)
Souvik Paul; Nicolas Kourtellis

http://arxiv.org/abs/2303.00387
DOLOS: A Novel Architecture for Moving Target Defense. (8%)
Giulio Pagnotta; Gaspari Fabio De; Dorjan Hitaj; Mauro Andreolini; Michele Colajanni; Luigi V. Mancini

http://arxiv.org/abs/2303.00302
Mitigating Backdoors in Federated Learning with FLD. (2%)
Yihang Lin; Pengyuan Zhou; Zhiqian Wu; Yong Liao

http://arxiv.org/abs/2303.00333
Competence-Based Analysis of Language Models. (1%)
Adam Davies; Jize Jiang; ChengXiang Zhai

http://arxiv.org/abs/2302.14353
A semantic backdoor attack against Graph Convolutional Networks. (98%)
Jiazhu Dai; Zhipeng Xiong

http://arxiv.org/abs/2303.00215
Single Image Backdoor Inversion via Robust Smoothed Classifiers. (88%)
Mingjie Sun; J. Zico Kolter

http://arxiv.org/abs/2303.00200
Feature Extraction Matters More: Universal Deepfake Disruption through Attacking Ensemble Feature Extractors. (67%)
Long Tang; Dengpan Ye; Zhenhao Lu; Yunming Zhang; Shengshan Hu; Yue Xu; Chuanxi Chen

http://arxiv.org/abs/2302.14677
Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger. (11%)
Yi Yu; Yufei Wang; Wenhan Yang; Shijian Lu; Yap-peng Tan; Alex C. Kot

http://arxiv.org/abs/2302.14500
FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases. (1%)
Chong Fu; Xuhong Zhang; Shouling Ji; Ting Wang; Peng Lin; Yanghe Feng; Jianwei Yin

http://arxiv.org/abs/2302.14301
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking. (99%)
Chang Liu; Yinpeng Dong; Wenzhao Xiang; Xiao Yang; Hang Su; Jun Zhu; Yuefeng Chen; Yuan He; Hui Xue; Shibao Zheng

http://arxiv.org/abs/2302.14267
Adversarial Attack with Raindrops. (99%)
Jiyuan Liu; Bingyi Lu; Mingkang Xiong; Tao Zhang; Huilin Xiong

http://arxiv.org/abs/2302.13570
Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Study. (99%)
Fabian Woitschek; Georg Schneider

http://arxiv.org/abs/2302.13520
Aegis: Mitigating Targeted Bit-flip Attacks against Deep Neural Networks. (98%)
Jialai Wang; Ziyuan Zhang; Meiqi Wang; Han Qiu; Tianwei Zhang; Qi Li; Zongpeng Li; Tao Wei; Chao Zhang

http://arxiv.org/abs/2302.13519
CBA: Contextual Background Attack against Optical Aerial Detection in the Physical World. (98%)
Jiawei Lian; Xiaofei Wang; Yuru Su; Mingyang Ma; Shaohui Mei

http://arxiv.org/abs/2302.14302
Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain. (96%)
Chang Liu; Wenzhao Xiang; Yuan He; Hui Xue; Shibao Zheng; Hang Su

http://arxiv.org/abs/2302.13763
Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic. (83%)
Guodong Huang; Chuan Ma; Ming Ding; Yuwen Qian; Chunpeng Ge; Liming Fang; Zhe Liu

http://arxiv.org/abs/2302.14166
GLOW: Global Layout Aware Attacks on Object Detection. (81%)
Buyu Liu; BaoJun; Jianping Fan; Xi Peng; Kui Ren; Jun Yu

http://arxiv.org/abs/2302.13578
Online Black-Box Confidence Estimation of Deep Neural Networks. (16%)
Fabian Woitschek; Georg Schneider

http://arxiv.org/abs/2302.13851
Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks. (15%)
Mohammad Mohammadi; Jonathan Nöther; Debmalya Mandal; Adish Singla; Goran Radanovic

http://arxiv.org/abs/2302.13861
Differentially Private Diffusion Models Generate Useful Synthetic Images. (10%)
Sahra Ghalebikesabi; Leonard Berrada; Sven Gowal; Ira Ktena; Robert Stanforth; Jamie Hayes; Soham De; Samuel L. Smith; Olivia Wiles; Borja Balle

http://arxiv.org/abs/2302.14290
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation. (5%)
Gaurav Patel; Konda Reddy Mopuri; Qiang Qiu

http://arxiv.org/abs/2302.13487
Contextual adversarial attack against aerial detection in the physical world. (99%)
Jiawei Lian; Xiaofei Wang; Yuru Su; Mingyang Ma; Shaohui Mei

http://arxiv.org/abs/2302.13464
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators. (96%)
Keane Lucas; Matthew Jagielski; Florian Tramèr; Lujo Bauer; Nicholas Carlini

http://arxiv.org/abs/2302.13172
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation. (99%)
Shaoyan Pan; Shao-Yuan Lo; Min Huang; Chaoqiong Ma; Jacob Wynne; Tonghe Wang; Tian Liu; Xiaofeng Yang

http://arxiv.org/abs/2302.14059
Scalable Attribution of Adversarial Attacks via Multi-Task Learning. (99%)
Zhongyi Guo; Keji Han; Yao Ge; Wei Ji; Yun Li

http://arxiv.org/abs/2302.13056
SATBA: An Invisible Backdoor Attack Based On Spatial Attention. (74%)
Huasong Zhou; Xiaowei Xu; Xiaodong Wang; Leon Bevan Bullock

http://arxiv.org/abs/2302.13095
Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts. (1%)
Qihan Ren; Huiqi Deng; Yunuo Chen; Siyu Lou; Quanshi Zhang

http://arxiv.org/abs/2302.12758
Defending Against Backdoor Attacks by Layer-wise Feature Analysis. (68%)
Najeeb Moharram Jebreel; Josep Domingo-Ferrer; Yiming Li

http://arxiv.org/abs/2302.12959
Chaotic Variational Auto encoder-based Adversarial Machine Learning. (54%)
Pavan Venkata Sainadh Reddy; Yelleti Vivek; Gopi Pranay; Vadlamani Ravi

http://arxiv.org/abs/2302.12480
Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights? (12%)
Ruisi Cai; Zhenyu Zhang; Zhangyang Wang

http://arxiv.org/abs/2302.12366
Less is More: Data Pruning for Faster Adversarial Training. (99%)
Yize Li; Pu Zhao; Xue Lin; Bhavya Kailkhura; Ryan Goldhahn

http://arxiv.org/abs/2302.11982
A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots. (99%)
Boyang Zhang; Xinlei He; Yun Shen; Tianhao Wang; Yang Zhang

http://arxiv.org/abs/2302.12252
Boosting Adversarial Transferability using Dynamic Cues. (99%)
Muzammal Naseer; Ahmad Mahmood; Salman Khan; Fahad Khan

http://arxiv.org/abs/2302.12407
HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks. (98%)
Chao Hu; Ruishi Yu; Binqi Zeng; Yu Zhan; Ying Fu; Quan Zhang; Rongkai Liu; Heyuan Shi

http://arxiv.org/abs/2302.11963
Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective. (84%)
Zhengbao He; Tao Li; Sizhe Chen; Xiaolin Huang

http://arxiv.org/abs/2302.12173
More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models. (70%)
Kai Greshake; Sahar Abdelnabi; Shailesh Mishra; Christoph Endres; Thorsten Holz; Mario Fritz

http://arxiv.org/abs/2302.12351
On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space. (68%)
Yuyang Deng; Nidham Gazagnadou; Junyuan Hong; Mehrdad Mahdavi; Lingjuan Lyu

http://arxiv.org/abs/2302.12415
Harnessing the Speed and Accuracy of Machine Learning to Advance Cybersecurity. (2%)
Khatoon Mohammed

http://arxiv.org/abs/2302.11704
Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques. (99%)
Saminder Dhesi; Laura Fontes; Pedro Machado; Isibor Kennedy Ihianle; Farhad Fassihi Tash; David Ada Adama

http://arxiv.org/abs/2302.11328
PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks. (98%)
Deqiang Li; Shicheng Cui; Yun Li; Jia Xu; Fu Xiao; Shouhuai Xu

http://arxiv.org/abs/2302.11628
Provable Robustness Against a Union of $\ell_0$ Adversarial Attacks. (97%)
Zayd Hammoudeh; Daniel Lowd

http://arxiv.org/abs/2302.11408
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms. (33%)
Minzhou Pan; Yi Zeng; Lingjuan Lyu; Xue Lin; Ruoxi Jia

http://arxiv.org/abs/2302.12095
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective. (12%)
Jindong Wang; Xixu Hu; Wenxin Hou; Hao Chen; Runkai Zheng; Yidong Wang; Linyi Yang; Haojun Huang; Wei Ye; Xiubo Geng; Binxin Jiao; Yue Zhang; Xing Xie

http://arxiv.org/abs/2302.10980
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. (99%)
Sihui Dai; Saeed Mahloujifar; Chong Xiang; Vikash Sehwag; Pin-Yu Chen; Prateek Mittal

http://arxiv.org/abs/2302.10739
MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection. (99%)
Aqib Rashid; Jose Such

http://arxiv.org/abs/2302.10686
Interpretable Spectrum Transformation Attacks to Speaker Recognition. (98%)
Jiadi Yao; Hong Luo; Xiao-Lei Zhang

http://arxiv.org/abs/2302.10722
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker. (97%)
Sihui Dai; Wenxin Ding; Arjun Nitin Bhagoji; Daniel Cullina; Ben Y. Zhao; Haitao Zheng; Prateek Mittal

http://arxiv.org/abs/2302.10633
Generalization Bounds for Adversarial Contrastive Learning. (31%)
Xin Zou; Weiwei Liu

http://arxiv.org/abs/2303.01245
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training. (98%)
Rabiah Al-qudah; Moayad Aloqaily; Bassem Ouni; Mohsen Guizani; Thierry Lestable

http://arxiv.org/abs/2302.09902
Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing System. (97%)
Hao Lv; Bing Li; Lei Zhang; Cheng Liu; Ying Wang

http://arxiv.org/abs/2302.10164
Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts. (88%)
Francesco Croce; Sylvestre-Alvise Rebuffi; Evan Shelhamer; Sven Gowal

http://arxiv.org/abs/2302.10149
Poisoning Web-Scale Training Datasets is Practical. (83%)
Nicholas Carlini; Matthew Jagielski; Christopher A. Choquette-Choo; Daniel Paleka; Will Pearce; Hyrum Anderson; Andreas Terzis; Kurt Thomas; Florian Tramèr

http://arxiv.org/abs/2302.09814
Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network. (47%)
Xiaojian Yuan; Kejiang Chen; Jie Zhang; Weiming Zhang; Nenghai Yu; Yang Zhang

http://arxiv.org/abs/2302.10344
Model-based feature selection for neural networks: A mixed-integer programming approach. (22%)
Shudian Zhao; Calvin Tsay; Jan Kronqvist

http://arxiv.org/abs/2302.10341
Take Me Home: Reversing Distribution Shifts using Reinforcement Learning. (8%)
Vivian Lin; Kuk Jin Jang; Souradeep Dutta; Michele Caprio; Oleg Sokolsky; Insup Lee

http://arxiv.org/abs/2302.09923
Prompt Stealing Attacks Against Text-to-Image Generation Models. (1%)
Xinyue Shen; Yiting Qu; Michael Backes; Yang Zhang

http://arxiv.org/abs/2302.09491
X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection. (99%)
Aishan Liu; Jun Guo; Jiakai Wang; Siyuan Liang; Renshuai Tao; Wenbo Zhou; Cong Liu; Xianglong Liu; Dacheng Tao

http://arxiv.org/abs/2302.09575
Stationary Point Losses for Robust Model. (93%)
Weiwei Gao; Dazhi Zhang; Yao Li; Zhichang Guo; Ovanes Petrosian

http://arxiv.org/abs/2302.09578
On Feasibility of Server-side Backdoor Attacks on Split Learning. (76%)
Behrad Tajalli; Oguzhan Ersoy; Stjepan Picek

http://arxiv.org/abs/2302.09457
Adversarial Machine Learning: A Systematic Survey of Backdoor Attack, Weight Attack and Adversarial Example. (99%)
Baoyuan Wu; Li Liu; Zihao Zhu; Qingshan Liu; Zhaofeng He; Siwei Lyu

http://arxiv.org/abs/2302.09479
Delving into the Adversarial Robustness of Federated Learning. (98%)
Jie Zhang; Bo Li; Chen Chen; Lingjuan Lyu; Shuang Wu; Shouhong Ding; Chao Wu

http://arxiv.org/abs/2302.09309
Meta Style Adversarial Training for Cross-Domain Few-Shot Learning. (83%)
Yuqian Fu; Yu Xie; Yanwei Fu; Yu-Gang Jiang

http://arxiv.org/abs/2302.09270
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements. (67%)
Jiawen Deng; Jiale Cheng; Hao Sun; Zhexin Zhang; Minlie Huang

http://arxiv.org/abs/2302.09462
MedViT: A Robust Vision Transformer for Generalized Medical Image Classification. (12%)
Omid Nejati Manzari; Hamid Ahmadabadi; Hossein Kashiani; Shahriar B. Shokouhi; Ahmad Ayatollahi

http://arxiv.org/abs/2302.09420
RobustNLP: A Technique to Defend NLP Models Against Backdoor Attacks. (11%)
Marwan Omar

http://arxiv.org/abs/2302.09344
Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics. (2%)
Nihal Murali; Aahlad Puli; Ke Yu; Rajesh Ranganath; Kayhan Batmanghelich

http://arxiv.org/abs/2302.08973
Measuring Equality in Machine Learning Security Defenses. (96%)
Luke E. Richards; Edward Raff; Cynthia Matuszek

http://arxiv.org/abs/2302.09190
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions. (5%)
Manish Nagireddy; Moninder Singh; Samuel C. Hoffman; Evaline Ju; Karthikeyan Natesan Ramamurthy; Kush R. Varshney

http://arxiv.org/abs/2302.09207
RetVec: Resilient and Efficient Text Vectorizer. (4%)
Elie Bursztein; Marina Zhang; Owen Vallis; Xinyu Jia; Alexey Kurakin

http://arxiv.org/abs/2302.08257
On the Effect of Adversarial Training Against Invariance-based Adversarial Examples. (99%)
Roland Rauter; Martin Nocker; Florian Merkle; Pascal Schöttle

http://arxiv.org/abs/2302.08637
High-frequency Matters: An Overwriting Attack and defense for Image-processing Neural Network Watermarking. (67%)
Huajie Chen; Tianqing Zhu; Chi Liu; Shui Yu; Wanlei Zhou

http://arxiv.org/abs/2302.08466
Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack using Public Data. (3%)
Pratik Karmakar; Debabrota Basu

http://arxiv.org/abs/2302.10802
A Novel Noise Injection-based Training Scheme for Better Model Robustness. (2%)
Zeliang Zhang; Jinyang Jiang; Minjie Chen; Zhiyuan Wang; Yijie Peng; Zhaofei Yu

http://arxiv.org/abs/2302.08066
Masking and Mixing Adversarial Training. (99%)
Hiroki Adachi; Tsubasa Hirakawa; Takayoshi Yamashita; Hironobu Fujiyoshi; Yasunori Ishii; Kazuki Kozuka

http://arxiv.org/abs/2302.08048
Robust Mid-Pass Filtering Graph Convolutional Networks. (98%)
Jincheng Huang; Lun Du; Xu Chen; Qiang Fu; Shi Han; Dongmei Zhang

http://arxiv.org/abs/2302.08051
Graph Adversarial Immunization for Certifiable Robustness. (98%)
Shuchang Tao; Huawei Shen; Qi Cao; Yunfan Wu; Liang Hou; Xueqi Cheng

http://arxiv.org/abs/2302.07769
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars. (87%)
Abhiroop Bhattacharjee; Abhishek Moitra; Priyadarshini Panda

http://arxiv.org/abs/2302.07956
Tight Auditing of Differentially Private Machine Learning. (41%)
Milad Nasr; Jamie Hayes; Thomas Steinke; Borja Balle; Florian Tramèr; Matthew Jagielski; Nicholas Carlini; Andreas Terzis

http://arxiv.org/abs/2302.07717
Field-sensitive Data Flow Integrity. (1%)
So Shizukuishi; Yoshitaka Arahori; Katsuhiko Gondow

http://arxiv.org/abs/2302.07608
Uncertainty-Estimation with Normalized Logits for Out-of-Distribution Detection. (1%)
Mouxiao Huang; Yu Qiao

http://arxiv.org/abs/2302.06912
Regret-Based Defense in Adversarial Reinforcement Learning. (99%)
Roman Belaire; Pradeep Varakantham; Thanh Nguyen; David Lo

http://arxiv.org/abs/2302.07221
On the Role of Randomization in Adversarially Robust Classification. (99%)
Lucas Gnecco-Heredia; Yann Chevaleyre; Benjamin Negrevergne; Laurent Meunier; Muni Sreenivas Pydi

http://arxiv.org/abs/2302.07363
Attacking Fake News Detectors via Manipulating News Social Engagement. (83%)
Haoran Wang; Yingtong Dou; Canyu Chen; Lichao Sun; Philip S. Yu; Kai Shu

http://arxiv.org/abs/2302.07173
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning. (31%)
Shenghui Li; Edith C. -H. Ngai; Thiemo Voigt

http://arxiv.org/abs/2302.07011
A Modern Look at the Relationship between Sharpness and Generalization. (10%)
Maksym Andriushchenko; Francesco Croce; Maximilian Müller; Matthias Hein; Nicolas Flammarion

http://arxiv.org/abs/2302.07225
Bounding Training Data Reconstruction in DP-SGD. (8%)
Jamie Hayes; Saeed Mahloujifar; Borja Balle

http://arxiv.org/abs/2302.07347
Security Defense For Smart Contracts: A Comprehensive Survey. (1%)
Nikolay Ivanov; Chenning Li; Qiben Yan; Zhiyuan Sun; Zhichao Cao; Xiapu Luo

http://arxiv.org/abs/2302.07324
READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises. (1%)
Chenglei Si; Zhengyan Zhang; Yingfa Chen; Xiaozhi Wang; Zhiyuan Liu; Maosong Sun

http://arxiv.org/abs/2302.06279
Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data. (98%)
Gorka Abad; Oguzhan Ersoy; Stjepan Picek; Aitor Urbieta

http://arxiv.org/abs/2302.06588
Raising the Cost of Malicious AI-Powered Image Editing. (82%)
Hadi Salman; Alaa Khaddaj; Guillaume Leclerc; Andrew Ilyas; Aleksander Madry

http://arxiv.org/abs/2302.07735
Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge. (8%)
Ali Al-Kaswan; Maliheh Izadi; Deursen Arie van

http://arxiv.org/abs/2302.06801
Backdoor Learning for NLP: Recent Advances, Challenges, and Future Research Directions. (1%)
Marwan Omar

http://arxiv.org/abs/2302.05892
TextDefense: Adversarial Text Detection based on Word Importance Entropy. (99%)
Lujia Shen; Xuhong Zhang; Shouling Ji; Yuwen Pu; Chunpeng Ge; Xing Yang; Yanghe Feng

http://arxiv.org/abs/2302.05794
Mutation-Based Adversarial Attacks on Neural Text Detectors. (69%)
Gongbo Liang; Jesus Guerrero; Izzat Alsmadi

http://arxiv.org/abs/2302.05703
HateProof: Are Hateful Meme Detection Systems really Robust? (13%)
Piush Aggarwal; Pranit Chawla; Mithun Das; Punyajoy Saha; Binny Mathew; Torsten Zesch; Animesh Mukherjee

http://arxiv.org/abs/2302.05706
MTTM: Metamorphic Testing for Textual Content Moderation Software. (2%)
Wenxuan Wang; Jen-tse Huang; Weibin Wu; Jianping Zhang; Yizhan Huang; Shuqing Li; Pinjia He; Michael Lyu

http://arxiv.org/abs/2302.05807
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. (1%)
Jeremiah Zhe Liu; Krishnamurthy Dj Dvijotham; Jihyeon Lee; Quan Yuan; Martin Strobel; Balaji Lakshminarayanan; Deepak Ramachandran

http://arxiv.org/abs/2302.05628
High Recovery with Fewer Injections: Practical Binary Volumetric Injection Attacks against Dynamic Searchable Encryption. (1%)
Xianglong Zhang; Wei Wang; Peng Xu; Laurence T. Yang; Kaitai Liang

http://arxiv.org/abs/2302.05086
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples. (98%)
Qizhang Li; Yiwen Guo; Wangmeng Zuo; Hao Chen

http://arxiv.org/abs/2303.01263
Unnoticeable Backdoor Attacks on Graph Neural Networks. (80%)
Enyan Dai; Minhua Lin; Xiang Zhang; Suhang Wang

http://arxiv.org/abs/2302.05120
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text Attacks. (73%)
Piotr Gaiński; Klaudia Bałazy

http://arxiv.org/abs/2302.10896
IB-RAR: Information Bottleneck as Regularizer for Adversarial Robustness. (98%)
Xiaoyun Xu; Guilherme Perin; Stjepan Picek

http://arxiv.org/abs/2302.04578
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples. (98%)
Chumeng Liang; Xiaoyu Wu; Yang Hua; Jiaru Zhang; Yiming Xue; Tao Song; Zhengui Xue; Ruhui Ma; Haibing Guan

http://arxiv.org/abs/2302.04977
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines. (81%)
Eugene Bagdasaryan; Vitaly Shmatikov

http://arxiv.org/abs/2302.04457
Imperceptible Sample-Specific Backdoor to DNN with Denoising Autoencoder. (62%)
Jiliang Zhang; Jing Xu; Zhi Zhang; Yansong Gao

http://arxiv.org/abs/2302.04638
Better Diffusion Models Further Improve Adversarial Training. (22%)
Zekai Wang; Tianyu Pang; Chao Du; Min Lin; Weiwei Liu; Shuicheng Yan

http://arxiv.org/abs/2302.04700
Augmenting NLP data to counter Annotation Artifacts for NLI Tasks. (16%)
Armaan Singh Bhullar

http://arxiv.org/abs/2302.06455
Incremental Satisfiability Modulo Theory for Verification of Deep Neural Networks. (1%)
Pengfei Yang; Zhiming Chi; Zongxin Liu; Mengyu Zhao; Cheng-Chao Huang; Shaowei Cai; Lijun Zhang

http://arxiv.org/abs/2302.04025
WAT: Improve the Worst-class Robustness in Adversarial Training. (99%)
Boqi Li; Weiwei Liu

http://arxiv.org/abs/2302.04379
Exploiting Certified Defences to Attack Randomised Smoothing. (99%)
Andrew C. Cullen; Paul Montague; Shijie Liu; Sarah M. Erfani; Benjamin I. P. Rubinstein

http://arxiv.org/abs/2302.04246
Shortcut Detection with Variational Autoencoders. (13%)
Nicolas M. Müller; Simon Roschmann; Shahbaz Khan; Philip Sperl; Konstantin Böttinger

http://arxiv.org/abs/2302.04332
Continuous Learning for Android Malware Detection. (13%)
Yizheng Chen; Zhoujie Ding; David Wagner

http://arxiv.org/abs/2302.04116
Training-free Lexical Backdoor Attacks on Language Models. (8%)
Yujin Huang; Terry Yue Zhuo; Qiongkai Xu; Han Hu; Xingliang Yuan; Chunyang Chen

http://arxiv.org/abs/2302.10296
On Function-Coupled Watermarks for Deep Neural Networks. (2%)
Xiangyu Wen; Yu Li; Wei Jiang; Qiang Xu

http://arxiv.org/abs/2302.04369
Unsupervised Learning of Initialization in Deep Neural Networks via Maximum Mean Discrepancy. (1%)
Cheolhyoung Lee; Kyunghyun Cho

http://arxiv.org/abs/2302.03657
Toward Face Biometric De-identification using Adversarial Examples. (98%)
Mahdi Ghafourian; Julian Fierrez; Luis Felipe Gomez; Ruben Vera-Rodriguez; Aythami Morales; Zohra Rezgui; Raymond Veldhuis

http://arxiv.org/abs/2302.03322
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence. (83%)
Simin Li; Jun Guo; Jingqiao Xiu; Pu Feng; Xin Yu; Jiakai Wang; Aishan Liu; Wenjun Wu; Xianglong Liu

http://arxiv.org/abs/2302.03262
Membership Inference Attacks against Diffusion Models. (64%)
Tomoya Matsumoto; Takayuki Miura; Naoto Yanai

http://arxiv.org/abs/2302.03684
Temporal Robustness against Data Poisoning. (12%)
Wenxiao Wang; Soheil Feizi

http://arxiv.org/abs/2302.03465
Robustness Implies Fairness in Casual Algorithmic Recourse. (2%)
Ahmad-Reza Ehyaei; Amir-Hossein Karimi; Bernhard Schölkopf; Setareh Maghsudi

http://arxiv.org/abs/2302.03335
Low-Latency Communication using Delay-Aware Relays Against Reactive Adversaries. (1%)
Vivek Chaudhary; J. Harshan

http://arxiv.org/abs/2302.02568
Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend. (99%)
Ning Lu; Shengcai Liu; Zhirui Zhang; Qi Wang; Haifeng Liu; Ke Tang

http://arxiv.org/abs/2302.03251
SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency. (92%)
Junfeng Guo; Yiming Li; Xun Chen; Hanqing Guo; Lichao Sun; Cong Liu

http://arxiv.org/abs/2302.03015
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness. (87%)
Yuancheng Xu; Yanchao Sun; Micah Goldblum; Tom Goldstein; Furong Huang

http://arxiv.org/abs/2302.02829
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. (75%)
Jan Schuchardt; Aleksandar Bojchevski; Johannes Gasteiger; Stephan Günnemann

http://arxiv.org/abs/2302.02907
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. (67%)
Salah Ghamizi; Jingfeng Zhang; Maxime Cordy; Mike Papadakis; Masashi Sugiyama; Yves Le Traon

http://arxiv.org/abs/2302.02607
Target-based Surrogates for Stochastic Optimization. (1%)
Jonathan Wilder Lavington; Sharan Vaswani; Reza Babanezhad; Mark Schmidt; Nicolas Le Roux

http://arxiv.org/abs/2302.02924
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks. (1%)
Emanuele Ledda; Giorgio Fumera; Fabio Roli

http://arxiv.org/abs/2302.03098
One-shot Empirical Privacy Estimation for Federated Learning. (1%)
Galen Andrew; Peter Kairouz; Sewoong Oh; Alina Oprea; H. Brendan McMahan; Vinith Suriyakumar

http://arxiv.org/abs/2302.02502
On the Role of Contrastive Representation Learning in Adversarial Robustness: An Empirical Study. (54%)
Fatemeh Ghofrani; Mehdi Yaghouti; Pooyan Jamshidi

http://arxiv.org/abs/2302.02503
Leaving Reality to Imagination: Robust Classification via Generated Datasets. (2%)
Hritik Bansal; Aditya Grover

http://arxiv.org/abs/2302.02213
CosPGD: a unified white-box adversarial attack for pixel-wise prediction tasks. (99%)
Shashank Agnihotri; Steffen Jung; Margret Keuper

http://arxiv.org/abs/2302.02216
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection. (86%)
Federica Granese; Marco Romanelli; Siddharth Garg; Pablo Piantanida

http://arxiv.org/abs/2302.02300
Run-Off Election: Improved Provable Defense against Data Poisoning Attacks. (83%)
Keivan Rezaei; Kiarash Banihashem; Atoosa Chegini; Soheil Feizi

http://arxiv.org/abs/2302.02162
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Decision Tree Models. (80%)
Abdullah Caglar Oksuz; Anisa Halimi; Erman Ayday

http://arxiv.org/abs/2302.02208
Certified Robust Control under Adversarial Perturbations. (78%)
Jinghan Yang; Hunmin Kim; Wenbin Wan; Naira Hovakimyan; Yevgeniy Vorobeychik

http://arxiv.org/abs/2302.02023
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification. (96%)
Lingfeng Shen; Ze Zhang; Haiyun Jiang; Ying Chen

http://arxiv.org/abs/2302.02012
DeTorrent: An Adversarial Padding-only Traffic Analysis Defense. (73%)
James K Holland; Jason Carpenter; Se Eun Oh; Nicholas Hopper

http://arxiv.org/abs/2302.01740
SoK: A Systematic Evaluation of Backdoor Trigger Characteristics in Image Classification. (61%)
Gorka Abad; Jing Xu; Stefanos Koffas; Behrad Tajalli; Stjepan Picek; Mauro Conti

http://arxiv.org/abs/2302.01629
Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels. (15%)
Simone Bombari; Shayan Kiyani; Marco Mondelli

http://arxiv.org/abs/2302.01961
Asymmetric Certified Robustness via Feature-Convex Neural Networks. (8%)
Samuel Pfrommer; Brendon G. Anderson; Julien Piet; Somayeh Sojoudi

http://arxiv.org/abs/2302.01677
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks. (2%)
Zeyu Qin; Liuyi Yao; Daoyuan Chen; Yaliang Li; Bolin Ding; Minhao Cheng

http://arxiv.org/abs/2302.02042
BarrierBypass: Out-of-Sight Clean Voice Command Injection Attacks through Physical Barriers. (2%)
Payton Walker; Tianfang Zhang; Cong Shi; Nitesh Saxena; Yingying Chen

http://arxiv.org/abs/2302.01855
From Robustness to Privacy and Back. (2%)
Hilal Asi; Jonathan Ullman; Lydia Zakynthinou

http://arxiv.org/abs/2302.01972
DCA: Delayed Charging Attack on the Electric Shared Mobility System. (1%)
Shuocheng Guo; Hanlin Chen; Mizanur Rahman; Xinwu Qian

http://arxiv.org/abs/2302.02031
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning. (1%)
Jacob Alexander Markson Brown; Xi Jiang; Van Tran; Arjun Nitin Bhagoji; Nguyen Phong Hoang; Nick Feamster; Prateek Mittal; Vinod Yegneswaran

http://arxiv.org/abs/2302.00944
TransFool: An Adversarial Attack against Neural Machine Translation Models. (99%)
Sahar Sadrizadeh; Ljiljana Dolamic; Pascal Frossard

http://arxiv.org/abs/2302.01056
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense. (99%)
Zunzhi You; Daochang Liu; Bohyung Han; Chang Xu

http://arxiv.org/abs/2302.01375
On the Robustness of Randomized Ensembles to Adversarial Perturbations. (75%)
Hassan Dbouk; Naresh R. Shanbhag

http://arxiv.org/abs/2302.01404
Provably Bounding Neural Network Preimages. (64%)
Suhas Kotha; Christopher Brix; Zico Kolter; Krishnamurthy Dvijotham; Huan Zhang

http://arxiv.org/abs/2302.01459
A sliced-Wasserstein distance-based approach for out-of-class-distribution detection. (62%)
Mohammad Shifat E Rabbi; Abu Hasnat Mohammad Rubaiyat; Yan Zhuang; Gustavo K Rohde

http://arxiv.org/abs/2302.01381
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. (13%)
Zhouxing Shi; Nicholas Carlini; Ananth Balashankar; Ludwig Schmidt; Cho-Jui Hsieh; Alex Beutel; Yao Qin

http://arxiv.org/abs/2302.00947
SPECWANDS: An Efficient Priority-based Scheduler Against Speculation Contention Attacks. (10%)
Bowen Tang; Chenggang Wu; Pen-Chung Yew; Yinqian Zhang; Mengyao Xie; Yuanming Lai; Yan Kang; Wei Wang; Qiang Wei; Zhe Wang

http://arxiv.org/abs/2302.01474
Defensive ML: Defending Architectural Side-channels with Adversarial Obfuscation. (2%)
Hyoungwook Nam; Raghavendra Pradyumna Pothukuchi; Bo Li; Nam Sung Kim; Josep Torrellas

http://arxiv.org/abs/2302.01440
Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications. (1%)
Chengyu Dong

http://arxiv.org/abs/2302.01428
Dataset Distillation Fixes Dataset Reconstruction Attacks. (1%)
Noel Loo; Ramin Hasani; Mathias Lechner; Daniela Rus

http://arxiv.org/abs/2302.00747
Universal Soldier: Using Universal Adversarial Perturbations for Detecting Backdoor Attacks. (99%)
Xiaoyun Xu; Oguzhan Ersoy; Stjepan Picek

http://arxiv.org/abs/2302.00537
Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection. (92%)
Aqib Rashid; Jose Such

http://arxiv.org/abs/2302.00509
Exploring Semantic Perturbations on Grover. (56%)
Pranav Kulkarni; Ziqing Ji; Yan Xu; Marko Neskovic; Kevin Nolan

http://arxiv.org/abs/2302.01762
BackdoorBox: A Python Toolbox for Backdoor Learning. (10%)
Yiming Li; Mengxi Ya; Yang Bai; Yong Jiang; Shu-Tao Xia

http://arxiv.org/abs/2301.13869
Reverse engineering adversarial attacks with fingerprints from adversarial examples. (99%)
David Aaron Embedded Intelligence Nicholson; Vincent Embedded Intelligence Emanuele

http://arxiv.org/abs/2302.00094
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models. (97%)
Son Quoc Tran; Phong Nguyen-Thuan Do; Uyen Le; Matt Kretchmar

http://arxiv.org/abs/2301.13694
Are Defenses for Graph Neural Networks Robust? (80%)
Felix Mujkanovic; Simon Geisler; Stephan Günnemann; Aleksandar Bojchevski

http://arxiv.org/abs/2301.13487
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks. (75%)
Zhiyuan Cheng; James Liang; Guanhong Tao; Dongfang Liu; Xiangyu Zhang

http://arxiv.org/abs/2301.13486
Robust Linear Regression: Gradient-descent, Early-stopping, and Beyond. (47%)
Meyer Scetbon; Elvis Dohmatob

http://arxiv.org/abs/2301.13803
Fairness-aware Vision Transformer via Debiased Self-Attention. (47%)
Yao Qiang; Chengyin Li; Prashant Khanduri; Dongxiao Zhu

http://arxiv.org/abs/2301.13838
Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression. (12%)
Zhuoran Liu; Zhengyu Zhao; Martha Larson

http://arxiv.org/abs/2301.13577
DRAINCLoG: Detecting Rogue Accounts with Illegally-obtained NFTs using Classifiers Learned on Graphs. (1%)
Hanna Kim; Jian Cui; Eugene Jang; Chanhee Lee; Yongjae Lee; Jin-Woo Chung; Seungwon Shin

http://arxiv.org/abs/2301.13807
Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search. (1%)
Sepehr Sharifi; Donghwan Shin; Lionel C. Briand; Nathan Aschbacher

http://arxiv.org/abs/2301.12680
Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness. (99%)
Bao Gia Doan; Shuiqiao Yang; Paul Montague; Vel Olivier De; Tamas Abraham; Seyit Camtepe; Salil S. Kanhere; Ehsan Abbasnejad; Damith C. Ranasinghe

http://arxiv.org/abs/2301.12968
Improving Adversarial Transferability with Scheduled Step Size and Dual Example. (99%)
Zeliang Zhang; Peihan Liu; Xiaosen Wang; Chenliang Xu

http://arxiv.org/abs/2301.13122
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification. (99%)
João Vitorino; Isabel Praça; Eva Maia

http://arxiv.org/abs/2302.01757
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion. (99%)
Zhuoqun Huang; Neil G. Marchant; Keane Lucas; Lujo Bauer; Olga Ohrimenko; Benjamin I. P. Rubinstein

http://arxiv.org/abs/2301.12896
Identifying Adversarially Attackable and Robust Samples. (99%)
Vyas Raina; Mark Gales

http://arxiv.org/abs/2301.12868
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex. (98%)
Terry Yue Zhuo; Zhuang Li; Yujin Huang; Fatemeh Shiri; Weiqing Wang; Gholamreza Haffari; Yuan-Fang Li

http://arxiv.org/abs/2301.13096
Anchor-Based Adversarially Robust Zero-Shot Learning Driven by Language. (96%)
Xiao Li; Wei Zhang; Yining Liu; Zhanhao Hu; Bo Zhang; Xiaolin Hu

http://arxiv.org/abs/2301.13356
Inference Time Evidences of Adversarial Attacks for Forensic on Transformers. (87%)
Hugo Lemarchant; Liangzi Li; Yiming Qian; Yuta Nakashima; Hajime Nagahara

http://arxiv.org/abs/2301.13028
On the Efficacy of Metrics to Describe Adversarial Attacks. (82%)
Tommaso Puccetti; Tommaso Zoppi; Andrea Ceccarelli

http://arxiv.org/abs/2301.12993
Benchmarking Robustness to Adversarial Image Obfuscations. (74%)
Florian Stimberg; Ayan Chakrabarti; Chun-Ta Lu; Hussein Hazimeh; Otilia Stretcu; Wei Qiao; Yintao Liu; Merve Kaya; Cyrus Rashtchian; Ariel Fuxman; Mehmet Tek; Sven Gowal

http://arxiv.org/abs/2301.13188
Extracting Training Data from Diffusion Models. (5%)
Nicholas Carlini; Jamie Hayes; Milad Nasr; Matthew Jagielski; Vikash Sehwag; Florian Tramèr; Borja Balle; Daphne Ippolito; Eric Wallace

http://arxiv.org/abs/2301.13340
Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning. (2%)
Chaoxi Niu; Guansong Pang; Ling Chen

http://arxiv.org/abs/2301.12831
M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System. (1%)
Chenqi Kong; Kexin Zheng; Yibing Liu; Shiqi Wang; Anderson Rocha; Haoliang Li

http://arxiv.org/abs/2301.12549
Unlocking Deterministic Robustness Certification on ImageNet. (98%)
Kai Hu; Andy Zou; Zifan Wang; Klas Leino; Matt Fredrikson

http://arxiv.org/abs/2301.12487
Mitigating Adversarial Effects of False Data Injection Attacks in Power Grid. (93%)
Farhin Farhad Riya; Shahinul Hoque; Jinyuan Stella Sun; Jiangnan Li; Hairong Qi

http://arxiv.org/abs/2301.12554
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing. (83%)
Yatong Bai; Brendon G. Anderson; Aerin Kim; Somayeh Sojoudi

http://arxiv.org/abs/2301.12576
Uncovering Adversarial Risks of Test-Time Adaptation. (82%)
Tong Wu; Feiran Jia; Xiangyu Qi; Jiachen T. Wang; Vikash Sehwag; Saeed Mahloujifar; Prateek Mittal

http://arxiv.org/abs/2301.12595
Adversarial Attacks on Adversarial Bandits. (69%)
Yuzhe Ma; Zhijin Zhou

http://arxiv.org/abs/2301.12456
Towards Verifying the Geometric Robustness of Large-scale Neural Networks. (54%)
Fu Wang; Peipei Xu; Wenjie Ruan; Xiaowei Huang

http://arxiv.org/abs/2301.12637
Lateralized Learning for Multi-Class Visual Classification Tasks. (13%)
Abubakar Siddique; Will N. Browne; Gina M. Grimshaw

http://arxiv.org/abs/2301.12527
Diverse, Difficult, and Odd Instances (D2O): A New Test Set for Object Classification. (3%)
Ali Borji

http://arxiv.org/abs/2301.12643
Adversarial Style Augmentation for Domain Generalization. (2%)
Yabin Zhang; Bin Deng; Ruihuang Li; Kui Jia; Lei Zhang

http://arxiv.org/abs/2301.12589
Confidence-Aware Calibration and Scoring Functions for Curriculum Learning. (1%)
Shuang Ao; Stefan Rueger; Advaith Siddharthan

http://arxiv.org/abs/2301.12277
Node Injection for Class-specific Network Poisoning. (82%)
Ansh Kumar Sharma; Rahul Kukreja; Mayank Kharbanda; Tanmoy Chakraborty

http://arxiv.org/abs/2302.12002
Out-of-distribution Detection with Energy-based Models. (82%)
Sven Elflein

http://arxiv.org/abs/2301.12318
Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering. (13%)
Rui Zhu; Di Tang; Siyuan Tang; Guanhong Tao; Shiqing Ma; Xiaofeng Wang; Haixu Tang

http://arxiv.org/abs/2301.12151
Selecting Models based on the Risk of Damage Caused by Adversarial Attacks. (1%)
Jona Klemenc; Holger Trittenbach

http://arxiv.org/abs/2301.12046
Semantic Adversarial Attacks on Face Recognition through Significant Attributes. (99%)
Yasmeen M. Khedr; Yifeng Xiong; Kun He

http://arxiv.org/abs/2301.11544
Targeted Attacks on Timeseries Forecasting. (99%)
Yuvaraj Govindarajulu; Avinash Amballa; Pavan Kulkarni; Manojkumar Parmar

http://arxiv.org/abs/2301.11546
Adapting Step-size: A Unified Perspective to Analyze and Improve Gradient-based Methods for Adversarial Attacks. (98%)
Wei Tao; Lei Bao; Long Sheng; Gaowei Wu; Qing Tao

http://arxiv.org/abs/2301.11824
PECAN: A Deterministic Certified Defense Against Backdoor Attacks. (97%)
Yuhao Zhang; Aws Albarghouthi; Loris D'Antoni

http://arxiv.org/abs/2301.12001
Vertex-based reachability analysis for verifying ReLU deep neural networks. (93%)
João Zago; Eduardo Camponogara; Eric Antonelo

http://arxiv.org/abs/2301.11912
OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks. (92%)
Xingwu Guo; Ziwei Zhou; Yueling Zhang; Guy Katz; Min Zhang

http://arxiv.org/abs/2301.11806
PCV: A Point Cloud-Based Network Verifier. (88%)
Arup Kumar Sarker; Farzana Yasmin Ahmad; Matthew B. Dwyer

http://arxiv.org/abs/2301.11553
Robust Transformer with Locality Inductive Bias and Feature Normalization. (88%)
Omid Nejati Manzari; Hossein Kashiani; Hojat Asgarian Dehkordi; Shahriar Baradaran Shokouhi

http://arxiv.org/abs/2301.12036
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense. (87%)
Diyi Liu; Lanmin Liu; Lee D Han

http://arxiv.org/abs/2301.11578
Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers. (80%)
Sungmin Cha; Sungjun Cho; Dasol Hwang; Honglak Lee; Taesup Moon; Moontae Lee

http://arxiv.org/abs/2301.11783
Certified Invertibility in Neural Networks via Mixed-Integer Programming. (76%)
Tianqi Cui; Thomas Bertalan; George J. Pappas; Manfred Morari; Ioannis G. Kevrekidis; Mahyar Fazlyab

http://arxiv.org/abs/2301.11457
Attacking Important Pixels for Anchor-free Detectors. (99%)
Yunxu Xie; Shu Hu; Xin Wang; Quanyu Liao; Bin Zhu; Xi Wu; Siwei Lyu

http://arxiv.org/abs/2301.11324
Certified Interpretability Robustness for Class Activation Mapping. (92%)
Alex Gu; Tsui-Wei Weng; Pin-Yu Chen; Sijia Liu; Luca Daniel

http://arxiv.org/abs/2301.11050
Minerva: A File-Based Ransomware Detector. (68%)
Dorjan Hitaj; Giulio Pagnotta; Gaspari Fabio De; Carli Lorenzo De; Luigi V. Mancini

http://arxiv.org/abs/2301.10964
Interaction-level Membership Inference Attack Against Federated Recommender Systems. (31%)
Wei Yuan; Chaoqun Yang; Quoc Viet Hung Nguyen; Lizhen Cui; Tieke He; Hongzhi Yin

http://arxiv.org/abs/2301.10766
On the Adversarial Robustness of Camera-based 3D Object Detection. (99%)
Shaoyuan Xie; Zichao Li; Zeyu Wang; Cihang Xie

http://arxiv.org/abs/2301.10822
RobustPdM: Designing Robust Predictive Maintenance against Adversarial Attacks. (99%)
Ayesha Siddique; Ripan Kumar Kundu; Gautam Raj Mode; Khaza Anuarul Hoque

http://arxiv.org/abs/2301.10412
BDMMT: Backdoor Sample Detection for Language Models through Model Mutation Testing. (98%)
Jiali Wei; Ming Fan; Wenjing Jiao; Wuxia Jin; Ting Liu

http://arxiv.org/abs/2301.10454
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection. (96%)
Mohammad Azizmalayeri; Arman Zarei; Alireza Isavand; Mohammad Taghi Manzuri; Mohammad Hossein Rohban

http://arxiv.org/abs/2301.10576
A Study on FGSM Adversarial Training for Neural Retrieval. (75%)
Simon Lupart; Stéphane Clinchant

http://arxiv.org/abs/2301.10908
Distilling Cognitive Backdoor Patterns within an Image. (5%)
Hanxun Huang; Xingjun Ma; Sarah Erfani; James Bailey

http://arxiv.org/abs/2301.10608
Connecting metrics for shape-texture knowledge in computer vision. (1%)
Tiago Oliveira; Tiago Marques; Arlindo L. Oliveira

http://arxiv.org/abs/2301.11289
Blockchain-aided Secure Semantic Communication for AI-Generated Content in Metaverse. (13%)
Yijing Lin; Hongyang Du; Dusit Niyato; Jiangtian Nie; Jiayi Zhang; Yanyu Cheng; Zhaohui Yang

http://arxiv.org/abs/2301.09892
Learning Effective Strategies for Moving Target Defense with Switching Costs. (1%)
Vignesh Viswanathan; Megha Bose; Praveen Paruchuri

http://arxiv.org/abs/2301.09879
Data Augmentation Alone Can Improve Adversarial Training. (1%)
Lin Li; Michael Spratling

http://arxiv.org/abs/2301.09740
DODEM: DOuble DEfense Mechanism Against Adversarial Attacks Towards Secure Industrial Internet of Things Analytics. (99%)
Onat Gungor; Tajana Rosing; Baris Aksanli

http://arxiv.org/abs/2301.09305
Practical Adversarial Attacks Against AI-Driven Power Allocation in a Distributed MIMO Network. (92%)
Ömer Faruk Tuna; Fehmi Emre Kadan; Leyli Karaçay

http://arxiv.org/abs/2301.09508
BayBFed: Bayesian Backdoor Defense for Federated Learning. (78%)
Kavita Kumari; Phillip Rieger; Hossein Fereidooni; Murtuza Jadliwala; Ahmad-Reza Sadeghi

http://arxiv.org/abs/2301.09732
Backdoor Attacks in Peer-to-Peer Federated Learning. (68%)
Gokberk Yar; Cristina Nita-Rotaru; Alina Oprea

http://arxiv.org/abs/2301.09069
Provable Unrestricted Adversarial Training without Compromise with Generalizability. (99%)
Lilin Zhang; Ning Yang; Yanchao Sun; Philip S. Yu

http://arxiv.org/abs/2301.09072
ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning. (8%)
Shangqing Liu; Bozhi Wu; Xiaofei Xie; Guozhu Meng; Yang Liu

http://arxiv.org/abs/2301.08842
Limitations of Piecewise Linearity for Efficient Robustness Certification. (95%)
Klas Leino

http://arxiv.org/abs/2301.08751
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning. (16%)
Soumyadeep Pal; Ren Wang; Yuguang Yao; Sijia Liu

http://arxiv.org/abs/2301.08881
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness. (8%)
Shuaichen Chang; Jun Wang; Mingwen Dong; Lin Pan; Henghui Zhu; Alexander Hanbo Li; Wuwei Lan; Sheng Zhang; Jiarong Jiang; Joseph Lilien; Steve Ash; William Yang Wang; Zhiguo Wang; Vittorio Castelli; Patrick Ng; Bing Xiang

http://arxiv.org/abs/2301.08428
Defending SDN against packet injection attacks using deep learning. (2%)
Anh Tuan Phu; Bo Li; Faheem Ullah; Tanvir Ul Huque; Ranesh Naha; Ali Babar; Hung Nguyen

http://arxiv.org/abs/2301.08170
On the Vulnerability of Backdoor Defenses for Federated Learning. (62%)
Pei Fang; Jinghui Chen

http://arxiv.org/abs/2301.08401
On the Relationship Between Information-Theoretic Privacy Metrics And Probabilistic Information Privacy. (31%)
Chong Xiao Wang; Wee Peng Tay

http://arxiv.org/abs/2301.08092
RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation. (16%)
Utkarsh Nath; Yancheng Wang; Yingzhen Yang

http://arxiv.org/abs/2301.08114
Enhancing Deep Learning with Scenario-Based Override Rules: a Case Study. (1%)
Adiel Ashrov; Guy Katz

http://arxiv.org/abs/2301.06871
Denoising Diffusion Probabilistic Models as a Defense against Adversarial Attacks. (98%)
Lars Lien Ankile; Anna Midgley; Sebastian Weisshaar

http://arxiv.org/abs/2301.07487
Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness. (68%)
Ezgi Korkmaz

http://arxiv.org/abs/2301.07284
Label Inference Attack against Split Learning under Regression Setting. (8%)
Shangyu Xie; Xin Yang; Yuanshun Yao; Tianyi Liu; Taiqing Wang; Jiankai Sun

http://arxiv.org/abs/2301.06393
$\beta$-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture Search. (1%)
Peng Ye; Tong He; Baopu Li; Tao Chen; Lei Bai; Wanli Ouyang

http://arxiv.org/abs/2301.06442
Modeling Uncertain Feature Representation for Domain Generalization. (1%)
Xiaotong Li; Zixuan Hu; Jun Liu; Yixiao Ge; Yongxing Dai; Ling-Yu Duan

http://arxiv.org/abs/2301.06241
BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense. (4%)
Siyuan Cheng; Guanhong Tao; Yingqi Liu; Shengwei An; Xiangzhe Xu; Shiwei Feng; Guangyu Shen; Kaiyuan Zhang; Qiuling Xu; Shiqing Ma; Xiangyu Zhang

http://arxiv.org/abs/2301.07099
Adaptive Deep Neural Network Inference Optimization with EENet. (1%)
Fatih Ilhan; Ka-Ho Chow; Sihao Hu; Tiansheng Huang; Selim Tekin; Wenqi Wei; Yanzhao Wu; Myungjin Lee; Ramana Kompella; Hugo Latapie; Gaowen Liu; Ling Liu

http://arxiv.org/abs/2301.05506
On the feasibility of attacking Thai LPR systems with adversarial examples. (99%)
Chissanupong Jiamsuchon; Jakapan Suaboot; Norrathep Rattanavipanon

http://arxiv.org/abs/2301.05264
Security-Aware Approximate Spiking Neural Networks. (87%)
Syed Tihaam Ahmad; Ayesha Siddique; Khaza Anuarul Hoque

http://arxiv.org/abs/2301.05250
Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks. (3%)
Yi Shi; Yalin E. Sagduyu; Tugba Erpek

http://arxiv.org/abs/2301.04785
Phase-shifted Adversarial Training. (82%)
Yeachan Kim; Seongyeon Kim; Ihyeok Seo; Bonggun Shin

http://arxiv.org/abs/2301.04554
Universal Detection of Backdoor Attacks via Density-based Clustering and Centroids Analysis. (78%)
Wei Guo; Benedetta Tondi; Mauro Barni

http://arxiv.org/abs/2301.04093
On the Robustness of AlphaFold: A COVID-19 Case Study. (73%)
Ismail Alkhouri; Sumit Jha; Andre Beckus; George Atia; Alvaro Velasquez; Rickard Ewetz; Arvind Ramanathan; Susmit Jha

http://arxiv.org/abs/2301.03826
CDA: Contrastive-adversarial Domain Adaptation. (38%)
Nishant Yadav; Mahbubul Alam; Ahmed Farahat; Dipanjan Ghosh; Chetan Gupta; Auroop R. Ganguly

http://arxiv.org/abs/2301.04230
User-Centered Security in Natural Language Processing. (12%)
Chris Emmery

http://arxiv.org/abs/2301.04218
Leveraging Diffusion For Strong and High Quality Face Morphing Attacks. (3%)
Zander W. Blasingame; Chen Liu

http://arxiv.org/abs/2301.03760
Over-The-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting. (99%)
Fei Xiao; Yong Huang; Yingying Zuo; Wei Kuang; Wei Wang

http://arxiv.org/abs/2301.03703
On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense. (98%)
Asadullah Hill Galib; Bidhan Bashyal

http://arxiv.org/abs/2301.04017
Is Federated Learning a Practical PET Yet? (13%)
Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot

http://arxiv.org/abs/2301.03724
SoK: Hardware Defenses Against Speculative Execution Attacks. (1%)
Guangyuan Hu; Zecheng He; Ruby Lee

http://arxiv.org/abs/2301.03110
RobArch: Designing Robust Architectures against Adversarial Attacks. (76%)
ShengYun Peng; Weilin Xu; Cory Cornelius; Kevin Li; Rahul Duggal; Duen Horng Chau; Jason Martin

http://arxiv.org/abs/2302.05294
MoreauGrad: Sparse and Robust Interpretation of Neural Networks via Moreau Envelope. (1%)
Jingwei Zhang; Farzan Farnia

http://arxiv.org/abs/2301.02905
REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service. (99%)
Wenjie Qu; Jinyuan Jia; Neil Zhenqiang Gong

http://arxiv.org/abs/2301.04472
Adversarial training with informed data selection. (99%)
Marcele O. K. Mendonça; Javier Maroto; Pascal Frossard; Paulo S. R. Diniz

http://arxiv.org/abs/2301.02412
Code Difference Guided Adversarial Example Generation for Deep Code Models. (99%)
Zhao Tian; Junjie Chen; Zhi Jin

http://arxiv.org/abs/2301.02496
Stealthy Backdoor Attack for Code Models. (98%)
Zhou Yang; Bowen Xu; Jie M. Zhang; Hong Jin Kang; Jieke Shi; Junda He; David Lo

http://arxiv.org/abs/2301.02615
Silent Killer: A Stealthy, Clean-Label, Black-Box Backdoor Attack. (98%)
Tzvi Lederer; Gallil Maimon; Lior Rokach

http://arxiv.org/abs/2301.02288
gRoMA: a Tool for Measuring the Global Robustness of Deep Neural Networks. (96%)
Natan Levy; Raz Yerushalmi; Guy Katz

http://arxiv.org/abs/2301.02039
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. (61%)
Yan Scholten; Jan Schuchardt; Simon Geisler; Aleksandar Bojchevski; Stephan Günnemann

http://arxiv.org/abs/2301.02344
TrojanPuzzle: Covertly Poisoning Code-Suggestion Models. (4%)
Hojjat Aghakhani; Wei Dai; Andre Manoel; Xavier Fernandes; Anant Kharkar; Christopher Kruegel; Giovanni Vigna; David Evans; Ben Zorn; Robert Sim

http://arxiv.org/abs/2302.10291
Can Large Language Models Change User Preference Adversarially? (1%)
Varshini Subhash

http://arxiv.org/abs/2301.01832
Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting. (98%)
Wangkun Xu; Fei Teng

http://arxiv.org/abs/2301.01495
Beckman Defense. (84%)
A. V. Subramanyam

http://arxiv.org/abs/2301.01731
GUAP: Graph Universal Attack Through Adversarial Patching. (81%)
Xiao Zang; Jie Chen; Bo Yuan

http://arxiv.org/abs/2301.01885
Enhancement attacks in biomedical machine learning. (1%)
Matthew Rosenblatt; Javid Dadashkarimi; Dustin Scheinost

http://arxiv.org/abs/2301.01343
Explainability and Robustness of Deep Visual Classification Models. (92%)
Jindong Gu

http://arxiv.org/abs/2301.00986
Look, Listen, and Attack: Backdoor Attacks Against Video Action Recognition. (83%)
Hasan Abed Al Kader Hammoud; Shuming Liu; Mohammed Alkhrashi; Fahad AlBalawi; Bernard Ghanem

http://arxiv.org/abs/2301.01197
Backdoor Attacks Against Dataset Distillation. (50%)
Yugeng Liu; Zheng Li; Michael Backes; Yun Shen; Yang Zhang

http://arxiv.org/abs/2301.01044
Analysis of Label-Flip Poisoning Attack on Machine Learning Based Malware Detector. (33%)
Kshitiz Aryal; Maanak Gupta; Mahmoud Abdelsalam

http://arxiv.org/abs/2301.00896
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos. (92%)
Wei Xingxing; Wang Songping; Yan Huanqian

http://arxiv.org/abs/2301.00364
Generalizable Black-Box Adversarial Attack with Meta Learning. (99%)
Fei Yin; Yong Zhang; Baoyuan Wu; Yan Feng; Jingyi Zhang; Yanbo Fan; Yujiu Yang

http://arxiv.org/abs/2301.01223
ExploreADV: Towards exploratory attack for Neural Networks. (99%)
Tianzuo Luo; Yuyi Zhong; Siaucheng Khoo

http://arxiv.org/abs/2301.00435
Trojaning semi-supervised learning model via poisoning wild images on the web. (47%)
Le Feng; Zhenxing Qian; Sheng Li; Xinpeng Zhang

http://arxiv.org/abs/2301.01218
Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence. (99%)
Han Fang; Jiyi Zhang; Yupeng Qiu; Ke Xu; Chengfang Fang; Ee-Chien Chang

http://arxiv.org/abs/2212.14875
Guidance Through Surrogate: Towards a Generic Diagnostic Attack. (99%)
Muzammal Naseer; Salman Khan; Fatih Porikli; Fahad Shahbaz Khan

http://arxiv.org/abs/2212.14597
Defense Against Adversarial Attacks on Audio DeepFake Detection. (91%)
Piotr Kawa; Marcin Plata; Piotr Syga

http://arxiv.org/abs/2212.14677
Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification. (82%)
Pedro Miguel Sánchez Sánchez; Alberto Huertas Celdrán; Gérôme Bovet; Gregorio Martínez Pérez

http://arxiv.org/abs/2301.01217
Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples. (22%)
Jiaming Zhang; Xingjun Ma; Qi Yi; Jitao Sang; Yugang Jiang; Yaowei Wang; Changsheng Xu

http://arxiv.org/abs/2301.00108
Targeted k-node Collapse Problem: Towards Understanding the Robustness of Local k-core Structure. (1%)
Yuqian Lv; Bo Zhou; Jinhuan Wang; Qi Xuan

http://arxiv.org/abs/2212.14315
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice. (68%)
Giovanni Apruzzese; Hyrum S. Anderson; Savino Dambra; David Freeman; Fabio Pierazzi; Kevin A. Roundy

http://arxiv.org/abs/2212.14268
Detection of out-of-distribution samples using binary neuron activation patterns. (11%)
Bartlomiej Olber; Krystian Radlak; Adam Popowicz; Michal Szczepankiewicz; Krystian Chachula

http://arxiv.org/abs/2212.13707
Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions. (99%)
Kamilya Smagulova; Mohammed E. Fouda; Ahmed Eltawil

http://arxiv.org/abs/2212.14115
Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks. (98%)
Junlin Wu; Hussein Sibai; Yevgeniy Vorobeychik

http://arxiv.org/abs/2212.13700
Publishing Efficient On-device Models Increases Adversarial Vulnerability. (95%)
Sanghyun Hong; Nicholas Carlini; Alexey Kurakin

http://arxiv.org/abs/2212.14049
Differentiable Search of Accurate and Robust Architectures. (92%)
Yuwei Ou; Xiangning Xie; Shangce Gao; Yanan Sun; Kay Chen Tan; Jiancheng Lv

http://arxiv.org/abs/2212.14106
Robust Ranking Explanations. (76%)
Chao Chen; Chenghua Guo; Guixiang Ma; Xi Zhang; Sihong Xie

http://arxiv.org/abs/2212.13929
Evaluating Generalizability of Deep Learning Models Using Indian-COVID-19 CT Dataset. (1%)
Suba S; Nita Parekh; Ramesh Loganathan; Vikram Pudi; Chinnababu Sunkavalli

http://arxiv.org/abs/2212.13607
EDoG: Adversarial Edge Detection For Graph Neural Networks. (98%)
Xiaojun Xu; Yue Yu; Hanzhang Wang; Alok Lal; Carl A. Gunter; Bo Li

http://arxiv.org/abs/2212.13667
Learning When to Use Adaptive Adversarial Image Perturbations against Autonomous Vehicles. (86%)
Hyung-Jin Yoon; Hamidreza Jafarnejadsani; Petros Voulgaris

http://arxiv.org/abs/2302.03523
Sparse Mixture Once-for-all Adversarial Training for Efficient In-Situ Trade-Off Between Accuracy and Robustness of DNNs. (62%)
Souvik Kundu; Sairam Sundaresan; Sharath Nittur Sridhar; Shunlin Lu; Han Tang; Peter A. Beerel

http://arxiv.org/abs/2212.13675
XMAM:X-raying Models with A Matrix to Reveal Backdoor Attacks for Federated Learning. (56%)
Jianyi Zhang; Fangjiao Zhang; Qichao Jin; Zhiqiang Wang; Xiaodong Lin; Xiali Hei

http://arxiv.org/abs/2212.12995
Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks. (99%)
Xingxing Wei; Ying Guo; Jie Yu; Bo Zhang

http://arxiv.org/abs/2212.12732
Frequency Regularization for Improving Adversarial Robustness. (99%)
Binxiao Huang; Chaofan Tao; Rui Lin; Ngai Wong

http://arxiv.org/abs/2212.12641
Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty. (61%)
Genki Osada; Takahashi Tsubasa; Budrul Ahsan; Takashi Nishide

http://arxiv.org/abs/2212.12380
Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Models. (1%)
Natale Loris Di; Bratislav Svetozarevic; Philipp Heer; Colin Neil Jones

http://arxiv.org/abs/2212.11778
Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning. (98%)
Yalin E. Sagduyu

http://arxiv.org/abs/2212.11760
Aliasing is a Driver of Adversarial Attacks. (80%)
Adrián Rodríguez-Muñoz; Antonio Torralba

http://arxiv.org/abs/2212.11810
GAN-based Domain Inference Attack. (2%)
Yuechun Gu; Keke Chen

http://arxiv.org/abs/2212.11614
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation. (1%)
Shu Lok Tsang; Maxwell T. West; Sarah M. Erfani; Muhammad Usman

http://arxiv.org/abs/2212.11005
Revisiting Residual Networks for Adversarial Robustness: An Architectural Perspective. (80%)
Shihua Huang; Zhichao Lu; Kalyanmoy Deb; Vishnu Naresh Boddeti

http://arxiv.org/abs/2212.11205
Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks. (67%)
Yalin E. Sagduyu; Tugba Erpek; Sennur Ulukus; Aylin Yener

http://arxiv.org/abs/2212.11209
A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression. (13%)
Deepak Maurya; Jean Honorio

http://arxiv.org/abs/2212.10230
A Comprehensive Study and Comparison of the Robustness of 3D Object Detectors Against Adversarial Attacks. (98%)
Yifan Zhang; Junhui Hou; Yixuan Yuan

http://arxiv.org/abs/2212.10006
Multi-head Uncertainty Inference for Adversarial Attack Detection. (98%)
Yuqi Yang; Songyun Yang; Jiyang Xie. Zhongwei Si; Kai Guo; Ke Zhang; Kongming Liang

http://arxiv.org/abs/2212.10258
In and Out-of-Domain Text Adversarial Robustness via Label Smoothing. (98%)
Yahan Yang; Soham Dan; Dan Roth; Insup Lee

http://arxiv.org/abs/2212.10438
Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks. (96%)
Yalin E. Sagduyu; Tugba Erpek; Sennur Ulukus; Aylin Yener

http://arxiv.org/abs/2212.10556
Unleashing the Power of Visual Prompting At the Pixel Level. (92%)
Junyang Wu; Xianhang Li; Chen Wei; Huiyu Wang; Alan Yuille; Yuyin Zhou; Cihang Xie

http://arxiv.org/abs/2212.10318
Learned Systems Security. (78%)
Roei Schuster; Jin Peng Zhou; Paul Grubbs; Thorsten Eisenhofer; Nicolas Papernot

http://arxiv.org/abs/2212.10717
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks. (22%)
Jimmy Z. Di; Jack Douglas; Jayadev Acharya; Gautam Kamath; Ayush Sekhari

http://arxiv.org/abs/2212.10264
ReCode: Robustness Evaluation of Code Generation Models. (10%)
Shiqi Wang; Zheng Li; Haifeng Qian; Chenghao Yang; Zijian Wang; Mingyue Shang; Varun Kumar; Samson Tan; Baishakhi Ray; Parminder Bhatia; Ramesh Nallapati; Murali Krishna Ramanathan; Dan Roth; Bing Xiang

http://arxiv.org/abs/2212.10002
Defending Against Poisoning Attacks in Open-Domain Question Answering. (8%)
Orion Weller; Aleem Khan; Nathaniel Weir; Dawn Lawrie; Durme Benjamin Van

http://arxiv.org/abs/2212.10221
SoK: Analysis of Root Causes and Defense Strategies for Attacks on Microarchitectural Optimizations. (5%)
Nadja Ramhöj Holtryd; Madhavan Manivannan; Per Stenström

http://arxiv.org/abs/2212.10534
DISCO: Distilling Phrasal Counterfactuals with Large Language Models. (1%)
Zeming Chen; Qiyue Gao; Kyle Richardson; Antoine Bosselut; Ashish Sabharwal

http://arxiv.org/abs/2212.09254
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization. (99%)
Bairu Hou; Jinghan Jia; Yihua Zhang; Guanhua Zhang; Yang Zhang; Sijia Liu; Shiyu Chang

http://arxiv.org/abs/2212.09994
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation. (75%)
Xinyu Pi; Bing Wang; Yan Gao; Jiaqi Guo; Zhoujun Li; Jian-Guang Lou

http://arxiv.org/abs/2212.09360
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends. (50%)
Yonghao Xu; Tao Bai; Weikang Yu; Shizhen Chang; Peter M. Atkinson; Pedram Ghamisi

http://arxiv.org/abs/2212.09668
Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects. (26%)
Yalin E. Sagduyu; Sennur Ulukus; Aylin Yener

http://arxiv.org/abs/2212.09979
Flareon: Stealthy any2any Backdoor Injection via Poisoned Augmentation. (2%)
Tianrui Qin; Xianghuan He; Xitong Gao; Yiren Zhao; Kejiang Ye; Cheng-Zhong Xu

http://arxiv.org/abs/2212.09458
Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruning. (1%)
Yingchun Wang; Jingcai Guo; Song Guo; Weizhan Zhang; Jie Zhang

http://arxiv.org/abs/2212.09155
Estimating the Adversarial Robustness of Attributions in Text with Transformers. (99%)
Adam Ivankay; Mattia Rigotti; Ivan Girardi; Chiara Marchiori; Pascal Frossard

http://arxiv.org/abs/2212.09035
Minimizing Maximum Model Discrepancy for Transferable Black-box Targeted Attacks. (99%)
Anqi Zhao; Tong Chu; Yahao Liu; Wen Li; Jingjing Li; Lixin Duan

http://arxiv.org/abs/2301.06083
Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition. (99%)
Qian Li; Yuxiao Hu; Ye Liu; Dongxiao Zhang; Xin Jin; Yuntian Chen

http://arxiv.org/abs/2212.09067
Fine-Tuning Is All You Need to Mitigate Backdoor Attacks. (4%)
Zeyang Sha; Xinlei He; Pascal Berrang; Mathias Humbert; Yang Zhang

http://arxiv.org/abs/2212.09000
Confidence-aware Training of Smoothed Classifiers for Certified Robustness. (86%)
Jongheon Jeong; Seojin Kim; Jinwoo Shin

http://arxiv.org/abs/2212.09006
A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness. (2%)
Tiantian Feng; Rajat Hebbar; Nicholas Mehlman; Xuan Shi; Aditya Kommineni; and Shrikanth Narayanan

http://arxiv.org/abs/2212.08853
HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation. (1%)
Hongyi Yuan; Zheng Yuan; Chuanqi Tan; Fei Huang; Songfang Huang

http://arxiv.org/abs/2212.08341
Adversarial Example Defense via Perturbation Grading Strategy. (99%)
Shaowei Zhu; Wanli Lyu; Bin Li; Zhaoxia Yin; Bin Luo

http://arxiv.org/abs/2212.08427
WebAssembly Diversification for Malware Evasion. (5%)
Javier Cabrera-Arteaga; Martin Monperrus; Tim Toady; Benoit Baudry

http://arxiv.org/abs/2212.08568
Biomedical image analysis competitions: The state of current participation practice. (4%)
Matthias Eisenmann; Annika Reinke; Vivienn Weru; Minu Dietlinde Tizabi; Fabian Isensee; Tim J. Adler; Patrick Godau; Veronika Cheplygina; Michal Kozubek; Sharib Ali; Anubha Gupta; Jan Kybic; Alison Noble; Solórzano Carlos Ortiz de; Samiksha Pachade; Caroline Petitjean; Daniel Sage; Donglai Wei; Elizabeth Wilden; Deepak Alapatt; Vincent Andrearczyk; Ujjwal Baid; Spyridon Bakas; Niranjan Balu; Sophia Bano; Vivek Singh Bawa; Jorge Bernal; Sebastian Bodenstedt; Alessandro Casella; Jinwook Choi; Olivier Commowick; Marie Daum; Adrien Depeursinge; Reuben Dorent; Jan Egger; Hannah Eichhorn; Sandy Engelhardt; Melanie Ganz; Gabriel Girard; Lasse Hansen; Mattias Heinrich; Nicholas Heller; Alessa Hering; Arnaud Huaulmé; Hyunjeong Kim; Bennett Landman; Hongwei Bran Li; Jianning Li; Jun Ma; Anne Martel; Carlos Martín-Isla; Bjoern Menze; Chinedu Innocent Nwoye; Valentin Oreiller; Nicolas Padoy; Sarthak Pati; Kelly Payette; Carole Sudre; Wijnen Kimberlin van; Armine Vardazaryan; Tom Vercauteren; Martin Wagner; Chuanbo Wang; Moi Hoon Yap; Zeyun Yu; Chun Yuan; Maximilian Zenk; Aneeq Zia; David Zimmerer; Rina Bao; Chanyeol Choi; Andrew Cohen; Oleh Dzyubachyk; Adrian Galdran; Tianyuan Gan; Tianqi Guo; Pradyumna Gupta; Mahmood Haithami; Edward Ho; Ikbeom Jang; Zhili Li; Zhengbo Luo; Filip Lux; Sokratis Makrogiannis; Dominik Müller; Young-tack Oh; Subeen Pang; Constantin Pape; Gorkem Polat; Charlotte Rosalie Reed; Kanghyun Ryu; Tim Scherr; Vajira Thambawita; Haoyu Wang; Xinliang Wang; Kele Xu; Hung Yeh; Doyeob Yeo; Yixuan Yuan; Yan Zeng; Xin Zhao; Julian Abbing; Jannes Adam; Nagesh Adluru; Niklas Agethen; Salman Ahmed; Yasmina Al Khalil; Mireia Alenyà; Esa Alhoniemi; Chengyang An; Talha Anwar; Tewodros Weldebirhan Arega; Netanell Avisdris; Dogu Baran Aydogan; Yingbin Bai; Maria Baldeon Calisto; Berke Doga Basaran; Marcel Beetz; Cheng Bian; Hao Bian; Kevin Blansit; Louise Bloch; Robert Bohnsack; Sara Bosticardo; Jack Breen; Mikael Brudfors; Raphael Brüngel; Mariano Cabezas; Alberto Cacciola; Zhiwei Chen; Yucong Chen; Daniel Tianming Chen; Minjeong Cho; Min-Kook Choi; Chuantao Xie Chuantao Xie; Dana Cobzas; Julien Cohen-Adad; Jorge Corral Acero; Sujit Kumar Das; Oliveira Marcela de; Hanqiu Deng; Guiming Dong; Lars Doorenbos; Cory Efird; Di Fan; Mehdi Fatan Serj; Alexandre Fenneteau; Lucas Fidon; Patryk Filipiak; René Finzel; Nuno R. Freitas; Christoph M. Friedrich; Mitchell Fulton; Finn Gaida; Francesco Galati; Christoforos Galazis; Chang Hee Gan; Zheyao Gao; Shengbo Gao; Matej Gazda; Beerend Gerats; Neil Getty; Adam Gibicar; Ryan Gifford; Sajan Gohil; Maria Grammatikopoulou; Daniel Grzech; Orhun Güley; Timo Günnemann; Chunxu Guo; Sylvain Guy; Heonjin Ha; Luyi Han; Il Song Han; Ali Hatamizadeh; Tian He; Jimin Heo; Sebastian Hitziger; SeulGi Hong; SeungBum Hong; Rian Huang; Ziyan Huang; Markus Huellebrand; Stephan Huschauer; Mustaffa Hussain; Tomoo Inubushi; Ece Isik Polat; Mojtaba Jafaritadi; SeongHun Jeong; Bailiang Jian; Yuanhong Jiang; Zhifan Jiang; Yueming Jin; Smriti Joshi; Abdolrahim Kadkhodamohammadi; Reda Abdellah Kamraoui; Inha Kang; Junghwa Kang; Davood Karimi; April Khademi; Muhammad Irfan Khan; Suleiman A. Khan; Rishab Khantwal; Kwang-Ju Kim; Timothy Kline; Satoshi Kondo; Elina Kontio; Adrian Krenzer; Artem Kroviakov; Hugo Kuijf; Satyadwyoom Kumar; Rosa Francesco La; Abhi Lad; Doohee Lee; Minho Lee; Chiara Lena; Hao Li; Ling Li; Xingyu Li; Fuyuan Liao; KuanLun Liao; Arlindo Limede Oliveira; Chaonan Lin; Shan Lin; Akis Linardos; Marius George Linguraru; Han Liu; Tao Liu; Di Liu; Yanling Liu; João Lourenço-Silva; Jingpei Lu; Jiangshan Lu; Imanol Luengo; Christina B. Lund; Huan Minh Luu; Yi Lv; Yi Lv; Uzay Macar; Leon Maechler; Sina Mansour L.; Kenji Marshall; Moona Mazher; Richard McKinley; Alfonso Medela; Felix Meissen; Mingyuan Meng; Dylan Miller; Seyed Hossein Mirjahanmardi; Arnab Mishra; Samir Mitha; Hassan Mohy-ud-Din; Tony Chi Wing Mok; Gowtham Krishnan Murugesan; Enamundram Naga Karthik; Sahil Nalawade; Jakub Nalepa; Mohamed Naser; Ramin Nateghi; Hammad Naveed; Quang-Minh Nguyen; Cuong Nguyen Quoc; Brennan Nichyporuk; Bruno Oliveira; David Owen; Jimut Bahan Pal; Junwen Pan; Wentao Pan; Winnie Pang; Bogyu Park; Vivek Pawar; Kamlesh Pawar; Michael Peven; Lena Philipp; Tomasz Pieciak; Szymon Plotka; Marcel Plutat; Fattaneh Pourakpour; Domen Preložnik; Kumaradevan Punithakumar; Abdul Qayyum; Sandro Queirós; Arman Rahmim; Salar Razavi; Jintao Ren; Mina Rezaei; Jonathan Adam Rico; ZunHyan Rieu; Markus Rink; Johannes Roth; Yusely Ruiz-Gonzalez; Numan Saeed; Anindo Saha; Mostafa Salem; Ricardo Sanchez-Matilla; Kurt Schilling; Wei Shao; Zhiqiang Shen; Ruize Shi; Pengcheng Shi; Daniel Sobotka; Théodore Soulier; Bella Specktor Fadida; Danail Stoyanov; Timothy Sum Hon Mun; Xiaowu Sun; Rong Tao; Franz Thaler; Antoine Théberge; Felix Thielke; Helena Torres; Kareem A. Wahid; Jiacheng Wang; YiFei Wang; Wei Wang; Xiong Wang; Jianhui Wen; Ning Wen; Marek Wodzinski; Ye Wu; Fangfang Xia; Tianqi Xiang; Chen Xiaofei; Lizhan Xu; Tingting Xue; Yuxuan Yang; Lin Yang; Kai Yao; Huifeng Yao; Amirsaeed Yazdani; Michael Yip; Hwanseung Yoo; Fereshteh Yousefirizi; Shunkai Yu; Lei Yu; Jonathan Zamora; Ramy Ashraf Zeineldin; Dewen Zeng; Jianpeng Zhang; Bokai Zhang; Jiapeng Zhang; Fan Zhang; Huahong Zhang; Zhongchen Zhao; Zixuan Zhao; Jiachen Zhao; Can Zhao; Qingshuo Zheng; Yuheng Zhi; Ziqi Zhou; Baosheng Zou; Klaus Maier-Hein; Paul F. Jäger; Annette Kopp-Schneider; Lena Maier-Hein

http://arxiv.org/abs/2212.08649
Better May Not Be Fairer: Can Data Augmentation Mitigate Subgroup Degradation? (1%)
Ming-Chang Chiu; Pin-Yu Chen; Xuezhe Ma

http://arxiv.org/abs/2212.08650
On Human Visual Contrast Sensitivity and Machine Vision Robustness: A Comparative Study. (1%)
Ming-Chang Chiu; Yingfei Wang; Derrick Eui Gyu Kim; Pin-Yu Chen; Xuezhe Ma

http://arxiv.org/abs/2212.07992
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks. (98%)
Nikolaos Antoniou; Efthymios Georgiou; Alexandros Potamianos

http://arxiv.org/abs/2212.08130
On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices. (84%)
Salah Ghamizi; Maxime Cordy; Michail Papadakis; Yves Le Traon

http://arxiv.org/abs/2212.08044
Are Multimodal Models Robust to Image and Text Perturbations? (5%)
Jielin Qiu; Yi Zhu; Xingjian Shi; Florian Wenzel; Zhiqiang Tang; Ding Zhao; Bo Li; Mu Li

http://arxiv.org/abs/2212.10628
Holistic risk assessment of inference attacks in machine learning. (4%)
Yang Yang

http://arxiv.org/abs/2212.12307
Defending against cybersecurity threats to the payments and banking system. (2%)
Williams Haruna; Toyin Ajiboro Aremu; Yetunde Ajao Modupe

http://arxiv.org/abs/2301.03595
White-box Inference Attacks against Centralized Machine Learning and Federated Learning. (1%)
Jingyi Ge

http://arxiv.org/abs/2212.07495
SAIF: Sparse Adversarial and Interpretable Attack Framework. (99%)
Tooba Imtiaz; Morgan Kohler; Jared Miller; Zifeng Wang; Mario Sznaier; Octavia Camps; Jennifer Dy

http://arxiv.org/abs/2212.07591
Dissecting Distribution Inference. (88%)
Anshuman Suri; Yifu Lu; Yanjin Chen; David Evans

http://arxiv.org/abs/2212.07283
Generative Robust Classification. (11%)
Xuwang Yin

http://arxiv.org/abs/2212.14109
Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial Networks. (8%)
Abdelmageed Ahmed Hassan; Mohamed Sayed Hussein; Ahmed Shehata AboMoustafa; Sarah Hossam Elmowafy

http://arxiv.org/abs/2212.07558
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection. (1%)
Mohanad Sarhan; Gayan Kulatilleke; Wai Weng Lo; Siamak Layeghy; Marius Portmann

http://arxiv.org/abs/2212.06431
Object-fabrication Targeted Attack for Object Detection. (99%)
Xuchong Zhang; Changfeng Sun; Haoliang Han; Hang Wang; Hongbin Sun; Nanning Zheng

http://arxiv.org/abs/2212.06822
Adversarial Attacks and Defences for Skin Cancer Classification. (99%)
Vinay Jogani; Joy Purohit; Ishaan Shivhare; Samina Attari; Shraddha Surtkar

http://arxiv.org/abs/2212.06776
Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection. (99%)
Peter Lorenz; Margret Keuper; Janis Keuper

http://arxiv.org/abs/2212.06836
Towards Efficient and Domain-Agnostic Evasion Attack with High-dimensional Categorical Inputs. (80%)
Hongyan Bao; Yufei Han; Yujun Zhou; Xin Gao; Xiangliang Zhang

http://arxiv.org/abs/2212.07016
Understanding Zero-Shot Adversarial Robustness for Large-Scale Models. (73%)
Chengzhi Mao; Scott Geng; Junfeng Yang; Xin Wang; Carl Vondrick

http://arxiv.org/abs/2212.06493
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection. (56%)
Zhenyu Wu; Lin Wang; Wei Wang; Qing Xia; Chenglizhao Chen; Aimin Hao; Shuo Li

http://arxiv.org/abs/2212.13989
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs. (56%)
Helene Orsini; Hongyan Bao; Yujun Zhou; Xiangrui Xu; Yufei Han; Longyang Yi; Wei Wang; Xin Gao; Xiangliang Zhang

http://arxiv.org/abs/2212.06428
Privacy-preserving Security Inference Towards Cloud-Edge Collaborative Using Differential Privacy. (1%)
Yulong Wang; Xingshu Chen; Qixu Wang

http://arxiv.org/abs/2212.06643
Boosting Semi-Supervised Learning with Contrastive Complementary Labeling. (1%)
Qinyi Deng; Yong Guo; Zhibang Yang; Haolin Pan; Jian Chen

http://arxiv.org/abs/2212.05917
SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation. (98%)
Wanqing Zhu; Jia-Li Yin; Bo-Hao Chen; Ximeng Liu

http://arxiv.org/abs/2212.07815
Adversarially Robust Video Perception by Seeing Motion. (98%)
Lingyu Zhang; Chengzhi Mao; Junfeng Yang; Carl Vondrick

http://arxiv.org/abs/2212.06123
A Survey on Reinforcement Learning Security with Application to Autonomous Driving. (96%)
Ambra Demontis; Maura Pintor; Luca Demetrio; Kathrin Grosse; Hsiao-Ying Lin; Chengfang Fang; Battista Biggio; Fabio Roli

http://arxiv.org/abs/2212.05709
HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design. (96%)
Hui Wei; Zhixiang Wang; Xuemei Jia; Yinqiang Zheng; Hao Tang; Shin'ichi Satoh; Zheng Wang

http://arxiv.org/abs/2212.06079
Robust Perception through Equivariance. (96%)
Chengzhi Mao; Lingyu Zhang; Abhishek Joshi; Junfeng Yang; Hao Wang; Carl Vondrick

http://arxiv.org/abs/2212.06295
Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety. (75%)
Joshua Albrecht; Ellie Kitanidis; Abraham J. Fetterman

http://arxiv.org/abs/2212.06325
AFLGuard: Byzantine-robust Asynchronous Federated Learning. (15%)
Minghong Fang; Jia Liu; Neil Zhenqiang Gong; Elizabeth S. Bentley

http://arxiv.org/abs/2212.05827
Carpet-bombing patch: attacking a deep network without usual requirements. (2%)
Pol Labarbarie; Adrien Chan-Hon-Tong; Stéphane Herbin; Milad Leyli-Abadi

http://arxiv.org/abs/2212.06361
Numerical Stability of DeepGOPlus Inference. (1%)
Inés Gonzalez Pepe; Yohan Chatelain; Gregory Kiar; Tristan Glatard

http://arxiv.org/abs/2212.05630
DISCO: Adversarial Defense with Local Implicit Functions. (99%)
Chih-Hui Ho; Nuno Vasconcelos

http://arxiv.org/abs/2212.05680
REAP: A Large-Scale Realistic Adversarial Patch Benchmark. (98%)
Nabeel Hingun; Chawin Sitawarin; Jerry Li; David Wagner

http://arxiv.org/abs/2212.05387
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments. (99%)
Xiaogang Xu; Hengshuang Zhao; Philip Torr; Jiaya Jia

http://arxiv.org/abs/2212.05399
Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense. (93%)
Yang Yu; Qi Liu; Likang Wu; Runlong Yu; Sanshi Lei Yu; Zaixi Zhang

http://arxiv.org/abs/2212.05337
Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking. (93%)
Dennis Gross; Thiago D. Simao; Nils Jansen; Guillermo A. Perez

http://arxiv.org/abs/2212.05380
Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors. (54%)
Giovanni Apruzzese; V. S. Subrahmanian

http://arxiv.org/abs/2212.05400
How to Backdoor Diffusion Models? (12%)
Sheng-Yen Chou; Pin-Yu Chen; Tsung-Yi Ho

http://arxiv.org/abs/2212.05327
Identifying the Source of Vulnerability in Explanation Discrepancy: A Case Study in Neural Text Classification. (1%)
Ruixuan Tang; Hanjie Chen; Yangfeng Ji

http://arxiv.org/abs/2212.04985
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization. (98%)
Lin Li; Michael Spratling

http://arxiv.org/abs/2212.04875
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding. (2%)
Minh-Long Luu; Zeyi Huang; Eric P. Xing; Yong Jae Lee; Haohan Wang

http://arxiv.org/abs/2212.04871
Spurious Features Everywhere -- Large-Scale Detection of Harmful Spurious Features in ImageNet. (1%)
Yannic Neuhaus; Maximilian Augustin; Valentyn Boreiko; Matthias Hein

http://arxiv.org/abs/2212.05015
Robustness Implies Privacy in Statistical Estimation. (1%)
Samuel B. Hopkins; Gautam Kamath; Mahbod Majid; Shyam Narayanan

http://arxiv.org/abs/2212.04687
Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models. (1%)
Rui Zhu; Di Tang; Siyuan Tang; XiaoFeng Wang; Haixu Tang

http://arxiv.org/abs/2212.11138
QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks. (1%)
Yedi Zhang; Zhe Zhao; Fu Song; Min Zhang; Taolue Chen; Jun Sun

http://arxiv.org/abs/2212.04138
Targeted Adversarial Attacks against Neural Network Trajectory Predictors. (99%)
Kaiyuan Tan; Jun Wang; Yiannis Kantaros

http://arxiv.org/abs/2212.04454
XRand: Differentially Private Defense against Explanation-Guided Attacks. (68%)
Truc Nguyen; Phung Lai; NhatHai Phan; My T. Thai

http://arxiv.org/abs/2212.04656
Robust Graph Representation Learning via Predictive Coding. (22%)
Billy Byiringiro; Tommaso Salvatori; Thomas Lukasiewicz

http://arxiv.org/abs/2212.04008
Use of Cryptography in Malware Obfuscation. (1%)
Hassan Jameel Asghar; Benjamin Zi Hao Zhao; Muhammad Ikram; Giang Nguyen; Dali Kaafar; Sean Lamont; Daniel Coscia

http://arxiv.org/abs/2212.03334
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning. (96%)
Hongbin Liu; Wenjie Qu; Jinyuan Jia; Neil Zhenqiang Gong

http://arxiv.org/abs/2212.02531
Enhancing Quantum Adversarial Robustness by Randomized Encodings. (99%)
Weiyuan Gong; Dong Yuan; Weikang Li; Dong-Ling Deng

http://arxiv.org/abs/2212.03069
Multiple Perturbation Attack: Attack Pixelwise Under Different $\ell_p$-norms For Better Adversarial Performance. (99%)
Ngoc N. Tran; Anh Tuan Bui; Dinh Phung; Trung Le

http://arxiv.org/abs/2212.02127
FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration. (92%)
Žiga Babnik; Peter Peer; Vitomir Štruc

http://arxiv.org/abs/2212.02042
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning. (76%)
Mingyuan Fan; Cen Chen; Chengyu Wang; Wenmeng Zhou; Jun Huang; Ximeng Liu; Wenzhong Guo

http://arxiv.org/abs/2212.02457
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria. (8%)
Tengyuan Liang

http://arxiv.org/abs/2212.02705
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning? (3%)
Songyang Han; Sanbao Su; Sihong He; Shuo Han; Haizhao Yang; Fei Miao

http://arxiv.org/abs/2212.02648
Spuriosity Rankings: Sorting Data for Spurious Correlation Robustness. (1%)
Mazda Moayeri; Wenxiao Wang; Sahil Singla; Soheil Feizi

http://arxiv.org/abs/2212.02663
Efficient Malware Analysis Using Metric Embeddings. (1%)
Ethan M. Rudd; David Krisiloff; Scott Coull; Daniel Olszewski; Edward Raff; James Holt

http://arxiv.org/abs/2212.02003
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense. (98%)
Bao Gia Doan; Ehsan Abbasnejad; Javen Qinfeng Shi; Damith C. Ranasinghe

http://arxiv.org/abs/2212.01806
Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks. (88%)
Xiao Li; Ziqi Wang; Bo Zhang; Fuchun Sun; Xiaolin Hu

http://arxiv.org/abs/2212.01957
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness. (82%)
Huy Phan; Miao Yin; Yang Sui; Bo Yuan; Saman Zonouz

http://arxiv.org/abs/2212.01976
FedCC: Robust Federated Learning against Model Poisoning Attacks. (45%)
Hyejun Jeong; Hamin Son; Seohu Lee; Jayun Hyun; Tai-Myoung Chung

http://arxiv.org/abs/2212.01767
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder. (8%)
Qi Tian; Kun Kuang; Kelu Jiang; Furui Liu; Zhihua Wang; Fei Wu

http://arxiv.org/abs/2212.01688
LDL: A Defense for Label-Based Membership Inference Attacks. (83%)
Arezoo Rajabi; Dinuka Sahabandu; Luyao Niu; Bhaskar Ramasubramanian; Radha Poovendran

http://arxiv.org/abs/2212.01716
Security Analysis of SplitFed Learning. (8%)
Momin Ahmad Khan; Virat Shejwalkar; Amir Houmansadr; Fatima Muhammad Anwar

http://arxiv.org/abs/2212.01082
Membership Inference Attacks Against Semantic Segmentation Models. (45%)
Tomas Chobola; Dmitrii Usynin; Georgios Kaissis

http://arxiv.org/abs/2212.01346
Guaranteed Conformance of Neurosymbolic Models to Natural Constraints. (1%)
Kaustubh Sridhar; Souradeep Dutta; James Weimer; Insup Lee

http://arxiv.org/abs/2212.00612
Purifier: Defending Data Inference Attacks via Transforming Confidence Scores. (89%)
Ziqi Yang; Lijin Wang; Da Yang; Jie Wan; Ziming Zhao; Ee-Chien Chang; Fan Zhang; Kui Ren

http://arxiv.org/abs/2212.00884
Pareto Regret Analyses in Multi-objective Multi-armed Bandit. (41%)
Mengfan Xu; Diego Klabjan

http://arxiv.org/abs/2212.00325
All You Need Is Hashing: Defending Against Data Reconstruction Attack in Vertical Federated Learning. (3%)
Pengyu Qiu; Xuhong Zhang; Shouling Ji; Yuwen Pu; Ting Wang

http://arxiv.org/abs/2212.00311
Generalizing and Improving Jacobian and Hessian Regularization. (1%)
Chenwei Cui; Zehao Yan; Guangshen Liu; Liangfu Lu

http://arxiv.org/abs/2212.00952
On the Limit of Explaining Black-box Temporal Graph Neural Networks. (1%)
Minh N. Vu; My T. Thai

http://arxiv.org/abs/2212.00951
SimpleMind adds thinking to deep neural networks. (1%)
Youngwon Choi; M. Wasil Wahi-Anwar; Matthew S. Brown

http://arxiv.org/abs/2211.17071
Towards Interpreting Vulnerability of Multi-Instance Learning via Customized and Universal Adversarial Perturbations. (97%)
Yu-Xuan Zhang; Hua Meng; Xue-Mei Cao; Zhengchun Zhou; Mei Yang; Avik Ranjan Adhikary

http://arxiv.org/abs/2212.03095
Interpretation of Neural Networks is Susceptible to Universal Adversarial Perturbations. (84%)
Haniyeh Ehsani Oskouie; Farzan Farnia

http://arxiv.org/abs/2211.16808
Efficient Adversarial Input Generation via Neural Net Patching. (75%)
Tooba Khan; Kumar Madhukar; Subodh Vishnu Sharma

http://arxiv.org/abs/2211.16806
Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection. (69%)
Kun Xiang; Xing Zhang; Jinwen She; Jinpeng Liu; Haohan Wang; Shiqi Deng; Shancheng Jiang

http://arxiv.org/abs/2211.17244
Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations. (38%)
Hong-Ming Chiu; Richard Y. Zhang

http://arxiv.org/abs/2211.16908
Improved Smoothed Analysis of 2-Opt for the Euclidean TSP. (8%)
Bodo Manthey; Rhijn Jesse van

http://arxiv.org/abs/2211.16080
Understanding and Enhancing Robustness of Concept-based Models. (99%)
Sanchit Sinha; Mengdi Huai; Jianhui Sun; Aidong Zhang

http://arxiv.org/abs/2211.16247
Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion. (99%)
Kui Zhang; Hang Zhou; Jie Zhang; Qidong Huang; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2211.16253
Advancing Deep Metric Learning Through Multiple Batch Norms And Multi-Targeted Adversarial Examples. (88%)
Inderjeet Singh; Kazuya Kakizaki; Toshinori Araki

http://arxiv.org/abs/2211.16093
Penalizing Confident Predictions on Largely Perturbed Inputs Does Not Improve Out-of-Distribution Generalization in Question Answering. (83%)
Kazutoshi Shinoda; Saku Sugawara; Akiko Aizawa

http://arxiv.org/abs/2211.16187
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks. (73%)
Mathias Lechner; Đorđe Žikelić; Krishnendu Chatterjee; Thomas A. Henzinger; Daniela Rus

http://arxiv.org/abs/2211.16040
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification. (54%)
Suorong Yang; Jinqiao Li; Jian Zhao; Furao Shen

http://arxiv.org/abs/2211.16316
A3T: Accuracy Aware Adversarial Training. (10%)
Enes Altinisik; Safa Messaoud; Husrev Taha Sencar; Sanjay Chawla

http://arxiv.org/abs/2211.16228
Building Resilience to Out-of-Distribution Visual Data via Input Optimization and Model Finetuning. (1%)
Christopher J. Holder; Majid Khonji; Jorge Dias; Muhammad Shafique

http://arxiv.org/abs/2212.00727
Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces. (99%)
Xiaoqing Chen; Dongrui Wu

http://arxiv.org/abs/2211.15926
Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning. (95%)
Eldor Abdukhamidov; Mohammed Abuhamad; Simon S. Woo; Eric Chan-Tin; Tamer Abuhmed

http://arxiv.org/abs/2211.15875
Training Time Adversarial Attack Aiming the Vulnerability of Continual Learning. (83%)
Gyojin Han; Jaehyun Choi; Hyeong Gwon Hong; Junmo Kim

http://arxiv.org/abs/2211.15900
Towards More Robust Interpretation via Local Gradient Alignment. (76%)
Sunghwan Joo; Seokhyeon Jeong; Juyeon Heo; Adrian Weller; Taesup Moon

http://arxiv.org/abs/2211.15762
Understanding the Impact of Adversarial Robustness on Accuracy Disparity. (31%)
Yuzheng Hu; Fan Wu; Hongyang Zhang; Han Zhao

http://arxiv.org/abs/2211.15844
How Important are Good Method Names in Neural Code Generation? A Model Robustness Perspective. (13%)
Guang Yang; Yu Zhou; Wenhua Yang; Tao Yue; Xiang Chen; Taolue Chen

http://arxiv.org/abs/2211.15180
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning. (8%)
Xiaoyue Duan; Guoliang Kang; Runqi Wang; Shumin Han; Song Xue; Tian Wang; Baochang Zhang

http://arxiv.org/abs/2211.15556
Attack on Unfair ToS Clause Detection: A Case Study using Universal Adversarial Triggers. (8%)
Shanshan Xu; Irina Broda; Rashid Haddad; Marco Negrini; Matthias Grabmair

http://arxiv.org/abs/2211.15223
Gamma-convergence of a nonlocal perimeter arising in adversarial machine learning. (3%)
Leon Bungert; Kerrek Stinson

http://arxiv.org/abs/2211.15718
CoNAL: Anticipating Outliers with Large Language Models. (1%)
Albert Xu; Xiang Ren; Robin Jia

http://arxiv.org/abs/2211.15897
Learning Antidote Data to Individual Unfairness. (1%)
Peizhao Li; Ethan Xia; Hongfu Liu

http://arxiv.org/abs/2211.15030
Imperceptible Adversarial Attack via Invertible Neural Networks. (99%)
Zihan Chen; Ziyue Wang; Junjie Huang; Wentao Zhao; Xiao Liu; Dejian Guan

http://arxiv.org/abs/2211.14860
Foiling Explanations in Deep Neural Networks. (98%)
Snir Vitrack Tamam; Raz Lapid; Moshe Sipper

http://arxiv.org/abs/2211.14769
Navigation as the Attacker Wishes? Towards Building Byzantine-Robust Embodied Agents under Federated Learning. (84%)
Yunchao Zhang; Zonglin Di; Kaiwen Zhou; Cihang Xie; Xin Wang

http://arxiv.org/abs/2211.14794
Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs. (50%)
Guangrun Wang; Philip H. S. Torr

http://arxiv.org/abs/2211.14952
Federated Learning Attacks and Defenses: A Survey. (47%)
Yao Chen; Yijie Gui; Hong Lin; Wensheng Gan; Yongdong Wu

http://arxiv.org/abs/2211.14966
Adversarial Rademacher Complexity of Deep Neural Networks. (47%)
Jiancong Xiao; Yanbo Fan; Ruoyu Sun; Zhi-Quan Luo

http://arxiv.org/abs/2211.14669
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning. (99%)
Ethan Rathbun; Kaleel Mahmood; Sohaib Ahmad; Caiwen Ding; Dijk Marten van

http://arxiv.org/abs/2211.14088
Boundary Adversarial Examples Against Adversarial Overfitting. (99%)
Muhammad Zaid Hameed; Beat Buesser

http://arxiv.org/abs/2211.14424
Supervised Contrastive Prototype Learning: Augmentation Free Robust Neural Network. (98%)
Iordanis Fostiropoulos; Laurent Itti

http://arxiv.org/abs/2211.14065
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating. (3%)
Yixin Liu; Yizhen Zheng; Daokun Zhang; Vincent CS Lee; Shirui Pan

http://arxiv.org/abs/2211.13991
TrustGAN: Training safe and trustworthy deep learning models through generative adversarial networks. (1%)
Hélion du Mas des Bourboux

http://arxiv.org/abs/2211.13775
SAGA: Spectral Adversarial Geometric Attack on 3D Meshes. (98%)
Tomer Stolik; Itai Lang; Shai Avidan

http://arxiv.org/abs/2211.13535
Tracking Dataset IP Use in Deep Neural Networks. (96%)
Seonhye Park; Alsharif Abuadbba; Shuo Wang; Kristen Moore; Yansong Gao; Hyoungshick Kim; Surya Nepal

http://arxiv.org/abs/2211.13474
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility. (92%)
Lei Wang; Hongyu Yang; Yi Lin; Suwan Yin; Yuankai Wu

http://arxiv.org/abs/2211.15382
Neural Network Complexity of Chaos and Turbulence. (41%)
Tim Whittaker; Romuald A. Janik; Yaron Oz

http://arxiv.org/abs/2211.13644
Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models. (8%)
Jacob Shams; Ben Nassi; Ikuya Morikawa; Toshiya Shimizu; Asaf Shabtai; Yuval Elovici

http://arxiv.org/abs/2211.13772
Generative Joint Source-Channel Coding for Semantic Image Transmission. (1%)
Ecenaz Erdemir; Tze-Yang Tung; Pier Luigi Dragotti; Deniz Gunduz

http://arxiv.org/abs/2211.13737
CycleGANWM: A CycleGAN watermarking method for ownership verification. (1%)
Dongdong Lin; Benedetta Tondi; Bin Li; Mauro Barni

http://arxiv.org/abs/2211.13171
Query Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition. (99%)
Rohit Gupta; Naveed Akhtar; Gaurav Kumar Nayak; Ajmal Mian; Mubarak Shah

http://arxiv.org/abs/2211.12990
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners. (99%)
Elre T. Oldewage; John Bronskill; Richard E. Turner

http://arxiv.org/abs/2211.12713
Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles. (76%)
Shengcai Liu; Fu Peng; Ke Tang

http://arxiv.org/abs/2211.13305
Dual Graphs of Polyhedral Decompositions for the Detection of Adversarial Attacks. (62%)
Huma Jamil; Yajing Liu; Christina Cole; Nathaniel Blanchard; Emily J. King; Michael Kirby; Christopher Peterson

http://arxiv.org/abs/2211.12864
Privacy-Enhancing Optical Embeddings for Lensless Classification. (11%)
Eric Bezzam; Martin Vetterli; Matthieu Simeoni

http://arxiv.org/abs/2211.13345
Principled Data-Driven Decision Support for Cyber-Forensic Investigations. (1%)
Soodeh Atefi; Sakshyam Panda; Manos Panaousis; Aron Laszka

http://arxiv.org/abs/2211.13416
Data Provenance Inference in Machine Learning. (1%)
Mingxue Xu; Xiang-Yang Li

http://arxiv.org/abs/2211.12681
Benchmarking Adversarially Robust Quantum Machine Learning at Scale. (99%)
Maxwell T. West; Sarah M. Erfani; Christopher Leckie; Martin Sevior; Lloyd C. L. Hollenberg; Muhammad Usman

http://arxiv.org/abs/2211.12294
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples. (99%)
Shengshan Hu; Junwei Zhang; Wei Liu; Junhui Hou; Minghui Li; Leo Yu Zhang; Hai Jin; Lichao Sun

http://arxiv.org/abs/2211.12314
Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Traces. (98%)
Shengbang Fang; Matthew C Stamm

http://arxiv.org/abs/2211.12044
Backdoor Cleansing with Unlabeled Data. (75%)
Lu Pang; Tao Sun; Haibin Ling; Chao Chen

http://arxiv.org/abs/2211.12624
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization. (70%)
Zifan Wang; Nan Ding; Tomer Levinboim; Xi Chen; Radu Soricut

http://arxiv.org/abs/2211.12087
SoK: Inference Attacks and Defenses in Human-Centered Wireless Sensing. (69%)
Wei Sun; Tingjun Chen; Neil Gong

http://arxiv.org/abs/2211.11236
Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization. (99%)
Jiafeng Wang; Zhaoyu Chen; Kaixun Jiang; Dingkang Yang; Lingyi Hong; Yan Wang; Wenqiang Zhang

http://arxiv.org/abs/2211.11312
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack. (99%)
Yunfeng Diao; He Wang; Tianjia Shao; Yong-Liang Yang; Kun Zhou; David Hogg

http://arxiv.org/abs/2211.12005
Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors. (99%)
Sizhe Chen; Geng Yuan; Xinwen Cheng; Yifan Gong; Minghai Qin; Yanzhi Wang; Xiaolin Huang

http://arxiv.org/abs/2211.11880
Addressing Mistake Severity in Neural Networks with Semantic Knowledge. (92%)
Natalie Abreu; Nathan Vaska; Victoria Helus

http://arxiv.org/abs/2211.11489
Efficient Generalization Improvement Guided by Random Weight Perturbation. (68%)
Tao Li; Weihao Yan; Zehao Lei; Yingwen Wu; Kun Fang; Ming Yang; Xiaolin Huang

http://arxiv.org/abs/2211.11711
CLAWSAT: Towards Both Robust and Accurate Code Models. (56%)
Jinghan Jia; Shashank Srikant; Tamara Mitrovska; Chuang Gan; Shiyu Chang; Sijia Liu; Una-May O'Reilly

http://arxiv.org/abs/2211.11835
Fairness Increases Adversarial Vulnerability. (54%)
Cuong Tran; Keyu Zhu; Ferdinando Fioretto; Henternyck Pascal Van

http://arxiv.org/abs/2211.14440
Don't Watch Me: A Spatio-Temporal Trojan Attack on Deep-Reinforcement-Learning-Augment Autonomous Driving. (10%)
Yinbo Yu; Jiajia Liu

http://arxiv.org/abs/2211.11321
SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks. (8%)
Sunder Ali Khowaja; Parus Khuwaja; Kapal Dev; Angelos Antonopoulos

http://arxiv.org/abs/2211.11958
A Survey on Backdoor Attack and Defense in Natural Language Processing. (2%)
Xuan Sheng; Zhaoyang Han; Piji Li; Xiangmao Chang

http://arxiv.org/abs/2211.11635
Understanding and Improving Visual Prompting: A Label-Mapping Perspective. (2%)
Aochuan Chen; Yuguang Yao; Pin-Yu Chen; Yihua Zhang; Sijia Liu

http://arxiv.org/abs/2211.11300
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text. (1%)
Qianhui Wu; Huiqiang Jiang; Haonan Yin; Börje F. Karlsson; Chin-Yew Lin

http://arxiv.org/abs/2211.11434
Privacy in Practice: Private COVID-19 Detection in X-Ray Images. (1%)
Lucas Lange; Maja Schneider; Erhard Rahm

http://arxiv.org/abs/2211.11357
A Tale of Frozen Clouds: Quantifying the Impact of Algorithmic Complexity Vulnerabilities in Popular Web Servers. (1%)
Masudul Hasan Masud Bhuiyan; Cristian-Alexandru Staicu

http://arxiv.org/abs/2211.10896
Spectral Adversarial Training for Robust Graph Neural Network. (99%)
Jintang Li; Jiaying Peng; Liang Chen; Zibin Zheng; Tingting Liang; Qing Ling

http://arxiv.org/abs/2211.10933
Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification. (81%)
Wenli Sun; Xinyang Jiang; Shuguang Dou; Dongsheng Li; Duoqian Miao; Cheng Deng; Cairong Zhao

http://arxiv.org/abs/2211.11127
Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training. (47%)
Jiaxu Tian; Dapeng Zhi; Si Liu; Peixin Wang; Guy Katz; Min Zhang

http://arxiv.org/abs/2211.11030
Adversarial Cheap Talk. (8%)
Chris Lu; Timon Willi; Alistair Letcher; Jakob Foerster

http://arxiv.org/abs/2211.11039
Deep Composite Face Image Attacks: Generation, Vulnerability and Detection. (2%)
Jag Mohan Singh; Raghavendra Ramachandra

http://arxiv.org/abs/2211.10938
AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation. (2%)
Hyungmin Kim; Sungho Suh; Sunghyun Baek; Daehwan Kim; Daun Jeong; Hansang Cho; Junmo Kim

http://arxiv.org/abs/2211.10670
Towards Adversarial Robustness of Deep Vision Algorithms. (92%)
Hanshu Yan

http://arxiv.org/abs/2211.10661
Phonemic Adversarial Attack against Audio Recognition in Real World. (87%)
Jiakai Wang; Zhendong Chen; Zixin Yin; Qinghong Yang; Xianglong Liu

http://arxiv.org/abs/2211.10752
Towards Robust Dataset Learning. (82%)
Yihan Wu; Xinda Li; Florian Kerschbaum; Heng Huang; Hongyang Zhang

http://arxiv.org/abs/2211.10782
Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning. (80%)
Mingxuan Ju; Yujie Fan; Chuxu Zhang; Yanfang Ye

http://arxiv.org/abs/2211.10747
Exploring validation metrics for offline model-based optimisation with diffusion models. (75%)
Christopher Beckham; Alexandre Piche; David Vazquez; Christopher Pal

http://arxiv.org/abs/2211.10843
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization. (9%)
Amirmohammad Pasdar; Young Choon Lee; Seok-Hee Hong

http://arxiv.org/abs/2211.10603
Investigating the Security of EV Charging Mobile Applications As an Attack Surface. (1%)
K. Sarieddine; M. A. Sayed; S. Torabi; R. Atallah; C. Assi

http://arxiv.org/abs/2211.10033
Adversarial Stimuli: Attacking Brain-Computer Interfaces via Perturbed Sensory Events. (98%)
Bibek Upadhayay; Vahid Behzadan

http://arxiv.org/abs/2211.10227
Adversarial Detection by Approximation of Ensemble Boundary. (91%)
T. Windeatt

http://arxiv.org/abs/2211.10209
Leveraging Algorithmic Fairness to Mitigate Blackbox Attribute Inference Attacks. (68%)
Jan Aalmoes; Vasisht Duddu; Antoine Boutet

http://arxiv.org/abs/2211.10370
Invariant Learning via Diffusion Dreamed Distribution Shifts. (10%)
Priyatham Kattakinda; Alexander Levine; Soheil Feizi

http://arxiv.org/abs/2211.10062
Intrusion Detection in Internet of Things using Convolutional Neural Networks. (1%)
Martin Kodys; Zhi Lu; Kar Wai Fok; Vrizlynn L. L. Thing

http://arxiv.org/abs/2211.10095
Improving Robustness of TCM-based Robust Steganography with Variable Robustness. (1%)
Jimin Zhang; Xianfeng Zhao; Xiaolei He

http://arxiv.org/abs/2211.10530
Provable Defense against Backdoor Policies in Reinforcement Learning. (1%)
Shubham Kumar Bharti; Xuezhou Zhang; Adish Singla; Xiaojin Zhu

http://arxiv.org/abs/2211.10586
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. (1%)
Justin Cui; Ruochen Wang; Si Si; Cho-Jui Hsieh

http://arxiv.org/abs/2211.10024
Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks. (99%)
Stephen Casper; Kaivalya Hariharan; Dylan Hadfield-Menell

http://arxiv.org/abs/2211.09565
Towards Good Practices in Evaluating Transfer Adversarial Attacks. (93%)
Zhengyu Zhao; Hanwei Zhang; Renjue Li; Ronan Sicre; Laurent Amsaleg; Michael Backes

http://arxiv.org/abs/2211.09782
Assessing Neural Network Robustness via Adversarial Pivotal Tuning. (92%)
Peter Ebert Christensen; Vésteinn Snæbjarnarson; Andrea Dittadi; Serge Belongie; Sagie Benaim

http://arxiv.org/abs/2211.09717
UPTON: Unattributable Authorship Text via Data Poisoning. (86%)
Ziyao Wang; Thai Le; Dongwon Lee

http://arxiv.org/abs/2211.09363
Generalizable Deepfake Detection with Phase-Based Motion Analysis. (50%)
Ekta Prashnani; Michael Goebel; B. S. Manjunath

http://arxiv.org/abs/2211.09345
More Effective Centrality-Based Attacks on Weighted Networks. (15%)
Balume Mburano; Weisheng Si; Qing Cao; Wei Xing Zheng

http://arxiv.org/abs/2211.09959
Potential Auto-driving Threat: Universal Rain-removal Attack. (2%)
Jinchegn Hu; Jihao Li; Zhuoran Hou; Jingjing Jiang; Cunjia Liu; Yuanjian Zhang

http://arxiv.org/abs/2211.09859
Data-Centric Debugging: mitigating model failures via targeted data collection. (1%)
Sahil Singla; Atoosa Malemir Chegini; Mazda Moayeri; Soheil Feiz

http://arxiv.org/abs/2211.10012
A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning. (1%)
Guanqin Zhang; Jiankun Sun; Feng Xu; H. M. N. Dilum Bandara; Shiping Chen; Yulei Sui; Tim Menzies

http://arxiv.org/abs/2211.09945
VeriSparse: Training Verified Locally Robust Sparse Neural Networks from Scratch. (1%)
Sawinder Kaur; Yi Xiao; Asif Salekin

http://arxiv.org/abs/2211.09773
T-SEA: Transfer-based Self-Ensemble Attack on Object Detection. (99%)
Hao Huang; Ziyan Chen; Huanran Chen; Yongtao Wang; Kevin Zhang

http://arxiv.org/abs/2211.08706
Efficiently Finding Adversarial Examples with DNN Preprocessing. (99%)
Avriti Chauhan; Mohammad Afzal; Hrishikesh Karmarkar; Yizhak Elboher; Kumar Madhukar; Guy Katz

http://arxiv.org/abs/2211.08686
Improving Interpretability via Regularization of Neural Activation Sensitivity. (92%)
Ofir Moshe; Gil Fidel; Ron Bitton; Asaf Shabtai

http://arxiv.org/abs/2211.08859
Attacking Object Detector Using A Universal Targeted Label-Switch Patch. (86%)
Avishag Shapira; Ron Bitton; Dan Avraham; Alon Zolfi; Yuval Elovici; Asaf Shabtai

http://arxiv.org/abs/2211.08942
Differentially Private Optimizers Can Learn Adversarially Robust Models. (83%)
Yuan Zhang; Zhiqi Bu

http://arxiv.org/abs/2211.09321
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection. (56%)
Yang Yang; Hongjian Sun; Jialei Gong; Di Yu

http://arxiv.org/abs/2211.09273
Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning. (38%)
Brian Testa; Yi Xiao; Harshit Sharma; Avery Gump; Asif Salekin

http://arxiv.org/abs/2211.09110
Holistic Evaluation of Language Models. (2%)
Percy Liang; Rishi Bommasani; Tony Lee; Dimitris Tsipras; Dilara Soylu; Michihiro Yasunaga; Yian Zhang; Deepak Narayanan; Yuhuai Wu; Ananya Kumar; Benjamin Newman; Binhang Yuan; Bobby Yan; Ce Zhang; Christian Cosgrove; Christopher D. Manning; Christopher Ré; Diana Acosta-Navas; Drew A. Hudson; Eric Zelikman; Esin Durmus; Faisal Ladhak; Frieda Rong; Hongyu Ren; Huaxiu Yao; Jue Wang; Keshav Santhanam; Laurel Orr; Lucia Zheng; Mert Yuksekgonul; Mirac Suzgun; Nathan Kim; Neel Guha; Niladri Chatterji; Omar Khattab; Peter Henderson; Qian Huang; Ryan Chi; Sang Michael Xie; Shibani Santurkar; Surya Ganguli; Tatsunori Hashimoto; Thomas Icard; Tianyi Zhang; Vishrav Chaudhary; William Wang; Xuechen Li; Yifan Mai; Yuhui Zhang; Yuta Koreeda

http://arxiv.org/abs/2211.08804
Analysis and Detectability of Offline Data Poisoning Attacks on Linear Systems. (1%)
Alessio Russo; Alexandre Proutiere

http://arxiv.org/abs/2211.08068
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation. (99%)
Zhihao Zhu; Chenwang Wu; Min Zhou; Hao Liao; Defu Lian; Enhong Chen

http://arxiv.org/abs/2211.08384
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning. (99%)
Yiran Huang; Yexu Zhou; Michael Hefenbrock; Till Riedel; Likun Fang; Michael Beigl

http://arxiv.org/abs/2211.08008
MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack. (99%)
Yunrui Yu; Xitong Gao; Cheng-Zhong Xu

http://arxiv.org/abs/2211.08657
Person Text-Image Matching via Text-Featur Interpretability Embedding and External Attack Node Implantation. (92%)
Fan Li; Hang Zhou; Huafeng Li; Yafei Zhang; Zhengtao Yu

http://arxiv.org/abs/2211.07915
Backdoor Attacks on Time Series: A Generative Approach. (70%)
Yujing Jiang; Xingjun Ma; Sarah Monazam Erfani; James Bailey

http://arxiv.org/abs/2211.08229
CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive Learning. (61%)
Jinghuai Zhang; Hongbin Liu; Jinyuan Jia; Neil Zhenqiang Gong

http://arxiv.org/abs/2211.08453
Improved techniques for deterministic l2 robustness. (22%)
Sahil Singla; Soheil Feizi

http://arxiv.org/abs/2211.08044
Backdoor Attacks for Remote Sensing Data with Wavelet Transform. (12%)
Nikolaus Dräger; Yonghao Xu; Pedram Ghamisi

http://arxiv.org/abs/2211.07263
Efficient Adversarial Training with Robust Early-Bird Tickets. (92%)
Zhiheng Xi; Rui Zheng; Tao Gui; Qi Zhang; Xuanjing Huang

http://arxiv.org/abs/2211.07383
Attacking Face Recognition with T-shirts: Database, Vulnerability Assessment and Detection. (13%)
M. Ibsen; C. Rathgeb; F. Brechtel; R. Klepp; K. Pöppelmann; A. George; S. Marcel; C. Busch

http://arxiv.org/abs/2211.07455
Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems. (5%)
Jialiang Xu; Mengyu Zhou; Xinyi He; Shi Han; Dongmei Zhang

http://arxiv.org/abs/2211.07650
Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations. (5%)
Shea Cardozo; Gabriel Islas Montero; Dmitry Kazhdan; Botty Dimanov; Maleakhi Wijaya; Mateja Jamnik; Pietro Lio

http://arxiv.org/abs/2211.07277
Robustifying Deep Vision Models Through Shape Sensitization. (2%)
Aditay Tripathi; Rishubh Singh; Anirban Chakraborty; Pradeep Shenoy

http://arxiv.org/abs/2211.09810
Certifying Robustness of Convolutional Neural Networks with Tight Linear Approximation. (26%)
Yuan Xiao; Tongtong Bai; Mingzheng Gu; Chunrong Fang; Zhenyu Chen

http://arxiv.org/abs/2211.06788
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization. (75%)
Liang Xiao; Jiaolong Xu; Dawei Zhao; Erke Shang; Qi Zhu; Bin Dai

http://arxiv.org/abs/2211.06757
DriftRec: Adapting diffusion models to blind JPEG restoration. (1%)
Simon Welker; Henry N. Chapman; Timo Gerkmann

http://arxiv.org/abs/2211.06571
Generating Textual Adversaries with Minimal Perturbation. (98%)
Xingyi Zhao; Lu Zhang; Depeng Xu; Shuhan Yuan

http://arxiv.org/abs/2211.06508
On the robustness of non-intrusive speech quality model by adversarial examples. (98%)
Hsin-Yi Lin; Huan-Hsin Tseng; Yu Tsao

http://arxiv.org/abs/2211.06500
An investigation of security controls and MITRE ATT\&CK techniques. (47%)
Md Rayhanur Rahman; Laurie Williams

http://arxiv.org/abs/2211.06495
Investigating co-occurrences of MITRE ATT\&CK Techniques. (12%)
Md Rayhanur Rahman; Laurie Williams

http://arxiv.org/abs/2211.06056
Remapped Cache Layout: Thwarting Cache-Based Side-Channel Attacks with a Hardware Defense. (9%)
Wei Song; Rui Hou; Peng Liu; Xiaoxin Li; Peinan Li; Lutan Zhao; Xiaofei Fu; Yifei Sun; Dan Meng

http://arxiv.org/abs/2211.05854
Test-time adversarial detection and robustness for localizing humans using ultra wide band channel impulse responses. (99%)
Abhiram Kolli; Muhammad Jehanzeb Mirza; Horst Possegger; Horst Bischof

http://arxiv.org/abs/2211.05523
Impact of Adversarial Training on Robustness and Generalizability of Language Models. (99%)
Enes Altinisik; Hassan Sajjad; Husrev Taha Sencar; Safa Messaoud; Sanjay Chawla

http://arxiv.org/abs/2211.05446
Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples. (98%)
Meng Chen; Li Lu; Jiadi Yu; Yingying Chen; Zhongjie Ba; Feng Lin; Kui Ren

http://arxiv.org/abs/2211.05410
Stay Home Safe with Starving Federated Data. (80%)
Jaechul Roh; Yajun Fang

http://arxiv.org/abs/2211.05371
MSDT: Masked Language Model Scoring Defense in Text Domain. (38%)
Jaechul Roh; Minhao Cheng; Yajun Fang

http://arxiv.org/abs/2211.09954
Robust DNN Surrogate Models with Uncertainty Quantification via Adversarial Training. (3%)
Lixiang Zhang; Jia Li

http://arxiv.org/abs/2211.05347
Mitigating Forgetting in Online Continual Learning via Contrasting Semantically Distinct Augmentations. (1%)
Sheng-Feng Yu; Wei-Chen Chiu

http://arxiv.org/abs/2211.04780
On the Robustness of Explanations of Deep Neural Network Models: A Survey. (50%)
Amlan Jyoti; Karthik Balaji Ganesh; Manoj Gayala; Nandita Lakshmi Tunuguntla; Sandesh Kamath; Vineeth N Balasubramanian

http://arxiv.org/abs/2211.05184
Are All Edges Necessary? A Unified Framework for Graph Purification. (5%)
Zishan Gu; Jintang Li; Liang Chen

http://arxiv.org/abs/2211.05249
QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems. (3%)
Ana-Maria Cretu; Florimond Houssiau; Antoine Cully; Montjoye Yves-Alexandre de

http://arxiv.org/abs/2211.04946
Accountable and Explainable Methods for Complex Reasoning over Text. (2%)
Pepa Atanasova

http://arxiv.org/abs/2211.04686
Directional Privacy for Deep Learning. (1%)
Pedro Faustini; Natasha Fernandes; Shakila Tonni; Annabelle McIver; Mark Dras

http://arxiv.org/abs/2211.04205
Preserving Semantics in Textual Adversarial Attacks. (99%)
David Herel; Hugo Cisneros; Tomas Mikolov

http://arxiv.org/abs/2211.04364
NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries? (98%)
Saadia Gabriel; Hamid Palangi; Yejin Choi

http://arxiv.org/abs/2211.11534
How Fraudster Detection Contributes to Robust Recommendation. (67%)
Yuni Lai; Kai Zhou

http://arxiv.org/abs/2211.04674
Lipschitz Continuous Algorithms for Graph Problems. (16%)
Soh Kumabe; Yuichi Yoshida

http://arxiv.org/abs/2211.04177
Learning advisor networks for noisy image classification. (1%)
Simone Ricci; Tiberio Uricchio; Bimbo Alberto Del

http://arxiv.org/abs/2211.03769
Are AlphaZero-like Agents Robust to Adversarial Perturbations? (99%)
Li-Cheng Lan; Huan Zhang; Ti-Rong Wu; Meng-Yu Tsai; I-Chen Wu; Cho-Jui Hsieh

http://arxiv.org/abs/2211.03509
Black-Box Attack against GAN-Generated Image Detector with Contrastive Perturbation. (82%)
Zijie Lou; Gang Cao; Man Lin

http://arxiv.org/abs/2211.03714
Deviations in Representations Induced by Adversarial Attacks. (70%)
Daniel Steinberg; Paul Munro

http://arxiv.org/abs/2211.03933
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System. (1%)
Zong-Zhi Lin; Thomas D. Pike; Mark M. Bailey; Nathaniel D. Bastian

http://arxiv.org/abs/2211.03637
Interpreting deep learning output for out-of-distribution detection. (1%)
Damian Matuszewski; Ida-Maria Sintorn

http://arxiv.org/abs/2211.03489
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks. (1%)
Naoya Tezuka; Hideya Ochiai; Yuwei Sun; Hiroshi Esaki

http://arxiv.org/abs/2211.03073
Contrastive Weighted Learning for Near-Infrared Gaze Estimation. (31%)
Adam Lee

http://arxiv.org/abs/2211.02878
Textual Manifold-based Defense Against Natural Language Adversarial Examples. (99%)
Dang Minh Nguyen; Luu Anh Tuan

http://arxiv.org/abs/2211.02885
Stateful Detection of Adversarial Reprogramming. (96%)
Yang Zheng; Xiaoyi Feng; Zhaoqiang Xia; Xiaoyue Jiang; Maura Pintor; Ambra Demontis; Battista Biggio; Fabio Roli

http://arxiv.org/abs/2211.03013
Robust Lottery Tickets for Pre-trained Language Models. (83%)
Rui Zheng; Rong Bao; Yuhao Zhou; Di Liang; Sirui Wang; Wei Wu; Tao Gui; Qi Zhang; Xuanjing Huang

http://arxiv.org/abs/2211.02468
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning. (99%)
Anaelia Ovalle; Evan Czyzycki; Cho-Jui Hsieh

http://arxiv.org/abs/2211.02272
Logits are predictive of network type. (68%)
Ali Borji

http://arxiv.org/abs/2211.02675
An Adversarial Robustness Perspective on the Topology of Neural Networks. (64%)
Morgane Goibert; Thomas Ricatte; Elvis Dohmatob

http://arxiv.org/abs/2211.04449
Fairness-aware Regression Robust to Adversarial Attacks. (38%)
Yulu Jin; Lifeng Lai

http://arxiv.org/abs/2211.02755
Extension of Simple Algorithms to the Matroid Secretary Problem. (9%)
Simon Park

http://arxiv.org/abs/2211.02646
Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions. (3%)
Gaurav Verma; Vishwa Vinay; Ryan A. Rossi; Srijan Kumar

http://arxiv.org/abs/2211.02578
Data Models for Dataset Drift Controls in Machine Learning With Images. (1%)
Luis Oala; Marco Aversa; Gabriel Nobis; Kurt Willis; Yoan Neuenschwander; Michèle Buck; Christian Matek; Jerome Extermann; Enrico Pomarico; Wojciech Samek; Roderick Murray-Smith; Christoph Clausen; Bruno Sanguinetti

http://arxiv.org/abs/2211.01671
Physically Adversarial Attacks and Defenses in Computer Vision: A Survey. (99%)
Xingxing Wei; Bangzheng Pu; Jiefan Lu; Baoyuan Wu

http://arxiv.org/abs/2211.02223
Adversarial Defense via Neural Oscillation inspired Gradient Masking. (98%)
Chunming Jiang; Yilei Zhang

http://arxiv.org/abs/2211.01875
M-to-N Backdoor Paradigm: A Stealthy and Fuzzy Attack to Deep Learning Models. (98%)
Linshan Hou; Zhongyun Hua; Yuhong Li; Leo Yu Zhang

http://arxiv.org/abs/2211.01598
Robust Few-shot Learning Without Using any Adversarial Samples. (89%)
Gaurav Kumar Nayak; Ruchit Rawal; Inder Khatri; Anirban Chakraborty

http://arxiv.org/abs/2211.01579
Data-free Defense of Black Box Models Against Adversarial Attacks. (84%)
Gaurav Kumar Nayak; Inder Khatri; Shubham Randive; Ruchit Rawal; Anirban Chakraborty

http://arxiv.org/abs/2211.01621
Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise. (38%)
Christian Heider Nielsen; Zheng-Hua Tan

http://arxiv.org/abs/2211.01592
Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems. (33%)
Chong Chen; Ying Gao; Leyu Shi; Siquan Huang

http://arxiv.org/abs/2211.02245
Unintended Memorization and Timing Attacks in Named Entity Recognition Models. (12%)
Rana Salal Ali; Benjamin Zi Hao Zhao; Hassan Jameel Asghar; Tham Nguyen; Ian David Wood; Dali Kaafar

http://arxiv.org/abs/2211.01182
Defending with Errors: Approximate Computing for Robustness of Deep Neural Networks. (99%)
Amira Guesmi; Ihsen Alouani; Khaled N. Khasawneh; Mouna Baklouti; Tarek Frikha; Mohamed Abid; Nael Abu-Ghazaleh

http://arxiv.org/abs/2211.01093
Improving transferability of 3D adversarial attacks with scale and shear transformations. (99%)
Jinali Zhang; Yinpeng Dong; Jun Zhu; Jihong Zhu; Minchi Kuang; Xiaming Yuan

http://arxiv.org/abs/2211.00887
Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise. (99%)
Jhih-Cing Huang; Yu-Lin Tsai; Chao-Han Huck Yang; Cheng-Fang Su; Chia-Mu Yu; Pin-Yu Chen; Sy-Yen Kuo

http://arxiv.org/abs/2211.01112
Adversarial Attack on Radar-based Environment Perception Systems. (99%)
Amira Guesmi; Ihsen Alouani

http://arxiv.org/abs/2211.01236
Isometric Representations in Neural Networks Improve Robustness. (62%)
Kosio Beshkov; Jonas Verhellen; Mikkel Elle Lepperød

http://arxiv.org/abs/2211.01806
BATT: Backdoor Attack with Transformation-based Triggers. (56%)
Tong Xu; Yiming Li; Yong Jiang; Shu-Tao Xia

http://arxiv.org/abs/2211.05638
Untargeted Backdoor Attack against Object Detection. (50%)
Chengxiao Luo; Yiming Li; Yong Jiang; Shu-Tao Xia

http://arxiv.org/abs/2211.09728
Generative Adversarial Training Can Improve Neural Language Models. (33%)
Sajad Movahedi; Azadeh Shakery

http://arxiv.org/abs/2211.05631
Backdoor Defense via Suppressing Model Shortcuts. (3%)
Sheng Yang; Yiming Li; Yong Jiang; Shu-Tao Xia

http://arxiv.org/abs/2211.01202
Human-in-the-Loop Mixup. (1%)
Katherine M. Collins; Umang Bhatt; Weiyang Liu; Vihari Piratla; Ilia Sucholutsky; Bradley Love; Adrian Weller

http://arxiv.org/abs/2211.00525
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training. (99%)
Junhao Dong; Seyed-Mohsen Moosavi-Dezfooli; Jianhuang Lai; Xiaohua Xie

http://arxiv.org/abs/2211.00825
LMD: A Learnable Mask Network to Detect Adversarial Examples for Speaker Verification. (99%)
Xing Chen; Jie Wang; Xiao-Lei Zhang; Wei-Qiang Zhang; Kunde Yang

http://arxiv.org/abs/2211.00322
DensePure: Understanding Diffusion Models towards Adversarial Robustness. (98%)
Chaowei Xiao; Zhongzhu Chen; Kun Jin; Jiongxiao Wang; Weili Nie; Mingyan Liu; Anima Anandkumar; Bo Li; Dawn Song

http://arxiv.org/abs/2211.00269
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. (87%)
Jianan Zhou; Jianing Zhu; Jingfeng Zhang; Tongliang Liu; Gang Niu; Bo Han; Masashi Sugiyama

http://arxiv.org/abs/2211.00366
Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metrics. (82%)
Ekaterina Shumitskaya; Anastasia Antsiferova; Dmitriy Vatolin

http://arxiv.org/abs/2211.00453
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning. (80%)
Virat Shejwalkar; Lingjuan Lyu; Amir Houmansadr

http://arxiv.org/abs/2211.00748
Maximum Likelihood Distillation for Robust Modulation Classification. (69%)
Javier Maroto; Gérôme Bovet; Pascal Frossard

http://arxiv.org/abs/2211.00294
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness. (45%)
Wenhao Wu; Wei Li; Jiachen Liu; Xinyan Xiao; Ziqiang Cao; Sujian Li; Hua Wu

http://arxiv.org/abs/2211.00463
Amplifying Membership Exposure via Data Poisoning. (22%)
Yufei Chen; Chao Shen; Yun Shen; Cong Wang; Yang Zhang

http://arxiv.org/abs/2211.00273
ActGraph: Prioritization of Test Cases Based on Deep Neural Network Activation Graph. (13%)
Jinyin Chen; Jie Ge; Haibin Zheng

http://arxiv.org/abs/2210.17140
Scoring Black-Box Models for Adversarial Robustness. (98%)
Jian Vora; Pranay Reddy Samala

http://arxiv.org/abs/2211.00239
ARDIR: Improving Robustness using Knowledge Distillation of Internal Representation. (88%)
Tomokatsu Takahashi; Masanori Yamada; Yuuki Yamanaka; Tomoya Yamashita

http://arxiv.org/abs/2210.17376
SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability. (33%)
Dipkamal Bhusal; Rosalyn Shin; Ajay Ashok Shewale; Monish Kumar Manikya Veerabhadran; Michael Clifford; Sara Rampazzi; Nidhi Rastogi

http://arxiv.org/abs/2210.17546
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy. (16%)
Daphne Ippolito; Florian Tramèr; Milad Nasr; Chiyuan Zhang; Matthew Jagielski; Katherine Lee; Christopher A. Choquette-Choo; Nicholas Carlini

http://arxiv.org/abs/2210.17029
Poison Attack and Defense on Deep Source Code Processing Models. (99%)
Jia Li; Zhuo Li; Huangzhao Zhang; Ge Li; Zhi Jin; Xing Hu; Xin Xia

http://arxiv.org/abs/2210.17004
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution. (99%)
Aiwei Liu; Honghai Yu; Xuming Hu; Shu'ang Li; Li Lin; Fukun Ma; Yawen Yang; Lijie Wen

http://arxiv.org/abs/2210.16765
Benchmarking Adversarial Patch Against Aerial Detection. (99%)
Jiawei Lian; Shaohui Mei; Shun Zhang; Mingyang Ma

http://arxiv.org/abs/2210.16777
Symmetric Saliency-based Adversarial Attack To Speaker Identification. (92%)
Jiadi Yao; Xing Chen; Xiao-Lei Zhang; Wei-Qiang Zhang; Kunde Yang

http://arxiv.org/abs/2210.16940
FI-ODE: Certified and Robust Forward Invariance in Neural ODEs. (61%)
Yujia Huang; Ivan Dario Jimenez Rodriguez; Huan Zhang; Yuanyuan Shi; Yisong Yue

http://arxiv.org/abs/2210.16915
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games. (9%)
The Viet Bui; Tien Mai; Thanh H. Nguyen

http://arxiv.org/abs/2210.16690
On the Need of Neuromorphic Twins to Detect Denial-of-Service Attacks on Communication Networks. (10%)
Holger Boche; Rafael F. Schaefer; H. Vincent Poor; Frank H. P. Fitzek

http://arxiv.org/abs/2210.15997
Universal Adversarial Directions. (99%)
Ching Lam Choi; Farzan Farnia

http://arxiv.org/abs/2210.16117
Improving the Transferability of Adversarial Attacks on Face Recognition with Beneficial Perturbation Feature Augmentation. (99%)
Fengfan Zhou; Hefei Ling; Yuxuan Shi; Jiazhong Chen; Zongyi Li; Ping Li

http://arxiv.org/abs/2210.16346
Improving Hyperspectral Adversarial Robustness Under Multiple Attacks. (98%)
Nicholas Soucy; Salimeh Yasaei Sekeh

http://arxiv.org/abs/2210.16371
Distributed Black-box Attack against Image Classification Cloud Services. (95%)
Han Wu; Sareh Rowlands; Johan Wahlstrom

http://arxiv.org/abs/2210.15944
RoChBert: Towards Robust BERT Fine-tuning for Chinese. (75%)
Zihan Zhang; Jinfeng Li; Ning Shi; Bo Yuan; Xiangyu Liu; Rong Zhang; Hui Xue; Donghong Sun; Chao Zhang

http://arxiv.org/abs/2210.16451
Robust Boosting Forests with Richer Deep Feature Hierarchy. (56%)
Jianqiao Wangni

http://arxiv.org/abs/2210.16140
Localized Randomized Smoothing for Collective Robustness Certification. (26%)
Jan Schuchardt; Tom Wollschläger; Aleksandar Bojchevski; Stephan Günnemann

http://arxiv.org/abs/2210.16114
Towards Reliable Neural Specifications. (11%)
Chuqin Geng; Nham Le; Xiaojie Xu; Zhaoyue Wang; Arie Gurfinkel; Xujie Si

http://arxiv.org/abs/2210.16258
On the Vulnerability of Data Points under Multiple Membership Inference Attacks and Target Models. (1%)
Mauro Conti; Jiaxin Li; Stjepan Picek

http://arxiv.org/abs/2210.15700
TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems. (99%)
Islam Debicha; Richard Bauwens; Thibault Debatty; Jean-Michel Dricot; Tayeb Kenaza; Wim Mees

http://arxiv.org/abs/2210.15291
Isometric 3D Adversarial Examples in the Physical World. (99%)
Yibo Miao; Yinpeng Dong; Jun Zhu; Xiao-Shan Gao

http://arxiv.org/abs/2210.15392
LeNo: Adversarial Robust Salient Object Detection Networks with Learnable Noise. (92%)
He Tang; He Wang

http://arxiv.org/abs/2210.15221
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack. (92%)
Yu Cao; Dianqi Li; Meng Fang; Tianyi Zhou; Jun Gao; Yibing Zhan; Dacheng Tao

http://arxiv.org/abs/2210.15318
Efficient and Effective Augmentation Strategy for Adversarial Training. (56%)
Sravanti Addepalli; Samyak Jain; R. Venkatesh Babu

http://arxiv.org/abs/2210.15764
Noise Injection Node Regularization for Robust Learning. (2%)
Noam Levi; Itay M. Bloch; Marat Freytsis; Tomer Volansky

http://arxiv.org/abs/2210.15176
Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather. (1%)
Jinlong Li; Runsheng Xu; Jin Ma; Qin Zou; Jiaqi Ma; Hongkai Yu

http://arxiv.org/abs/2210.15068
Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting. (99%)
Pengyue Hou; Jie Han; Xingyu Li

http://arxiv.org/abs/2210.17316
There is more than one kind of robustness: Fooling Whisper with adversarial examples. (98%)
Raphael Olivier; Bhiksha Raj

http://arxiv.org/abs/2210.14957
Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness. (86%)
Jiahao Zhao; Wenji Mao

http://arxiv.org/abs/2210.14814
BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples. (75%)
Mohaddeseh Bastan; Mihai Surdeanu; Niranjan Balasubramanian

http://arxiv.org/abs/2210.14999
EIPSIM: Modeling Secure IP Address Allocation at Cloud Scale. (11%)
Eric University of Wisconsin-Madison Pauley; Kyle Pennsylvania State University Domico; Blaine University of Wisconsin-Madison Hoak; Ryan University of Wisconsin-Madison Sheatsley; Quinn University of Wisconsin-Madison Burke; Yohan University of Wisconsin-Madison Beugin; Patrick University of Wisconsin-Madison McDaniel

http://arxiv.org/abs/2210.15140
V-Cloak: Intelligibility-, Naturalness- & Timbre-Preserving Real-Time Voice Anonymization. (10%)
Jiangyi Zhejiang University Deng; Fei Zhejiang University Teng; Yanjiao Zhejiang University Chen; Xiaofu Wuhan University Chen; Zhaohui Wuhan University Wang; Wenyuan Zhejiang University Xu

http://arxiv.org/abs/2210.15127
Rethinking the Reverse-engineering of Trojan Triggers. (5%)
Zhenting Wang; Kai Mei; Hailun Ding; Juan Zhai; Shiqing Ma

http://arxiv.org/abs/2210.14632
Cover Reproducible Steganography via Deep Generative Models. (1%)
Kejiang Chen; Hang Zhou; Yaofei Wang; Menghan Li; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2210.14622
DEMIS: A Threat Model for Selectively Encrypted Visual Surveillance Data. (1%)
Ifeoluwapo Aribilola; Mamoona Naveed Asghar; Brian Lee

http://arxiv.org/abs/2210.15042
Privately Fine-Tuning Large Language Models with Differential Privacy. (1%)
Rouzbeh Behnia; Mohamamdreza Ebrahimi; Jason Pacheco; Balaji Padmanabhan

http://arxiv.org/abs/2210.15446
LP-BFGS attack: An adversarial attack based on the Hessian with limited pixels. (99%)
Jiebao Zhang; Wenhua Qian; Rencan Nie; Jinde Cao; Dan Xu

http://arxiv.org/abs/2210.14405
Adversarially Robust Medical Classification via Attentive Convolutional Neural Networks. (99%)
Isaac Wasserman

http://arxiv.org/abs/2210.14018
A White-Box Adversarial Attack Against a Digital Twin. (99%)
Wilson Patterson; Ivan Fernandez; Subash Neupane; Milan Parmar; Sudip Mittal; Shahram Rahimi

http://arxiv.org/abs/2210.15429
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants. (98%)
James Lee Hu; Mohammadreza Ebrahimi; Weifeng Li; Xin Li; Hsinchun Chen

http://arxiv.org/abs/2210.14404
Adversarial Purification with the Manifold Hypothesis. (98%)
Zhaoyuan Yang; Zhiwei Xu; Jing Zhang; Richard Hartley; Peter Tu

http://arxiv.org/abs/2210.14410
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes. (98%)
Sina Baharlouei; Fatemeh Sheikholeslami; Meisam Razaviyayn; Zico Kolter

http://arxiv.org/abs/2210.14283
Accelerating Certified Robustness Training via Knowledge Transfer. (73%)
Pratik Vaishnavi; Kevin Eykholt; Amir Rahmati

http://arxiv.org/abs/2210.14229
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network. (64%)
Huan Hua; Jun Yan; Xi Fang; Weiquan Huang; Huilin Yin; Wancheng Ge

http://arxiv.org/abs/2210.13762
Towards Robust Recommender Systems via Triple Cooperative Defense. (61%)
Qingyang Wang; Defu Lian; Chenwang Wu; Enhong Chen

http://arxiv.org/abs/2210.13915
Towards Formal Approximated Minimal Explanations of Neural Networks. (13%)
Shahaf Bassan; Guy Katz

http://arxiv.org/abs/2210.13815
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification. (13%)
Yulin Zhu; Liang Tong; Kai Zhou

http://arxiv.org/abs/2211.12851
A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks. (3%)
M. Kuzlu; F. O. Catak; S. Sarp; U. Cali; O Gueler

http://arxiv.org/abs/2210.14376
Robustness of Locally Differentially Private Graph Analysis Against Poisoning. (1%)
Jacob Imola; Amrita Roy Chowdhury; Kamalika Chaudhuri

http://arxiv.org/abs/2210.12952
Ares: A System-Oriented Wargame Framework for Adversarial ML. (99%)
Farhan Ahmed; Pratik Vaishnavi; Kevin Eykholt; Amir Rahmati

http://arxiv.org/abs/2210.13660
SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning. (99%)
Giovanni Apruzzese; Mauro Conti; Ying Yuan

http://arxiv.org/abs/2210.13710
Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs. (96%)
Haibin Zheng; Haiyang Xiong; Jinyin Chen; Haonan Ma; Guohan Huang

http://arxiv.org/abs/2210.13631
On the Robustness of Dataset Inference. (88%)
Sebastian Szyller; Rui Zhang; Jian Liu; N. Asokan

http://arxiv.org/abs/2210.14225
Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor. (16%)
Zhao Yang; Fengyang Deng; Linxi Han

http://arxiv.org/abs/2210.12945
Revisiting Sparse Convolutional Model for Visual Recognition. (11%)
Xili Dai; Mingyang Li; Pengyuan Zhai; Shengbang Tong; Xingjian Gao; Shao-Lun Huang; Zhihui Zhu; Chong You; Yi Ma

http://arxiv.org/abs/2210.12873
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning. (68%)
Kaiyuan Zhang; Guanhong Tao; Qiuling Xu; Siyuan Cheng; Shengwei An; Yingqi Liu; Shiwei Feng; Guangyu Shen; Pin-Yu Chen; Shiqing Ma; Xiangyu Zhang

http://arxiv.org/abs/2210.13463
Adversarial Pretraining of Self-Supervised Deep Networks: Past, Present and Future. (45%)
Guo-Jun Qi; Mubarak Shah

http://arxiv.org/abs/2210.12396
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation. (99%)
Fan Yin; Yao Li; Cho-Jui Hsieh; Kai-Wei Chang

http://arxiv.org/abs/2210.13982
Hindering Adversarial Attacks with Implicit Neural Representations. (92%)
Andrei A. Rusu; Dan A. Calian; Sven Gowal; Raia Hadsell

http://arxiv.org/abs/2210.12598
GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections. (81%)
Junyuan Fang; Haixian Wen; Jiajing Wu; Qi Xuan; Zibin Zheng; Chi K. Tse

http://arxiv.org/abs/2210.12606
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. (26%)
Maria-Florina Balcan; Rattana Pukdee; Pradeep Ravikumar; Hongyang Zhang

http://arxiv.org/abs/2210.12367
Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation. (4%)
Wenhao Wu; Wei Li; Jiachen Liu; Xinyan Xiao; Sujian Li; Yajuan Lyu

http://arxiv.org/abs/2210.12030
Evolution of Neural Tangent Kernels under Benign and Adversarial Training. (99%)
Noel Loo; Ramin Hasani; Alexander Amini; Daniela Rus

http://arxiv.org/abs/2210.12179
The Dark Side of AutoML: Towards Architectural Backdoor Search. (68%)
Ren Pang; Changjiang Li; Zhaohan Xi; Shouling Ji; Ting Wang

http://arxiv.org/abs/2210.11841
Diffusion Visual Counterfactual Explanations. (10%)
Maximilian Augustin; Valentyn Boreiko; Francesco Croce; Matthias Hein

http://arxiv.org/abs/2210.12233
TCAB: A Large-Scale Text Classification Attack Benchmark. (10%)
Kalyani Asthana; Zhouhang Xie; Wencong You; Adam Noack; Jonathan Brophy; Sameer Singh; Daniel Lowd

http://arxiv.org/abs/2210.11726
A critical review of cyber-physical security for building automation systems. (2%)
Guowen Li; Lingyu Ren; Yangyang Fu; Zhiyao Yang; Veronica Adetola; Jin Wen; Qi Zhu; Teresa Wu; K. Selcuk Candanf; Zheng O'Neill

http://arxiv.org/abs/2210.11735
Extracted BERT Model Leaks More Information than You Think! (1%)
Xuanli He; Chen Chen; Lingjuan Lyu; Qiongkai Xu

http://arxiv.org/abs/2210.11598
Identifying Human Strategies for Generating Word-Level Adversarial Examples. (98%)
Maximilian Mozes; Bennett Kleinberg; Lewis D. Griffin

http://arxiv.org/abs/2210.15427
Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks. (98%)
Jiyang Guan; Jian Liang; Ran He

http://arxiv.org/abs/2210.11498
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models. (98%)
Hannah Chen; Yangfeng Ji; David Evans

http://arxiv.org/abs/2210.11513
Learning Sample Reweighting for Accuracy and Adversarial Robustness. (93%)
Chester Holtz; Tsui-Wei Weng; Gal Mishne

http://arxiv.org/abs/2210.11407
Similarity of Neural Architectures using Adversarial Attack Transferability. (86%)
Jaehui Hwang; Dongyoon Han; Byeongho Heo; Song Park; Sanghyuk Chun; Jong-Seok Lee

http://arxiv.org/abs/2210.11592
New data poison attacks on machine learning classifiers for mobile exfiltration. (80%)
Miguel A. Ramirez; Sangyoung Yoon; Ernesto Damiani; Hussam Al Hamadi; Claudio Agostino Ardagna; Nicola Bena; Young-Ji Byon; Tae-Yeon Kim; Chung-Suk Cho; Chan Yeob Yeun

http://arxiv.org/abs/2210.11242
Attacking Motion Estimation with Adversarial Snow. (16%)
Jenny Schmalfuss; Lukas Mehl; Andrés Bruhn

http://arxiv.org/abs/2210.11049
How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers. (13%)
Guangsheng Zhang; Bo Liu; Huan Tian; Tianqing Zhu; Ming Ding; Wanlei Zhou

http://arxiv.org/abs/2210.11061
Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario. (4%)
Pedro Miguel Sánchez Sánchez; Alberto Huertas Celdrán; Enrique Tomás Martínez Beltrán; Daniel Demeter; Gérôme Bovet; Gregorio Martínez Pérez; Burkhard Stiller

http://arxiv.org/abs/2210.11082
Apple of Sodom: Hidden Backdoors in Superior Sentence Embeddings via Contrastive Learning. (3%)
Xiaoyi Chen; Baisong Xin; Shengfang Zhai; Shiqing Ma; Qingni Shen; Zhonghai Wu

http://arxiv.org/abs/2210.11620
LOT: Layer-wise Orthogonal Training on Improving $\ell_2$ Certified Robustness. (3%)
Xiaojun Xu; Linyi Li; Bo Li

http://arxiv.org/abs/2210.10485
Learning Transferable Adversarial Robust Representations via Multi-view Consistency. (99%)
Minseon Kim; Hyeonjeong Ha; Dong Bok Lee; Sung Ju Hwang

http://arxiv.org/abs/2210.10482
Effective Targeted Attacks for Adversarial Self-Supervised Learning. (99%)
Minseon Kim; Hyeonjeong Ha; Sooel Son; Sung Ju Hwang

http://arxiv.org/abs/2210.14164
No-Box Attacks on 3D Point Cloud Classification. (93%)
Hanieh Naderi; Chinthaka Dinesh; Ivan V. Bajic; Shohreh Kasaei

http://arxiv.org/abs/2210.10886
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis. (83%)
Ruinan Jin; Xiaoxiao Li

http://arxiv.org/abs/2210.13235
Chaos Theory and Adversarial Robustness. (73%)
Jonathan S. Kent

http://arxiv.org/abs/2210.11237
Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey. (69%)
Hui Cao; Wenlong Zou; Yinkun Wang; Ting Song; Mengjun Liu

http://arxiv.org/abs/2210.10683
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP. (64%)
Yangyi Chen; Hongcheng Gao; Ganqu Cui; Fanchao Qi; Longtao Huang; Zhiyuan Liu; Maosong Sun

http://arxiv.org/abs/2210.10936
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information. (41%)
Xiaoyu Cao; Jinyuan Jia; Zaixi Zhang; Neil Zhenqiang Gong

http://arxiv.org/abs/2210.10880
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning. (16%)
Ruihan Wu; Xiangyu Chen; Chuan Guo; Kilian Q. Weinberger

http://arxiv.org/abs/2210.10378
Variational Model Perturbation for Source-Free Domain Adaptation. (1%)
Mengmeng Jing; Xiantong Zhen; Jingjing Li; Cees G. M. Snoek

http://arxiv.org/abs/2210.09852
Scaling Adversarial Training to Large Perturbation Bounds. (98%)
Sravanti Addepalli; Samyak Jain; Gaurang Sriramanan; R. Venkatesh Babu

http://arxiv.org/abs/2210.09671
Not All Poisons are Created Equal: Robust Training against Data Poisoning. (97%)
Yu Yang; Tian Yu Liu; Baharan Mirzasoleiman

http://arxiv.org/abs/2210.09658
ROSE: Robust Selective Fine-tuning for Pre-trained Language Models. (73%)
Lan Jiang; Hao Zhou; Yankai Lin; Peng Li; Jie Zhou; Rui Jiang

http://arxiv.org/abs/2210.10667
Analysis of Master Vein Attacks on Finger Vein Recognition Systems. (56%)
Huy H. Nguyen; Trung-Nghia Le; Junichi Yamagishi; Isao Echizen

http://arxiv.org/abs/2210.10272
Training set cleansing of backdoor poisoning by self-supervised representation learning. (56%)
H. Wang; S. Karami; O. Dia; H. Ritter; E. Emamjomeh-Zadeh; J. Chen; Z. Xiang; D. J. Miller; G. Kesidis

http://arxiv.org/abs/2210.10253
On the Adversarial Robustness of Mixture of Experts. (13%)
Joan Puigcerver; Rodolphe Jenatton; Carlos Riquelme; Pranjal Awasthi; Srinadh Bhojanapalli

http://arxiv.org/abs/2210.10114
Transferable Unlearnable Examples. (8%)
Jie Ren; Han Xu; Yuxuan Wan; Xingjun Ma; Lichao Sun; Jiliang Tang

http://arxiv.org/abs/2210.09940
Automatic Detection of Fake Key Attacks in Secure Messaging. (8%)
Tarun Kumar Yadav; Devashish Gosain; Amir Herzberg; Daniel Zappala; Kent Seamons

http://arxiv.org/abs/2210.09643
Improving Adversarial Robustness by Contrastive Guided Diffusion Process. (2%)
Yidong Ouyang; Liyan Xie; Guang Cheng

http://arxiv.org/abs/2210.09405
Towards Generating Adversarial Examples on Mixed-type Data. (99%)
Han Xu; Menghai Pan; Zhimeng Jiang; Huiyuan Chen; Xiaoting Li; Mahashweta Das; Hao Yang

http://arxiv.org/abs/2210.08870
Differential Evolution based Dual Adversarial Camouflage: Fooling Human Eyes and Object Detectors. (99%)
Jialiang Sun; Tingsong Jiang; Wen Yao; Donghua Wang; Xiaoqian Chen

http://arxiv.org/abs/2210.09364
Probabilistic Categorical Adversarial Attack & Adversarial Training. (99%)
Pengfei He; Han Xu; Jie Ren; Yuxuan Wan; Zitao Liu; Jiliang Tang

http://arxiv.org/abs/2210.09194
Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class. (96%)
Khoa D. Doan; Yingjie Lao; Ping Li

http://arxiv.org/abs/2210.08929
DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers. (87%)
Gaurav Kumar Nayak; Ruchit Rawal; Anirban Chakraborty

http://arxiv.org/abs/2210.08902
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations. (78%)
Julia El Zini; Mariette Awad

http://arxiv.org/abs/2210.09503
Towards Fair Classification against Poisoning Attacks. (76%)
Han Xu; Xiaorui Liu; Yuxuan Wan; Jiliang Tang

http://arxiv.org/abs/2210.09421
Deepfake Text Detection: Limitations and Opportunities. (41%)
Jiameng Pu; Zain Sarwar; Sifat Muhammad Abdullah; Abdullah Rehman; Yoonjin Kim; Parantapa Bhattacharya; Mobin Javed; Bimal Viswanath

http://arxiv.org/abs/2210.09482
You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving Frameworks. (15%)
Yulong Cao; S. Hrushikesh Bhupathiraju; Pirouz Naghavi; Takeshi Sugawara; Z. Morley Mao; Sara Rampazzi

http://arxiv.org/abs/2210.09545
Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models. (9%)
Zhiyuan Zhang; Lingjuan Lyu; Xingjun Ma; Chenguang Wang; Xu Sun

http://arxiv.org/abs/2210.09465
Understanding CNN Fragility When Learning With Imbalanced Data. (1%)
Damien Dablain; Kristen N. Jacobson; Colin Bellinger; Mark Roberts; Nitesh Chawla

http://arxiv.org/abs/2210.08472
Object-Attentional Untargeted Adversarial Attack. (99%)
Chao Zhou; Yuan-Gen Wang; Guopu Zhu

http://arxiv.org/abs/2210.08579
Nowhere to Hide: A Lightweight Unsupervised Detector against Adversarial Examples. (99%)
Hui Liu; Bo Zhao; Kehuan Zhang; Peng Liu

http://arxiv.org/abs/2210.08701
ODG-Q: Robust Quantization via Online Domain Generalization. (83%)
Chaofan Tao; Ngai Wong

http://arxiv.org/abs/2210.11235
Interpretable Machine Learning for Detection and Classification of Ransomware Families Based on API Calls. (1%)
Rawshan Ara Mowri; Madhuri Siddula; Kaushik Roy

http://arxiv.org/abs/2210.08388
RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging. (2%)
Ajay Jaiswal; Kumar Ashutosh; Justin F Rousseau; Yifan Peng; Zhangyang Wang; Ying Ding

http://arxiv.org/abs/2210.08159
Dynamics-aware Adversarial Attack of Adaptive Neural Networks. (89%)
An Tao; Yueqi Duan; Yingqi Wang; Jiwen Lu; Jie Zhou

http://arxiv.org/abs/2210.07540
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture. (87%)
Yichuan Mo; Dongxian Wu; Yifei Wang; Yiwen Guo; Yisen Wang

http://arxiv.org/abs/2210.08178
Is Face Recognition Safe from Realizable Attacks? (84%)
Sanjay Saha; Terence Sim

http://arxiv.org/abs/2210.07907
Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks. (76%)
Sishuo Chen; Wenkai Yang; Zhiyuan Zhang; Xiaohan Bi; Xu Sun

http://arxiv.org/abs/2210.07714
Close the Gate: Detecting Backdoored Models in Federated Learning based on Client-Side Deep Layer Output Analysis. (67%)
Phillip Technical University Darmstadt Rieger; Torsten University of Würzburg Krauß; Markus Technical University Darmstadt Miettinen; Alexandra University of Würzburg Dmitrienko; Ahmad-Reza Technical University Darmstadt Sadeghi

http://arxiv.org/abs/2210.06871
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition. (99%)
Shuai Jia; Bangjie Yin; Taiping Yao; Shouhong Ding; Chunhua Shen; Xiaokang Yang; Chao Ma

http://arxiv.org/abs/2210.06888
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient. (99%)
Farzad Nikfam; Alberto Marchisio; Maurizio Martina; Muhammad Shafique

http://arxiv.org/abs/2210.07346
Demystifying Self-supervised Trojan Attacks. (95%)
Changjiang Li; Ren Pang; Zhaohan Xi; Tianyu Du; Shouling Ji; Yuan Yao; Ting Wang

http://arxiv.org/abs/2210.06807
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors. (81%)
Qixun Wang; Yifei Wang; Hong Zhu; Yisen Wang

http://arxiv.org/abs/2210.07394
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation. (13%)
Zhouxing Shi; Yihan Wang; Huan Zhang; Zico Kolter; Cho-Jui Hsieh

http://arxiv.org/abs/2210.06789
Large-Scale Open-Set Classification Protocols for ImageNet. (2%)
Jesus Andres Palechor Anacona; Annesha Bhoumik; Manuel Günther

http://arxiv.org/abs/2210.06792
SoK: How Not to Architect Your Next-Generation TEE Malware? (1%)
Kubilay Ahmet Küçük; Steve Moyle; Andrew Martin; Alexandru Mereacre; Nicholas Allott

http://arxiv.org/abs/2210.06771
Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learning. (1%)
Peng Ye; Zhifeng Jiang; Wei Wang; Bo Li; Baochun Li

http://arxiv.org/abs/2210.07441
Characterizing the Influence of Graph Elements. (1%)
Zizhang Chen; Peizhao Li; Hongfu Liu; Pengyu Hong

http://arxiv.org/abs/2210.06670
A Game Theoretical vulnerability analysis of Adversarial Attack. (99%)
Khondker Fariha Hossain; Alireza Tavakkoli; Shamik Sengupta

http://arxiv.org/abs/2210.05968
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation. (99%)
Zeyu Qin; Yanbo Fan; Yi Liu; Li Shen; Yong Zhang; Jue Wang; Baoyuan Wu

http://arxiv.org/abs/2210.06284
Visual Prompting for Adversarial Robustness. (99%)
Aochuan Chen; Peter Lorenz; Yuguang Yao; Pin-Yu Chen; Sijia Liu

http://arxiv.org/abs/2210.05938
Robust Models are less Over-Confident. (96%)
Julia Grabinski; Paul Gavrikov; Janis Keuper; Margret Keuper

http://arxiv.org/abs/2210.06077
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity. (86%)
Andrew C. Cullen; Paul Montague; Shijie Liu; Sarah M. Erfani; Benjamin I. P. Rubinstein

http://arxiv.org/abs/2210.05927
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning. (82%)
Yongyuan Liang; Yanchao Sun; Ruijie Zheng; Furong Huang

http://arxiv.org/abs/2210.06704
COLLIDER: A Robust Training Framework for Backdoor Data. (81%)
Hadi M. Dolatabadi; Sarah Erfani; Christopher Leckie

http://arxiv.org/abs/2210.06428
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork. (76%)
Haotao Wang; Junyuan Hong; Aston Zhang; Jiayu Zhou; Zhangyang Wang

http://arxiv.org/abs/2210.05929
Few-shot Backdoor Attacks via Neural Tangent Kernels. (62%)
Jonathan Hayase; Sewoong Oh

http://arxiv.org/abs/2210.06516
How to Sift Out a Clean Data Subset in the Presence of Data Poisoning? (9%)
Yi Zeng; Minzhou Pan; Himanshu Jahagirdar; Ming Jin; Lingjuan Lyu; Ruoxi Jia

http://arxiv.org/abs/2210.06509
Understanding Impacts of Task Similarity on Backdoor Attack and Detection. (2%)
Di Tang; Rui Zhu; XiaoFeng Wang; Haixu Tang; Yi Chen

http://arxiv.org/abs/2210.06089
When are Local Queries Useful for Robust Learning? (1%)
Pascale Gourdeau; Varun Kanade; Marta Kwiatkowska; James Worrell

http://arxiv.org/abs/2210.05577
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness? (99%)
Nikolaos Tsilivis; Julia Kempe

http://arxiv.org/abs/2210.05373
Stable and Efficient Adversarial Training through Local Linearization. (91%)
Zhuorong Li; Daiwei Yu

http://arxiv.org/abs/2210.05276
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks. (86%)
Alberto Marchisio; Vojtech Mrazek; Andrea Massa; Beatrice Bussolino; Maurizio Martina; Muhammad Shafique

http://arxiv.org/abs/2210.06589
Adversarial Attack Against Image-Based Localization Neural Networks. (78%)
Meir Brand; Itay Naeh; Daniel Teitelman

http://arxiv.org/abs/2210.11264
Detecting Backdoors in Deep Text Classifiers. (76%)
You Guo; Jun Wang; Trevor Cohn

http://arxiv.org/abs/2210.05667
Human Body Measurement Estimation with Adversarial Augmentation. (33%)
Nataniel Ruiz; Miriam Bellver; Timo Bolkart; Ambuj Arora; Ming C. Lin; Javier Romero; Raja Bala

http://arxiv.org/abs/2210.05742
Curved Representation Space of Vision Transformers. (10%)
Juyeop Kim; Junha Park; Songkuk Kim; Jong-Seok Lee

http://arxiv.org/abs/2210.05279
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity. (1%)
Vazelhes William de; Hualin Zhang; Huimin Wu; Xiao-Tong Yuan; Bin Gu

http://arxiv.org/abs/2210.05177
Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach. (1%)
Peng Mi; Li Shen; Tianhe Ren; Yiyi Zhou; Xiaoshuai Sun; Rongrong Ji; Dacheng Tao

http://arxiv.org/abs/2210.05118
Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization. (92%)
Ziquan Liu; Antoni B. Chan

http://arxiv.org/abs/2210.04886
Revisiting adapters with adversarial training. (88%)
Sylvestre-Alvise Rebuffi; Francesco Croce; Sven Gowal

http://arxiv.org/abs/2210.04591
Universal Adversarial Perturbations: Efficiency on a small image dataset. (81%)
Waris ENSEIRB-MATMECA, UB Radji

http://arxiv.org/abs/2210.04871
Certified Training: Small Boxes are All You Need. (22%)
Mark Niklas Müller; Franziska Eckert; Marc Fischer; Martin Vechev

http://arxiv.org/abs/2210.06983
Denoising Masked AutoEncoders Help Robust Classification. (1%)
Quanlin Wu; Hang Ye; Yuntian Gu; Huishuai Zhang; Liwei Wang; Di He

http://arxiv.org/abs/2210.04311
Pruning Adversarially Robust Neural Networks without Adversarial Examples. (99%)
Tong Jian; Zifeng Wang; Yanzhi Wang; Jennifer Dy; Stratis Ioannidis

http://arxiv.org/abs/2210.04213
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective. (99%)
Yao Zhu; Yuefeng Chen; Xiaodan Li; Kejiang Chen; Yuan He; Xiang Tian; Bolun Zheng; Yaowu Chen; Qingming Huang

http://arxiv.org/abs/2210.04195
Online Training Through Time for Spiking Neural Networks. (1%)
Mingqing Xiao; Qingyan Meng; Zongpeng Zhang; Di He; Zhouchen Lin

http://arxiv.org/abs/2210.04052
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems. (99%)
Jiahui Chen; Yi Zhao; Qi Li; Xuewei Feng; Ke Xu

http://arxiv.org/abs/2210.04087
Symmetry Defense Against CNN Adversarial Perturbation Attacks. (99%)
Blerta Lindqvist

http://arxiv.org/abs/2210.04076
Robustness of Unsupervised Representation Learning without Labels. (54%)
Aleksandar Petrov; Marta Kwiatkowska

http://arxiv.org/abs/2210.03429
Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs. (99%)
Prashant Pandey; Aleti Vardhan; Mustafa Chasmai; Tanuj Sur; Brejesh Lall

http://arxiv.org/abs/2210.03372
Pre-trained Adversarial Perturbations. (99%)
Yuanhao Ban; Yinpeng Dong

http://arxiv.org/abs/2210.03895
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints. (93%)
Yinpeng Dong; Shouwei Ruan; Hang Su; Caixin Kang; Xingxing Wei; Jun Zhu

http://arxiv.org/abs/2210.03349
Game-Theoretic Understanding of Misclassification. (47%)
Kosuke Sumiyasu; Kazuhiko Kawamoto; Hiroshi Kera

http://arxiv.org/abs/2210.03543
A2: Efficient Automated Attacker for Boosting Adversarial Training. (41%)
Zhuoer Xu; Guanghui Zhu; Changhua Meng; Shiwen Cui; Zhenzhe Ying; Weiqiang Wang; Ming GU; Yihua Huang

http://arxiv.org/abs/2210.03696
NMTSloth: Understanding and Testing Efficiency Degradation of Neural Machine Translation Systems. (13%)
Simin Chen; Cong Liu; Mirazul Haque; Zihe Song; Wei Yang

http://arxiv.org/abs/2210.04688
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets. (9%)
Chen Gong; Zhou Yang; Yunpeng Bai; Junda He; Jieke Shi; Kecen Li; Arunesh Sinha; Bowen Xu; Xinwen Hou; David Lo; Tianhao Wang

http://arxiv.org/abs/2210.03688
A Wolf in Sheep's Clothing: Spreading Deadly Pathogens Under the Disguise of Popular Music. (2%)
Anomadarshi Barua; Yonatan Gizachew Achamyeleh; Mohammad Abdullah Al Faruque

http://arxiv.org/abs/2210.03879
Improving Fine-Grain Segmentation via Interpretable Modifications: A Case Study in Fossil Segmentation. (1%)
Indu Panigrahi; Ryan Manzuk; Adam Maloof; Ruth Fong

http://arxiv.org/abs/2210.03297
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. (99%)
Chawin Sitawarin; Florian Tramèr; Nicholas Carlini

http://arxiv.org/abs/2210.03003
Enhancing Code Classification by Mixup-Based Data Augmentation. (96%)
Zeming Dong; Qiang Hu; Yuejun Guo; Maxime Cordy; Mike Papadakis; Yves Le Traon; Jianjun Zhao

http://arxiv.org/abs/2210.02840
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet Detection. (92%)
Rizwan Hamid Randhawa; Nauman Aslam; Mohammad Alauthman; Muhammad Khalid; Husnain Rafiq

http://arxiv.org/abs/2210.02713
On Optimal Learning Under Targeted Data Poisoning. (82%)
Steve Hanneke; Amin Karbasi; Mohammad Mahmoody; Idan Mehalel; Shay Moran

http://arxiv.org/abs/2210.03150
Towards Out-of-Distribution Adversarial Robustness. (73%)
Adam Ibrahim; Charles Guille-Escuret; Ioannis Mitliagkas; Irina Rish; David Krueger; Pouya Bashivan

http://arxiv.org/abs/2210.03068
InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples. (61%)
Venelin Kovatchev; Mariona Taulé

http://arxiv.org/abs/2210.03250
Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup. (41%)
Huimin Zeng; Zhenrui Yue; Ziyi Kou; Lanyu Shang; Yang Zhang; Dong Wang

http://arxiv.org/abs/2210.03205
Synthetic Dataset Generation for Privacy-Preserving Machine Learning. (2%)
Efstathia Soufleri; Gobinda Saha; Kaushik Roy

http://arxiv.org/abs/2210.03123
Enhancing Mixup-Based Graph Learning for Language Processing via Hybrid Pooling. (1%)
Zeming Dong; Qiang Hu; Yuejun Guo; Maxime Cordy; Mike Papadakis; Yves Le Traon; Jianjun Zhao

http://arxiv.org/abs/2210.03239
Bad Citrus: Reducing Adversarial Costs with Model Distances. (1%)
Giorgio Severi; Will Pearce; Alina Oprea

http://arxiv.org/abs/2210.02041
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks. (99%)
Shengming Yuan; Qilong Zhang; Lianli Gao; Yaya Cheng; Jingkuan Song

http://arxiv.org/abs/2210.02618
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks. (98%)
Qi Peng; Wenlin Liu; Ruoxi Qin; Libin Hou; Bin Yan; Linyuan Wang

http://arxiv.org/abs/2210.02502
On Adversarial Robustness of Deep Image Deblurring. (83%)
Kanchana Vaishnavi Gandikota; Paramanand Chandramouli; Michael Moeller

http://arxiv.org/abs/2210.02577
A Closer Look at Robustness to L-infinity and Spatial Perturbations and their Composition. (81%)
Luke Rowe; Benjamin Thérien; Krzysztof Czarnecki; Hongyang Zhang

http://arxiv.org/abs/2210.02082
Jitter Does Matter: Adapting Gaze Estimation to New Domains. (78%)
Ruicong Liu; Yiwei Bao; Mingjie Xu; Haofei Wang; Yunfei Liu; Feng Lu

http://arxiv.org/abs/2210.02357
Image Masking for Robust Self-Supervised Monocular Depth Estimation. (38%)
Hemang Chawla; Kishaan Jeeveswaran; Elahe Arani; Bahram Zonooz

http://arxiv.org/abs/2210.02235
Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations. (38%)
Jialing Liao; Zheng Chen; Erik G. Larsson

http://arxiv.org/abs/2210.01787
Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective. (97%)
Bohang Zhang; Du Jiang; Di He; Liwei Wang

http://arxiv.org/abs/2210.01953
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework. (93%)
Anshuman Chhabra; Peizhao Li; Prasant Mohapatra; Hongfu Liu

http://arxiv.org/abs/2210.01371
A Study on the Efficiency and Generalization of Light Hybrid Retrievers. (86%)
Man Luo; Shashank Jain; Anchit Gupta; Arash Einolghozati; Barlas Oguz; Debojeet Chatterjee; Xilun Chen; Chitta Baral; Peyman Heidari

http://arxiv.org/abs/2210.02447
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models. (81%)
Fan Liu; Hao Liu; Wenzhao Jiang

http://arxiv.org/abs/2210.01834
Invariant Aggregator for Defending against Federated Backdoor Attacks. (80%)
Xiaoyang Wang; Dimitrios Dimitriadis; Sanmi Koyejo; Shruti Tople

http://arxiv.org/abs/2210.01940
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses. (75%)
Anshuman Chhabra; Ashwin Sekhari; Prasant Mohapatra

http://arxiv.org/abs/2210.04625
Robustness Certification of Visual Perception Models via Camera Motion Smoothing. (70%)
Hanjiang Hu; Zuxin Liu; Linyi Li; Jiacheng Zhu; Ding Zhao

http://arxiv.org/abs/2210.01632
Backdoor Attacks in the Supply Chain of Masked Image Modeling. (68%)
Xinyue Shen; Xinlei He; Zheng Li; Yun Shen; Michael Backes; Yang Zhang

http://arxiv.org/abs/2210.01742
CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning. (67%)
Charles Guille-Escuret; Pau Rodriguez; David Vazquez; Ioannis Mitliagkas; Joao Monteiro

http://arxiv.org/abs/2210.01111
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples. (99%)
Jinyuan Jia; Wenjie Qu; Neil Zhenqiang Gong

http://arxiv.org/abs/2210.00753
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection. (97%)
Xuanjun Chen; Haibin Wu; Helen Meng; Hung-yi Lee; Jyh-Shing Roger Jang

http://arxiv.org/abs/2210.00960
Stability Analysis and Generalization Bounds of Adversarial Training. (96%)
Jiancong Xiao; Yanbo Fan; Ruoyu Sun; Jue Wang; Zhi-Quan Luo

http://arxiv.org/abs/2210.02191
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks. (92%)
Huimin Zeng; Zhenrui Yue; Yang Zhang; Ziyi Kou; Lanyu Shang; Dong Wang

http://arxiv.org/abs/2210.01075
Decompiling x86 Deep Neural Network Executables. (83%)
Zhibo Liu; Yuanyuan Yuan; Shuai Wang; Xiaofei Xie; Lei Ma

http://arxiv.org/abs/2210.01288
Strength-Adaptive Adversarial Training. (80%)
Chaojian Yu; Dawei Zhou; Li Shen; Jun Yu; Bo Han; Mingming Gong; Nannan Wang; Tongliang Liu

http://arxiv.org/abs/2210.01002
ASGNN: Graph Neural Networks with Adaptive Structure. (68%)
Zepeng Zhang; Songtao Lu; Zengfeng Huang; Ziping Zhao

http://arxiv.org/abs/2210.00957
UnGANable: Defending Against GAN-based Face Manipulation. (2%)
Zheng Li; Ning Yu; Ahmed Salem; Michael Backes; Mario Fritz; Yang Zhang

http://arxiv.org/abs/2210.00557
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis. (99%)
Jiancong Xiao; Zeyu Qin; Yanbo Fan; Baoyuan Wu; Jue Wang; Zhi-Quan Luo

http://arxiv.org/abs/2210.00430
Understanding Adversarial Robustness Against On-manifold Adversarial Examples. (99%)
Jiancong Xiao; Liusha Yang; Yanbo Fan; Jue Wang; Zhi-Quan Luo

http://arxiv.org/abs/2210.00584
FLCert: Provably Secure Federated Learning against Poisoning Attacks. (74%)
Xiaoyu Cao; Zaixi Zhang; Jinyuan Jia; Neil Zhenqiang Gong

http://arxiv.org/abs/2210.00621
Optimization for Robustness Evaluation beyond $\ell_p$ Metrics. (16%)
Hengyue Liang; Buyun Liang; Ying Cui; Tim Mitchell; Ju Sun

http://arxiv.org/abs/2210.00649
Automated Security Analysis of Exposure Notification Systems. (1%)
Kevin Morio; Ilkan Esiyok; Dennis Jackson; Robert Künnemann

http://arxiv.org/abs/2210.00292
DeltaBound Attack: Efficient decision-based attack in low queries regime. (96%)
Lorenzo Rossi

http://arxiv.org/abs/2210.00008
Adversarial Attacks on Transformers-Based Malware Detectors. (91%)
Yash Jakhotiya; Heramb Patil; Jugal Rawlani; Dr. Sunil B. Mane

http://arxiv.org/abs/2210.00417
Voice Spoofing Countermeasures: Taxonomy, State-of-the-art, experimental analysis of generalizability, open challenges, and the way forward. (5%)
Awais Khan; Khalid Mahmood Malik; James Ryan; Mikul Saravanan

http://arxiv.org/abs/2209.15246
Your Out-of-Distribution Detection Method is Not Robust! (99%)
Mohammad Azizmalayeri; Arshia Soltani Moakhar; Arman Zarei; Reihaneh Zohrabi; Mohammad Taghi Manzuri; Mohammad Hossein Rohban

http://arxiv.org/abs/2210.00062
Learning Robust Kernel Ensembles with Kernel Average Pooling. (99%)
Pouya Bashivan; Adam Ibrahim; Amirozhan Dehghani; Yifei Ren

http://arxiv.org/abs/2210.00122
Adversarial Robustness of Representation Learning for Knowledge Graphs. (95%)
Peru Bhardwaj

http://arxiv.org/abs/2209.15304
Hiding Visual Information via Obfuscating Adversarial Perturbations. (92%)
Zhigang Su; Dawei Zhou; Nannan Wangu; Decheng Li; Zhen Wang; Xinbo Gao

http://arxiv.org/abs/2210.00178
On the tightness of linear relaxation based robustness certification methods. (78%)
Cheng Tang

http://arxiv.org/abs/2209.15266
Data Poisoning Attacks Against Multimodal Encoders. (73%)
Ziqing Yang; Xinlei He; Zheng Li; Michael Backes; Mathias Humbert; Pascal Berrang; Yang Zhang

http://arxiv.org/abs/2210.00108
ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks. (73%)
Tim Clifford; Ilia Shumailov; Yiren Zhao; Ross Anderson; Robert Mullins

http://arxiv.org/abs/2209.15179
Physical Adversarial Attack meets Computer Vision: A Decade Survey. (99%)
Hui Wei; Hao Tang; Xuemei Jia; Zhixiang Wang; Hanxun Yu; Zhubo Li; Shin'ichi Satoh; Gool Luc Van; Zheng Wang

http://arxiv.org/abs/2209.14826
Towards Lightweight Black-Box Attacks against Deep Neural Networks. (99%)
Chenghao Sun; Yonggang Zhang; Wan Chaoqun; Qizhou Wang; Ya Li; Tongliang Liu; Bo Han; Xinmei Tian

http://arxiv.org/abs/2209.15042
Generalizability of Adversarial Robustness Under Distribution Shifts. (83%)
Kumail Alhamoud; Hasan Abed Al Kader Hammoud; Motasem Alfarra; Bernard Ghanem

http://arxiv.org/abs/2209.14692
Digital and Physical Face Attacks: Reviewing and One Step Further. (2%)
Chenqi Kong; Shiqi Wang; Haoliang Li

http://arxiv.org/abs/2209.14673
Chameleon Cache: Approximating Fully Associative Caches with Random Replacement to Prevent Contention-Based Cache Attacks. (1%)
Thomas Unterluggauer; Austin Harris; Scott Constable; Fangfei Liu; Carlos Rozas

http://arxiv.org/abs/2209.14262
A Survey on Physical Adversarial Attack in Computer Vision. (99%)
Donghua Wang; Wen Yao; Tingsong Jiang; Guijian Tang; Xiaoqian Chen

http://arxiv.org/abs/2209.14105
Exploring the Relationship between Architecture and Adversarially Robust Generalization. (99%)
Aishan Liu; Shiyu Tang; Siyuan Liang; Ruihao Gong; Boxi Wu; Xianglong Liu; Dacheng Tao

http://arxiv.org/abs/2209.14243
A Closer Look at Evaluating the Bit-Flip Attack Against Deep Neural Networks. (67%)
Kevin Hector; Mathieu Dumont; Pierre-Alain Moellic; Jean-Max Dutertre

http://arxiv.org/abs/2209.14161
Supervised Contrastive Learning as Multi-Objective Optimization for Fine-Tuning Large Pre-trained Language Models. (47%)
Youness Moukafih; Mounir Ghogho; Kamel Smaili

http://arxiv.org/abs/2209.14013
On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach. (31%)
Marco Anisetti; Claudio A. Ardagna; Alessandro Balestrucci; Nicola Bena; Ernesto Damiani; Chan Yeob Yeun

http://arxiv.org/abs/2209.14169
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention. (1%)
Ziyu Guo; Renrui Zhang; Longtian Qiu; Xianzheng Ma; Xupeng Miao; Xuming He; Bin Cui

http://arxiv.org/abs/2209.14375
Improving alignment of dialogue agents via targeted human judgements. (1%)
Amelia Glaese; Nat McAleese; Maja Trębacz; John Aslanides; Vlad Firoiu; Timo Ewalds; Maribeth Rauh; Laura Weidinger; Martin Chadwick; Phoebe Thacker; Lucy Campbell-Gillingham; Jonathan Uesato; Po-Sen Huang; Ramona Comanescu; Fan Yang; Abigail See; Sumanth Dathathri; Rory Greig; Charlie Chen; Doug Fritz; Jaume Sanchez Elias; Richard Green; Soňa Mokrá; Nicholas Fernando; Boxi Wu; Rachel Foley; Susannah Young; Iason Gabriel; William Isaac; John Mellor; Demis Hassabis; Koray Kavukcuoglu; Lisa Anne Hendricks; Geoffrey Irving

http://arxiv.org/abs/2209.13353
Suppress with a Patch: Revisiting Universal Adversarial Patch Attacks against Object Detection. (74%)
Svetlana Pavlitskaya; Jonas Hendl; Sebastian Kleim; Leopold Müller; Fabian Wylczoch; J. Marius Zöllner

http://arxiv.org/abs/2209.14053
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training. (33%)
Saehyung Lee; Hyungyu Lee

http://arxiv.org/abs/2209.13785
Attacking Compressed Vision Transformers. (33%)
Swapnil Parekh; Devansh Shah; Pratyush Shukla

http://arxiv.org/abs/2209.13007
Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals. (8%)
Ferhat Ozgur Catak; Murat Kuzlu; Salih Sarp; Evren Catak; Umit Cali

http://arxiv.org/abs/2210.00875
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection. (5%)
Yiming Li; Yang Bai; Yong Jiang; Yong Yang; Shu-Tao Xia; Bo Li

http://arxiv.org/abs/2209.13620
Reconstruction-guided attention improves the robustness and shape processing of neural networks. (2%)
Seoyoung Ahn; Hossein Adeli; Gregory J. Zelinsky

http://arxiv.org/abs/2209.13815
A Learning-based Honeypot Game for Collaborative Defense in UAV Networks. (1%)
Yuntao Wang; Zhou Su; Abderrahim Benslimane; Qichao Xu; Minghui Dai; Ruidong Li

http://arxiv.org/abs/2210.00874
Stability Via Adversarial Training of Neural Network Stochastic Control of Mean-Field Type. (1%)
Julian Barreiro-Gomez; Salah Eddine Choutri; Boualem Djehiche

http://arxiv.org/abs/2209.13382
Measuring Overfitting in Convolutional Neural Networks using Adversarial Perturbations and Label Noise. (1%)
Svetlana Pavlitskaya; Joël Oswald; J. Marius Zöllner

http://arxiv.org/abs/2209.13113
FG-UAP: Feature-Gathering Universal Adversarial Perturbation. (99%)
Zhixing Ye; Xinwen Cheng; Xiaolin Huang

http://arxiv.org/abs/2209.13400
Activation Learning by Local Competitions. (64%)
Hongchao Zhou

http://arxiv.org/abs/2209.12549
Multi-Task Adversarial Training Algorithm for Multi-Speaker Neural Text-to-Speech. (1%)
Yusuke Nakai; Yuki Saito; Kenta Udagawa; Hiroshi Saruwatari

http://arxiv.org/abs/2209.14974
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification. (1%)
Adrien Bennetot; Gianni Franchi; Ser Javier Del; Raja Chatila; Natalia Diaz-Rodriguez

http://arxiv.org/abs/2209.12195
SPRITZ-1.5C: Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks. (81%)
Ehsan Nowroozi; Mohammadreza Mohammadi; Erkay Savas; Mauro Conti; Yassine Mekdad

http://arxiv.org/abs/2209.11964
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning. (99%)
Zhengwei Fang; Rui Wang; Tao Huang; Liping Jing

http://arxiv.org/abs/2209.11715
The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices. (13%)
Wanlun Ma; Derui Wang; Ruoxi Sun; Minhui Xue; Sheng Wen; Yang Xiang

http://arxiv.org/abs/2209.11020
Privacy Attacks Against Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models. (50%)
Sohaib Ahmad; Benjamin Fuller; Kaleel Mahmood

http://arxiv.org/abs/2209.10729
Fair Robust Active Learning by Joint Inconsistency. (99%)
Tsung-Han Wu; Shang-Tse Chen; Winston H. Hsu

http://arxiv.org/abs/2209.10652
Toy Models of Superposition. (45%)
Nelson Elhage; Tristan Hume; Catherine Olsson; Nicholas Schiefer; Tom Henighan; Shauna Kravec; Zac Hatfield-Dodds; Robert Lasenby; Dawn Drain; Carol Chen; Roger Grosse; Sam McCandlish; Jared Kaplan; Dario Amodei; Martin Wattenberg; Christopher Olah

http://arxiv.org/abs/2209.10381
DARTSRepair: Core-failure-set Guided DARTS for Network Robustness to Common Corruptions. (13%)
Xuhong Ren; Jianlang Chen; Felix Juefei-Xu; Wanli Xue; Qing Guo; Lei Ma; Jianjun Zhao; Shengyong Chen

http://arxiv.org/abs/2209.10222
Fairness Reprogramming. (1%)
Guanhua Zhang; Yihua Zhang; Yang Zhang; Wenqi Fan; Qing Li; Sijia Liu; Shiyu Chang

http://arxiv.org/abs/2209.09577
Understanding Real-world Threats to Deep Learning Models in Android Apps. (99%)
Zizhuang Deng; Kai Chen; Guozhu Meng; Xiaodong Zhang; Ke Xu; Yao Cheng

http://arxiv.org/abs/2209.09996
Audit and Improve Robustness of Private Neural Networks on Encrypted Data. (99%)
Jiaqi Xue; Lei Xu; Lin Chen; Weidong Shi; Kaidi Xu; Qian Lou

http://arxiv.org/abs/2209.09502
GAMA: Generative Adversarial Multi-Object Scene Attacks. (99%)
Abhishek Aich; Calvin-Khang Ta; Akash Gupta; Chengyu Song; Srikanth V. Krishnamurthy; M. Salman Asif; Amit K. Roy-Chowdhury

http://arxiv.org/abs/2209.09688
Sparse Vicious Attacks on Graph Neural Networks. (98%)
Giovanni Trappolini; Valentino Maiorca; Silvio Severino; Emanuele Rodolà; Fabrizio Silvestri; Gabriele Tolomei

http://arxiv.org/abs/2209.09883
Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks. (98%)
Abhishek Aich; Shasha Li; Chengyu Song; M. Salman Asif; Srikanth V. Krishnamurthy; Amit K. Roy-Chowdhury

http://arxiv.org/abs/2209.09841
Rethinking Data Augmentation in Knowledge Distillation for Object Detection. (68%)
Jiawei Liang; Siyuan Liang; Aishan Liu; Mingli Zhu; Danni Yuan; Chenye Xu; Xiaochun Cao

http://arxiv.org/abs/2209.09557
CANflict: Exploiting Peripheral Conflicts for Data-Link Layer Attacks on Automotive Networks. (1%)
Alvise de Faveri Tron; Stefano Longari; Michele Carminati; Mario Polino; Stefano Zanero

http://arxiv.org/abs/2209.09835
EM-Fault It Yourself: Building a Replicable EMFI Setup for Desktop and Server Hardware. (1%)
Niclas Kühnapfel; Robert Buhren; Hans Niklas Jacob; Thilo Krachenfels; Christian Werling; Jean-Pierre Seifert

http://arxiv.org/abs/2209.11739
Adversarial Catoptric Light: An Effective, Stealthy and Robust Physical-World Attack to DNNs. (99%)
Chengyin Hu; Weiwen Shi

http://arxiv.org/abs/2209.09652
Adversarial Color Projection: A Projector-Based Physical Attack to DNNs. (99%)
Chengyin Hu; Weiwen Shi

http://arxiv.org/abs/2209.08724
On the Adversarial Transferability of ConvMixer Models. (99%)
Ryota Iijima; Miki Tanaka; Isao Echizen; Hitoshi Kiya

http://arxiv.org/abs/2209.08744
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction. (96%)
Yulong Cao; Chaowei Xiao; Anima Anandkumar; Danfei Xu; Marco Pavone

http://arxiv.org/abs/2209.08541
Distribution inference risks: Identifying and mitigating sources of leakage. (1%)
Valentin Hartmann; Léo Meynent; Maxime Peyrard; Dimitrios Dimitriadis; Shruti Tople; Robert West

http://arxiv.org/abs/2209.13523
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models. (99%)
Raphael Olivier; Hadi Abdullah; Bhiksha Raj

http://arxiv.org/abs/2209.08412
Characterizing Internal Evasion Attacks in Federated Learning. (98%)
Taejin Kim; Shubhranshu Singh; Nikhil Madaan; Carlee Joe-Wong

http://arxiv.org/abs/2209.08262
A study on the deviations in performance of FNNs and CNNs in the realm of grayscale adversarial images. (4%)
Durga Shree Nagabushanam; Steve Mathew; Chiranji Lal Chowdhary

http://arxiv.org/abs/2209.08130
Robust Ensemble Morph Detection with Domain Generalization. (99%)
Hossein Kashiani; Shoaib Meraj Sami; Sobhan Soleymani; Nasser M. Nasrabadi

http://arxiv.org/abs/2209.07790
A Large-scale Multiple-objective Method for Black-box Attack against Object Detection. (99%)
Siyuan Liang; Longkang Li; Yanbo Fan; Xiaojun Jia; Jingzhi Li; Baoyuan Wu; Xiaochun Cao

http://arxiv.org/abs/2209.07735
Enhance the Visual Representation via Discrete Adversarial Training. (97%)
Xiaofeng Mao; Yuefeng Chen; Ranjie Duan; Yao Zhu; Gege Qi; Shaokai Ye; Xiaodan Li; Rong Zhang; Hui Xue

http://arxiv.org/abs/2209.07807
Model Inversion Attacks against Graph Neural Networks. (92%)
Zaixi Zhang; Qi Liu; Zhenya Huang; Hao Wang; Chee-Kong Lee; Enhong Chen

http://arxiv.org/abs/2209.07788
PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition. (62%)
Qidong Huang; Xiaoyi Dong; Dongdong Chen; Hang Zhou; Weiming Zhang; Kui Zhang; Gang Hua; Nenghai Yu

http://arxiv.org/abs/2209.08116
Cascading Failures in Power Grids. (33%)
Rounak Meyur

http://arxiv.org/abs/2209.09024
Dataset Inference for Self-Supervised Models. (16%)
Adam Dziedzic; Haonan Duan; Muhammad Ahmad Kaleem; Nikita Dhawan; Jonas Guan; Yannis Cattan; Franziska Boenisch; Nicolas Papernot

http://arxiv.org/abs/2209.07754
On the Robustness of Graph Neural Diffusion to Topology Perturbations. (15%)
Yang Song; Qiyu Kang; Sijie Wang; Zhao Kai; Wee Peng Tay

http://arxiv.org/abs/2209.08064
A Systematic Evaluation of Node Embedding Robustness. (11%)
Alexandru Mara; Jefrey Lijffijt; Stephan Günnemann; Bie Tijl De

http://arxiv.org/abs/2209.07936
PA-Boot: A Formally Verified Authentication Protocol for Multiprocessor Secure Boot. (1%)
Zhuoruo Zhang; Chenyang Yu; Rui Chang; Mingshuai Chen; Bo Feng; He Huang; Qinming Dai; Wenbo Shen; Yongwang Zhao

http://arxiv.org/abs/2209.07534
Improving Robust Fairness via Balance Adversarial Training. (99%)
Chunyu Sun; Chenye Xu; Chengyuan Yao; Siyuan Liang; Yichao Wu; Ding Liang; XiangLong Liu; Aishan Liu

http://arxiv.org/abs/2209.07399
A Light Recipe to Train Robust Vision Transformers. (98%)
Edoardo Debenedetti; Vikash Sehwag; Prateek Mittal

http://arxiv.org/abs/2209.09117
Part-Based Models Improve Adversarial Robustness. (92%)
Chawin Sitawarin; Kornrapat Pongmala; Yizheng Chen; Nicholas Carlini; David Wagner

http://arxiv.org/abs/2209.07592
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness. (80%)
Mazda Moayeri; Kiarash Banihashem; Soheil Feizi

http://arxiv.org/abs/2209.07369
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. (80%)
Omar Montasser; Steve Hanneke; Nathan Srebro

http://arxiv.org/abs/2209.07491
Defending Root DNS Servers Against DDoS Using Layered Defenses. (15%)
A S M Rizvi; Jelena Mirkovic; John Heidemann; Wesley Hardaker; Robert Story

http://arxiv.org/abs/2209.07125
BadRes: Reveal the Backdoors through Residual Connection. (2%)
Mingrui He; Tianyu Chen; Haoyi Zhou; Shanghang Zhang; Jianxin Li

http://arxiv.org/abs/2209.07699
Adversarial Cross-View Disentangled Graph Contrastive Learning. (1%)
Qianlong Wen; Zhongyu Ouyang; Chunhui Zhang; Yiyue Qian; Yanfang Ye; Chuxu Zhang

http://arxiv.org/abs/2209.07601
Towards Improving Calibration in Object Detection Under Domain Shift. (1%)
Muhammad Akhtar Munir; Muhammad Haris Khan; M. Saquib Sarfraz; Mohsen Ali

http://arxiv.org/abs/2209.06931
Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries. (99%)
Alexander Cann; Ian Colbert; Ihab Amer

http://arxiv.org/abs/2209.06971
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack. (99%)
Junxuan Huang; Yatong An; Lu cheng; Bai Chen; Junsong Yuan; Chunming Qiao

http://arxiv.org/abs/2209.06691
Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models. (99%)
Chen Wu; Ruqing Zhang; Jiafeng Guo; Wei Chen; Yixing Fan; Rijke Maarten de; Xueqi Cheng

http://arxiv.org/abs/2209.06506
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models. (97%)
Jiawei Liu; Yangyang Kang; Di Tang; Kaisong Song; Changlong Sun; Xiaofeng Wang; Wei Lu; Xiaozhong Liu

http://arxiv.org/abs/2209.06953
On the interplay of adversarial robustness and architecture components: patches, convolution and attention. (67%)
Francesco Croce; Matthias Hein

http://arxiv.org/abs/2209.06997
M^4I: Multi-modal Models Membership Inference. (54%)
Pingyi Hu; Zihan Wang; Ruoxi Sun; Hu Wang; Minhui Xue

http://arxiv.org/abs/2209.06954
Finetuning Pretrained Vision-Language Models with Correlation Information Bottleneck for Robust Visual Question Answering. (12%)
Jingjing Jiang; Ziyi Liu; Nanning Zheng

http://arxiv.org/abs/2209.06866
Robust Constrained Reinforcement Learning. (9%)
Yue Wang; Fei Miao; Shaofeng Zou

http://arxiv.org/abs/2209.05785
Adversarial Coreset Selection for Efficient Robust Training. (99%)
Hadi M. Dolatabadi; Sarah Erfani; Christopher Leckie

http://arxiv.org/abs/2209.06388
TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack. (99%)
Yanyun Wang; Dehui Du; Haibo Hu; Zi Liang; Yuanhao Liu

http://arxiv.org/abs/2209.06300
PINCH: An Adversarial Extraction Attack Framework for Deep Learning Models. (92%)
William Hackett; Stefan Trawicki; Zhengxin Yu; Neeraj Suri; Peter Garraghan

http://arxiv.org/abs/2209.05980
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation. (78%)
Maksym Yatsura; Kaspar Sakmann; N. Grace Hua; Matthias Hein; Jan Hendrik Metzen

http://arxiv.org/abs/2209.05957
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks. (68%)
Hussain Hussain; Meng Cao; Sandipan Sikdar; Denis Helic; Elisabeth Lex; Markus Strohmaier; Roman Kern

http://arxiv.org/abs/2209.06292
ADMM based Distributed State Observer Design under Sparse Sensor Attacks. (22%)
Vinaya Mary Prinse; Rachel Kalpana Kalaimani

http://arxiv.org/abs/2209.05742
A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game. (15%)
Ke Ma; Qianqian Xu; Jinshan Zeng; Guorong Li; Xiaochun Cao; Qingming Huang

http://arxiv.org/abs/2209.05724
Defense against Privacy Leakage in Federated Learning. (12%)
Jing Wu; Munawar Hayat; Mingyi Zhou; Mehrtash Harandi

http://arxiv.org/abs/2209.06397
Federated Learning based on Defending Against Data Poisoning Attacks in IoT. (1%)
Jiayin Li; Wenzhong Guo; Xingshuo Han; Jianping Cai; Ximeng Liu

http://arxiv.org/abs/2209.05244
Adaptive Perturbation Generation for Multiple Backdoors Detection. (95%)
Yuhang Wang; Huafeng Shi; Rui Min; Ruijia Wu; Siyuan Liang; Yichao Wu; Ding Liang; Aishan Liu

http://arxiv.org/abs/2209.05055
CARE: Certifiably Robust Learning with Reasoning via Variational Inference. (75%)
Jiawei Zhang; Linyi Li; Ce Zhang; Bo Li

http://arxiv.org/abs/2209.05692
Sample Complexity of an Adversarial Attack on UCB-based Best-arm Identification Policy. (69%)
Varsha Pendyala

http://arxiv.org/abs/2209.05446
Boosting Robustness Verification of Semantic Feature Neighborhoods. (54%)
Anan Kabaha; Dana Drachsler-Cohen

http://arxiv.org/abs/2209.05130
Semantic-Preserving Adversarial Code Comprehension. (1%)
Yiyang Li; Hongqiu Wu; Hai Zhao

http://arxiv.org/abs/2209.05407
Holistic Segmentation. (1%)
Stefano Gasperini; Alvaro Marcos-Ramiro; Michael Schmidt; Nassir Navab; Benjamin Busam; Federico Tombari

http://arxiv.org/abs/2209.05668
Class-Level Logit Perturbation. (1%)
Mengyang Li; Fengguang Su; Ou Wu; Ji Zhang

http://arxiv.org/abs/2209.04930
Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization. (99%)
Ehsan Nowroozi; Mohammadreza Mohammadi; Pargol Golmohammadi; Yassine Mekdad; Mauro Conti; Selcuk Uluagac

http://arxiv.org/abs/2209.06113
Generate novel and robust samples from data: accessible sharing without privacy concerns. (5%)
David Banh; Alan Huang

http://arxiv.org/abs/2209.04779
Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense. (99%)
Bowen Peng; Bo Peng; Jie Zhou; Jianyue Xie; Li Liu

http://arxiv.org/abs/2209.04521
The Space of Adversarial Strategies. (99%)
Ryan Sheatsley; Blaine Hoak; Eric Pauley; Patrick McDaniel

http://arxiv.org/abs/2209.04547
Defend Data Poisoning Attacks on Voice Authentication. (54%)
Ke Li; Cameron Baird; Dan Lin

http://arxiv.org/abs/2209.04293
Robust-by-Design Classification via Unitary-Gradient Neural Networks. (41%)
Fabio Brau; Giulio Rossolini; Alessandro Biondi; Giorgio Buttazzo

http://arxiv.org/abs/2209.04113
Robust and Lossless Fingerprinting of Deep Neural Networks via Pooled Membership Inference. (10%)
Hanzhou Wu

http://arxiv.org/abs/2209.04326
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System. (1%)
Xin Li; Yao Qiang; Chengyin Li; Sijia Liu; Dongxiao Zhu

http://arxiv.org/abs/2209.03716
Incorporating Locality of Images to Generate Targeted Transferable Adversarial Examples. (99%)
Zhipeng Wei; Jingjing Chen; Zuxuan Wu; Yu-Gang Jiang

http://arxiv.org/abs/2209.04028
Evaluating the Security of Aircraft Systems. (92%)
Edan Habler; Ron Bitton; Asaf Shabtai

http://arxiv.org/abs/2209.04030
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks. (64%)
Chulin Xie; Yunhui Long; Pin-Yu Chen; Qinbin Li; Arash Nourian; Sanmi Koyejo; Bo Li

http://arxiv.org/abs/2209.03622
A Survey of Recent Advances in Deep Learning Models for Detecting Malware in Desktop and Mobile Platforms. (1%)
Pascal Maniriho; Abdun Naser Mahmood; Mohammad Jabed Morshed Chowdhury

http://arxiv.org/abs/2209.03839
FADE: Enabling Large-Scale Federated Adversarial Training on Resource-Constrained Edge Devices. (1%)
Minxue Tang; Jianyi Zhang; Mingyuan Ma; Louis DiValentin; Aolin Ding; Amin Hassanzadeh; Hai Li; Yiran Chen

http://arxiv.org/abs/2209.02997
On the Transferability of Adversarial Examples between Encrypted Models. (99%)
Miki Tanaka; Isao Echizen; Hitoshi Kiya

http://arxiv.org/abs/2209.03358
Securing the Spike: On the Transferabilty and Security of Spiking Neural Networks to Adversarial Examples. (99%)
Nuo Xu; Kaleel Mahmood; Haowen Fang; Ethan Rathbun; Caiwen Ding; Wujie Wen

http://arxiv.org/abs/2209.03540
Reward Delay Attacks on Deep Reinforcement Learning. (70%)
Anindya Sarkar; Jiarui Feng; Yevgeniy Vorobeychik; Christopher Gill; Ning Zhang

http://arxiv.org/abs/2209.03755
Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systems. (47%)
Sahar Abdelnabi; Mario Fritz

http://arxiv.org/abs/2209.03463
Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots. (15%)
Wai Man Si; Michael Backes; Jeremy Blackburn; Cristofaro Emiliano De; Gianluca Stringhini; Savvas Zannettou; Yand Zhang

http://arxiv.org/abs/2209.03431
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation. (1%)
Houssem Ben Braiek; Thomas Reid; Foutse Khomh

http://arxiv.org/abs/2209.03225
Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs. (1%)
Syed Qutub; Florian Geissler; Yang Peng; Ralf Grafe; Michael Paulitsch; Gereon Hinz; Alois Knoll

http://arxiv.org/abs/2209.03547
MalDetConv: Automated Behaviour-based Malware Detection Framework Based on Natural Language Processing and Deep Learning Techniques. (1%)
Pascal Maniriho; Abdun Naser Mahmood; Mohammad Jabed Morshed Chowdhury

http://arxiv.org/abs/2209.02453
Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detection. (99%)
Sun RuiJin; Guo ShiZe; Guo JinHong; Xing ChangYou; Yang LuMing; Guo Xi; Pan ZhiSong

http://arxiv.org/abs/2209.02684
Bag of Tricks for FGSM Adversarial Training. (96%)
Zichao Li; Li Liu; Zeyu Wang; Yuyin Zhou; Cihang Xie

http://arxiv.org/abs/2209.02369
Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation. (82%)
Koki Mukai; Soichiro Kumano; Toshihiko Yamasaki

http://arxiv.org/abs/2209.02902
Defending Against Backdoor Attack on Graph Nerual Network by Explainability. (80%)
Bingchen Jiang; Zhao Li

http://arxiv.org/abs/2209.02339
MACAB: Model-Agnostic Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-World. (56%)
Hua Ma; Yinshan Li; Yansong Gao; Zhi Zhang; Alsharif Abuadbba; Anmin Fu; Said F. Al-Sarawi; Nepal Surya; Derek Abbott

http://arxiv.org/abs/2209.02329
Multimodal contrastive learning for remote sensing tasks. (1%)
Umangi Jain; Alex Wilson; Varun Gulshan

http://arxiv.org/abs/2209.02826
Annealing Optimization for Progressive Learning with Stochastic Approximation. (1%)
Christos Mavridis; John Baras

http://arxiv.org/abs/2209.02869
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps. (1%)
Alireza Ganjdanesh; Shangqian Gao; Heng Huang

http://arxiv.org/abs/2209.02299
A Survey of Machine Unlearning. (1%)
Thanh Tam Nguyen; Thanh Trung Huynh; Phi Le Nguyen; Alan Wee-Chung Liew; Hongzhi Yin; Quoc Viet Hung Nguyen

http://arxiv.org/abs/2209.02128
Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples. (98%)
Hezekiah J. Branch; Jonathan Rodriguez Cefalu; Jeremy McHugh; Leyla Hujer; Aditya Bahl; Daniel del Castillo Iglesias; Ron Heichman; Ramesh Darwishi

http://arxiv.org/abs/2209.02167
White-Box Adversarial Policies in Deep Reinforcement Learning. (98%)
Stephen Casper; Taylor Killian; Gabriel Kreiman; Dylan Hadfield-Menell

http://arxiv.org/abs/2209.01782
"Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution. (69%)
Yuyou Gan; Yuhao Mao; Xuhong Zhang; Shouling Ji; Yuwen Pu; Meng Han; Jianwei Yin; Ting Wang

http://arxiv.org/abs/2209.01962
Adversarial Detection: Attacking Object Detection in Real Time. (64%)
Han Wu; Syed Yunas; Sareh Rowlands; Wenjie Ruan; Johan Wahlstrom

http://arxiv.org/abs/2209.01882
PromptAttack: Prompt-based Attack for Language Models via Gradient Search. (16%)
Yundi Shi; Piji Li; Changchun Yin; Zhaoyang Han; Lu Zhou; Zhe Liu

http://arxiv.org/abs/2209.01994
Federated Zero-Shot Learning for Visual Recognition. (2%)
Zhi Chen; Yadan Luo; Sen Wang; Jingjing Li; Zi Huang

http://arxiv.org/abs/2209.03148
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training. (2%)
Derek Everett; Andre T. Nguyen; Luke E. Richards; Edward Raff

http://arxiv.org/abs/2209.01721
An Adaptive Black-box Defense against Trojan Attacks (TrojDef). (98%)
Guanxiong Liu; Abdallah Khreishah; Fatima Sharadgah; Issa Khalil

http://arxiv.org/abs/2209.01711
Hide & Seek: Seeking the (Un)-Hidden key in Provably-Secure Logic Locking Techniques. (11%)
Satwik Patnaik; Nimisha Limaye; Ozgur Sinanoglu

http://arxiv.org/abs/2209.01710
Synergistic Redundancy: Towards Verifiable Safety for Autonomous Vehicles. (1%)
Ayoosh Bansal; Simon Yu; Hunmin Kim; Bo Li; Naira Hovakimyan; Marco Caccamo; Lui Sha

http://arxiv.org/abs/2209.02430
Adversarial Color Film: Effective Physical-World Attack to DNNs. (98%)
Chengyin Hu; Weiwen Shi

http://arxiv.org/abs/2209.02132
Impact of Scaled Image on Robustness of Deep Neural Networks. (98%)
Chengyin Hu; Weiwen Shi

http://arxiv.org/abs/2209.01100
Property inference attack; Graph neural networks; Privacy attacks and defense; Trustworthy machine learning. (95%)
Xiuling Wang; Wendy Hui Wang

http://arxiv.org/abs/2209.02832
Impact of Colour Variation on Robustness of Deep Neural Networks. (92%)
Chengyin Hu; Weiwen Shi

http://arxiv.org/abs/2209.00892
Scalable Adversarial Attack Algorithms on Influence Maximization. (68%)
Lichao Sun; Xiaobin Rui; Wei Chen

http://arxiv.org/abs/2209.01292
Are Attribute Inference Attacks Just Imputation? (31%)
Bargav Jayaraman; David Evans

http://arxiv.org/abs/2209.00812
Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well? (9%)
Yue Liu; Chakkrit Tantithamthavorn; Li Li; Yepang Liu

http://arxiv.org/abs/2209.01199
Revisiting Outer Optimization in Adversarial Training. (5%)
Ali Dabouei; Fariborz Taherkhani; Sobhan Soleymani; Nasser M. Nasrabadi

http://arxiv.org/abs/2209.00269
Adversarial for Social Privacy: A Poisoning Strategy to Degrade User Identity Linkage. (98%)
Jiangli Shao; Yongqing Wang; Boshen Shi; Hao Gao; Huawei Shen; Xueqi Cheng

http://arxiv.org/abs/2209.00757
Universal Fourier Attack for Time Series. (12%)
Elizabeth Coda; Brad Clymer; Chance DeSmet; Yijing Watkins; Michael Girard

http://arxiv.org/abs/2209.00005
Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning. (99%)
Zhiyuan He; Yijun Yang; Pin-Yu Chen; Qiang Xu; Tsung-Yi Ho

http://arxiv.org/abs/2209.02406
Unrestricted Adversarial Samples Based on Non-semantic Feature Clusters Substitution. (99%)
MingWei Zhou; Xiaobing Pei

http://arxiv.org/abs/2208.14933
Membership Inference Attacks by Exploiting Loss Trajectory. (70%)
Yiyong Liu; Zhengyu Zhao; Michael Backes; Yang Zhang

http://arxiv.org/abs/2208.14937
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research. (13%)
Zhibo Zhang; Hussam Al Hamadi; Ernesto Damiani; Chan Yeob Yeun; Fatma Taher

http://arxiv.org/abs/2208.14888
Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation. (1%)
JoonHo Lee; Gyemin Lee

http://arxiv.org/abs/2208.14672
Vulnerability of Distributed Inverter VAR Control in PV Distributed Energy System. (1%)
Bo Tu; Wen-Tai Li; Chau Yuen

http://arxiv.org/abs/2209.00462
MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation. (1%)
Nitzan Avidan; Moti Freiman

http://arxiv.org/abs/2208.14302
A Black-Box Attack on Optical Character Recognition Systems. (99%)
Samet Bayram; Kenneth Barner

http://arxiv.org/abs/2209.02408
Robustness and invariance properties of image classifiers. (99%)
Apostolos Modas

http://arxiv.org/abs/2208.14127
Solving the Capsulation Attack against Backdoor-based Deep Neural Network Watermarks by Reversing Triggers. (1%)
Fangqi Li; Shilin Wang; Yun Zhu

http://arxiv.org/abs/2208.14488
Constraining Representations Yields Models That Know What They Don't Know. (1%)
Joao Monteiro; Pau Rodriguez; Pierre-Andre Noel; Issam Laradji; David Vazquez

http://arxiv.org/abs/2208.13838
Towards Adversarial Purification using Denoising AutoEncoders. (99%)
Dvij Kalaria; Aritra Hazra; Partha Pratim Chakrabarti

http://arxiv.org/abs/2208.13904
Reducing Certified Regression to Certified Classification for General Poisoning Attacks. (54%)
Zayd Hammoudeh; Daniel Lowd

http://arxiv.org/abs/2208.13405
Interpreting Black-box Machine Learning Models for High Dimensional Datasets. (1%)
Md. Rezaul Karim; Md. Shajalal; Alex Graß; Till Döhmen; Sisay Adugna Chala; Christian Beecks; Stefan Decker

http://arxiv.org/abs/2208.13182
Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies. (99%)
Yinghua Zhang; Yangqiu Song; Kun Bai; Qiang Yang

http://arxiv.org/abs/2208.13058
Adversarial Robustness for Tabular Data through Cost and Utility Awareness. (99%)
Klim Kireev; Bogdan Kulynych; Carmela Troncoso

http://arxiv.org/abs/2208.13066
SA: Sliding attack for synthetic speech detection with resistance to clipping and self-splicing. (99%)
Deng JiaCheng; Dong Li; Yan Diqun; Wang Rangding; Zeng Jiaming

http://arxiv.org/abs/2208.13049
TrojViT: Trojan Insertion in Vision Transformers. (15%)
Mengxin Zheng; Qian Lou; Lei Jiang

http://arxiv.org/abs/2208.12926
Overparameterized (robust) models from computational constraints. (13%)
Sanjam Garg; Somesh Jha; Saeed Mahloujifar; Mohammad Mahmoody; Mingyuan Wang

http://arxiv.org/abs/2208.13032
RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems. (1%)
Emna Baccour; Aiman Erbad; Amr Mohamed; Mounir Hamdi; Mohsen Guizani

http://arxiv.org/abs/2208.12815
What Does the Gradient Tell When Attacking the Graph Structure. (69%)
Zihan Liu; Ge Wang; Yun Luo; Stan Z. Li

http://arxiv.org/abs/2208.12911
Network-Level Adversaries in Federated Learning. (54%)
Giorgio Severi; Matthew Jagielski; Gökberk Yar; Yuxuan Wang; Alina Oprea; Cristina Nita-Rotaru

http://arxiv.org/abs/2208.12897
ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning. (45%)
Vasudev JV Gohil; Hao JV Guo; Satwik JV Patnaik; JV Jeyavijayan; Rajendran

http://arxiv.org/abs/2208.12511
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective for Adversarial Training. (4%)
Zihui Wu; Haichang Gao; Bingqian Zhou; Xiaoyan Guo; Shudong Zhang

http://arxiv.org/abs/2208.12230
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm. (99%)
Xinyi Wang; Simon Yusuf Enoch; Dong Seong Kim

http://arxiv.org/abs/2208.12348
SNAP: Efficient Extraction of Private Properties with Poisoning. (89%)
Harsh Chaudhari; John Abascal; Alina Oprea; Matthew Jagielski; Florian Tramèr; Jonathan Ullman

http://arxiv.org/abs/2208.14191
FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods. (78%)
Lichen Jia; Bowen Tang; Chenggang Wu; Zhe Wang; Zihan Jiang; Yuanming Lai; Yan Kang; Ning Liu; Jingfeng Zhang

http://arxiv.org/abs/2208.12428
Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs. (31%)
Prashant Pandey; Mustafa Chasmai; Tanuj Sur; Brejesh Lall

http://arxiv.org/abs/2208.12084
Calibrated Selective Classification. (15%)
Adam Fisch; Tommi Jaakkola; Regina Barzilay

http://arxiv.org/abs/2208.12003
XDRI Attacks - and - How to Enhance Resilience of Residential Routers. (4%)
Philipp Jeitner; Haya Shulman; Lucas Teichmann; Michael Waidner

http://arxiv.org/abs/2208.12268
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning. (1%)
Haodong Zhao; Wei Du; Fangqi Li; Peixuan Li; Gongshen Liu

http://arxiv.org/abs/2208.11667
Attacking Neural Binary Function Detection. (99%)
Joshua Bundt; Michael Davinroy; Ioannis Agadakos; Alina Oprea; William Robertson

http://arxiv.org/abs/2208.11613
Unrestricted Black-box Adversarial Attack Using GAN with Limited Queries. (99%)
Dongbin Na; Sangwoo Ji; Jong Kim

http://arxiv.org/abs/2208.11436
Trace and Detect Adversarial Attacks on CNNs using Feature Response Maps. (98%)
Mohammadreza Amirian; Friedhelm Schwenker; Thilo Stadelmann

http://arxiv.org/abs/2208.11839
A Perturbation Resistant Transformation and Classification System for Deep Neural Networks. (98%)
Nathaniel Dean; Dilip Sarkar

http://arxiv.org/abs/2208.11739
Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation. (92%)
Qiyuan Chen; Raed Al Kontar; Maher Nouiehed; Jessie Yang; Corey Lester

http://arxiv.org/abs/2208.11435
Bidirectional Contrastive Split Learning for Visual Question Answering. (38%)
Yuwei Sun; Hideya Ochiai

http://arxiv.org/abs/2208.11264
Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability. (99%)
Shahroz Tariq; Binh M. Le; Simon S. Woo

http://arxiv.org/abs/2208.10773
Adversarial Vulnerability of Temporal Feature Networks for Object Detection. (99%)
Svetlana Pavlitskaya; Nikolai Polley; Michael Weber; J. Marius Zöllner

http://arxiv.org/abs/2208.10878
Transferability Ranking of Adversarial Examples. (99%)
Mosh Levy; Yuval Elovici; Yisroel Mirsky

http://arxiv.org/abs/2208.11180
Auditing Membership Leakages of Multi-Exit Networks. (76%)
Zheng Li; Yiyong Liu; Xinlei He; Ning Yu; Michael Backes; Yang Zhang

http://arxiv.org/abs/2208.10895
A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey. (13%)
Elahe Arani; Shruthi Gowda; Ratnajit Mukherjee; Omar Magdy; Senthilkumar Kathiresan; Bahram Zonooz

http://arxiv.org/abs/2208.10973
Robust DNN Watermarking via Fixed Embedding Weights with Optimized Distribution. (10%)
Benedetta Tondi; Andrea Costanzo; Mauro Barni

http://arxiv.org/abs/2208.10373
Fight Fire With Fire: Reversing Skin Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism. (99%)
Yongwei Wang; Yuan Li; Zhiqi Shen

http://arxiv.org/abs/2208.10688
Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection. (98%)
Simin Li; Huangxinxin Xu; Jiakai Wang; Aishan Liu; Fazhi He; Xianglong Liu; Dacheng Tao

http://arxiv.org/abs/2208.10576
Different Spectral Representations in Optimized Artificial Neural Networks and Brains. (93%)
Richard C. Gerum; Cassidy Pirlot; Alona Fyshe; Joel Zylberberg

http://arxiv.org/abs/2208.10445
Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models. (87%)
Xinlei He; Zheng Li; Weilin Xu; Cory Cornelius; Yang Zhang

http://arxiv.org/abs/2208.10481
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning. (86%)
Eugene Bykovets; Yannick Metz; Mennatallah El-Assady; Daniel A. Keim; Joachim M. Buhmann

http://arxiv.org/abs/2208.10608
RIBAC: Towards Robust and Imperceptible Backdoor Attack against Compact DNN. (62%)
Huy Phan; Cong Shi; Yi Xie; Tianfang Zhang; Zhuohang Li; Tianming Zhao; Jian Liu; Yan Wang; Yingying Chen; Bo Yuan

http://arxiv.org/abs/2208.10531
Toward Better Target Representation for Source-Free and Black-Box Domain Adaptation. (31%)
Qucheng Peng; Zhengming Ding; Lingjuan Lyu; Lichao Sun; Chen Chen

http://arxiv.org/abs/2208.10618
Optimal Bootstrapping of PoW Blockchains. (1%)
Ranvir Rana; Dimitris Karakostas; Sreeram Kannan; Aggelos Kiayias; Pramod Viswanath

http://arxiv.org/abs/2208.09801
PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition. (99%)
Jiachen Sun; Weili Nie; Zhiding Yu; Z. Morley Mao; Chaowei Xiao

http://arxiv.org/abs/2208.09967
Inferring Sensitive Attributes from Model Explanations. (56%)
Vasisht Duddu; Antoine Boutet

http://arxiv.org/abs/2208.09894
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning. (10%)
Kerem Ozfatura; Emre Ozfatura; Alptekin Kupcu; Deniz Gunduz

http://arxiv.org/abs/2208.09915
MockingBERT: A Method for Retroactively Adding Resilience to NLP Models. (4%)
Jan Jezabek; Akash Singh

http://arxiv.org/abs/2208.10010
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs. (1%)
Yijun Tian; Chuxu Zhang; Zhichun Guo; Xiangliang Zhang; Nitesh V. Chawla

http://arxiv.org/abs/2208.09913
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective. (1%)
Chanwoo Park; Sangdoo Yun; Sanghyuk Chun

http://arxiv.org/abs/2208.09602
Analyzing Adversarial Robustness of Vision Transformers against Spatial and Spectral Attacks. (86%)
Gihyun Kim; Jong-Seok Lee

http://arxiv.org/abs/2208.09764
GAIROSCOPE: Injecting Data from Air-Gapped Computers to Nearby Gyroscopes. (33%)
Mordechai Guri

http://arxiv.org/abs/2208.09741
Sensor Security: Current Progress, Research Challenges, and Future Roadmap. (10%)
Anomadarshi Barua; Mohammad Abdullah Al Faruque

http://arxiv.org/abs/2208.10940
Evaluating Out-of-Distribution Detectors Through Adversarial Generation of Outliers. (5%)
Sangwoong Yoon; Jinwon Choi; Yonghyeon Lee; Yung-Kyun Noh; Frank Chongwoo Park

http://arxiv.org/abs/2208.09710
Adversarial contamination of networks in the setting of vertex nomination: a new trimming method. (1%)
Sheyda Peyman; Minh Tang; Vince Lyzinski

http://arxiv.org/abs/2208.09195
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks. (99%)
Husheng Han; Xing Hu; Kaidi Xu; Pucheng Dang; Ying Wang; Yongwei Zhao; Zidong Du; Qi Guo; Yanzhi Yang; Tianshi Chen

http://arxiv.org/abs/2208.09466
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment. (92%)
Vyas Raina; Mark Gales

http://arxiv.org/abs/2208.09336
Dispersed Pixel Perturbation-based Imperceptible Backdoor Trigger for Image Classifier Models. (76%)
Yulong Wang; Minghui Zhao; Shenghong Li; Xin Yuan; Wei Ni

http://arxiv.org/abs/2208.09449
A Novel Plug-and-Play Approach for Adversarially Robust Generalization. (54%)
Deepak Maurya; Adarsh Barik; Jean Honorio

http://arxiv.org/abs/2208.09418
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability. (8%)
Wei Huang; Xingyu Zhao; Gaojie Jin; Xiaowei Huang

http://arxiv.org/abs/2208.09316
UKP-SQuARE v2 Explainability and Adversarial Attacks for Trustworthy QA. (1%)
Rachneet Sachdeva; Haritz Puerto; Tim Baumgärtner; Sewin Tariverdian; Hao Zhang; Kexin Wang; Hossain Shaikh Saadi; Leonardo F. R. Ribeiro; Iryna Gurevych

http://arxiv.org/abs/2208.08697
Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries. (99%)
Manaar Alam; Shubhajit Datta; Debdeep Mukhopadhyay; Arijit Mondal; Partha Pratim Chakrabarti

http://arxiv.org/abs/2208.08677
Enhancing Targeted Attack Transferability via Diversified Weight Pruning. (99%)
Hung-Jui Wang; Yu-Yu Wu; Shang-Tse Chen

http://arxiv.org/abs/2208.08664
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance. (45%)
Bahjat Kawar; Roy Ganz; Michael Elad

http://arxiv.org/abs/2208.08689
Reverse Engineering of Integrated Circuits: Tools and Techniques. (33%)
Abhijitt Dhavlle

http://arxiv.org/abs/2208.09139
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization. (10%)
Anshul Nasery; Sravanti Addepalli; Praneeth Netrapalli; Prateek Jain

http://arxiv.org/abs/2208.08831
Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning. (3%)
Olivia Wiles; Isabela Albuquerque; Sven Gowal

http://arxiv.org/abs/2208.08662
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy. (2%)
Wenqiang Ruan; Mingxin Xu; Wenjing Fang; Li Wang; Lei Wang; Weili Han

http://arxiv.org/abs/2208.08745
Profiler: Profile-Based Model to Detect Phishing Emails. (1%)
Mariya Shmalko; Alsharif Abuadbba; Raj Gaire; Tingmin Wu; Hye-Young Paik; Surya Nepal

http://arxiv.org/abs/2208.08083
Two Heads are Better than One: Robust Learning Meets Multi-branch Models. (99%)
Dong Huang; Qingwen Bu; Yuhao Qing; Haowen Pi; Sen Wang; Heming Cui

http://arxiv.org/abs/2208.08297
An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks. (99%)
Raz Lapid; Zvika Haramaty; Moshe Sipper

http://arxiv.org/abs/2208.09285
Shadows Aren't So Dangerous After All: A Fast and Robust Defense Against Shadow-Based Adversarial Attacks. (98%)
Andrew Wang; Wyatt Mayor; Ryan Smith; Gopal Nookula; Gregory Ditzler

http://arxiv.org/abs/2208.08433
Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System. (70%)
Abdur R. Shahid; Ahmed Imteaj; Peter Y. Wu; Diane A. Igoche; Tauhidul Alam

http://arxiv.org/abs/2208.08071
An Efficient Multi-Step Framework for Malware Packing Identification. (41%)
Jong-Wouk Kim; Yang-Sae Moon; Mi-Jung Choi

http://arxiv.org/abs/2208.08270
On the Privacy Effect of Data Enhancement via the Lens of Memorization. (31%)
Xiao Li; Qiongxiu Li; Zhanhao Hu; Xiaolin Hu

http://arxiv.org/abs/2208.08114
An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models. (26%)
Jihyeon Hyeong; Jayoung Kim; Noseong Park; Sushil Jajodia

http://arxiv.org/abs/2208.08085
Efficient Detection and Filtering Systems for Distributed Training. (26%)
Konstantinos Konstantinidis; Aditya Ramamoorthy

http://arxiv.org/abs/2208.08569
ObfuNAS: A Neural Architecture Search-based DNN Obfuscation Approach. (2%)
Tong Zhou; Shaolei Ren; Xiaolin Xu

http://arxiv.org/abs/2208.08524
DF-Captcha: A Deepfake Captcha for Preventing Fake Calls. (1%)
Yisroel Mirsky

http://arxiv.org/abs/2208.08509
Analyzing Robustness of End-to-End Neural Models for Automatic Speech Recognition. (1%)
Goutham Rajendran; Wei Zou

http://arxiv.org/abs/2208.08029
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search. (82%)
Huijun Liu; Jie Yu; Shasha Li; Jun Ma; Bin Ji

http://arxiv.org/abs/2208.08052
Imperceptible and Robust Backdoor Attack in 3D Point Cloud. (68%)
Kuofeng Gao; Jiawang Bai; Baoyuan Wu; Mengxi Ya; Shu-Tao Xia

http://arxiv.org/abs/2208.08025
AutoCAT: Reinforcement Learning for Automated Exploration of Cache-Timing Attacks. (13%)
Mulong Luo; Wenjie Xiong; Geunbae Lee; Yueying Li; Xiaomeng Yang; Amy Zhang; Yuandong Tian; Hsien-Hsin S. Lee; G. Edward Suh

http://arxiv.org/abs/2208.08003
Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise. (1%)
Yihao Xue; Kyle Whitecross; Baharan Mirzasoleiman

http://arxiv.org/abs/2208.07174
Man-in-the-Middle Attack against Object Detection Systems. (96%)
Han Wu; Sareh Rowlands; Johan Wahlstrom

http://arxiv.org/abs/2208.07316
MENLI: Robust Evaluation Metrics from Natural Language Inference. (92%)
Yanran Chen; Steffen Eger

http://arxiv.org/abs/2208.07272
Training-Time Attacks against k-Nearest Neighbors. (2%)
Ara Vartanian; Will Rosenbaum; Scott Alfeld

http://arxiv.org/abs/2208.07476
CTI4AI: Threat Intelligence Generation and Sharing after Red Teaming AI Models. (1%)
Chuyen Nguyen; Caleb Morgan; Sudip Mittal

http://arxiv.org/abs/2208.06984
A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design. (99%)
Jialiang Sun; Wen Yao; Tingsong Jiang; Xiaoqian Chen

http://arxiv.org/abs/2208.06776
Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection. (92%)
Haibin Zheng; Haiyang Xiong; Haonan Ma; Guohan Huang; Jinyin Chen

http://arxiv.org/abs/2208.06962
InvisibiliTee: Angle-agnostic Cloaking from Person-Tracking Systems with a Tee. (92%)
Yaxian Li; Bingqing Zhang; Guoping Zhao; Mingyu Zhang; Jiajun Liu; Ziwei Wang; Jirong Wen

http://arxiv.org/abs/2208.10273
Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning. (67%)
Ashish Gupta; Tie Luo; Mao V. Ngo; Sajal K. Das

http://arxiv.org/abs/2208.06651
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification. (99%)
Beini Xie; Heng Chang; Xin Wang; Tian Bian; Shiji Zhou; Daixin Wang; Zhiqiang Zhang; Wenwu Zhu

http://arxiv.org/abs/2208.10224
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attacks. (99%)
Tian Yu Liu; Yu Yang; Baharan Mirzasoleiman

http://arxiv.org/abs/2208.06592
Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer. (62%)
Tong Wang; Yuan Yao; Feng Xu; Miao Xu; Shengwei An; Ting Wang

http://arxiv.org/abs/2208.06222
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator. (99%)
Xiangbo Gao; Cheng Luo; Qinliang Lin; Weicheng Xie; Minmin Liu; Linlin Shen; Keerthy Kusumam; Siyang Song

http://arxiv.org/abs/2208.06538
MaskBlock: Transferable Adversarial Examples with Bayes Approach. (99%)
Mingyuan Fan; Cen Chen; Ximeng Liu; Wenzhong Guo

http://arxiv.org/abs/2208.10279
Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks. (98%)
Ferhat Ozgur Catak; Murat Kuzlu; Evren Catak; Umit Cali; Ozgur Guler

http://arxiv.org/abs/2208.06228
Unifying Gradients to Improve Real-world Robustness for Deep Networks. (96%)
Yingwen Wu; Sizhe Chen; Kun Fang; Xiaolin Huang

http://arxiv.org/abs/2208.06176
A Knowledge Distillation-Based Backdoor Attack in Federated Learning. (93%)
Yifan Wang; Wei Fan; Keke Yang; Naji Alhusaini; Jing Li

http://arxiv.org/abs/2208.06163
Dropout is NOT All You Need to Prevent Gradient Leakage. (62%)
Daniel Scheliga; Patrick Mäder; Marco Seeland

http://arxiv.org/abs/2208.06537
Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons. (2%)
Mingyuan Fan; Yang Liu; Cen Chen; Ximeng Liu; Wenzhong Guo

http://arxiv.org/abs/2208.06481
PRIVEE: A Visual Analytic Workflow for Proactive Privacy Risk Inspection of Open Data. (2%)
Kaustav Bhattacharjee; Akm Islam; Jaideep Vaidya; Aritra Dasgupta

http://arxiv.org/abs/2208.05650
Diverse Generative Perturbations on Attention Space for Transferable Adversarial Attacks. (99%)
Woo Jae Kim; Seunghoon Hong; Sung-Eui Yoon

http://arxiv.org/abs/2208.05740
General Cutting Planes for Bound-Propagation-Based Neural Network Verification. (68%)
Huan Zhang; Shiqi Wang; Kaidi Xu; Linyi Li; Bo Li; Suman Jana; Cho-Jui Hsieh; J. Zico Kolter

http://arxiv.org/abs/2208.06092
On deceiving malware classification with section injection. (5%)
Silva Adeilson Antonio da; Mauricio Pamplona Segundo

http://arxiv.org/abs/2208.06018
A Probabilistic Framework for Mutation Testing in Deep Neural Networks. (1%)
Florian Tambon; Foutse Khomh; Giuliano Antoniol

http://arxiv.org/abs/2208.05969
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment. (1%)
Jie Zhu; Leye Wang; Xiao Han

http://arxiv.org/abs/2208.05895
Shielding Federated Learning Systems against Inference Attacks with ARM TrustZone. (1%)
Aghiles Ait Messaoud; Sonia Ben Mokhtar; Vlad Nitu; Valerio Schiavoni

http://arxiv.org/abs/2208.05285
Explaining Machine Learning DGA Detectors from DNS Traffic Data. (13%)
Giorgio Piras; Maura Pintor; Luca Demetrio; Battista Biggio

http://arxiv.org/abs/2208.05395
A Sublinear Adversarial Training Algorithm. (3%)
Yeqi Gao; Lianke Qin; Zhao Song; Yitan Wang

http://arxiv.org/abs/2208.05190
DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias. (1%)
Yu Zheng; Chen Gao; Jingtao Ding; Lingling Yi; Depeng Jin; Yong Li; Meng Wang

http://arxiv.org/abs/2208.05073
Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services. (98%)
Ahmed Omara; Burak Kantarci

http://arxiv.org/abs/2208.05083
Reducing Exploitability with Population Based Training. (67%)
Pavel Czempin; Adam Gleave

http://arxiv.org/abs/2208.04838
Robust Machine Learning for Malware Detection over Time. (9%)
Daniele Angioni; Luca Demetrio; Maura Pintor; Battista Biggio

http://arxiv.org/abs/2208.03944
Robust and Imperceptible Black-box DNN Watermarking Based on Fourier Perturbation Analysis and Frequency Sensitivity Clustering. (75%)
Yong Liu; Hanzhou Wu; Xinpeng Zhang

http://arxiv.org/abs/2208.04943
PerD: Perturbation Sensitivity-based Neural Trojan Detection Framework on NLP Applications. (67%)
Diego Garcia-soto; Huili Chen; Farinaz Koushanfar

http://arxiv.org/abs/2208.03923
Adversarial robustness of VAEs through the lens of local geometry. (47%)
Asif Khan; Amos Storkey

http://arxiv.org/abs/2208.03948
AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning. (26%)
Tianxing Zhang; Hanzhou Wu; Xiaofeng Lu; Guangling Sun

http://arxiv.org/abs/2208.03958
Abutting Grating Illusion: Cognitive Challenge to Neural Network Models. (1%)
Jinyu Fan; Yi Zeng

http://arxiv.org/abs/2208.04062
Testing of Machine Learning Models with Limited Samples: An Industrial Vacuum Pumping Application. (1%)
Ayan Chatterjee; Bestoun S. Ahmed; Erik Hallin; Anton Engman

http://arxiv.org/abs/2208.03635
Federated Adversarial Learning: A Framework with Convergence Analysis. (80%)
Xiaoxiao Li; Zhao Song; Jiaming Yang

http://arxiv.org/abs/2208.05514
Are Gradients on Graph Structure Reliable in Gray-box Attacks? (13%)
Zihan Liu; Yun Luo; Lirong Wu; Siyuan Li; Zicheng Liu; Stan Z. Li

http://arxiv.org/abs/2208.03610
Blackbox Attacks via Surrogate Ensemble Search. (99%)
Zikui Cai; Chengyu Song; Srikanth Krishnamurthy; Amit Roy-Chowdhury; M. Salman Asif

http://arxiv.org/abs/2208.03567
On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning. (22%)
Congyu Fang; Hengrui Jia; Anvith Thudi; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Varun Chandrasekaran; Nicolas Papernot

http://arxiv.org/abs/2208.03466
Preventing or Mitigating Adversarial Supply Chain Attacks; a legal analysis. (3%)
Kaspar Rosager Ludvigsen; Shishir Nagaraja; Angela Daly

http://arxiv.org/abs/2208.03161
Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations. (73%)
Jan Nikolas Morshuis; Sergios Gatidis; Matthias Hein; Christian F. Baumgartner

http://arxiv.org/abs/2208.03111
Data-free Backdoor Removal based on Channel Lipschitzness. (64%)
Runkai Zheng; Rongjun Tang; Jianze Li; Li Liu

http://arxiv.org/abs/2208.03309
Lethal Dose Conjecture on Data Poisoning. (2%)
Wenxiao Wang; Alexander Levine; Soheil Feizi

http://arxiv.org/abs/2208.03399
LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles. (1%)
Li Yang; Abdallah Shami; Gary Stevens; Rusett Stephen De

http://arxiv.org/abs/2208.03160
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks. (1%)
Bernd Prach; Christoph H. Lampert

http://arxiv.org/abs/2208.02851
Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification. (99%)
Faris Almalik; Mohammad Yaqub; Karthik Nandakumar

http://arxiv.org/abs/2208.01919
Spectrum Focused Frequency Adversarial Attacks for Automatic Modulation Classification. (99%)
Sicheng College of Information and Communication Engineering, Harbin Engineering University, Harbin Zhang; Jiarun College of Information and Communication Engineering, Harbin Engineering University, Harbin Yu; Zhida College of Information and Communication Engineering, Harbin Engineering University, Harbin Bao; Shiwen Department of Electrical & Computer Engineering, Auburn University, Auburn Mao; Yun College of Information and Communication Engineering, Harbin Engineering University, Harbin Lin

http://arxiv.org/abs/2208.02310
Design of secure and robust cognitive system for malware detection. (99%)
Sanket Shukla

http://arxiv.org/abs/2208.02430
A New Kind of Adversarial Example. (99%)
Ali Borji

http://arxiv.org/abs/2208.02250
Adversarial Attacks on ASR Systems: An Overview. (98%)
Xiao Zhang; Hao Tan; Xuan Huang; Denghui Zhang; Keke Tang; Zhaoquan Gu

http://arxiv.org/abs/2208.01844
Multiclass ASMA vs Targeted PGD Attack in Image Segmentation. (96%)
Johnson University of Toronto Vo; Jiabao University of Toronto Xie; Sahil University of Toronto Patel

http://arxiv.org/abs/2208.02820
MOVE: Effective and Harmless Ownership Verification via Embedded External Features. (84%)
Yiming Li; Linghui Zhu; Xiaojun Jia; Yang Bai; Yong Jiang; Shu-Tao Xia; Xiaochun Cao

http://arxiv.org/abs/2208.01853
Robust Graph Neural Networks using Weighted Graph Laplacian. (13%)
Bharat Runwal; Vivek; Sandeep Kumar

http://arxiv.org/abs/2208.01819
Adversarial Camouflage for Node Injection Attack on Graphs. (81%)
Shuchang Tao; Qi Cao; Huawei Shen; Yunfan Wu; Liang Hou; Xueqi Cheng

http://arxiv.org/abs/2208.01705
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry. (2%)
Mark Penrod; Harrison Termotto; Varshini Reddy; Jiayu Yao; Finale Doshi-Velez; Weiwei Pan

http://arxiv.org/abs/2208.01356
SCFI: State Machine Control-Flow Hardening Against Fault Attacks. (1%)
Pascal Nasahl; Martin Unterguggenberger; Rishub Nagpal; Robert Schilling; David Schrammel; Stefan Mangard

http://arxiv.org/abs/2208.01220
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction. (98%)
Jiacheng Zhu; Jielin Qiu; Zhuolin Yang; Douglas Weber; Michael A. Rosenberg; Emerson Liu; Bo Li; Ding Zhao

http://arxiv.org/abs/2208.00906
Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem. (81%)
Zheng Wang; Wenjie Ruan

http://arxiv.org/abs/2208.01113
On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel. (75%)
Shubhi Shukla; Manaar Alam; Sarani Bhattacharya; Debdeep Mukhopadhyay; Pabitra Mitra

http://arxiv.org/abs/2208.00862
Attacking Adversarial Defences by Smoothing the Loss Landscape. (26%)
Panagiotis Eustratiadis; Henry Gouk; Da Li; Timothy Hospedales

http://arxiv.org/abs/2208.00498
DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning. (99%)
Mohammad Hossein Samavatian; Saikat Majumdar; Kristin Barber; Radu Teodorescu

http://arxiv.org/abs/2208.00428
Robust Real-World Image Super-Resolution against Adversarial Attacks. (99%)
Jiutao Yue; Haofeng Li; Pengxu Wei; Guanbin Li; Liang Lin

http://arxiv.org/abs/2208.00539
Is current research on adversarial robustness addressing the right problem? (97%)
Ali Borji

http://arxiv.org/abs/2208.00328
enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks. (5%)
Alessio Colucci; Andreas Steininger; Muhammad Shafique

http://arxiv.org/abs/2208.00331
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks. (2%)
Muhammad Abdullah Hanif; Giuseppe Maria Sarda; Alberto Marchisio; Guido Masera; Maurizio Martina; Muhammad Shafique

http://arxiv.org/abs/2208.00094
Robust Trajectory Prediction against Adversarial Attacks. (99%)
Yulong Cao; Danfei Xu; Xinshuo Weng; Zhuoqing Mao; Anima Anandkumar; Chaowei Xiao; Marco Pavone

http://arxiv.org/abs/2208.00081
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity Analysis. (56%)
Tao Li; Haozhe Lei; Quanyan Zhu

http://arxiv.org/abs/2207.14381
Pro-tuning: Unified Prompt Tuning for Vision Tasks. (1%)
Xing Nie; Bolin Ni; Jianlong Chang; Gaomeng Meng; Chunlei Huo; Zhaoxiang Zhang; Shiming Xiang; Qi Tian; Chunhong Pan

http://arxiv.org/abs/2207.13381
Look Closer to Your Enemy: Learning to Attack via Teacher-student Mimicking. (99%)
Mingejie Wang; Zhiqing Tang; Sirui Li; Dingwen Xiao

http://arxiv.org/abs/2207.13326
Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processing. (96%)
Daizong Liu; Wei Hu; Xin Li

http://arxiv.org/abs/2207.13572
Membership Inference Attacks via Adversarial Examples. (73%)
Hamid Jalalzai; Elie Kadoche; Rémi Leluc; Vincent Plassier

http://arxiv.org/abs/2207.13417
Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips. (69%)
Jiawang Bai; Kuofeng Gao; Dihong Gong; Shu-Tao Xia; Zhifeng Li; Wei Liu

http://arxiv.org/abs/2207.13321
DynaMarks: Defending Against Deep Learning Model Extraction Using Dynamic Watermarking. (47%)
Abhishek Chakraborty; Daniel Xing; Yuntao Liu; Ankur Srivastava

http://arxiv.org/abs/2207.13766
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks. (22%)
Mauro Conti; Jiaxin Li; Stjepan Picek; Jing Xu

http://arxiv.org/abs/2207.13867
Generative Steganography Network. (1%)
Ping Wei; Sheng Li; Xinpeng Zhang; Ge Luo; Zhenxing Qian; Qing Zhou

http://arxiv.org/abs/2207.13129
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity. (99%)
Martin Gubri; Maxime Cordy; Mike Papadakis; Yves Le Traon; Koushik Sen

http://arxiv.org/abs/2207.13192
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception. (99%)
Rui Duan; Zhe Qu; Shangqing Zhao; Leah Ding; Yao Liu; Zhuo Lu

http://arxiv.org/abs/2207.12816
Generative Extraction of Audio Classifiers for Speaker Identification. (73%)
Tejumade Afonja; Lucas Bourtoule; Varun Chandrasekaran; Sageev Oore; Nicolas Papernot

http://arxiv.org/abs/2207.13243
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks. (8%)
Tilman Räuker; Anson Ho; Stephen Casper; Dylan Hadfield-Menell

http://arxiv.org/abs/2207.12545
$p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations. (99%)
Adam Dziedzic; Stephan Rabanser; Mohammad Yaghini; Armin Ale; Murat A. Erdogdu; Nicolas Papernot

http://arxiv.org/abs/2207.12203
Improving Adversarial Robustness via Mutual Information Estimation. (99%)
Dawei Zhou; Nannan Wang; Xinbo Gao; Bo Han; Xiaoyu Wang; Yibing Zhan; Tongliang Liu

http://arxiv.org/abs/2207.12391
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness. (99%)
Jindong Gu; Hengshuang Zhao; Volker Tresp; Philip Torr

http://arxiv.org/abs/2207.11971
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer. (75%)
Yingyi Chen; Xi Shen; Yahui Liu; Qinghua Tao; Johan A. K. Suykens

http://arxiv.org/abs/2207.12327
Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment. (9%)
Tian Liu; Xueyang Hu; Tao Shu

http://arxiv.org/abs/2207.12535
Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning. (2%)
Xinlei He; Hongbin Liu; Neil Zhenqiang Gong; Yang Zhang

http://arxiv.org/abs/2207.12405
Versatile Weight Attack via Flipping Limited Bits. (86%)
Jiawang Bai; Baoyuan Wu; Zhifeng Li; Shu-tao Xia

http://arxiv.org/abs/2207.11727
Can we achieve robustness from data alone? (82%)
Nikolaos Tsilivis; Jingtong Su; Julia Kempe

http://arxiv.org/abs/2207.11694
Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability. (69%)
Quanshi Zhang; Xin Wang; Jie Ren; Xu Cheng; Shuyun Lin; Yisen Wang; Xiangming Zhu

http://arxiv.org/abs/2207.11788
Privacy Against Inference Attacks in Vertical Federated Learning. (2%)
Borzoo Rassouli; Morteza Varasteh; Deniz Gunduz

http://arxiv.org/abs/2207.11722
Semantic-guided Multi-Mask Image Harmonization. (1%)
Xuqian Ren; Yifan Liu

http://arxiv.org/abs/2207.11378
Do Perceptually Aligned Gradients Imply Adversarial Robustness? (99%)
Roy Ganz; Bahjat Kawar; Michael Elad

http://arxiv.org/abs/2207.11177
Provable Defense Against Geometric Transformations. (47%)
Rem Yang; Jacob Laurel; Sasa Misailovic; Gagandeep Singh

http://arxiv.org/abs/2207.10942
Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation. (41%)
Qiang Hu; Yuejun Guo; Xiaofei Xie; Maxime Cordy; Lei Ma; Mike Papadakis; Yves Le Traon

http://arxiv.org/abs/2207.11327
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion. (1%)
Zhengqi Gao; Fan-Keng Sun; Mingran Yang; Sucheng Ren; Zikai Xiong; Marc Engeler; Antonio Burazer; Linda Wildling; Luca Daniel; Duane S. Boning

http://arxiv.org/abs/2207.10719
Synthetic Dataset Generation for Adversarial Machine Learning Research. (99%)
Xiruo Liu; Shibani Singh; Cory Cornelius; Colin Busho; Mike Tan; Anindya Paul; Jason Martin

http://arxiv.org/abs/2207.10561
Careful What You Wish For: on the Extraction of Adversarially Trained Models. (99%)
Kacem Khaled; Gabriela Nicolescu; Magalhães Felipe Gohring de

http://arxiv.org/abs/2208.10251
Rethinking Textual Adversarial Defense for Pre-trained Language Models. (99%)
Jiayi Wang; Rongzhou Bao; Zhuosheng Zhang; Hai Zhao

http://arxiv.org/abs/2207.10290
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance. (98%)
Xiaoliang Liu; Furao Shen; Jian Zhao; Changhai Nie

http://arxiv.org/abs/2207.10307
Knowledge-enhanced Black-box Attacks for Recommendations. (92%)
Jingfan Chen; Wenqi Fan; Guanghui Zhu; Xiangyu Zhao; Chunfeng Yuan; Qing Li; Yihua Huang

http://arxiv.org/abs/2207.10498
Towards Efficient Adversarial Training on Vision Transformers. (92%)
Boxi Wu; Jindong Gu; Zhifeng Li; Deng Cai; Xiaofei He; Wei Liu

http://arxiv.org/abs/2207.10825
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation. (87%)
Tong Wu; Tianhao Wang; Vikash Sehwag; Saeed Mahloujifar; Prateek Mittal

http://arxiv.org/abs/2207.10862
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility. (83%)
Rohit Gupta; Naveed Akhtar; Ajmal Mian; Mubarak Shah

http://arxiv.org/abs/2207.10495
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing. (22%)
Michael Weiss; André García Gómez; Paolo Tonella

http://arxiv.org/abs/2207.10283
Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness. (99%)
Sekitoshi Kanai; Shin'ya Yamaguchi; Masanori Yamada; Hiroshi Takahashi; Kentaro Ohno; Yasutoshi Ida

http://arxiv.org/abs/2207.10170
Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers. (98%)
Tim Franzmeyer; Stephen McAleer; João F. Henriques; Jakob N. Foerster; Philip H. S. Torr; Adel Bibi; Witt Christian Schroeder de

http://arxiv.org/abs/2207.09640
Test-Time Adaptation via Conjugate Pseudo-labels. (10%)
Sachin Goyal; Mingjie Sun; Aditi Raghunathan; Zico Kolter

http://arxiv.org/abs/2207.10242
Malware Triage Approach using a Task Memory based on Meta-Transfer Learning Framework. (9%)
Jinting Zhu; Julian Jang-Jaccard; Ian Welch; Harith Al-Sahaf; Seyit Camtepe

http://arxiv.org/abs/2207.09755
A temporally and spatially local spike-based backpropagation algorithm to enable training in hardware. (1%)
Anmol Biswas; Vivek Saraswat; Udayan Ganguly

http://arxiv.org/abs/2207.09572
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. (99%)
Linbo Liu; Youngsuk Park; Trong Nghia Hoang; Hilaf Hasson; Jun Huan

http://arxiv.org/abs/2207.09209
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients. (41%)
Zaixi Zhang; Xiaoyu Cao; Jinyuan Jia; Neil Zhenqiang Gong

http://arxiv.org/abs/2207.09087
Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond. (26%)
Yuzheng Hu; Tianle Cai; Jinyong Shan; Shange Tang; Chaochao Cai; Ethan Song; Bo Li; Dawn Song

http://arxiv.org/abs/2207.09239
Assaying Out-Of-Distribution Generalization in Transfer Learning. (1%)
Florian Wenzel; Andrea Dittadi; Peter Vincent Gehler; Carl-Johann Simon-Gabriel; Max Horn; Dominik Zietlow; David Kernert; Chris Russell; Thomas Brox; Bernt Schiele; Bernhard Schölkopf; Francesco Locatello

http://arxiv.org/abs/2207.11237
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders. (99%)
Zhenrui Yue; Huimin Zeng; Ziyi Kou; Lanyu Shang; Dong Wang

http://arxiv.org/abs/2207.08859
Prior-Guided Adversarial Initialization for Fast Adversarial Training. (99%)
Xiaojun Jia; Yong Zhang; Xingxing Wei; Baoyuan Wu; Ke Ma; Jue Wang; Xiaochun Cao

http://arxiv.org/abs/2207.09031
Decorrelative Network Architecture for Robust Electrocardiogram Classification. (99%)
Christopher Wiedeman; Ge Wang

http://arxiv.org/abs/2207.08948
Multi-step domain adaptation by adversarial attack to $\mathcal{H} \Delta \mathcal{H}$-divergence. (96%)
Arip Asadulaev; Alexander Panfilov; Andrey Filchenkov

http://arxiv.org/abs/2207.08803
Adversarial Pixel Restoration as a Pretext Task for Transferable Perturbations. (91%)
Hashmat Shadab Malik; Shahina K Kunhimon; Muzammal Naseer; Salman Khan; Fahad Shahbaz Khan

http://arxiv.org/abs/2207.08940
Easy Batch Normalization. (69%)
Arip Asadulaev; Alexander Panfilov; Andrey Filchenkov

http://arxiv.org/abs/2207.08374
Adversarial Contrastive Learning via Asymmetric InfoNCE. (61%)
Qiying Yu; Jieming Lou; Xianyuan Zhan; Qizhang Li; Wangmeng Zuo; Yang Liu; Jingjing Liu

http://arxiv.org/abs/2207.08486
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications. (22%)
Ali Raza; Shujun Li; Kim-Phuc Tran; Ludovic Koehl

http://arxiv.org/abs/2207.08556
A Certifiable Security Patch for Object Tracking in Self-Driving Systems via Historical Deviation Modeling. (10%)
Xudong Pan; Qifan Xiao; Mi Zhang; Min Yang

http://arxiv.org/abs/2207.08898
Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification. (2%)
Sarwan Ali; Bikram Sahoo; Alexander Zelikovskiy; Pin-Yu Chen; Murray Patterson

http://arxiv.org/abs/2207.08178
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal. (99%)
Xinwei Liu; Jian Liu; Yang Bai; Jindong Gu; Tao Chen; Xiaojun Jia; Xiaochun Cao

http://arxiv.org/abs/2207.08089
Threat Model-Agnostic Adversarial Defense using Diffusion Models. (99%)
Tsachi Blau; Roy Ganz; Bahjat Kawar; Alex Bronstein; Michael Elad

http://arxiv.org/abs/2207.08137
Achieve Optimal Adversarial Accuracy for Adversarial Deep Learning using Stackelberg Game. (96%)
Xiao-Shan Gao; Shuang Liu; Lijia Yu

http://arxiv.org/abs/2207.08157
Automated Repair of Neural Networks. (16%)
Dor Cohen; Ofer Strichman

http://arxiv.org/abs/2207.08044
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking. (99%)
Xiangyu Yin; Wenjie Ruan; Jonathan Fieldsend

http://arxiv.org/abs/2207.07972
Certified Neural Network Watermarks with Randomized Smoothing. (1%)
Arpit Bansal; Ping-yeh Chiang; Michael Curry; Rajiv Jain; Curtis Wigington; Varun Manjunatha; John P Dickerson; Tom Goldstein

http://arxiv.org/abs/2207.08034
Progress and limitations of deep networks to recognize objects in unusual poses. (1%)
Amro Abbas; Stéphane Deny

http://arxiv.org/abs/2207.07941
MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks. (1%)
Ali Ramezani-Kebrya; Iman Tabrizian; Fartash Faghri; Petar Popovski

http://arxiv.org/abs/2207.08005
Exploring The Resilience of Control Execution Skips against False Data Injection Attacks. (1%)
Ipsita Koley; Sunandan Adhikary; Soumyajit Dey

http://arxiv.org/abs/2207.07793
Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training. (99%)
Xiaoyu Liang; Yaguan Qian; Jianchang Huang; Xiang Ling; Bin Wang; Chunming Wu; Wassim Swaileh

http://arxiv.org/abs/2207.07797
CARBEN: Composite Adversarial Robustness Benchmark. (98%)
Lei Hsiung; Yun-Yun Tsai; Pin-Yu Chen; Tsung-Yi Ho

http://arxiv.org/abs/2207.07803
Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders. (68%)
Jiahao Qi; Zhiqiang Gong; Xingyue Liu; Kangcheng Bin; Chen Chen; Yongqian Li; Wei Xue; Yu Zhang; Ping Zhong

http://arxiv.org/abs/2207.07347
Feasibility of Inconspicuous GAN-generated Adversarial Patches against Object Detection. (10%)
Svetlana Pavlitskaya; Bianca-Marina Codău; J. Marius Zöllner

http://arxiv.org/abs/2207.07292
PASS: Parameters Audit-based Secure and Fair Federated Learning Scheme against Free Rider. (5%)
Jianhua Wang

http://arxiv.org/abs/2207.07539
3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models. (1%)
Ronghui Mu; Wenjie Ruan; Leandro S. Marcolino; Qiang Ni

http://arxiv.org/abs/2207.06982
Adversarial Examples for Model-Based Control: A Sensitivity Analysis. (98%)
Po-han Department of Electrical and Computer Engineering, The University of Texas at Austin Li; Ufuk Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin Topcu; Sandeep P. Department of Electrical and Computer Engineering, The University of Texas at Austin Chinchali

http://arxiv.org/abs/2207.07032
Adversarial Attacks on Monocular Pose Estimation. (98%)
Hemang Chawla; Arnav Varma; Elahe Arani; Bahram Zonooz

http://arxiv.org/abs/2207.07208
Provably Adversarially Robust Nearest Prototype Classifiers. (83%)
Václav Voráček; Matthias Hein

http://arxiv.org/abs/2207.07256
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution. (70%)
Zhenyi Wang; Li Shen; Le Fang; Qiuling Suo; Tiehang Duan; Mingchen Gao

http://arxiv.org/abs/2207.06858
RSD-GAN: Regularized Sobolev Defense GAN Against Speech-to-Text Adversarial Attacks. (67%)
Mohammad Esmaeilpour; Nourhene Chaalia; Patrick Cardinal

http://arxiv.org/abs/2207.07209
Sound Randomized Smoothing in Floating-Point Arithmetics. (50%)
Václav Voráček; Matthias Hein

http://arxiv.org/abs/2207.07162
Audio-guided Album Cover Art Generation with Genetic Algorithms. (38%)
James Marien; Sam Leroux; Bart Dhoedt; Boom Cedric De

http://arxiv.org/abs/2207.06888
Distance Learner: Incorporating Manifold Prior to Model Training. (16%)
Aditya Chetan; Nipun Kwatra

http://arxiv.org/abs/2207.10802
Active Data Pattern Extraction Attacks on Generative Language Models. (11%)
Bargav Jayaraman; Esha Ghosh; Huseyin Inan; Melissa Chase; Sambuddha Roy; Wei Dai

http://arxiv.org/abs/2207.07180
Contrastive Adapters for Foundation Model Group Robustness. (1%)
Michael Zhang; Christopher Ré

http://arxiv.org/abs/2207.07232
Lipschitz Bound Analysis of Neural Networks. (1%)
Sarosij Bose

http://arxiv.org/abs/2207.06035
Perturbation Inactivation Based Adversarial Defense for Face Recognition. (99%)
Min Ren; Yuhao Zhu; Yunlong Wang; Zhenan Sun

http://arxiv.org/abs/2207.06154
On the Robustness of Bayesian Neural Networks to Adversarial Attacks. (93%)
Luca Bortolussi; Ginevra Carbone; Luca Laurenti; Andrea Patane; Guido Sanguinetti; Matthew Wicker

http://arxiv.org/abs/2207.06202
Adversarially-Aware Robust Object Detector. (91%)
Ziyi Dong; Pengxu Wei; Liang Lin

http://arxiv.org/abs/2207.06647
PIAT: Physics Informed Adversarial Training for Solving Partial Differential Equations. (15%)
Simin Shekarpaz; Mohammad Azizmalayeri; Mohammad Hossein Rohban

http://arxiv.org/abs/2207.06236
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities. (10%)
Subash Neupane; Jesse Ables; William Anderson; Sudip Mittal; Shahram Rahimi; Ioana Banicescu; Maria Seale

http://arxiv.org/abs/2207.06196
Interactive Machine Learning: A State of the Art Review. (4%)
Natnael A. Wondimu; Cédric Buche; Ubbo Visser

http://arxiv.org/abs/2207.06211
Sample-dependent Adaptive Temperature Scaling for Improved Calibration. (2%)
Tom Joy; Francesco Pinto; Ser-Nam Lim; Philip H. S. Torr; Puneet K. Dokania

http://arxiv.org/abs/2207.06282
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment. (1%)
Ahmed Haj Yahmed; Houssem Ben Braiek; Foutse Khomh; Sonia Bouzidi; Rania Zaatour

http://arxiv.org/abs/2207.05756
Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information. (99%)
Jiebao Zhang; Wenhua Qian; Rencan Nie; Jinde Cao; Dan Xu

http://arxiv.org/abs/2207.05451
Adversarial Robustness Assessment of NeuroEvolution Approaches. (99%)
Inês Valentim; Nuno Lourenço; Nuno Antunes

http://arxiv.org/abs/2207.05382
Frequency Domain Model Augmentation for Adversarial Attack. (99%)
Yuyang Long; Qilong Zhang; Boheng Zeng; Lianli Gao; Xianglong Liu; Jian Zhang; Jingkuan Song

http://arxiv.org/abs/2207.05548
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware. (92%)
Luca Demetrio; Battista Biggio; Fabio Roli

http://arxiv.org/abs/2207.05937
Game of Trojans: A Submodular Byzantine Approach. (87%)
Dinuka Sahabandu; Arezoo Rajabi; Luyao Niu; Bo Li; Bhaskar Ramasubramanian; Radha Poovendran

http://arxiv.org/abs/2207.05321
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures. (84%)
Jia Liu; Ran Cheng; Yaochu Jin

http://arxiv.org/abs/2207.05327
Certified Adversarial Robustness via Anisotropic Randomized Smoothing. (76%)
Hanbin Hong; Yuan Hong

http://arxiv.org/abs/2207.05801
RelaxLoss: Defending Membership Inference Attacks without Losing Utility. (26%)
Dingfan Chen; Ning Yu; Mario Fritz

http://arxiv.org/abs/2207.05902
Verifying Attention Robustness of Deep Neural Networks against Semantic Perturbations. (5%)
Satoshi Munakata; Caterina Urban; Haruki Yokoyama; Koji Yamamoto; Kazuki Munakata

http://arxiv.org/abs/2207.05436
Markov Decision Process For Automatic Cyber Defense. (4%)
Simon Yusuf Enoch; Simon Yusuf Enoch; Dong Seong Kim

http://arxiv.org/abs/2207.05796
Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction. (1%)
Charles Lu; Syed Rakin Ahmed; Praveer Singh; Jayashree Kalpathy-Cramer

http://arxiv.org/abs/2207.05641
Backdoor Attacks on Crowd Counting. (1%)
Yuhua Sun; Tailai Zhang; Xingjun Ma; Pan Zhou; Jian Lou; Zichuan Xu; Xing Di; Yu Cheng; Lichao

http://arxiv.org/abs/2207.04843
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems. (99%)
Ibrahim Aliyu; Engelenburg Selinde van; Muhammed Bashir Muazu; Jinsul Kim; Chang Gyoon Lim

http://arxiv.org/abs/2207.05127
RUSH: Robust Contrastive Learning via Randomized Smoothing. (98%)
Yijiang Pang; Boyang Liu; Jiayu Zhou

http://arxiv.org/abs/2207.05729
Physical Passive Patch Adversarial Attacks on Visual Odometry Systems. (98%)
Yaniv Nemcovsky; Matan Yaakoby; Alex M. Bronstein; Chaim Baskin

http://arxiv.org/abs/2207.05137
Towards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency. (83%)
Hassan Mahmood; Ehsan Elhamifar

http://arxiv.org/abs/2207.05225
Susceptibility of Continual Learning Against Adversarial Attacks. (75%)
Hikmat Khan; Pir Masoom Shah; Syed Farhan Alam Zaidi; Saif ul Islam

http://arxiv.org/abs/2207.05164
"Why do so?" -- A Practical Perspective on Machine Learning Security. (64%)
Kathrin Grosse; Lukas Bieringer; Tarek Richard Besold; Battista Biggio; Katharina Krombholz

http://arxiv.org/abs/2207.04718
Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches. (22%)
Zhiyuan Cheng; James Liang; Hongjun Choi; Guanhong Tao; Zhiwen Cao; Dongfang Liu; Xiangyu Zhang

http://arxiv.org/abs/2207.04892
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation. (1%)
Zhun Zhong; Yuyang Zhao; Gim Hee Lee; Nicu Sebe

http://arxiv.org/abs/2207.04497
One-shot Neural Backdoor Erasing via Adversarial Weight Masking. (33%)
Shuwen Chai; Jinghui Chen

http://arxiv.org/abs/2207.04434
Hiding Your Signals: A Security Analysis of PPG-based Biometric Authentication. (4%)
Lin Li; Chao Chen; Lei Pan; Yonghang Tai; Jun Zhang; Yang Xiang

http://arxiv.org/abs/2207.04307
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features. (98%)
Taha Belkhouja; Janardhan Rao Doppa

http://arxiv.org/abs/2207.04209
Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain. (98%)
Chang Yue; Peizhuo Lv; Ruigang Liang; Kai Chen

http://arxiv.org/abs/2207.04308
Dynamic Time Warping based Adversarial Framework for Time-Series Domain. (97%)
Taha Belkhouja; Yan Yan; Janardhan Rao Doppa

http://arxiv.org/abs/2207.04305
Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis. (67%)
Taha Belkhouja; Yan Yan; Janardhan Rao Doppa

http://arxiv.org/abs/2207.04129
Not all broken defenses are equal: The dead angles of adversarial accuracy. (99%)
Raphael Olivier; Bhiksha Raj

http://arxiv.org/abs/2207.13036
Improved and Interpretable Defense to Transferred Adversarial Examples by Jacobian Norm with Selective Input Gradient Regularization. (99%)
Deyin Liu; Lin Wu; Lingqiao Liu; Haifeng Zhao; Farid Boussaid; Mohammed Bennamoun

http://arxiv.org/abs/2207.03895
Defense Against Multi-target Trojan Attacks. (80%)
Haripriya Harikumar; Santu Rana; Kien Do; Sunil Gupta; Wei Zong; Willy Susilo; Svetha Venkastesh

http://arxiv.org/abs/2207.03689
Guiding the retraining of convolutional neural networks against adversarial inputs. (80%)
Francisco Durán López; Silverio Martínez-Fernández; Michael Felderer; Xavier Franch

http://arxiv.org/abs/2207.09912
Online Evasion Attacks on Recurrent Models:The Power of Hallucinating the Future. (68%)
Byunggill Joe; Insik Shin; Jihun Hamm

http://arxiv.org/abs/2207.04075
Models Out of Line: A Fourier Lens on Distribution Shift Robustness. (10%)
Sara Fridovich-Keil; Brian R. Bartoldson; James Diffenderfer; Bhavya Kailkhura; Peer-Timo Bremer

http://arxiv.org/abs/2207.03933
A law of adversarial risk, interpolation, and label noise. (1%)
Daniel Paleka; Amartya Sanyal

http://arxiv.org/abs/2207.03400
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network. (99%)
Seongjin Park; Haedong Jeong; Giyoung Jeon; Jaesik Choi

http://arxiv.org/abs/2207.03162
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. (11%)
Zhuo Huang; Xiaobo Xia; Li Shen; Bo Han; Mingming Gong; Chen Gong; Tongliang Liu

http://arxiv.org/abs/2207.03586
CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships. (5%)
Rebecca Roelofs; Liting Sun; Ben Caine; Khaled S. Refaat; Ben Sapp; Scott Ettinger; Wei Chai

http://arxiv.org/abs/2207.02963
The Weaknesses of Adversarial Camouflage in Overhead Imagery. (83%)
Etten Adam Van

http://arxiv.org/abs/2207.02639
Adversarial Robustness of Visual Dialog. (64%)
Lu Yu; Verena Rieser

http://arxiv.org/abs/2207.02764
Enhancing Adversarial Attacks on Single-Layer NVM Crossbar-Based Neural Networks with Power Consumption Information. (54%)
Cory Merkel

http://arxiv.org/abs/2207.02842
When does Bias Transfer in Transfer Learning? (10%)
Hadi Salman; Saachi Jain; Andrew Ilyas; Logan Engstrom; Eric Wong; Aleksander Madry

http://arxiv.org/abs/2207.03056
Privacy-preserving Reflection Rendering for Augmented Reality. (2%)
Yiqin Zhao; Sheng Wei; Tian Guo

http://arxiv.org/abs/2207.03036
Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space. (1%)
Wenqi Shao; Xun Zhao; Yixiao Ge; Zhaoyang Zhang; Lei Yang; Xiaogang Wang; Ying Shan; Ping Luo

http://arxiv.org/abs/2207.02391
Query-Efficient Adversarial Attack Based on Latin Hypercube Sampling. (99%)
Dan Wang; Jiayu Lin; Yuan-Gen Wang

http://arxiv.org/abs/2207.01982
Defending against the Label-flipping Attack in Federated Learning. (98%)
Najeeb Moharram Jebreel; Josep Domingo-Ferrer; David Sánchez; Alberto Blanco-Justicia

http://arxiv.org/abs/2207.02152
UniCR: Universally Approximated Certified Robustness via Randomized Smoothing. (93%)
Hanbin Hong; Binghui Wang; Yuan Hong

http://arxiv.org/abs/2207.02036
PRoA: A Probabilistic Robustness Assessment against Functional Perturbations. (92%)
Tianle Zhang; Wenjie Ruan; Jonathan E. Fieldsend

http://arxiv.org/abs/2207.02087
Learning to Accelerate Approximate Methods for Solving Integer Programming via Early Fixing. (38%)
Longkang Li; Baoyuan Wu

http://arxiv.org/abs/2207.02159
Robustness Analysis of Video-Language Models Against Visual and Language Perturbations. (1%)
Madeline C. Schiappa; Shruti Vyas; Hamid Palangi; Yogesh S. Rawat; Vibhav Vineet

http://arxiv.org/abs/2207.01991
Conflicting Interactions Among Protection Mechanisms for Machine Learning Models. (1%)
Sebastian Szyller; N. Asokan

http://arxiv.org/abs/2207.01847
PoF: Post-Training of Feature Extractor for Improving Generalization. (1%)
Ikuro Sato; Ryota Yamada; Masayuki Tanaka; Nakamasa Inoue; Rei Kawakami

http://arxiv.org/abs/2207.02158
Class-Specific Semantic Reconstruction for Open Set Recognition. (1%)
Hongzhi Huang; Yu Wang; Qinghua Hu; Ming-Ming Cheng

http://arxiv.org/abs/2207.01396
Hessian-Free Second-Order Adversarial Examples for Adversarial Learning. (99%)
Yaguan Qian; Yuqi Wang; Bin Wang; Zhaoquan Gu; Yuhan Guo; Wassim Swaileh

http://arxiv.org/abs/2207.01531
Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples. (98%)
Giovanni Apruzzese; Rodion Vladimirov; Aliya Tastemirova; Pavel Laskov

http://arxiv.org/abs/2207.01795
Task-agnostic Defense against Adversarial Patch Attacks. (98%)
Ke Xu; Yao Xiao; Zhaoheng Zheng; Kaijie Cai; Ram Nevatia

http://arxiv.org/abs/2207.01398
Large-scale Robustness Analysis of Video Action Recognition Models. (70%)
Madeline C. Schiappa; Naman Biyani; Shruti Vyas; Hamid Palangi; Vibhav Vineet; Yogesh Rawat

http://arxiv.org/abs/2207.01548
Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness. (1%)
Saeid Asgari Taghanaki; Ali Gholami; Fereshte Khani; Kristy Choi; Linh Tran; Ran Zhang; Aliasghar Khani

http://arxiv.org/abs/2207.01149
RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries. (99%)
Keshav Kasichainula; Hadi Mansourifar; Weidong Shi

http://arxiv.org/abs/2207.01156
Removing Batch Normalization Boosts Adversarial Training. (98%)
Haotao Wang; Aston Zhang; Shuai Zheng; Xingjian Shi; Mu Li; Zhangyang Wang

http://arxiv.org/abs/2207.01106
Anomaly Detection with Adversarially Learned Perturbations of Latent Space. (13%)
Vahid Reza Khazaie; Anthony Wong; John Taylor Jewell; Yalda Mohsenzadeh

http://arxiv.org/abs/2207.01059
Identifying the Context Shift between Test Benchmarks and Production Data. (1%)
Matthew Groh

http://arxiv.org/abs/2207.00872
FL-Defender: Combating Targeted Attacks in Federated Learning. (80%)
Najeeb Jebreel; Josep Domingo-Ferrer

http://arxiv.org/abs/2207.00762
Backdoor Attack is a Devil in Federated GAN-based Medical Image Synthesis. (11%)
Ruinan Jin; Xiaoxiao Li

http://arxiv.org/abs/2207.00740
PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection. (1%)
Zhi Lu; Vrizlynn L. L. Thing

http://arxiv.org/abs/2207.00694
Efficient Adversarial Training With Data Pruning. (99%)
Maximilian Kaufmann; Yiren Zhao; Ilia Shumailov; Robert Mullins; Nicolas Papernot

http://arxiv.org/abs/2207.00278
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label. (99%)
Shengshan Hu; Ziqi Zhou; Yechao Zhang; Leo Yu Zhang; Yifeng Zheng; Yuanyuan HE; Hai Jin

http://arxiv.org/abs/2206.15128
Detecting and Recovering Adversarial Examples from Extracting Non-robust and Highly Predictive Adversarial Perturbations. (99%)
Mingyu Dong; Jiahao Chen; Diqun Yan; Jingxing Gao; Li Dong; Rangding Wang

http://arxiv.org/abs/2207.00099
Measuring Forgetting of Memorized Training Examples. (83%)
Matthew Jagielski; Om Thakkar; Florian Tramèr; Daphne Ippolito; Katherine Lee; Nicholas Carlini; Eric Wallace; Shuang Song; Abhradeep Thakurta; Nicolas Papernot; Chiyuan Zhang

http://arxiv.org/abs/2206.15415
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors. (80%)
Federica Granese; Marine Picot; Marco Romanelli; Francisco Messina; Pablo Piantanida

http://arxiv.org/abs/2207.00012
Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN. (16%)
Kuan Li; Yang Liu; Xiang Ao; Jianfeng Chi; Jinghua Feng; Hao Yang; Qing He

http://arxiv.org/abs/2207.00091
Threat Assessment in Machine Learning based Systems. (13%)
Lionel Nganyewou Tidjon; Foutse Khomh

http://arxiv.org/abs/2207.00137
Robustness of Epinets against Distributional Shifts. (1%)
Xiuyuan Lu; Ian Osband; Seyed Mohammad Asghari; Sven Gowal; Vikranth Dwaracherla; Zheng Wen; Roy Benjamin Van

http://arxiv.org/abs/2207.00118
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State. (1%)
Xinshao Wang; Yang Hua; Elyor Kodirov; Sankha Subhra Mukherjee; David A. Clifton; Neil M. Robertson

http://arxiv.org/abs/2206.15369
No Reason for No Supervision: Improved Generalization in Supervised Models. (1%)
Mert Bulent Sariyildiz; Yannis Kalantidis; Karteek Alahari; Diane Larlus

http://arxiv.org/abs/2206.15274
Augment like there's no tomorrow: Consistently performing neural networks for medical imaging. (1%)
Joona Pohjonen; Carolin Stürenberg; Atte Föhr; Reija Randen-Brady; Lassi Luomala; Jouni Lohi; Esa Pitkänen; Antti Rannikko; Tuomas Mirtti

http://arxiv.org/abs/2206.14772
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound. (92%)
Palma Alessandro De; Rudy Bunel; Krishnamurthy Dvijotham; M. Pawan Kumar; Robert Stanforth

http://arxiv.org/abs/2206.14477
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models. (33%)
Lele Wang; Bin Liu

http://arxiv.org/abs/2206.14729
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks. (10%)
Venelin Kovatchev; Trina Chatterjee; Venkata S Govindarajan; Jifan Chen; Eunsol Choi; Gabriella Chronis; Anubrata Das; Katrin Erk; Matthew Lease; Junyi Jessy Li; Yating Wu; Kyle Mahowald

http://arxiv.org/abs/2206.14724
Private Graph Extraction via Feature Explanations. (10%)
Iyiola E. Olatunji; Mandeep Rathee; Thorben Funke; Megha Khosla

http://arxiv.org/abs/2206.14502
RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness. (2%)
Francesco Pinto; Harry Yang; Ser-Nam Lim; Philip H. S. Torr; Puneet K. Dokania

http://arxiv.org/abs/2206.13991
Increasing Confidence in Adversarial Robustness Evaluations. (99%)
Roland S. Zimmermann; Wieland Brendel; Florian Tramer; Nicholas Carlini

http://arxiv.org/abs/2206.14020
Rethinking Adversarial Examples for Location Privacy Protection. (93%)
Trung-Nghia Le; Ta Gu; Huy H. Nguyen; Isao Echizen

http://arxiv.org/abs/2206.14346
A Deep Learning Approach to Create DNS Amplification Attacks. (92%)
Jared Mathews; Prosenjit Chatterjee; Shankar Banik; Cory Nance

http://arxiv.org/abs/2206.14004
On the amplification of security and privacy risks by post-hoc explanations in machine learning models. (31%)
Pengrui Quan; Supriyo Chakraborty; Jeya Vikranth Jeyakumar; Mani Srivastava

http://arxiv.org/abs/2206.14157
How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection. (12%)
Mantas Mazeika; Bo Li; David Forsyth

http://arxiv.org/abs/2206.14322
An Empirical Study of Challenges in Converting Deep Learning Models. (5%)
Moses Jack Openja; Amin Jack Nikanjam; Ahmed Haj Jack Yahmed; Foutse Jack Khomh; Zhen Jack Ming; Jiang

http://arxiv.org/abs/2206.14076
Reasoning about Moving Target Defense in Attack Modeling Formalisms. (2%)
Gabriel Ballot; Vadim Malvone; Jean Leneutre; Etienne Borde

http://arxiv.org/abs/2206.13903
AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE. (1%)
Changjie Lu; Shen Zheng; Zirui Wang; Omar Dib; Gaurav Gupta

http://arxiv.org/abs/2206.13083
Adversarial Example Detection in Deployed Tree Ensembles. (99%)
Laurens Devos; Wannes Meert; Jesse Davis

http://arxiv.org/abs/2206.13104
Towards Secrecy-Aware Attacks Against Trust Prediction in Signed Graphs. (38%)
Yulin Zhu; Tomasz Michalak; Xiapu Luo; Kai Zhou

http://arxiv.org/abs/2206.13405
Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers. (15%)
Georg Siedel; Silvia Vock; Andrey Morozov; Stefan Voß

http://arxiv.org/abs/2206.13594
Cyber Network Resilience against Self-Propagating Malware Attacks. (13%)
Alesia Chernikova; Nicolò Gozzi; Simona Boboila; Priyanka Angadi; John Loughner; Matthew Wilden; Nicola Perra; Tina Eliassi-Rad; Alina Oprea

http://arxiv.org/abs/2206.14615
Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models. (4%)
Mahmoud Yaseen; Xu Wu

http://arxiv.org/abs/2206.12963
Self-Healing Robust Neural Networks via Closed-Loop Control. (45%)
Zhuotong Chen; Qianxiao Li; Zheng Zhang

http://arxiv.org/abs/2206.13032
De-END: Decoder-driven Watermarking Network. (1%)
Han Fang; Zhaoyang Jia; Yupeng Qiu; Jiyi Zhang; Weiming Zhang; Ee-Chien Chang

http://arxiv.org/abs/2206.12725
Empirical Evaluation of Physical Adversarial Patch Attacks Against Overhead Object Detection Models. (99%)
Gavin S. Hartnett; Li Ang Zhang; Caolionn O'Connell; Andrew J. Lohn; Jair Aguirre

http://arxiv.org/abs/2206.12685
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising. (99%)
Sandhya Aneja; Nagender Aneja; Pg Emeroylariffion Abas; Abdul Ghani Naim

http://arxiv.org/abs/2206.12590
RSTAM: An Effective Black-Box Impersonation Attack on Face Recognition using a Mobile and Compact Printer. (99%)
Xiaoliang Liu; Furao Shen; Jian Zhao; Changhai Nie

http://arxiv.org/abs/2206.12714
Defending Multimodal Fusion Models against Single-Source Adversaries. (81%)
Karren Yang; Wan-Yi Lin; Manash Barman; Filipe Condessa; Zico Kolter

http://arxiv.org/abs/2206.12654
BackdoorBench: A Comprehensive Benchmark of Backdoor Learning. (12%)
Baoyuan Wu; Hongrui Chen; Mingda Zhang; Zihao Zhu; Shaokui Wei; Danni Yuan; Chao Shen; Hongyuan Zha

http://arxiv.org/abs/2206.12735
Cascading Failures in Smart Grids under Random, Targeted and Adaptive Attacks. (1%)
Sushmita Ruj; Arindam Pal

http://arxiv.org/abs/2206.12381
Defending Backdoor Attacks on Vision Transformer via Patch Processing. (99%)
Khoa D. Doan; Yingjie Lao; Peng Yang; Ping Li

http://arxiv.org/abs/2206.12169
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems. (96%)
Wenzheng Hou; Qianqian Xu; Zhiyong Yang; Shilong Bao; Yuan He; Qingming Huang

http://arxiv.org/abs/2206.12227
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective. (92%)
Mark Huasong Meng; Guangdong Bai; Sin Gee Teo; Zhe Hou; Yan Xiao; Yun Lin; Jin Song Dong

http://arxiv.org/abs/2206.12284
Robustness of Explanation Methods for NLP Models. (82%)
Shriya Atmakuri; Tejas Chheda; Dinesh Kandula; Nishant Yadav; Taesung Lee; Hessel Tuinhof

http://arxiv.org/abs/2206.12100
zPROBE: Zero Peek Robustness Checks for Federated Learning. (4%)
Zahra Ghodsi; Mojan Javaheripi; Nojan Sheybani; Xinqiao Zhang; Ke Huang; Farinaz Koushanfar

http://arxiv.org/abs/2207.03576
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems. (2%)
D'Jeff Kanda Nkashama; Arian Soltani; Jean-Charles Verdier; Marc Frappier; Pierre-Martin Tardif; Froduald Kabanza

http://arxiv.org/abs/2206.12251
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs. (99%)
Chengyin Hu; Weiwen Shi

http://arxiv.org/abs/2206.11480
A Framework for Understanding Model Extraction Attack and Defense. (98%)
Xun Xian; Mingyi Hong; Jie Ding

http://arxiv.org/abs/2206.11750
Towards End-to-End Private Automatic Speaker Recognition. (76%)
Francisco Teixeira; Alberto Abad; Bhiksha Raj; Isabel Trancoso

http://arxiv.org/abs/2206.11724
BERT Rankers are Brittle: a Study using Adversarial Document Perturbations. (75%)
Yumeng Wang; Lijun Lyu; Avishek Anand

http://arxiv.org/abs/2206.11981
Never trust, always verify : a roadmap for Trustworthy AI? (1%)
Lionel Nganyewou Tidjon; Foutse Khomh

http://arxiv.org/abs/2206.11939
Measuring Representational Robustness of Neural Networks Through Shared Invariances. (1%)
Vedant Nanda; Till Speicher; Camila Kolling; John P. Dickerson; Krishna P. Gummadi; Adrian Weller

http://arxiv.org/abs/2206.10988
AdvSmo: Black-box Adversarial Attack by Smoothing Linear Structure of Texture. (99%)
Hui Xia; Rui Zhang; Shuliang Jiang; Zi Kang

http://arxiv.org/abs/2206.12292
InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle. (98%)
Mengting Xu; Tao Zhang; Zhongnian Li; Daoqiang Zhang

http://arxiv.org/abs/2206.10858
Robust Universal Adversarial Perturbations. (97%)
Changming Xu; Gagandeep Singh

http://arxiv.org/abs/2206.10875
Guided Diffusion Model for Adversarial Purification from Random Noise. (68%)
Quanlin Wu; Hang Ye; Yuntian Gu

http://arxiv.org/abs/2206.10915
Understanding the effect of sparsity on neural networks robustness. (61%)
Lukas Timpl; Rahim Entezari; Hanie Sedghi; Behnam Neyshabur; Olga Saukh

http://arxiv.org/abs/2206.11433
Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles. (41%)
Chen Lin; Si Chen; Meifang Zeng; Sheng Zhang; Min Gao; Hui Li

http://arxiv.org/abs/2206.10809
SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors? (99%)
Hui Xia; Rui Zhang; Zi Kang; Shuliang Jiang

http://arxiv.org/abs/2207.00425
Transferable Graph Backdoor Attack. (99%)
Shuiqiao Yang; Bao Gia Doan; Paul Montague; Vel Olivier De; Tamas Abraham; Seyit Camtepe; Damith C. Ranasinghe; Salil S. Kanhere

http://arxiv.org/abs/2206.10550
(Certified!!) Adversarial Robustness for Free! (84%)
Nicholas Dj Carlini; Florian Dj Tramer; Dj Krishnamurthy; Dvijotham; J. Zico Kolter

http://arxiv.org/abs/2206.10158
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems. (81%)
Yanchao Sun; Ruijie Zheng; Parisa Hassanzadeh; Yongyuan Liang; Soheil Feizi; Sumitra Ganesh; Furong Huang

http://arxiv.org/abs/2206.10708
FlashSyn: Flash Loan Attack Synthesis via Counter Example Driven Approximation. (68%)
Zhiyang Chen; Sidi Mohamed Beillahi; Fan Long

http://arxiv.org/abs/2206.10673
Natural Backdoor Datasets. (33%)
Emily Wenger; Roma Bhattacharjee; Arjun Nitin Bhagoji; Josephine Passananti; Emilio Andere; Haitao Zheng; Ben Y. Zhao

http://arxiv.org/abs/2206.10469
The Privacy Onion Effect: Memorization is Relative. (22%)
Nicholas Carlini; Matthew Jagielski; Nicolas Papernot; Andreas Terzis; Florian Tramer; Chiyuan Zhang

http://arxiv.org/abs/2206.10110
ProML: A Decentralised Platform for Provenance Management of Machine Learning Software Systems. (1%)
Nguyen Khoi Tran; Bushra Sabir; M. Ali Babar; Nini Cui; Mehran Abolhasan; Justin Lipman

http://arxiv.org/abs/2206.09868
Understanding Robust Learning through the Lens of Representation Similarities. (99%)
Christian Cianfarani; Arjun Nitin Bhagoji; Vikash Sehwag; Ben Zhao; Prateek Mittal

http://arxiv.org/abs/2206.09628
Diversified Adversarial Attacks based on Conjugate Gradient Method. (98%)
Keiichiro Yamamura; Haruki Sato; Nariaki Tateiwa; Nozomi Hata; Toru Mitsutake; Issa Oe; Hiroki Ishikura; Katsuki Fujisawa

http://arxiv.org/abs/2206.10057
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum. (76%)
Junlin Wu; Yevgeniy Vorobeychik

http://arxiv.org/abs/2206.09682
SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles. (5%)
Chejian Xu; Wenhao Ding; Weijie Lyu; Zuxin Liu; Shuai Wang; Yihan He; Hanjiang Hu; Ding Zhao; Bo Li

http://arxiv.org/abs/2206.09880
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities. (1%)
Julian Bitterwolf; Alexander Meinke; Maximilian Augustin; Matthias Hein

http://arxiv.org/abs/2206.09491
On the Limitations of Stochastic Pre-processing Defenses. (99%)
Yue Gao; Ilia Shumailov; Kassem Fawaz; Nicolas Papernot

http://arxiv.org/abs/2206.09391
Towards Adversarial Attack on Vision-Language Pre-training Models. (98%)
Jiaming Zhang; Qi Yi; Jitao Sang

http://arxiv.org/abs/2206.09458
A Universal Adversarial Policy for Text Classifiers. (98%)
Gallil Maimon; Lior Rokach

http://arxiv.org/abs/2206.09410
JPEG Compression-Resistant Low-Mid Adversarial Perturbation against Unauthorized Face Recognition System. (68%)
Jiaming Zhang; Qi Yi; Jitao Sang

http://arxiv.org/abs/2206.11228
Adversarially trained neural representations may already be as robust as corresponding biological neural representations. (31%)
Chong Guo; Michael J. Lee; Guillaume Leclerc; Joel Dapello; Yug Rao; Aleksander Madry; James J. DiCarlo

http://arxiv.org/abs/2207.03574
Demystifying the Adversarial Robustness of Random Transformation Defenses. (99%)
Chawin Sitawarin; Zachary Golan-Strieb; David Wagner

http://arxiv.org/abs/2206.09238
On the Role of Generalization in Transferability of Adversarial Examples. (99%)
Yilin Wang; Farzan Farnia

http://arxiv.org/abs/2206.09272
DECK: Model Hardening for Defending Pervasive Backdoors. (98%)
Guanhong Tao; Yingqi Liu; Siyuan Cheng; Shengwei An; Zhuo Zhang; Qiuling Xu; Guangyu Shen; Xiangyu Zhang

http://arxiv.org/abs/2206.09122
Measuring Lower Bounds of Local Differential Privacy via Adversary Instantiations in Federated Learning. (10%)
Marin Matsumoto; Tsubasa Takahashi; Seng Pei Liew; Masato Oguchi

http://arxiv.org/abs/2206.09305
Adversarial Scrutiny of Evidentiary Statistical Software. (2%)
Rediet Abebe; Moritz Hardt; Angela Jin; John Miller; Ludwig Schmidt; Rebecca Wexler

http://arxiv.org/abs/2206.08738
Detecting Adversarial Examples in Batches -- a geometrical approach. (99%)
Danush Kumar Venkatesh; Peter Steinbach

http://arxiv.org/abs/2206.08638
Minimum Noticeable Difference based Adversarial Privacy Preserving Image Generation. (99%)
Wen Sun; Jian Jin; Weisi Lin

http://arxiv.org/abs/2206.08575
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization. (99%)
Deokjae Lee; Seungyong Moon; Junhyeok Lee; Hyun Oh Song

http://arxiv.org/abs/2206.09075
Comment on Transferability and Input Transformation with Additive Noise. (99%)
Hoki Kim; Jinseong Park; Jaewook Lee

http://arxiv.org/abs/2207.00411
Adversarial Robustness is at Odds with Lazy Training. (98%)
Yunjuan Wang; Enayat Ullah; Poorya Mianjy; Raman Arora

http://arxiv.org/abs/2206.08788
Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection. (83%)
Jinyin Chen; Chengyu Jia; Haibin Zheng; Ruoxi Chen; Chenbo Fu

http://arxiv.org/abs/2206.11225
RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval. (81%)
Yihan Wu; Hongyang Zhang; Heng Huang

http://arxiv.org/abs/2206.09099
The Consistency of Adversarial Training for Binary Classification. (26%)
Natalie S. Frank; Jonathan Niles-Weed

http://arxiv.org/abs/2206.09098
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification. (15%)
Natalie S. Frank

http://arxiv.org/abs/2206.08675
Understanding Robust Overfitting of Adversarial Training and Beyond. (8%)
Chaojian Yu; Bo Han; Li Shen; Jun Yu; Chen Gong; Mingming Gong; Tongliang Liu

http://arxiv.org/abs/2206.08170
Adversarial Privacy Protection on Speech Enhancement. (99%)
Mingyu Dong; Diqun Yan; Rangding Wang

http://arxiv.org/abs/2206.08316
Boosting the Adversarial Transferability of Surrogate Model with Dark Knowledge. (99%)
Dingcheng Yang; Zihao Xiao; Wenjian Yu

http://arxiv.org/abs/2206.07953
Analysis and Extensions of Adversarial Training for Video Classification. (93%)
Kaleab A. Kinfu; René Vidal

http://arxiv.org/abs/2206.07912
Double Sampling Randomized Smoothing. (89%)
Linyi Li; Jiawei Zhang; Tao Xie; Bo Li

http://arxiv.org/abs/2206.08260
Adversarial Robustness of Graph-based Anomaly Detection. (76%)
Yulin Zhu; Yuni Lai; Kaifa Zhao; Xiapu Luo; Mingquan Yuan; Jian Ren; Kai Zhou

http://arxiv.org/abs/2206.08514
A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks. (68%)
Ganqu Cui; Lifan Yuan; Bingxiang He; Yangyi Chen; Zhiyuan Liu; Maosong Sun

http://arxiv.org/abs/2206.08477
Backdoor Attacks on Vision Transformers. (31%)
Akshayvarun Subramanya; Aniruddha Saha; Soroush Abbasi Koohpayegani; Ajinkya Tejankar; Hamed Pirsiavash

http://arxiv.org/abs/2206.08304
Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey. (22%)
Abhijith Sharma; Yijun Bian; Phil Munz; Apurva Narayan

http://arxiv.org/abs/2206.08242
Catastrophic overfitting is a bug but also a feature. (16%)
Guillermo Ortiz-Jiménez; Jorge Pau de; Amartya Sanyal; Adel Bibi; Puneet K. Dokania; Pascal Frossard; Gregory Rogéz; Philip H. S. Torr

http://arxiv.org/abs/2206.08451
I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences. (5%)
Daryna Oliynyk; Rudolf Mayer; Andreas Rauber

http://arxiv.org/abs/2206.08255
Gradient-Based Adversarial and Out-of-Distribution Detection. (2%)
Jinsol Lee; Mohit Prabhushankar; Ghassan AlRegib

http://arxiv.org/abs/2206.07918
"Understanding Robustness Lottery": A Comparative Visual Analysis of Neural Network Pruning Approaches. (1%)
Zhimin Li; Shusen Liu; Xin Yu; Kailkhura Bhavya; Jie Cao; Diffenderfer James Daniel; Peer-Timo Bremer; Valerio Pascucci

http://arxiv.org/abs/2206.07314
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. (99%)
Ruize Gao; Jiongxiao Wang; Kaiwen Zhou; Feng Liu; Binghui Xie; Gang Niu; Bo Han; James Cheng

http://arxiv.org/abs/2206.07321
Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection. (99%)
Abderrahmen Amich; Ata Kaboudi; Birhanu Eshete

http://arxiv.org/abs/2206.07840
Architectural Backdoors in Neural Networks. (83%)
Mikel Bober-Irizar; Ilia Shumailov; Yiren Zhao; Robert Mullins; Nicolas Papernot

http://arxiv.org/abs/2206.07406
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning. (75%)
Jonah O'Brien Weiss; Tiago Alves; Sandip Kundu

http://arxiv.org/abs/2206.07839
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness. (74%)
Tianlong Chen; Huan Zhang; Zhenyu Zhang; Shiyu Chang; Sijia Liu; Pin-Yu Chen; Zhangyang Wang

http://arxiv.org/abs/2206.07813
A Search-Based Testing Approach for Deep Reinforcement Learning Agents. (62%)
Amirhossein Zolfagharian; Manel Abdellatif; Lionel Briand; Mojtaba Bagherzadeh; Ramesh S

http://arxiv.org/abs/2206.07311
Can pruning improve certified robustness of neural networks? (56%)
Zhangheng Li; Tianlong Chen; Linyi Li; Bo Li; Zhangyang Wang

http://arxiv.org/abs/2206.07736
Improving Diversity with Adversarially Learned Transformations for Domain Generalization. (33%)
Tejas Gokhale; Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Chitta Baral; Yezhou Yang

http://arxiv.org/abs/2206.07842
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning. (11%)
Tianlong Chen; Sijia Liu; Shiyu Chang; Lisa Amini; Zhangyang Wang

http://arxiv.org/abs/2206.07387
The Manifold Hypothesis for Gradient-Based Explanations. (2%)
Sebastian Bordt; Uddeshya Upadhyay; Zeynep Akata; Luxburg Ulrike von

http://arxiv.org/abs/2206.07459
READ: Aggregating Reconstruction Error into Out-of-distribution Detection. (1%)
Wenyu Jiang; Hao Cheng; Mingcai Chen; Shuai Feng; Yuxin Ge; Chongjun Wang

http://arxiv.org/abs/2206.06737
Adversarial Vulnerability of Randomized Ensembles. (99%)
Hassan Dbouk; Naresh R. Shanbhag

http://arxiv.org/abs/2206.06592
Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training. (99%)
B. R. Manoj; Meysam Sadeghi; Erik G. Larsson

http://arxiv.org/abs/2206.07144
Efficiently Training Low-Curvature Neural Networks. (92%)
Suraj Srinivas; Kyle Matoba; Himabindu Lakkaraju; Francois Fleuret

http://arxiv.org/abs/2206.07179
Proximal Splitting Adversarial Attacks for Semantic Segmentation. (92%)
Jérôme Rony; Jean-Christophe Pesquet; Ismail Ben Ayed

http://arxiv.org/abs/2206.06854
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective. (89%)
Mathieu IRIT, UT Serrurier; Franck UT Mamalet; Thomas UT Fel; Louis UT3, UT, IRIT Béthune; Thibaut UT Boissin

http://arxiv.org/abs/2206.07188
Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising. (88%)
Zikang Xiong; Joe Eappen; He Zhu; Suresh Jagannathan

http://arxiv.org/abs/2206.06761
Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO. (86%)
Javier Rando; Nasib Naimi; Thomas Baumann; Max Mathys

http://arxiv.org/abs/2206.07018
Turning a Curse Into a Blessing: Enabling Clean-Data-Free Defenses by Model Inversion. (68%)
Si Chen; Yi Zeng; Won Park; Ruoxi Jia

http://arxiv.org/abs/2206.07282
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises. (62%)
Minkyu Choi; Yizhen Zhang; Kuan Han; Xiaokai Wang; Zhongming Liu

http://arxiv.org/abs/2206.07150
Attacks on Perception-Based Control Systems: Modeling and Fundamental Limits. (2%)
Amir Khazraei; Henry Pfister; Miroslav Pajic

http://arxiv.org/abs/2206.07277
A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise. (1%)
Jongwoo Ko; Bongsoo Yi; Se-Young Yun

http://arxiv.org/abs/2206.07284
A Survey on Gradient Inversion: Attacks, Defenses and Future Directions. (1%)
Rui Zhang; Song Guo; Junxiao Wang; Xin Xie; Dacheng Tao

http://arxiv.org/abs/2206.06496
Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations. (99%)
Kaustubh Sridhar; Souradeep Dutta; Ramneet Kaur; James Weimer; Oleg Sokolsky; Insup Lee

http://arxiv.org/abs/2206.06257
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale. (99%)
Gaoyuan Zhang; Songtao Lu; Yihua Zhang; Xiangyi Chen; Pin-Yu Chen; Quanfu Fan; Lee Martie; Lior Horesh; Mingyi Hong; Sijia Liu

http://arxiv.org/abs/2206.05898
Pixel to Binary Embedding Towards Robustness for CNNs. (47%)
Ikki Kishida; Hideki Nakayama

http://arxiv.org/abs/2206.06232
Towards Understanding Sharpness-Aware Minimization. (1%)
Maksym Andriushchenko; Nicolas Flammarion

http://arxiv.org/abs/2206.06299
An adversarially robust data-market for spatial, crowd-sourced data. (1%)
Aida Manzano Kharman; Christian Jursitzky; Quan Zhou; Pietro Ferraro; Jakub Marecek; Pierre Pinson; Robert Shorten

http://arxiv.org/abs/2206.05981
Efficient Human-in-the-loop System for Guiding DNNs Attention. (1%)
Yi He; Xi Yang; Chia-Ming Chang; Haoran Xie; Takeo Igarashi

http://arxiv.org/abs/2206.05751
Consistent Attack: Universal Adversarial Perturbation on Embodied Vision Navigation. (98%)
Chengyang Ying; You Qiaoben; Xinning Zhou; Hang Su; Wenbo Ding; Jianyong Ai

http://arxiv.org/abs/2206.05678
Security of Machine Learning-Based Anomaly Detection in Cyber Physical Systems. (92%)
Zahra Jadidi; Shantanu Pal; Nithesh Nayak K; Arawinkumaar Selvakkumar; Chih-Chia Chang; Maedeh Beheshti; Alireza Jolfaei

http://arxiv.org/abs/2206.06371
Darknet Traffic Classification and Adversarial Attacks. (81%)
Nhien Rust-Nguyen; Mark Stamp

http://arxiv.org/abs/2206.05846
InBiaseD: Inductive Bias Distillation to Improve Generalization and Robustness through Shape-awareness. (26%)
Shruthi Gowda; Bahram Zonooz; Elahe Arani

http://arxiv.org/abs/2206.05821
RSSD: Defend against Ransomware with Hardware-Isolated Network-Storage Codesign and Post-Attack Analysis. (9%)
Benjamin Reidys; Peng Liu; Jian Huang

http://arxiv.org/abs/2206.10341
Neurotoxin: Durable Backdoors in Federated Learning. (5%)
Zhengming Zhang; Ashwinee Panda; Linyue Song; Yaoqing Yang; Michael W. Mahoney; Joseph E. Gonzalez; Kannan Ramchandran; Prateek Mittal

http://arxiv.org/abs/2206.05664
An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines. (4%)
Wen Su; Qingna Li

http://arxiv.org/abs/2206.05511
Improving the Adversarial Robustness of NLP Models by Information Bottleneck. (99%)
Cenyuan Zhang; Xiang Zhou; Yixin Wan; Xiaoqing Zheng; Kai-Wei Chang; Cho-Jui Hsieh

http://arxiv.org/abs/2206.10334
Defending Adversarial Examples by Negative Correlation Ensemble. (99%)
Wenjian Luo; Hongwei Zhang; Linghao Kong; Zhijian Chen; Ke Tang

http://arxiv.org/abs/2206.05565
NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks. (81%)
Nuo Xu; Binghui Wang; Ran Ran; Wujie Wen; Parv Venkitasubramaniam

http://arxiv.org/abs/2206.05483
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. (69%)
Xiong Peng; Feng Liu; Jingfen Zhang; Long Lan; Junjie Ye; Tongliang Liu; Bo Han

http://arxiv.org/abs/2206.05289
Localized adversarial artifacts for compressed sensing MRI. (76%)
Rima Alaifari; Giovanni S. Alberti; Tandri Gauksson

http://arxiv.org/abs/2206.05406
Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency. (70%)
Jinyin Chen; Mingjun Li; Tao Liu; Haibin Zheng; Yao Cheng; Changting Lin

http://arxiv.org/abs/2206.05359
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning. (33%)
Shenghui Li; Edith Ngai; Fanghua Ye; Li Ju; Tianru Zhang; Thiemo Voigt

http://arxiv.org/abs/2206.04881
Enhancing Clean Label Backdoor Attack with Two-phase Specific Triggers. (9%)
Nan Luo; Yuanzhang Li; Yajie Wang; Shangbo Wu; Yu-an Tan; Quanxin Zhang

http://arxiv.org/abs/2206.04887
Deep Leakage from Model in Federated Learning. (3%)
Zihao Zhao; Mengen Luo; Wenbo Ding

http://arxiv.org/abs/2206.04890
Adversarial Counterfactual Environment Model Learning. (1%)
Xiong-Hui Chen; Yang Yu; Zheng-Mao Zhu; Zhihua Yu; Zhenjun Chen; Chenghe Wang; Yinan Wu; Hongqiu Wu; Rong-Jun Qin; Ruijin Ding; Fangsheng Huang

http://arxiv.org/abs/2206.04365
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models. (99%)
Federico Nesti; Giulio Rossolini; Gianluca D'Amico; Alessandro Biondi; Giorgio Buttazzo

http://arxiv.org/abs/2206.04783
ReFace: Real-time Adversarial Attacks on Face Recognition Systems. (99%)
Shehzeen Hussain; Todd Huster; Chris Mesterharm; Paarth Neekhara; Kevin An; Malhar Jere; Harshvardhan Sikka; Farinaz Koushanfar

http://arxiv.org/abs/2206.04316
Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks. (98%)
Huishuai Zhang; Da Yu; Yiping Lu; Di He

http://arxiv.org/abs/2206.04463
Meet You Halfway: Explaining Deep Learning Mysteries. (92%)
Oriel BenShmuel

http://arxiv.org/abs/2206.04472
Early Transferability of Adversarial Examples in Deep Neural Networks. (86%)
Oriel BenShmuel

http://arxiv.org/abs/2206.04310
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing. (86%)
Zhongkai Hao; Chengyang Ying; Yinpeng Dong; Hang Su; Jun Zhu; Jian Song

http://arxiv.org/abs/2206.04615
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. (84%)
Aarohi Shammie Srivastava; Abhinav Shammie Rastogi; Abhishek Shammie Rao; Abu Awal Md Shammie Shoeb; Abubakar Shammie Abid; Adam Shammie Fisch; Adam R. Shammie Brown; Adam Shammie Santoro; Aditya Shammie Gupta; Adrià Shammie Garriga-Alonso; Agnieszka Shammie Kluska; Aitor Shammie Lewkowycz; Akshat Shammie Agarwal; Alethea Shammie Power; Alex Shammie Ray; Alex Shammie Warstadt; Alexander W. Shammie Kocurek; Ali Shammie Safaya; Ali Shammie Tazarv; Alice Shammie Xiang; Alicia Shammie Parrish; Allen Shammie Nie; Aman Shammie Hussain; Amanda Shammie Askell; Amanda Shammie Dsouza; Ambrose Shammie Slone; Ameet Shammie Rahane; Anantharaman S. Shammie Iyer; Anders Shammie Andreassen; Andrea Shammie Madotto; Andrea Shammie Santilli; Andreas Shammie Stuhlmüller; Andrew Shammie Dai; Andrew Shammie La; Andrew Shammie Lampinen; Andy Shammie Zou; Angela Shammie Jiang; Angelica Shammie Chen; Anh Shammie Vuong; Animesh Shammie Gupta; Anna Shammie Gottardi; Antonio Shammie Norelli; Anu Shammie Venkatesh; Arash Shammie Gholamidavoodi; Arfa Shammie Tabassum; Arul Shammie Menezes; Arun Shammie Kirubarajan; Asher Shammie Mullokandov; Ashish Shammie Sabharwal; Austin Shammie Herrick; Avia Shammie Efrat; Aykut Shammie Erdem; Ayla Shammie Karakaş; B. Ryan Shammie Roberts; Bao Sheng Shammie Loe; Barret Shammie Zoph; Bartłomiej Shammie Bojanowski; Batuhan Shammie Özyurt; Behnam Shammie Hedayatnia; Behnam Shammie Neyshabur; Benjamin Shammie Inden; Benno Shammie Stein; Berk Shammie Ekmekci; Bill Yuchen Shammie Lin; Blake Shammie Howald; Cameron Shammie Diao; Cameron Shammie Dour; Catherine Shammie Stinson; Cedrick Shammie Argueta; César Ferri Shammie Ramírez; Chandan Shammie Singh; Charles Shammie Rathkopf; Chenlin Shammie Meng; Chitta Shammie Baral; Chiyu Shammie Wu; Chris Shammie Callison-Burch; Chris Shammie Waites; Christian Shammie Voigt; Christopher D. Shammie Manning; Christopher Shammie Potts; Cindy Shammie Ramirez; Clara E. Shammie Rivera; Clemencia Shammie Siro; Colin Shammie Raffel; Courtney Shammie Ashcraft; Cristina Shammie Garbacea; Damien Shammie Sileo; Dan Shammie Garrette; Dan Shammie Hendrycks; Dan Shammie Kilman; Dan Shammie Roth; Daniel Shammie Freeman; Daniel Shammie Khashabi; Daniel Shammie Levy; Daniel Moseguí Shammie González; Danielle Shammie Perszyk; Danny Shammie Hernandez; Danqi Shammie Chen; Daphne Shammie Ippolito; Dar Shammie Gilboa; David Shammie Dohan; David Shammie Drakard; David Shammie Jurgens; Debajyoti Shammie Datta; Deep Shammie Ganguli; Denis Shammie Emelin; Denis Shammie Kleyko; Deniz Shammie Yuret; Derek Shammie Chen; Derek Shammie Tam; Dieuwke Shammie Hupkes; Diganta Shammie Misra; Dilyar Shammie Buzan; Dimitri Coelho Shammie Mollo; Diyi Shammie Yang; Dong-Ho Shammie Lee; Ekaterina Shammie Shutova; Ekin Dogus Shammie Cubuk; Elad Shammie Segal; Eleanor Shammie Hagerman; Elizabeth Shammie Barnes; Elizabeth Shammie Donoway; Ellie Shammie Pavlick; Emanuele Shammie Rodola; Emma Shammie Lam; Eric Shammie Chu; Eric Shammie Tang; Erkut Shammie Erdem; Ernie Shammie Chang; Ethan A. Shammie Chi; Ethan Shammie Dyer; Ethan Shammie Jerzak; Ethan Shammie Kim; Eunice Engefu Shammie Manyasi; Evgenii Shammie Zheltonozhskii; Fanyue Shammie Xia; Fatemeh Shammie Siar; Fernando Shammie Martínez-Plumed; Francesca Shammie Happé; Francois Shammie Chollet; Frieda Shammie Rong; Gaurav Shammie Mishra; Genta Indra Shammie Winata; Melo Gerard Shammie de; Germán Shammie Kruszewski; Giambattista Shammie Parascandolo; Giorgio Shammie Mariani; Gloria Shammie Wang; Gonzalo Shammie Jaimovitch-López; Gregor Shammie Betz; Guy Shammie Gur-Ari; Hana Shammie Galijasevic; Hannah Shammie Kim; Hannah Shammie Rashkin; Hannaneh Shammie Hajishirzi; Harsh Shammie Mehta; Hayden Shammie Bogar; Henry Shammie Shevlin; Hinrich Shammie Schütze; Hiromu Shammie Yakura; Hongming Shammie Zhang; Hugh Mee Shammie Wong; Ian Shammie Ng; Isaac Shammie Noble; Jaap Shammie Jumelet; Jack Shammie Geissinger; Jackson Shammie Kernion; Jacob Shammie Hilton; Jaehoon Shammie Lee; Jaime Fernández Shammie Fisac; James B. Shammie Simon; James Shammie Koppel; James Shammie Zheng; James Shammie Zou; Jan Shammie Kocoń; Jana Shammie Thompson; Jared Shammie Kaplan; Jarema Shammie Radom; Jascha Shammie Sohl-Dickstein; Jason Shammie Phang; Jason Shammie Wei; Jason Shammie Yosinski; Jekaterina Shammie Novikova; Jelle Shammie Bosscher; Jennifer Shammie Marsh; Jeremy Shammie Kim; Jeroen Shammie Taal; Jesse Shammie Engel; Jesujoba Shammie Alabi; Jiacheng Shammie Xu; Jiaming Shammie Song; Jillian Shammie Tang; Joan Shammie Waweru; John Shammie Burden; John Shammie Miller; John U. Shammie Balis; Jonathan Shammie Berant; Jörg Shammie Frohberg; Jos Shammie Rozen; Jose Shammie Hernandez-Orallo; Joseph Shammie Boudeman; Joseph Shammie Jones; Joshua B. Shammie Tenenbaum; Joshua S. Shammie Rule; Joyce Shammie Chua; Kamil Shammie Kanclerz; Karen Shammie Livescu; Karl Shammie Krauth; Karthik Shammie Gopalakrishnan; Katerina Shammie Ignatyeva; Katja Shammie Markert; Kaustubh D. Shammie Dhole; Kevin Shammie Gimpel; Kevin Shammie Omondi; Kory Shammie Mathewson; Kristen Shammie Chiafullo; Ksenia Shammie Shkaruta; Kumar Shammie Shridhar; Kyle Shammie McDonell; Kyle Shammie Richardson; Laria Shammie Reynolds; Leo Shammie Gao; Li Shammie Zhang; Liam Shammie Dugan; Lianhui Shammie Qin; Lidia Shammie Contreras-Ochando; Louis-Philippe Shammie Morency; Luca Shammie Moschella; Lucas Shammie Lam; Lucy Shammie Noble; Ludwig Shammie Schmidt; Luheng Shammie He; Luis Oliveros Shammie Colón; Luke Shammie Metz; Lütfi Kerem Shammie Şenel; Maarten Shammie Bosma; Maarten Shammie Sap; Hoeve Maartje Shammie ter; Maheen Shammie Farooqi; Manaal Shammie Faruqui; Mantas Shammie Mazeika; Marco Shammie Baturan; Marco Shammie Marelli; Marco Shammie Maru; Maria Jose Ramírez Shammie Quintana; Marie Shammie Tolkiehn; Mario Shammie Giulianelli; Martha Shammie Lewis; Martin Shammie Potthast; Matthew L. Shammie Leavitt; Matthias Shammie Hagen; Mátyás Shammie Schubert; Medina Orduna Shammie Baitemirova; Melody Shammie Arnaud; Melvin Shammie McElrath; Michael A. Shammie Yee; Michael Shammie Cohen; Michael Shammie Gu; Michael Shammie Ivanitskiy; Michael Shammie Starritt; Michael Shammie Strube; Michał Shammie Swędrowski; Michele Shammie Bevilacqua; Michihiro Shammie Yasunaga; Mihir Shammie Kale; Mike Shammie Cain; Mimee Shammie Xu; Mirac Shammie Suzgun; Mo Shammie Tiwari; Mohit Shammie Bansal; Moin Shammie Aminnaseri; Mor Shammie Geva; Mozhdeh Shammie Gheini; Mukund Varma Shammie T; Nanyun Shammie Peng; Nathan Shammie Chi; Nayeon Shammie Lee; Neta Gur-Ari Shammie Krakover; Nicholas Shammie Cameron; Nicholas Shammie Roberts; Nick Shammie Doiron; Nikita Shammie Nangia; Niklas Shammie Deckers; Niklas Shammie Muennighoff; Nitish Shirish Shammie Keskar; Niveditha S. Shammie Iyer; Noah Shammie Constant; Noah Shammie Fiedel; Nuan Shammie Wen; Oliver Shammie Zhang; Omar Shammie Agha; Omar Shammie Elbaghdadi; Omer Shammie Levy; Owain Shammie Evans; Pablo Antonio Moreno Shammie Casares; Parth Shammie Doshi; Pascale Shammie Fung; Paul Pu Shammie Liang; Paul Shammie Vicol; Pegah Shammie Alipoormolabashi; Peiyuan Shammie Liao; Percy Shammie Liang; Peter Shammie Chang; Peter Shammie Eckersley; Phu Mon Shammie Htut; Pinyu Shammie Hwang; Piotr Shammie Miłkowski; Piyush Shammie Patil; Pouya Shammie Pezeshkpour; Priti Shammie Oli; Qiaozhu Shammie Mei; Qing Shammie Lyu; Qinlang Shammie Chen; Rabin Shammie Banjade; Rachel Etta Shammie Rudolph; Raefer Shammie Gabriel; Rahel Shammie Habacker; Ramón Risco Shammie Delgado; Raphaël Shammie Millière; Rhythm Shammie Garg; Richard Shammie Barnes; Rif A. Shammie Saurous; Riku Shammie Arakawa; Robbe Shammie Raymaekers; Robert Shammie Frank; Rohan Shammie Sikand; Roman Shammie Novak; Roman Shammie Sitelew; Ronan Shammie LeBras; Rosanne Shammie Liu; Rowan Shammie Jacobs; Rui Shammie Zhang; Ruslan Shammie Salakhutdinov; Ryan Shammie Chi; Ryan Shammie Lee; Ryan Shammie Stovall; Ryan Shammie Teehan; Rylan Shammie Yang; Sahib Shammie Singh; Saif M. Shammie Mohammad; Sajant Shammie Anand; Sam Shammie Dillavou; Sam Shammie Shleifer; Sam Shammie Wiseman; Samuel Shammie Gruetter; Samuel R. Shammie Bowman; Samuel S. Shammie Schoenholz; Sanghyun Shammie Han; Sanjeev Shammie Kwatra; Sarah A. Shammie Rous; Sarik Shammie Ghazarian; Sayan Shammie Ghosh; Sean Shammie Casey; Sebastian Shammie Bischoff; Sebastian Shammie Gehrmann; Sebastian Shammie Schuster; Sepideh Shammie Sadeghi; Shadi Shammie Hamdan; Sharon Shammie Zhou; Shashank Shammie Srivastava; Sherry Shammie Shi; Shikhar Shammie Singh; Shima Shammie Asaadi; Shixiang Shane Shammie Gu; Shubh Shammie Pachchigar; Shubham Shammie Toshniwal; Shyam Shammie Upadhyay; Shammie Shyamolima; Debnath; Siamak Shakeri; Simon Thormeyer; Simone Melzi; Siva Reddy; Sneha Priscilla Makini; Soo-Hwan Lee; Spencer Torene; Sriharsha Hatwar; Stanislas Dehaene; Stefan Divic; Stefano Ermon; Stella Biderman; Stephanie Lin; Stephen Prasad; Steven T. Piantadosi; Stuart M. Shieber; Summer Misherghi; Svetlana Kiritchenko; Swaroop Mishra; Tal Linzen; Tal Schuster; Tao Li; Tao Yu; Tariq Ali; Tatsu Hashimoto; Te-Lin Wu; Théo Desbordes; Theodore Rothschild; Thomas Phan; Tianle Wang; Tiberius Nkinyili; Timo Schick; Timofei Kornev; Timothy Telleen-Lawton; Titus Tunduny; Tobias Gerstenberg; Trenton Chang; Trishala Neeraj; Tushar Khot; Tyler Shultz; Uri Shaham; Vedant Misra; Vera Demberg; Victoria Nyamai; Vikas Raunak; Vinay Ramasesh; Vinay Uday Prabhu; Vishakh Padmakumar; Vivek Srikumar; William Fedus; William Saunders; William Zhang; Wout Vossen; Xiang Ren; Xiaoyu Tong; Xinran Zhao; Xinyi Wu; Xudong Shen; Yadollah Yaghoobzadeh; Yair Lakretz; Yangqiu Song; Yasaman Bahri; Yejin Choi; Yichi Yang; Yiding Hao; Yifu Chen; Yonatan Belinkov; Yu Hou; Yufang Hou; Yuntao Bai; Zachary Seid; Zhuoye Zhao; Zijian Wang; Zijie J. Wang; Zirui Wang; Ziyi Wu

http://arxiv.org/abs/2206.04762
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training. (41%)
Tianlong Chen; Zhenyu Zhang; Sijia Liu; Yang Zhang; Shiyu Chang; Zhangyang Wang

http://arxiv.org/abs/2206.04530
DORA: Exploring outlier representations in Deep Neural Networks. (1%)
Kirill Bykov; Mayukh Deb; Dennis Grinwald; Klaus-Robert Müller; Marina M. -C. Höhne

http://arxiv.org/abs/2206.04823
Membership Inference via Backdooring. (1%)
Hongsheng Hu; Zoran Salcic; Gillian Dobbie; Jinjun Chen; Lichao Sun; Xuyun Zhang

http://arxiv.org/abs/2206.03727
Wavelet Regularization Benefits Adversarial Training. (99%)
Jun Yan; Huilin Yin; Xiaoyang Deng; Ziming Zhao; Wancheng Ge; Hao Zhang; Gerhard Rigoll

http://arxiv.org/abs/2206.03717
Latent Boundary-guided Adversarial Training. (99%)
Xiaowei Zhou; Ivor W. Tsang; Jie Yin

http://arxiv.org/abs/2206.04137
Adversarial Text Normalization. (73%)
Joanna Bitton; Maya Pavlova; Ivan Evtimov

http://arxiv.org/abs/2206.03693
Autoregressive Perturbations for Data Poisoning. (70%)
Pedro Sandoval-Segura; Vasu Singla; Jonas Geiping; Micah Goldblum; Tom Goldstein; David W. Jacobs

http://arxiv.org/abs/2206.03669
Toward Certified Robustness Against Real-World Distribution Shifts. (5%)
Haoze Wu; Teruhiro Tagomori; Alexander Robey; Fengjun Yang; Nikolai Matni; George Pappas; Hamed Hassani; Corina Pasareanu; Clark Barrett

http://arxiv.org/abs/2207.00421
Generative Adversarial Networks and Image-Based Malware Classification. (1%)
Huy Nguyen; Troia Fabio Di; Genya Ishigaki; Mark Stamp

http://arxiv.org/abs/2206.03691
Robust Deep Ensemble Method for Real-world Image Denoising. (1%)
Pengju Liu; Hongzhi Zhang; Jinghui Wang; Yuzhi Wang; Dongwei Ren; Wangmeng Zuo

http://arxiv.org/abs/2206.03178
Fooling Explanations in Text Classifiers. (99%)
Adam Ivankay; Ivan Girardi; Chiara Marchiori; Pascal Frossard

http://arxiv.org/abs/2206.03351
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems. (99%)
Guangke Chen; Zhe Zhao; Fu Song; Sen Chen; Lingling Fan; Yang Liu

http://arxiv.org/abs/2206.03393
Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition. (99%)
Guangke Chen; Zhe Zhao; Fu Song; Sen Chen; Lingling Fan; Feng Wang; Jiashui Wang

http://arxiv.org/abs/2206.03353
Adaptive Regularization for Adversarial Training. (98%)
Dongyoon Yang; Insung Kong; Yongdai Kim

http://arxiv.org/abs/2206.03362
Building Robust Ensembles via Margin Boosting. (83%)
Dinghuai Zhang; Hongyang Zhang; Aaron Courville; Yoshua Bengio; Pradeep Ravikumar; Arun Sai Suggala

http://arxiv.org/abs/2206.04677
On the Permanence of Backdoors in Evolving Models. (67%)
Huiying Li; Arjun Nitin Bhagoji; Yuxin Chen; Haitao Zheng; Ben Y. Zhao

http://arxiv.org/abs/2206.03317
Subject Membership Inference Attacks in Federated Learning. (4%)
Anshuman Suri; Pallika Kanani; Virendra J. Marathe; Daniel W. Peterson

http://arxiv.org/abs/2206.03466
Adversarial Reprogramming Revisited. (3%)
Matthias Englert; Ranko Lazic

http://arxiv.org/abs/2206.03575
Certifying Data-Bias Robustness in Linear Regression. (1%)
Anna P. Meyer; Aws Albarghouthi; Loris D'Antoni

http://arxiv.org/abs/2206.03482
Parametric Chordal Sparsity for SDP-based Neural Network Verification. (1%)
Anton Xue; Lars Lindemann; Rajeev Alur

http://arxiv.org/abs/2206.03452
Can CNNs Be More Robust Than Transformers? (1%)
Zeyu Wang; Yutong Bai; Yuyin Zhou; Cihang Xie

http://arxiv.org/abs/2206.02670
Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning. (99%)
Thomas Hickling; Nabil Aouf; Phillippa Spencer

http://arxiv.org/abs/2206.02417
Fast Adversarial Training with Adaptive Step Size. (98%)
Zhichao Huang; Yanbo Fan; Chen Liu; Weizhong Zhang; Yong Zhang; Mathieu Salzmann; Sabine Süsstrunk; Jue Wang

http://arxiv.org/abs/2206.02535
Certified Robustness in Federated Learning. (87%)
Motasem Alfarra; Juan C. Pérez; Egor Shulgin; Peter Richtárik; Bernard Ghanem

http://arxiv.org/abs/2206.02405
Robust Image Protection Countering Cropping Manipulation. (12%)
Qichao Ying; Hang Zhou; Zhenxing Qian; Sheng Li; Xinpeng Zhang

http://arxiv.org/abs/2206.02541
PCPT and ACPT: Copyright Protection and Traceability Scheme for DNN Model. (3%)
Xuefeng Fan; Hangyu Gui; Xiaoyi Zhou

http://arxiv.org/abs/2206.02435
Tackling covariate shift with node-based Bayesian neural networks. (1%)
Trung Trinh; Markus Heinonen; Luigi Acerbi; Samuel Kaski

http://arxiv.org/abs/2206.02345
Anomaly Detection with Test Time Augmentation and Consistency Evaluation. (1%)
Haowei He; Jiaye Teng; Yang Yuan

http://arxiv.org/abs/2206.02131
Federated Adversarial Training with Transformers. (98%)
Ahmed Aldahdooh; Wassim Hamidouche; Olivier Déforges

http://arxiv.org/abs/2206.02158
Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training. (98%)
Guodong Cao; Zhibo Wang; Xiaowei Dong; Zhifei Zhang; Hengchang Guo; Zhan Qin; Kui Ren

http://arxiv.org/abs/2206.02152
Which models are innately best at uncertainty estimation? (1%)
Ido Galil; Mohammed Dabbah; Ran El-Yaniv

http://arxiv.org/abs/2206.01904
Soft Adversarial Training Can Retain Natural Accuracy. (76%)
Abhijith Sharma; Apurva Narayan

http://arxiv.org/abs/2206.01898
Saliency Attack: Towards Imperceptible Black-box Adversarial Attack. (99%)
Zeyu Dai; Shengcai Liu; Ke Tang; Qing Li

http://arxiv.org/abs/2206.01715
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis. (96%)
Raphael Ettedgui; Alexandre Araujo; Rafael Pinot; Yann Chevaleyre; Jamal Atif

http://arxiv.org/abs/2206.01467
Evaluating Transfer-based Targeted Adversarial Perturbations against Real-World Computer Vision Systems based on Human Judgments. (92%)
Zhengyu Zhao; Nga Dang; Martha Larson

http://arxiv.org/abs/2206.01820
A Robust Backpropagation-Free Framework for Images. (80%)
Timothy Zee; Alexander G. Ororbia; Ankur Mali; Ifeoma Nwogu

http://arxiv.org/abs/2206.01705
Gradient Obfuscation Checklist Test Gives a False Sense of Security. (73%)
Nikola Popovic; Danda Pani Paudel; Thomas Probst; Gool Luc Van

http://arxiv.org/abs/2206.01832
Kallima: A Clean-label Framework for Textual Backdoor Attacks. (26%)
Xiaoyi Chen; Yinpeng Dong; Zeyu Sun; Shengfang Zhai; Qingni Shen; Zhonghai Wu

http://arxiv.org/abs/2206.00913
Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction. (99%)
Xiangyuan Yang; Jie Lin; Hanlin Zhang; Xinyu Yang; Peng Zhao

http://arxiv.org/abs/2206.00924
FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples. (99%)
Xiangyuan Yang; Jie Lin; Hanlin Zhang; Xinyu Yang; Peng Zhao

http://arxiv.org/abs/2206.01736
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection. (99%)
Linhai Ma; Liang Liang

http://arxiv.org/abs/2206.01733
Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline. (99%)
Junjian Li; Honglong Chen

http://arxiv.org/abs/2206.01034
Adversarial Laser Spot: Robust and Covert Physical Adversarial Attack to DNNs. (98%)
Chengyin Hu

http://arxiv.org/abs/2206.01367
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. (31%)
Amrith Setlur; Benjamin Eysenbach; Virginia Smith; Sergey Levine

http://arxiv.org/abs/2206.01737
MaxStyle: Adversarial Style Composition for Robust Medical Image Segmentation. (8%)
Chen Chen; Zeju Li; Cheng Ouyang; Matt Sinclair; Wenjia Bai; Daniel Rueckert

http://arxiv.org/abs/2206.01102
A temporal chrominance trigger for clean-label backdoor attack against anti-spoof rebroadcast detection. (4%)
Wei Guo; Benedetta Tondi; Mauro Barni

http://arxiv.org/abs/2206.01319
Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling. (1%)
Jian Hu; Haowen Zhong; Junchi Yan; Shaogang Gong; Guile Wu; Fei Yang

http://arxiv.org/abs/2206.00772
On the reversibility of adversarial attacks. (99%)
Chau Yi Li; Ricardo Sánchez-Matilla; Ali Shahin Shamsabadi; Riccardo Mazzon; Andrea Cavallaro

http://arxiv.org/abs/2206.00402
NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks. (99%)
Mahya Morid Ahmadi; Lilas Alrahis; Alessio Colucci; Ozgur Sinanoglu; Muhammad Shafique

http://arxiv.org/abs/2206.00489
Attack-Agnostic Adversarial Detection. (99%)
Jiaxin Cheng; Mohamed Hussein; Jay Billa; Wael AbdAlmageed

http://arxiv.org/abs/2206.00278
On the Perils of Cascading Robust Classifiers. (98%)
Ravi Mangal; Zifan Wang; Chi Zhang; Klas Leino; Corina Pasareanu; Matt Fredrikson

http://arxiv.org/abs/2206.00477
Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations. (98%)
Run Wang; Ziheng Huang; Zhikai Chen; Li Liu; Jing Chen; Lina Wang

http://arxiv.org/abs/2206.00352
Support Vector Machines under Adversarial Label Contamination. (97%)
Huang Xiao; Battista Biggio; Blaine Nelson; Han Xiao; Claudia Eckert; Fabio Roli

http://arxiv.org/abs/2206.00769
Defense Against Gradient Leakage Attacks via Learning to Obscure Data. (80%)
Yuxuan Wan; Han Xu; Xiaorui Liu; Jie Ren; Wenqi Fan; Jiliang Tang

http://arxiv.org/abs/2206.00513
The robust way to stack and bag: the local Lipschitz way. (70%)
Thulasi Tholeti; Sheetal Kalyani

http://arxiv.org/abs/2206.02539
Robustness Evaluation and Adversarial Training of an Instance Segmentation Model. (54%)
Jacob Bond; Andrew Lingg

http://arxiv.org/abs/2206.00700
RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model. (1%)
Hangzhi Guo; Feiran Jia; Jinghui Chen; Anna Squicciarini; Amulya Yadav

http://arxiv.org/abs/2205.15944
Hide and Seek: on the Stealthiness of Attacks against Deep Learning Systems. (99%)
Zeyan Liu; Fengjun Li; Jingqiang Lin; Zhu Li; Bo Luo

http://arxiv.org/abs/2205.15763
Exact Feature Collisions in Neural Networks. (95%)
Utku Ozbulak; Manvel Gasparyan; Shodhan Rao; Neve Wesley De; Messem Arnout Van

http://arxiv.org/abs/2206.00052
CodeAttack: Code-based Adversarial Attacks for Pre-Trained Programming Language Models. (93%)
Akshita Jha; Chandan K. Reddy

http://arxiv.org/abs/2206.00145
CASSOCK: Viable Backdoor Attacks against DNN in The Wall of Source-Specific Backdoor Defences. (83%)
Shang Wang; Yansong Gao; Anmin Fu; Zhi Zhang; Yuqing Zhang; Willy Susilo

http://arxiv.org/abs/2205.15592
Semantic Autoencoder and Its Potential Usage for Adversarial Attack. (81%)
Yurui Ming; Cuihuan Du; Chin-Teng Lin

http://arxiv.org/abs/2205.15582
An Effective Fusion Method to Enhance the Robustness of CNN. (80%)
Yating Ma; Zhichao Lian

http://arxiv.org/abs/2206.00192
Order-sensitive Shapley Values for Evaluating Conceptual Soundness of NLP Models. (64%)
Kaiji Lu; Anupam Datta

http://arxiv.org/abs/2206.00071
Generative Models with Information-Theoretic Protection Against Membership Inference Attacks. (10%)
Parisa Hassanzadeh; Robert E. Tillman

http://arxiv.org/abs/2205.15784
Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization. (1%)
Lorenzo Pacchiardi; Ritabrata Dutta

http://arxiv.org/abs/2205.15128
Domain Constraints in Feature Space: Strengthening Robustness of Android Malware Detection against Realizable Adversarial Examples. (99%)
Hamid Bostani; Zhuoran Liu; Zhengyu Zhao; Veelasha Moonsamy

http://arxiv.org/abs/2205.15357
Searching for the Essence of Adversarial Perturbations. (99%)
Dennis Y. Menn; Tzu-hsun Feng; Hung-yi Lee

http://arxiv.org/abs/2205.14851
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models. (99%)
Songlin Yang; Wei Wang; Chenye Xu; Ziwen He; Bo Peng; Jing Dong

http://arxiv.org/abs/2205.14969
Guided Diffusion Model for Adversarial Purification. (99%)
Jinyi Wang; Zhaoyang Lyu; Dahua Lin; Bo Dai; Hongfei Fu

http://arxiv.org/abs/2205.15130
Why Adversarial Training of ReLU Networks Is Difficult? (68%)
Xu Cheng; Hao Zhang; Yue Xin; Wen Shen; Jie Ren; Quanshi Zhang

http://arxiv.org/abs/2205.14926
CalFAT: Calibrated Federated Adversarial Training with Label Skewness. (67%)
Chen Chen; Yuchen Liu; Xingjun Ma; Lingjuan Lyu

http://arxiv.org/abs/2206.04793
Securing AI-based Healthcare Systems using Blockchain Technology: A State-of-the-Art Systematic Literature Review and Future Research Directions. (15%)
Rucha Shinde; Shruti Patil; Ketan Kotecha; Vidyasagar Potdar; Ganeshsree Selvachandran; Ajith Abraham

http://arxiv.org/abs/2205.14842
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning. (13%)
Yinglun Xu; Qi Zeng; Gagandeep Singh

http://arxiv.org/abs/2206.03584
White-box Membership Attack Against Machine Learning Based Retinopathy Classification. (10%)
Mounia Hamidouche; Reda Bellafqira; Gwenolé Quellec; Gouenou Coatrieux

http://arxiv.org/abs/2205.15419
Fool SHAP with Stealthily Biased Sampling. (2%)
Gabriel Laberge; Ulrich Aïvodji; Satoshi Hara; Mario Marchand.; Foutse Khomh

http://arxiv.org/abs/2205.15037
Snoopy: A Webpage Fingerprinting Framework with Finite Query Model for Mass-Surveillance. (2%)
Gargi Mitra; Prasanna Karthik Vairam; Sandip Saha; Nitin Chandrachoodan; V. Kamakoti

http://arxiv.org/abs/2205.14826
Robust Weight Perturbation for Adversarial Training. (99%)
Chaojian Yu; Bo Han; Mingming Gong; Li Shen; Shiming Ge; Bo Du; Tongliang Liu

http://arxiv.org/abs/2205.15743
Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks. (99%)
Eyad Shtaiwi; Ahmed El Ouadrhiri; Majid Moradikia; Salma Sultana; Ahmed Abdelhadi; Zhu Han

http://arxiv.org/abs/2205.14772
Unfooling Perturbation-Based Post Hoc Explainers. (98%)
Zachariah Carmichael; Walter J Scheirer

http://arxiv.org/abs/2205.14691
On the Robustness of Safe Reinforcement Learning under Observational Perturbations. (93%)
Zuxin Liu; Zijian Guo; Zhepeng Cen; Huan Zhang; Jie Tan; Bo Li; Ding Zhao

http://arxiv.org/abs/2205.14629
Superclass Adversarial Attack. (80%)
Soichiro Kumano; Hiroshi Kera; Toshihiko Yamasaki

http://arxiv.org/abs/2205.14576
Problem-Space Evasion Attacks in the Android OS: a Survey. (50%)
Harel Berger; Chen Hajaj; Amit Dvir

http://arxiv.org/abs/2206.11851
Context-based Virtual Adversarial Training for Text Classification with Noisy Labels. (11%)
Do-Myoung Lee; Yeachan Kim; Chang-gyun Seo

http://arxiv.org/abs/2205.14606
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks. (1%)
Binyan Hu; Yu Sun; A. K. Qin

http://arxiv.org/abs/2206.03583
Contributor-Aware Defenses Against Adversarial Backdoor Attacks. (98%)
Glenn Dawson; Muhammad Umer; Robi Polikar

http://arxiv.org/abs/2205.14497
BadDet: Backdoor Attacks on Object Detection. (92%)
Shih-Han Chan; Yinpeng Dong; Jun Zhu; Xiaolu Zhang; Jun Zhou

http://arxiv.org/abs/2205.14374
Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models. (62%)
Md Rafiqul Islam Rabin; Aftab Hussain; Mohammad Amin Alipour

http://arxiv.org/abs/2205.13807
fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systems. (96%)
Alberto Marchisio; Giovanni Caramia; Maurizio Martina; Muhammad Shafique

http://arxiv.org/abs/2205.13863
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power. (95%)
Binghui Li; Jikai Jin; Han Zhong; John E. Hopcroft; Liwei Wang

http://arxiv.org/abs/2205.14230
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction. (93%)
Ruochen Jiao; Xiangguo Liu; Takami Sato; Qi Alfred Chen; Qi Zhu

http://arxiv.org/abs/2205.14246
Defending Against Stealthy Backdoor Attacks. (73%)
Sangeet Sagar; Abhinav Bhatt; Abhijith Srinivas Bidaralli

http://arxiv.org/abs/2205.13892
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks. (13%)
Runlin Lei; Zhen Wang; Yaliang Li; Bolin Ding; Zhewei Wei

http://arxiv.org/abs/2205.13412
A Physical-World Adversarial Attack Against 3D Face Recognition. (99%)
Yanjie Li; Yiquan Li; Bin Xiao

http://arxiv.org/abs/2205.13152
Transferable Adversarial Attack based on Integrated Gradients. (99%)
Yi Huang; Adams Wai-Kin Kong

http://arxiv.org/abs/2205.13253
MALICE: Manipulation Attacks on Learned Image ComprEssion. (99%)
Kang Liu; Di Wu; Yiru Wang; Dan Feng; Benjamin Tan; Siddharth Garg

http://arxiv.org/abs/2205.13618
Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors. (98%)
Avishag Shapira; Alon Zolfi; Luca Demetrio; Battista Biggio; Asaf Shabtai

http://arxiv.org/abs/2205.13613
Circumventing Backdoor Defenses That Are Based on Latent Separability. (96%)
Xiangyu Qi; Tinghao Xie; Yiming Li; Saeed Mahloujifar; Prateek Mittal

http://arxiv.org/abs/2205.13502
An Analytic Framework for Robust Training of Artificial Neural Networks. (93%)
Ramin Barati; Reza Safabakhsh; Mohammad Rahmati

http://arxiv.org/abs/2205.13685
Adversarial attacks and defenses in Speaker Recognition Systems: A survey. (81%)
Jiahe Lan; Rui Zhang; Zheng Yan; Jie Wang; Yu Chen; Ronghui Hou

http://arxiv.org/abs/2205.13523
PerDoor: Persistent Non-Uniform Backdoors in Federated Learning using Adversarial Perturbations. (81%)
Manaar Alam; Esha Sarkar; Michail Maniatakos

http://arxiv.org/abs/2205.13383
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning. (81%)
Zhenting Wang; Juan Zhai; Shiqing Ma

http://arxiv.org/abs/2205.13702
R-HTDetector: Robust Hardware-Trojan Detection Based on Adversarial Training. (80%)
Kento Hasegawa; Seira Hidano; Kohei Nozawa; Shinsaku Kiyomoto; Nozomu Togawa

http://arxiv.org/abs/2205.13634
BagFlip: A Certified Defense against Data Poisoning. (75%)
Yuhao Zhang; Aws Albarghouthi; Loris D'Antoni

http://arxiv.org/abs/2205.13616
Towards A Proactive ML Approach for Detecting Backdoor Poison Samples. (67%)
Xiangyu Qi; Tinghao Xie; Jiachen T. Wang; Tong Wu; Saeed Mahloujifar; Prateek Mittal

http://arxiv.org/abs/2205.13680
Membership Inference Attack Using Self Influence Functions. (45%)
Gilad Cohen; Raja Giryes

http://arxiv.org/abs/2205.13268
MemeTector: Enforcing deep focus for meme detection. (1%)
Christos Koutlis; Manos Schinas; Symeon Papadopoulos

http://arxiv.org/abs/2205.13700
ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting. (1%)
Jingwei Guo; Kaizhu Huang; Rui Zhang; Xinping Yi

http://arxiv.org/abs/2205.12695
Surprises in adversarially-trained linear regression. (87%)
Antônio H. Ribeiro; Dave Zachariah; Thomas B. Schön

http://arxiv.org/abs/2205.12700
BITE: Textual Backdoor Attacks with Iterative Trigger Injection. (75%)
Jun Yan; Vansh Gupta; Xiang Ren

http://arxiv.org/abs/2205.12787
Impartial Games: A Challenge for Reinforcement Learning. (13%)
Bei Zhou; Søren Riis

http://arxiv.org/abs/2205.13042
How explainable are adversarially-robust CNNs? (8%)
Mehdi Nourelahi; Lars Kotthoff; Peijie Chen; Anh Nguyen

http://arxiv.org/abs/2205.12032
Defending a Music Recommender Against Hubness-Based Adversarial Attacks. (99%)
Katharina Hoedt; Arthur Flexer; Gerhard Widmer

http://arxiv.org/abs/2205.12134
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks. (99%)
Sizhe Chen; Zhehao Huang; Qinghua Tao; Yingwen Wu; Cihang Xie; Xiaolin Huang

http://arxiv.org/abs/2205.12331
Certified Robustness Against Natural Language Attacks by Causal Intervention. (98%)
Haiteng Zhao; Chang Ma; Xinshuai Dong; Anh Tuan Luu; Zhi-Hong Deng; Hanwang Zhang

http://arxiv.org/abs/2205.12141
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks. (92%)
Shutong Wu; Sizhe Chen; Cihang Xie; Xiaolin Huang

http://arxiv.org/abs/2205.11782
Fine-grained Poisoning Attacks to Local Differential Privacy Protocols for Mean and Variance Estimation. (64%)
Xiaoguang Li; Neil Zhenqiang Gong; Ninghui Li; Wenhai Sun; Hui Li

http://arxiv.org/abs/2205.11803
WeDef: Weakly Supervised Backdoor Defense for Text Classification. (56%)
Lesheng Jin; Zihan Wang; Jingbo Shang

http://arxiv.org/abs/2205.12396
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks. (50%)
Yijun Tian; Chuxu Zhang; Zhichun Guo; Yihong Ma; Ronald Metoyer; Nitesh V. Chawla

http://arxiv.org/abs/2205.12243
EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling. (10%)
Mitch Hill; Jonathan Mitchell; Chu Chen; Yuan Du; Mubarak Shah; Song-Chun Zhu

http://arxiv.org/abs/2205.11857
Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attacks. (9%)
Shijie Zhang; Hongzhi Yin

http://arxiv.org/abs/2205.12311
Fast & Furious: Modelling Malware Detection as Evolving Data Streams. (2%)
Fabrício Ceschin; Marcus Botacin; Heitor Murilo Gomes; Felipe Pinagé; Luiz S. Oliveira; André Grégio

http://arxiv.org/abs/2205.11819
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free. (2%)
Tianlong Chen; Zhenyu Zhang; Yihua Zhang; Shiyu Chang; Sijia Liu; Zhangyang Wang

http://arxiv.org/abs/2205.11845
CDFKD-MFS: Collaborative Data-free Knowledge Distillation via Multi-level Feature Sharing. (1%)
Zhiwei Hao; Yong Luo; Zhi Wang; Han Hu; Jianping An

http://arxiv.org/abs/2205.11156
Collaborative Adversarial Training. (98%)
Qizhang Li; Yiwen Guo; Wangmeng Zuo; Hao Chen

http://arxiv.org/abs/2205.11744
Alleviating Robust Overfitting of Adversarial Training With Consistency Regularization. (98%)
Shudong Zhang; Haichang Gao; Tianwei Zhang; Yunyi Zhou; Zihui Wu

http://arxiv.org/abs/2205.11551
Learning to Ignore Adversarial Attacks. (95%)
Yiming Zhang; Yangqiaoyu Zhou; Samuel Carton; Chenhao Tan

http://arxiv.org/abs/2205.11736
Towards a Defense against Backdoor Attacks in Continual Federated Learning. (50%)
Shuaiqi Wang; Jonathan Hayase; Giulia Fanti; Sewoong Oh

http://arxiv.org/abs/2205.11678
Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation. (10%)
Huarui He; Jie Wang; Zhanqiu Zhang; Feng Wu

http://arxiv.org/abs/2205.11693
RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesis. (1%)
Mohammad Esmaeilpour; Nourhene Chaalia; Adel Abusitta; Francois-Xavier Devailly; Wissem Maazoun; Patrick Cardinal

http://arxiv.org/abs/2205.10933
AutoJoin: Efficient Adversarial Training for Robust Maneuvering via Denoising Autoencoder and Joint Learning. (26%)
Michael Villarreal; Bibek Poudel; Ryan Wickman; Yu Shen; Weizi Li

http://arxiv.org/abs/2205.10848
Robust Quantity-Aware Aggregation for Federated Learning. (13%)
Jingwei Yi; Fangzhao Wu; Huishuai Zhang; Bin Zhu; Tao Qi; Guangzhong Sun; Xing Xie

http://arxiv.org/abs/2205.10952
Analysis of functional neural codes of deep learning models. (10%)
Jung Hoon Lee; Sujith Vijayan

http://arxiv.org/abs/2205.10686
Post-breach Recovery: Protection against White-box Adversarial Examples for Leaked DNN Models. (99%)
Shawn Shan; Wenxin Ding; Emily Wenger; Haitao Zheng; Ben Y. Zhao

http://arxiv.org/abs/2205.10617
Gradient Concealment: Free Lunch for Defending Adversarial Attacks. (99%)
Sen Pei; Jiaxi Sun; Xiaopeng Zhang; Gaofeng Meng

http://arxiv.org/abs/2205.10710
Phrase-level Textual Adversarial Attack with Label Preservation. (99%)
Yibin Lei; Yu Cao; Dianqi Li; Tianyi Zhou; Meng Fang; Mykola Pechenizkiy

http://arxiv.org/abs/2205.10539
On the Feasibility and Generality of Patch-based Adversarial Attacks on Semantic Segmentation Problems. (16%)
Soma Kontar; Andras Horvath

http://arxiv.org/abs/2205.10159
Getting a-Round Guarantees: Floating-Point Attacks on Certified Robustness. (99%)
Jiankai Jin; Olga Ohrimenko; Benjamin I. P. Rubinstein

http://arxiv.org/abs/2205.10457
Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification. (98%)
Jungeum Kim; Xiao Wang

http://arxiv.org/abs/2205.10098
Adversarial joint attacks on legged robots. (86%)
Takuto Otomo; Hiroshi Kera; Kazuhiko Kawamoto

http://arxiv.org/abs/2205.10022
Towards Consistency in Adversarial Classification. (82%)
Laurent Meunier; Raphaël Ettedgui; Rafael Pinot; Yann Chevaleyre; Jamal Atif

http://arxiv.org/abs/2205.10187
Adversarial Body Shape Search for Legged Robots. (80%)
Takaaki Azakami; Hiroshi Kera; Kazuhiko Kawamoto

http://arxiv.org/abs/2205.09986
SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning. (64%)
Harsh Chaudhari; Matthew Jagielski; Alina Oprea

http://arxiv.org/abs/2205.10144
The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networks. (11%)
Lukas S. Huber; Robert Geirhos; Felix A. Wichmann

http://arxiv.org/abs/2205.10292
Vulnerability Analysis and Performance Enhancement of Authentication Protocol in Dynamic Wireless Power Transfer Systems. (10%)
Tommaso Bianchi; Surudhi Asokraj; Alessandro Brighente; Mauro Conti; Radha Poovendran

http://arxiv.org/abs/2205.10232
Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization. (4%)
Ser Javier Del; Alejandro Barredo-Arrieta; Natalia Díaz-Rodríguez; Francisco Herrera; Andreas Holzinger

http://arxiv.org/abs/2205.09624
Focused Adversarial Attacks. (99%)
Thomas Cilloni; Charles Walter; Charles Fleming

http://arxiv.org/abs/2205.09592
Transferable Physical Attack against Object Detection with Separable Attention. (99%)
Yu Zhang; Zhiqiang Gong; Yichuang Zhang; YongQian Li; Kangcheng Bin; Jiahao Qi; Wei Xue; Ping Zhong

http://arxiv.org/abs/2205.09518
Gradient Aligned Attacks via a Few Queries. (99%)
Xiangyuan Yang; Jie Lin; Hanlin Zhang; Xinyu Yang; Peng Zhao

http://arxiv.org/abs/2205.09586
On Trace of PGD-Like Adversarial Attacks. (99%)
Mo Zhou; Vishal M. Patel

http://arxiv.org/abs/2205.09619
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification. (98%)
Leo Schwinn; Leon Bungert; An Nguyen; René Raab; Falk Pulsmeyer; Doina Precup; Björn Eskofier; Dario Zanca

http://arxiv.org/abs/2205.09522
Defending Against Adversarial Attacks by Energy Storage Facility. (96%)
Jiawei Li; Jianxiao Wang; Lin Chen; Yang Yu

http://arxiv.org/abs/2205.09362
Sparse Adversarial Attack in Multi-agent Reinforcement Learning. (82%)
Yizheng Hu; Zhihua Zhang

http://arxiv.org/abs/2205.09550
Data Valuation for Offline Reinforcement Learning. (1%)
Amir Abolfazli; Gregory Palmer; Daniel Kudenko

http://arxiv.org/abs/2205.08738
Passive Defense Against 3D Adversarial Point Clouds Through the Lens of 3D Steganalysis. (99%)
Jiahao Zhu

http://arxiv.org/abs/2205.08821
Property Unlearning: A Defense Strategy Against Property Inference Attacks. (84%)
Joshua Universität Hamburg Stock; Jens Universität Hamburg Wettlaufer; Daniel Universität Hamburg Demmler; Hannes Universität Hamburg Federrath

http://arxiv.org/abs/2205.09167
Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution. (56%)
Zhixin Pan; Prabhat Mishra

http://arxiv.org/abs/2205.09037
Empirical Advocacy of Bio-inspired Models for Robust Image Recognition. (38%)
Harshitha Machiraju; Oh-Hyeon Choung; Michael H. Herzog; Pascal Frossard

http://arxiv.org/abs/2205.08989
Constraining the Attack Space of Machine Learning Models with Distribution Clamping Preprocessing. (1%)
Ryan Feng; Somesh Jha; Atul Prakash

http://arxiv.org/abs/2205.09310
Mitigating Neural Network Overconfidence with Logit Normalization. (1%)
Hongxin Wei; Renchunzi Xie; Hao Cheng; Lei Feng; Bo An; Yixuan Li

http://arxiv.org/abs/2205.08728
RandoMix: A mixed sample data augmentation method with multiple mixed modes. (1%)
Xiaoliang Liu; Furao Shen; Jian Zhao; Changhai Nie

http://arxiv.org/abs/2205.08589
Hierarchical Distribution-Aware Testing of Deep Learning. (99%)
Wei Huang; Xingyu Zhao; Alec Banks; Victoria Cox; Xiaowei Huang

http://arxiv.org/abs/2205.08287
Bankrupting DoS Attackers Despite Uncertainty. (12%)
Trisha Chakraborty; Abir Islam; Valerie King; Daniel Rayborn; Jared Saia; Maxwell Young

http://arxiv.org/abs/2205.08265
A two-steps approach to improve the performance of Android malware detectors. (10%)
Nadia Daoudi; Kevin Allix; Tegawendé F. Bissyandé; Jacques Klein

http://arxiv.org/abs/2205.08685
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability. (2%)
Jinwei Xing; Takashi Nagata; Xinyun Zou; Emre Neftci; Jeffrey L. Krichmar

http://arxiv.org/abs/2205.08514
Recovering Private Text in Federated Learning of Language Models. (2%)
Samyak Gupta; Yangsibo Huang; Zexuan Zhong; Tianyu Gao; Kai Li; Danqi Chen

http://arxiv.org/abs/2205.08416
Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training. (1%)
Qingyu Li; Yilei Shi; Xiao Xiang Zhu

http://arxiv.org/abs/2205.07626
Attacking and Defending Deep Reinforcement Learning Policies. (99%)
Chao Wang

http://arxiv.org/abs/2205.07460
Diffusion Models for Adversarial Purification. (99%)
Weili Nie; Brandon Guo; Yujia Huang; Chaowei Xiao; Arash Vahdat; Anima Anandkumar

http://arxiv.org/abs/2205.07466
Robust Representation via Dynamic Feature Aggregation. (84%)
Haozhe Liu; Haoqin Ji; Yuexiang Li; Nanjun He; Haoqian Wu; Feng Liu; Linlin Shen; Yefeng Zheng

http://arxiv.org/abs/2205.07972
Sparse Visual Counterfactual Explanations in Image Space. (83%)
Valentyn Boreiko; Maximilian Augustin; Francesco Croce; Philipp Berens; Matthias Hein

http://arxiv.org/abs/2205.07890
On the Difficulty of Defending Self-Supervised Learning against Model Extraction. (67%)
Adam Dziedzic; Nikita Dhawan; Muhammad Ahmad Kaleem; Jonas Guan; Nicolas Papernot

http://arxiv.org/abs/2205.07711
Transferability of Adversarial Attacks on Synthetic Speech Detection. (47%)
Jiacheng Deng; Shunyi Chen; Li Dong; Diqun Yan; Rangding Wang

http://arxiv.org/abs/2205.07315
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks. (99%)
Siddhartha Datta

http://arxiv.org/abs/2205.07279
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection. (64%)
Fan Wang; Adams Wai-Kin Kong

http://arxiv.org/abs/2205.07229
RoMFAC: A robust mean-field actor-critic reinforcement learning against adversarial perturbations on states. (62%)
Ziyuan Zhou; Guanjun Liu

http://arxiv.org/abs/2205.07228
Automation Slicing and Testing for in-App Deep Learning Models. (1%)
Hao Wu; Yuhang Gong; Xiaopeng Ke; Hanzhong Liang; Minghao Li; Fengyuan Xu; Yunxin Liu; Sheng Zhong

http://arxiv.org/abs/2205.06986
Evaluating Membership Inference Through Adversarial Robustness. (98%)
Zhaoxi Zhang; Leo Yu Zhang; Xufei Zheng; Bilal Hussain Abbasi; Shengshan Hu

http://arxiv.org/abs/2205.06992
Verifying Neural Networks Against Backdoor Attacks. (2%)
Long H. Pham; Jun Sun

http://arxiv.org/abs/2205.06900
MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statistic. (98%)
Hang Wang; Zhen Xiang; David J. Miller; George Kesidis

http://arxiv.org/abs/2205.06469
l-Leaks: Membership Inference Attacks with Logits. (41%)
Shuhao Li; Yajie Wang; Yuanzhang Li; Yu-an Tan

http://arxiv.org/abs/2205.06504
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations. (26%)
Yongjie Wang; Hangwei Qian; Chunyan Miao

http://arxiv.org/abs/2205.06567
Millimeter-Wave Automotive Radar Spoofing. (2%)
Mihai Ordean; Flavio D. Garcia

http://arxiv.org/abs/2205.06127
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks. (75%)
Pascale Gourdeau; Varun Kanade; Marta Kwiatkowska; James Worrell

http://arxiv.org/abs/2205.06401
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning. (61%)
Hongbin Liu; Jinyuan Jia; Neil Zhenqiang Gong

http://arxiv.org/abs/2205.06369
How to Combine Membership-Inference Attacks on Multiple Updated Models. (11%)
Matthew Jagielski; Stanley Wu; Alina Oprea; Jonathan Ullman; Roxana Geambasu

http://arxiv.org/abs/2205.05909
Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World. (4%)
Xiaopei Zhu; Zhanhao Hu; Siyuan Huang; Jianmin Li; Xiaolin Hu

http://arxiv.org/abs/2205.06154
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness. (2%)
Ameya Joshi; Minh Pham; Minsu Cho; Leonid Boytsov; Filipe Condessa; J. Zico Kolter; Chinmay Hegde

http://arxiv.org/abs/2205.06064
Stalloris: RPKI Downgrade Attack. (1%)
Tomas Hlavacek; Philipp Jeitner; Donika Mirdita; Haya Shulman; Michael Waidner

http://arxiv.org/abs/2205.05439
Injection Attacks Reloaded: Tunnelling Malicious Payloads over DNS. (1%)
Philipp Jeitner; Haya Shulman

http://arxiv.org/abs/2205.05473
The Hijackers Guide To The Galaxy: Off-Path Taking Over Internet Resources. (1%)
Tianxiang Dai; Philipp Jeitner; Haya Shulman; Michael Waidner

http://arxiv.org/abs/2205.05573
A Longitudinal Study of Cryptographic API: a Decade of Android Malware. (1%)
Adam Janovsky; Davide Maiorca; Dominik Macko; Vashek Matyas; Giorgio Giacinto

http://arxiv.org/abs/2205.04723
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training. (1%)
Cheng Xue; Lequan Yu; Pengfei Chen; Qi Dou; Pheng-Ann Heng

http://arxiv.org/abs/2205.05050
White-box Testing of NLP models with Mask Neuron Coverage. (1%)
Arshdeep Sekhon; Yangfeng Ji; Matthew B. Dwyer; Yanjun Qi

http://arxiv.org/abs/2205.07859
Btech thesis report on adversarial attack detection and purification of adverserially attacked images. (99%)
Dvij Kalaria

http://arxiv.org/abs/2205.04638
Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector. (98%)
Xiaochun Lei; Chang Lu; Zetao Jiang; Zhaoting Gong; Xiang Cai; Linjun Lu

http://arxiv.org/abs/2205.04293
Do You Think You Can Hold Me? The Real Challenge of Problem-Space Evasion Attacks. (97%)
Harel Berger; Amit Dvir; Chen Hajaj; Rony Ronen

http://arxiv.org/abs/2205.04411
Model-Contrastive Learning for Backdoor Defense. (87%)
Zhihao Yue; Jun Xia; Zhiwei Ling; Ming Hu; Ting Wang; Xian Wei; Mingsong Chen

http://arxiv.org/abs/2205.04533
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations? (61%)
Alvin Chan; Yew-Soon Ong; Clement Tan

http://arxiv.org/abs/2205.04134
Federated Multi-Armed Bandits Under Byzantine Attacks. (2%)
Ilker Demirel; Yigit Yildirim; Cem Tekin

http://arxiv.org/abs/2205.04145
Verifying Integrity of Deep Ensemble Models by Lossless Black-box Watermarking with Sensitive Samples. (2%)
Lina Lin; Hanzhou Wu

http://arxiv.org/abs/2205.03809
Fingerprint Template Invertibility: Minutiae vs. Deep Templates. (68%)
Kanishka P. Wijewardena; Steven A. Grosz; Kai Cao; Anil K. Jain

http://arxiv.org/abs/2205.04007
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning. (22%)
Jingtao Li; Adnan Siraj Rakin; Xing Chen; Zhezhi He; Deliang Fan; Chaitali Chakrabarti

http://arxiv.org/abs/2205.03894
VPN: Verification of Poisoning in Neural Networks. (9%)
Youcheng Sun; Muhammad Usman; Divya Gopinath; Corina S. Păsăreanu

http://arxiv.org/abs/2205.03915
FOLPETTI: A Novel Multi-Armed Bandit Smart Attack for Wireless Networks. (4%)
Emilie Bout; Alessandro Brighente; Mauro Conti; Valeria Loscri

http://arxiv.org/abs/2205.03817
PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift. (1%)
Siyang Jiang; Wei Ding; Hsi-Wen Chen; Ming-Syan Chen

http://arxiv.org/abs/2206.05015
A Simple Yet Efficient Method for Adversarial Word-Substitute Attack. (99%)
Tianle Li; Yi Yang

http://arxiv.org/abs/2205.03546
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees. (92%)
Binghui Wang; Youqi Li; Pan Zhou

http://arxiv.org/abs/2205.03190
Imperceptible Backdoor Attack: From Input Space to Feature Representation. (68%)
Nan Zhong; Zhenxing Qian; Xinpeng Zhang

http://arxiv.org/abs/2205.03168
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data. (26%)
Joceline Ziegler; Bjarne Pfitzner; Heinrich Schulz; Axel Saalbach; Bert Arnrich

http://arxiv.org/abs/2205.03105
LPGNet: Link Private Graph Networks for Node Classification. (1%)
Aashish Kolluri; Teodora Baluta; Bryan Hooi; Prateek Saxena

http://arxiv.org/abs/2205.03205
Unlimited Lives: Secure In-Process Rollback with Isolated Domains. (1%)
Merve Gülmez; Thomas Nyman; Christoph Baumann; Jan Tobias Mühlberg

http://arxiv.org/abs/2205.02604
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems. (99%)
Gaurav Kumar Nayak; Ruchit Rawal; Rohit Lal; Himanshu Patil; Anirban Chakraborty

http://arxiv.org/abs/2205.08955
Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness. (78%)
Dávid Szeghy; Mahmoud Aslan; Áron Fóthi; Balázs Mészáros; Zoltán Ádám Milacski; András Lőrincz

http://arxiv.org/abs/2205.02652
Can collaborative learning be private, robust and scalable? (61%)
Dmitrii Usynin; Helena Klause; Daniel Rueckert; Georgios Kaissis

http://arxiv.org/abs/2205.02973
Large Scale Transfer Learning for Differentially Private Image Classification. (2%)
Harsh Mehta; Abhradeep Thakurta; Alexey Kurakin; Ashok Cutkosky

http://arxiv.org/abs/2205.02496
Are GAN-based Morphs Threatening Face Recognition? (1%)
Eklavya Sarkar; Pavel Korshunov; Laurent Colbois; Sébastien Marcel

http://arxiv.org/abs/2205.07853
Heterogeneous Domain Adaptation with Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity. (1%)
Mohammadreza Ebrahimi; Yidong Chai; Hao Helen Zhang; Hsinchun Chen

http://arxiv.org/abs/2205.02741
Based-CE white-box adversarial attack will not work using super-fitting. (99%)
Youhuan Yang; Lei Sun; Leyu Dai; Song Guo; Xiuqing Mao; Xiaoqin Wang; Bayi Xu

http://arxiv.org/abs/2205.02743
Rethinking Classifier And Adversarial Attack. (98%)
Youhuan Yang; Lei Sun; Leyu Dai; Song Guo; Xiuqing Mao; Xiaoqin Wang; Bayi Xu

http://arxiv.org/abs/2205.01992
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning. (98%)
Antonio Emanuele Cinà; Kathrin Grosse; Ambra Demontis; Sebastiano Vascon; Werner Zellinger; Bernhard A. Moser; Alina Oprea; Battista Biggio; Marcello Pelillo; Fabio Roli

http://arxiv.org/abs/2205.02392
Robust Conversational Agents against Imperceptible Toxicity Triggers. (92%)
Ninareh Mehrabi; Ahmad Beirami; Fred Morstatter; Aram Galstyan

http://arxiv.org/abs/2205.02414
Subverting Fair Image Search with Generative Adversarial Perturbations. (83%)
Avijit Ghosh; Matthew Jagielski; Christo Wilson

http://arxiv.org/abs/2205.01663
Adversarial Training for High-Stakes Reliability. (98%)
Daniel M. Ziegler; Seraphina Nix; Lawrence Chan; Tim Bauman; Peter Schmidt-Nielsen; Tao Lin; Adam Scherlis; Noa Nabeshima; Ben Weinstein-Raun; Haas Daniel de; Buck Shlegeris; Nate Thomas

http://arxiv.org/abs/2205.01714
Don't sweat the small stuff, classify the rest: Sample Shielding to protect text classifiers against adversarial attacks. (96%)
Jonathan Rusert; Padmini Srinivasan

http://arxiv.org/abs/2205.01493
On the uncertainty principle of neural networks. (3%)
Jun-Jie Zhang; Dong-Xiao Zhang; Jian-Nan Chen; Long-Gang Pang

http://arxiv.org/abs/2205.01794
Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars. (1%)
Kunal Pattanayak; Vikram Krishnamurthy; Christopher Berry

http://arxiv.org/abs/2205.01287
SemAttack: Natural Textual Attacks via Different Semantic Spaces. (96%)
Boxin Wang; Chejian Xu; Xiangyu Liu; Yu Cheng; Bo Li

http://arxiv.org/abs/2205.00807
Deep-Attack over the Deep Reinforcement Learning. (93%)
Yang Li; Quan Pan; Erik Cambria

http://arxiv.org/abs/2205.00637
Enhancing Adversarial Training with Feature Separability. (92%)
Yaxin Li; Xiaorui Liu; Han Xu; Wentao Wang; Jiliang Tang

http://arxiv.org/abs/2205.00953
BERTops: Studying BERT Representations under a Topological Lens. (92%)
Jatin Chauhan; Manohar Kaul

http://arxiv.org/abs/2205.01674
MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer. (83%)
Shoukun Sun; Min Xian; Aleksandar Vakanski; Hossny Ghanem

http://arxiv.org/abs/2205.00920
Revisiting Gaussian Neurons for Online Clustering with Unknown Number of Clusters. (1%)
Ole Christian Eidheim

http://arxiv.org/abs/2205.01094
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction. (98%)
Yong Xie; Dakuo Wang; Pin-Yu Chen; Jinjun Xiong; Sijia Liu; Sanmi Koyejo

http://arxiv.org/abs/2205.10117
DDDM: a Brain-Inspired Framework for Robust Classification. (76%)
Xiyuan Chen; Xingyu Li; Yi Zhou; Tianming Yang

http://arxiv.org/abs/2205.00633
Robust Fine-tuning via Perturbation and Interpolation from In-batch Instances. (9%)
Shoujie Tong; Qingxiu Dong; Damai Dai; Yifan song; Tianyu Liu; Baobao Chang; Zhifang Sui

http://arxiv.org/abs/2205.00403
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. (3%)
Jeremiah Zhe Liu; Shreyas Padhy; Jie Ren; Zi Lin; Yeming Wen; Ghassen Jerfel; Zack Nado; Jasper Snoek; Dustin Tran; Balaji Lakshminarayanan

http://arxiv.org/abs/2205.00566
Adversarial Plannning. (2%)
Valentin Vie; Ryan Sheatsley; Sophia Beyda; Sushrut Shringarputale; Kevin Chan; Trent Jaeger; Patrick McDaniel

http://arxiv.org/abs/2205.02116
Optimizing One-pixel Black-box Adversarial Attacks. (82%)
Tianxun Zhou; Shubhankar Agrawal; Prateek Manocha

http://arxiv.org/abs/2205.00199
Cracking White-box DNN Watermarks via Invariant Neuron Transforms. (26%)
Yifan Yan; Xudong Pan; Yining Wang; Mi Zhang; Min Yang

http://arxiv.org/abs/2205.00224
Loss Function Entropy Regularization for Diverse Decision Boundaries. (1%)
Chong Sue Sin

http://arxiv.org/abs/2205.00359
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees. (1%)
Jonathan Brophy; Zayd Hammoudeh; Daniel Lowd

http://arxiv.org/abs/2205.01226
Adversarial attacks on an optical neural network. (92%)
Shuming Jiao; Ziwei Song; Shuiying Xiang

http://arxiv.org/abs/2205.00047
Logically Consistent Adversarial Attacks for Soft Theorem Provers. (2%)
Alexander Gaskell; Yishu Miao; Lucia Specia; Francesca Toni

http://arxiv.org/abs/2205.00107
Bridging Differential Privacy and Byzantine-Robustness via Model Aggregation. (1%)
Heng Zhu; Qing Ling

http://arxiv.org/abs/2204.13853
Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations. (99%)
Na Liu; Mark Dras; Wei Emma Zhang

http://arxiv.org/abs/2204.13779
Formulating Robustness Against Unforeseen Attacks. (99%)
Sihui Dai; Saeed Mahloujifar; Prateek Mittal

http://arxiv.org/abs/2204.14187
Randomized Smoothing under Attack: How Good is it in Pratice? (84%)
Thibault Maho; Teddy Furon; Erwan Le Merrer

http://arxiv.org/abs/2204.13309
Improving robustness of language models from a geometry-aware perspective. (68%)
Bin Zhu; Zhaoquan Gu; Le Wang; Jinyin Chen; Qi Xuan

http://arxiv.org/abs/2204.13572
Mixup-based Deep Metric Learning Approaches for Incomplete Supervision. (50%)
Luiz H. Buris; Daniel C. G. Pedronette; Joao P. Papa; Jurandy Almeida; Gustavo Carneiro; Fabio A. Faria

http://arxiv.org/abs/2204.13784
AGIC: Approximate Gradient Inversion Attack on Federated Learning. (16%)
Jin Xu; Chi Hong; Jiyue Huang; Lydia Y. Chen; Jérémie Decouchant

http://arxiv.org/abs/2204.13814
An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks. (1%)
Hiba Tabbaa; Samir Ifzarne; Imad Hafidi

http://arxiv.org/abs/2204.13232
Adversarial Fine-tune with Dynamically Regulated Adversary. (99%)
Pengyue Hou; Ming Zhou; Jie Han; Petr Musilek; Xingyu Li

http://arxiv.org/abs/2204.13004
Defending Against Person Hiding Adversarial Patch Attack with a Universal White Frame. (98%)
Youngjoon Yu; Hong Joo Lee; Hakmin Lee; Yong Man Ro

http://arxiv.org/abs/2204.13172
An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework. (81%)
Ehsan Nowroozi; Abhishek; Mohammadreza Mohammadi; Mauro Conti

http://arxiv.org/abs/2204.12204
Boosting Adversarial Transferability of MLP-Mixer. (99%)
Haoran Lyu; Yajie Wang; Yu-an Tan; Huipeng Zhou; Yuhang Zhao; Quanxin Zhang

http://arxiv.org/abs/2204.12347
Restricted Black-box Adversarial Attack Against DeepFake Face Swapping. (99%)
Junhao Dong; Yuan Wang; Jianhuang Lai; Xiaohua Xie

http://arxiv.org/abs/2204.12680
Improving the Transferability of Adversarial Examples with Restructure Embedded Patches. (99%)
Huipeng Zhou; Yu-an Tan; Yajie Wang; Haoran Lyu; Shangbo Wu; Yuanzhang Li

http://arxiv.org/abs/2204.12393
On Fragile Features and Batch Normalization in Adversarial Training. (97%)
Nils Philipp Walter; David Stutz; Bernt Schiele

http://arxiv.org/abs/2204.12158
Mixed Strategies for Security Games with General Defending Requirements. (75%)
Rufan Bai; Haoxing Lin; Xinyu Yang; Xiaowei Wu; Minming Li; Weijia Jia

http://arxiv.org/abs/2204.13594
Poisoning Deep Learning based Recommender Model in Federated Learning Scenarios. (26%)
Dazhong Rong; Qinming He; Jianhai Chen

http://arxiv.org/abs/2204.12301
Designing Perceptual Puzzles by Differentiating Probabilistic Programs. (13%)
Kartik Chandra; Tzu-Mao Li; Joshua Tenenbaum; Jonathan Ragan-Kelley

http://arxiv.org/abs/2204.12495
Enhancing Privacy against Inversion Attacks in Federated Learning by using Mixing Gradients Strategies. (8%)
Shaltiel Eloul; Fran Silavong; Sanket Kamthe; Antonios Georgiadis; Sean J. Moran

http://arxiv.org/abs/2204.12378
Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks. (4%)
Jens Henriksson; Christian Berger; Markus Borg; Lars Tornberg; Sankar Raman Sathyamoorthy; Cristofer Englund

http://arxiv.org/abs/2204.12050
Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks. (99%)
Jiawei Zhang; Jinwei Wang; Hao Wang; Xiangyang Luo

http://arxiv.org/abs/2204.11985
When adversarial examples are excusable. (89%)
Pieter-Jan Kindermans; Charles Staats

http://arxiv.org/abs/2204.11596
A Simple Structure For Building A Robust Model. (81%)
Xiao Tan; JingBo Gao; Ruolin Li

http://arxiv.org/abs/2204.11853
Real or Virtual: A Video Conferencing Background Manipulation-Detection System. (67%)
Ehsan Nowroozi; Yassine Mekdad; Mauro Conti; Simone Milani; Selcuk Uluagac; Berrin Yanikoglu

http://arxiv.org/abs/2204.11790
Can Rationalization Improve Robustness? (12%)
Howard Chen; Jacqueline He; Karthik Narasimhan; Danqi Chen

http://arxiv.org/abs/2204.13597
PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings. (1%)
Moustafa Alzantot; Luis Garcia; Mani Srivastava

http://arxiv.org/abs/2204.11531
VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization. (1%)
Minghui Chen; Cheng Wen; Feng Zheng; Fengxiang He; Ling Shao

http://arxiv.org/abs/2204.11786
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications. (1%)
Han Cai; Ji Lin; Yujun Lin; Zhijian Liu; Haotian Tang; Hanrui Wang; Ligeng Zhu; Song Han

http://arxiv.org/abs/2205.01225
A Hybrid Defense Method against Adversarial Attacks on Traffic Sign Classifiers in Autonomous Vehicles. (99%)
Zadid Khan; Mashrur Chowdhury; Sakib Mahmud Khan

http://arxiv.org/abs/2204.11357
Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity. (81%)
Marco Marchetti; Edmond S. L. Ho

http://arxiv.org/abs/2204.11075
Smart App Attack: Hacking Deep Learning Models in Android Apps. (98%)
Yujin Huang; Chunyang Chen

http://arxiv.org/abs/2204.11022
Towards Data-Free Model Stealing in a Hard Label Setting. (13%)
Sunandini Sanyal; Sravanti Addepalli; R. Venkatesh Babu

http://arxiv.org/abs/2204.11028
Reinforced Causal Explainer for Graph Neural Networks. (1%)
Xiang Wang; Yingxin Wu; An Zhang; Fuli Feng; Xiangnan He; Tat-Seng Chua

http://arxiv.org/abs/2204.10839
How Sampling Impacts the Robustness of Stochastic Neural Networks. (99%)
Sina Däubener; Asja Fischer

http://arxiv.org/abs/2204.10933
A Tale of Two Models: Constructing Evasive Attacks on Edge Models. (83%)
Wei Hao; Aahil Awatramani; Jiayang Hu; Chengzhi Mao; Pin-Chun Chen; Eyal Cidon; Asaf Cidon; Junfeng Yang

http://arxiv.org/abs/2204.10606
Enhancing the Transferability via Feature-Momentum Adversarial Attack. (82%)
Xianglong; Yuezun Li; Haipeng Qu; Junyu Dong

http://arxiv.org/abs/2204.12281
Data-Efficient Backdoor Attacks. (76%)
Pengfei Xia; Ziqiang Li; Wei Zhang; Bin Li

http://arxiv.org/abs/2204.11837
A Mask-Based Adversarial Defense Scheme. (99%)
Weizhen Xu; Chenyi Zhang; Fangzhen Zhao; Liangda Fang

http://arxiv.org/abs/2204.10027
Is Neuron Coverage Needed to Make Person Detection More Robust? (98%)
Svetlana Pavlitskaya; Şiyar Yıkmış; J. Marius Zöllner

http://arxiv.org/abs/2204.10046
Testing robustness of predictions of trained classifiers against naturally occurring perturbations. (98%)
Sebastian Scher; Andreas Trügler

http://arxiv.org/abs/2204.10314
Adversarial Contrastive Learning by Permuting Cluster Assignments. (15%)
Muntasir Wahed; Afrina Tabassum; Ismini Lourentzou

http://arxiv.org/abs/2204.09975
Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation. (4%)
Jun Xia; Ting Wang; Jiepin Ding; Xian Wei; Mingsong Chen

http://arxiv.org/abs/2204.10072
Detecting Topology Attacks against Graph Neural Networks. (1%)
Senrong Xu; Yuan Yao; Liangyue Li; Wei Yang; Feng Xu; Hanghang Tong

http://arxiv.org/abs/2204.09397
Adversarial Scratches: Deployable Attacks to CNN Classifiers. (99%)
Loris Giulivi; Malhar Jere; Loris Rossi; Farinaz Koushanfar; Gabriela Ciocarlie; Briland Hitaj; Giacomo Boracchi

http://arxiv.org/abs/2204.09803
GUARD: Graph Universal Adversarial Defense. (99%)
Jintang Li; Jie Liao; Ruofan Wu; Liang Chen; Zibin Zheng; Jiawang Dan; Changhua Meng; Weiqiang Wang

http://arxiv.org/abs/2204.09838
Fast AdvProp. (98%)
Jieru Mei; Yucheng Han; Yutong Bai; Yixiao Zhang; Yingwei Li; Xianhang Li; Alan Yuille; Cihang Xie

http://arxiv.org/abs/2204.09398
Case-Aware Adversarial Training. (98%)
Mingyuan Fan; Yang Liu; Wenzhong Guo; Ximeng Liu; Jianhua Li

http://arxiv.org/abs/2204.09583
Improved Worst-Group Robustness via Classifier Retraining on Independent Splits. (1%)
Thien Hang Nguyen; Hongyang R. Zhang; Huy Le Nguyen

http://arxiv.org/abs/2204.08726
Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks. (99%)
Kenneth T. Co; David Martinez-Rego; Zhongyuan Hau; Emil C. Lupu

http://arxiv.org/abs/2204.09183
Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems. (86%)
Xugui Zhou; Maxfield Kouzel; Homa Alemzadeh

http://arxiv.org/abs/2204.08689
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation. (83%)
Siyu Lai; Zhen Yang; Fandong Meng; Xue Zhang; Yufeng Chen; Jinan Xu; Jie Zhou

http://arxiv.org/abs/2204.09502
UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples. (99%)
Rahim Taheri

http://arxiv.org/abs/2204.08189
Sardino: Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile Edge. (99%)
Qun Song; Zhenyu Yan; Wenjie Luo; Rui Tan

http://arxiv.org/abs/2204.10779
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval. (99%)
Xunguang Wang; Yiqun Lin; Xiaomeng Li

http://arxiv.org/abs/2204.08612
Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors. (98%)
Nyee Thoang Lim; Meng Yi Kuan; Muxin Pu; Mei Kuan Lim; Chun Yong Chong

http://arxiv.org/abs/2204.08570
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability. (75%)
Enyan Dai; Tianxiang Zhao; Huaisheng Zhu; Junjie Xu; Zhimeng Guo; Hui Liu; Jiliang Tang; Suhang Wang

http://arxiv.org/abs/2204.08623
CorrGAN: Input Transformation Technique Against Natural Corruptions. (70%)
Mirazul Haque; Christof J. Budnik; Wei Yang

http://arxiv.org/abs/2204.08615
Poisons that are learned faster are more effective. (64%)
Pedro Sandoval-Segura; Vasu Singla; Liam Fowl; Jonas Geiping; Micah Goldblum; David Jacobs; Tom Goldstein

http://arxiv.org/abs/2204.10192
Residue-Based Natural Language Adversarial Attack Detection. (99%)
Vyas Raina; Mark Gales

http://arxiv.org/abs/2204.07932
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning. (95%)
Jun Guo; Yonghong Chen; Yihang Hao; Zixin Yin; Yin Yu; Simin Li

http://arxiv.org/abs/2204.07772
SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment. (95%)
Marjan Golmaryami; Rahim Taheri; Zahra Pooranian; Mohammad Shojafar; Pei Xiao

http://arxiv.org/abs/2204.07752
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case. (67%)
Febrianti Wibawa; Ferhat Ozgur Catak; Salih Sarp; Murat Kuzlu; Umit Cali

http://arxiv.org/abs/2204.07373
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning. (92%)
Mathias Lechner; Alexander Amini; Daniela Rus; Thomas A. Henzinger

http://arxiv.org/abs/2204.07018
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks. (99%)
Mohammad Esmaeilpour; Patrick Cardinal; Alessandro Lameiras Koerich

http://arxiv.org/abs/2204.06974
Planting Undetectable Backdoors in Machine Learning Models. (99%)
Shafi Goldwasser; Michael P. Kim; Vinod Vaikuntanathan; Or Zamir

http://arxiv.org/abs/2204.07024
Q-TART: Quickly Training for Adversarial Robustness and in-Transferability. (50%)
Madan Ravi Ganesh; Salimeh Yasaei Sekeh; Jason J. Corso

http://arxiv.org/abs/2204.07246
Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification. (41%)
Jordan J. Bird

http://arxiv.org/abs/2204.06173
Task-Driven Data Augmentation for Vision-Based Robotic Control. (96%)
Shubhankar Agarwal; Sandeep P. Chinchali

http://arxiv.org/abs/2204.06241
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions. (87%)
Maria Rigaki; Sebastian Garcia

http://arxiv.org/abs/2204.06213
Defensive Patches for Robust Recognition in the Physical World. (80%)
Jiakai Wang; Zixin Yin; Pengfei Hu; Aishan Liu; Renshuai Tao; Haotong Qin; Xianglong Liu; Dacheng Tao

http://arxiv.org/abs/2204.06337
A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets. (78%)
Pervaiz Iqbal Khan; Imran Razzak; Andreas Dengel; Sheraz Ahmed

http://arxiv.org/abs/2204.06274
Overparameterized Linear Regression under Adversarial Attacks. (76%)
Antônio H. Ribeiro; Thomas B. Schön

http://arxiv.org/abs/2204.06273
Towards A Critical Evaluation of Robustness for Deep Learning Backdoor Countermeasures. (38%)
Huming Qiu; Hua Ma; Zhi Zhang; Alsharif Abuadbba; Wei Kang; Anmin Fu; Yansong Gao

http://arxiv.org/abs/2204.06624
A Natural Language Processing Approach for Instruction Set Architecture Identification. (1%)
Dinuka Sahabandu; Sukarno Mertoguno; Radha Poovendran

http://arxiv.org/abs/2204.06113
Liuer Mihou: A Practical Framework for Generating and Evaluating Grey-box Adversarial Attacks against NIDS. (99%)
Ke He; Dan Dongseong Kim; Jing Sun; Jeong Do Yoo; Young Hun Lee; Huy Kang Kim

http://arxiv.org/abs/2204.05764
Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks. (98%)
Štefan Pócoš; Iveta Bečková; Igor Farkaš

http://arxiv.org/abs/2205.01625
Toward Robust Spiking Neural Network Against Adversarial Perturbation. (98%)
Ling Liang; Kaidi Xu; Xing Hu; Lei Deng; Yuan Xie

http://arxiv.org/abs/2204.05986
Machine Learning Security against Data Poisoning: Are We There Yet? (92%)
Antonio Emanuele Cinà; Kathrin Grosse; Ambra Demontis; Battista Biggio; Fabio Roli; Marcello Pelillo

http://arxiv.org/abs/2204.06106
Optimal Membership Inference Bounds for Adaptive Composition of Sampled Gaussian Mechanisms. (11%)
Saeed Mahloujifar; Alexandre Sablayrolles; Graham Cormode; Somesh Jha

http://arxiv.org/abs/2204.05687
3DeformRS: Certifying Spatial Deformations on Point Clouds. (9%)
Gabriel Pérez S.; Juan C. Pérez; Motasem Alfarra; Silvio Giancola; Bernard Ghanem

http://arxiv.org/abs/2204.05432
A Simple Approach to Adversarial Robustness in Few-shot Image Classification. (98%)
Akshayvarun Subramanya; Hamed Pirsiavash

http://arxiv.org/abs/2204.05255
Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information. (92%)
Yi Zeng; Minzhou Pan; Hoang Anh Just; Lingjuan Lyu; Meikang Qiu; Ruoxi Jia

http://arxiv.org/abs/2204.05427
Generalizing Adversarial Explanations with Grad-CAM. (84%)
Tanmay Chakraborty; Utkarsh Trehan; Khawla Mallat; Jean-Luc Dugelay

http://arxiv.org/abs/2204.04890
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization. (83%)
Jungbeom Lee; Eunji Kim; Jisoo Mok; Sungroh Yoon

http://arxiv.org/abs/2204.05239
Exploring the Universal Vulnerability of Prompt-based Learning Paradigm. (47%)
Lei Xu; Yangyi Chen; Ganqu Cui; Hongcheng Gao; Zhiyuan Liu

http://arxiv.org/abs/2204.05376
medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space. (1%)
Amil Dravid; Florian Schiffers; Boqing Gong; Aggelos K. Katsaggelos

http://arxiv.org/abs/2204.04636
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks. (88%)
Edoardo Mosca; Shreyash Agarwal; Javier Rando-Ramirez; Georg Groh

http://arxiv.org/abs/2204.04768
Analysis of Power-Oriented Fault Injection Attacks on Spiking Neural Networks. (54%)
Karthikeyan Nagarajan; Junde Li; Sina Sayyah Ensan; Mohammad Nasim Imtiaz Khan; Sachhidh Kannan; Swaroop Ghosh

http://arxiv.org/abs/2204.04778
Measuring the False Sense of Security. (26%)
Carlos Gomes

http://arxiv.org/abs/2204.03851
Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser. (99%)
Sonal Joshi; Saurabh Kataria; Yiwen Shao; Piotr Zelasko; Jesus Villalba; Sanjeev Khudanpur; Najim Dehak

http://arxiv.org/abs/2204.03848
AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification. (99%)
Sonal Joshi; Saurabh Kataria; Jesus Villalba; Najim Dehak

http://arxiv.org/abs/2204.04259
Evaluating the Adversarial Robustness for Fourier Neural Operators. (92%)
Abolaji D. Adesoji; Pin-Yu Chen

http://arxiv.org/abs/2204.05758
Backdoor Attack against NLP models with Robustness-Aware Perturbation defense. (87%)
Shaik Mohammed Maqsood; Viveros Manuela Ceron; Addluri GowthamKrishna

http://arxiv.org/abs/2204.04329
An Adaptive Black-box Backdoor Detection Method for Deep Neural Networks. (45%)
Xinqiao Zhang; Huili Chen; Ke Huang; Farinaz Koushanfar

http://arxiv.org/abs/2204.04220
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment. (13%)
Qiang Hu; Yuejun Guo; Maxime Cordy; Xiaofei Xie; Wei Ma; Mike Papadakis; Yves Le Traon

http://arxiv.org/abs/2204.04090
Neural Tangent Generalization Attacks. (12%)
Chia-Hung Yuan; Shan-Hung Wu

http://arxiv.org/abs/2204.03994
Labeling-Free Comparison Testing of Deep Learning Models. (11%)
Yuejun Guo; Qiang Hu; Maxime Cordy; Xiaofei Xie; Mike Papadakis; Yves Le Traon

http://arxiv.org/abs/2204.03934
Does Robustness on ImageNet Transfer to Downstream Tasks? (2%)
Yutaro Yamada; Mayu Otani

http://arxiv.org/abs/2204.05227
The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system. (1%)
A. M. Vorontsov; G. A. Filimonov

http://arxiv.org/abs/2204.04063
Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings. (99%)
Yuhao Mao; Chong Fu; Saizhuo Wang; Shouling Ji; Xuhong Zhang; Zhenguang Liu; Jun Zhou; Alex X. Liu; Raheem Beyah; Ting Wang

http://arxiv.org/abs/2204.03694
Adaptive-Gravity: A Defense Against Adversarial Samples. (99%)
Ali Mirzaeian; Zhi Tian; Sai Manoj P D; Banafsheh S. Latibari; Ioannis Savidis; Houman Homayoun; Avesta Sasan

http://arxiv.org/abs/2204.03714
Using Multiple Self-Supervised Tasks Improves Model Robustness. (81%)
Matthew Lawhon; Chengzhi Mao; Junfeng Yang

http://arxiv.org/abs/2204.03214
Transformer-Based Language Models for Software Vulnerability Detection: Performance, Model's Security and Platforms. (69%)
Chandra Thapa; Seung Ick Jang; Muhammad Ejaz Ahmed; Seyit Camtepe; Josef Pieprzyk; Surya Nepal

http://arxiv.org/abs/2204.03397
Defending Active Directory by Combining Neural Network based Dynamic Program and Evolutionary Diversity Optimisation. (1%)
Diksha Goel; Max Hector Ward-Graham; Aneta Neumann; Frank Neumann; Hung Nguyen; Mingyu Guo

http://arxiv.org/abs/2204.02887
Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks. (99%)
Xu Han; Anmin Liu; Yifeng Xiong; Yanbo Fan; Kun He

http://arxiv.org/abs/2204.02738
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network. (95%)
Byung-Kwan Lee; Junho Kim; Yong Man Ro

http://arxiv.org/abs/2204.02735
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck. (93%)
Junho Kim; Byung-Kwan Lee; Yong Man Ro

http://arxiv.org/abs/2204.03154
Optimization Models and Interpretations for Three Types of Adversarial Perturbations against Support Vector Machines. (68%)
Wen Su; Qingna Li; Chunfeng Cui

http://arxiv.org/abs/2204.03141
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems. (62%)
Furkan Mumcu; Keval Doshi; Yasin Yilmaz

http://arxiv.org/abs/2204.02654
Adversarial Analysis of the Differentially-Private Federated Learning in Cyber-Physical Critical Infrastructures. (33%)
Md Tamjid Jim Hossain; Shahriar Jim Badsha; Jim Hung; La; Haoting Shen; Shafkat Islam; Ibrahim Khalil; Xun Yi

http://arxiv.org/abs/2204.02381
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning. (98%)
Nilaksh Das; Duen Horng Chau

http://arxiv.org/abs/2204.02481
Adversarial Robustness through the Lens of Convolutional Filters. (87%)
Paul Gavrikov; Janis Keuper

http://arxiv.org/abs/2204.02500
User-Level Differential Privacy against Attribute Inference Attack of Speech Emotion Recognition in Federated Learning. (2%)
Tiantian Feng; Raghuveer Peri; Shrikanth Narayanan

http://arxiv.org/abs/2204.02285
SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering. (1%)
Vipul Gupta; Zhuowan Li; Adam Kortylewski; Chenyu Zhang; Yingwei Li; Alan Yuille

http://arxiv.org/abs/2204.01975
GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation Learning. (1%)
Jinyin Chen; Shulong Hu; Haibin Zheng; Changyou Xing; Guomin Zhang

http://arxiv.org/abs/2204.01568
DAD: Data-free Adversarial Defense at Test Time. (99%)
Gaurav Kumar Nayak; Ruchit Rawal; Anirban Chakraborty

http://arxiv.org/abs/2204.01560
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition. (99%)
Jianfei Yang; Han Zou; Lihua Xie

http://arxiv.org/abs/2204.01738
Experimental quantum adversarial learning with programmable superconducting qubits. (99%)
Wenhui Ren; Weikang Li; Shibo Xu; Ke Wang; Wenjie Jiang; Feitong Jin; Xuhao Zhu; Jiachen Chen; Zixuan Song; Pengfei Zhang; Hang Dong; Xu Zhang; Jinfeng Deng; Yu Gao; Chuanyu Zhang; Yaozu Wu; Bing Zhang; Qiujiang Guo; Hekang Li; Zhen Wang; Jacob Biamonte; Chao Song; Dong-Ling Deng; H. Wang

http://arxiv.org/abs/2204.01321
PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models. (99%)
Chen Wu; Ruqing Zhang; Jiafeng Guo; Rijke Maarten de; Yixing Fan; Xueqi Cheng

http://arxiv.org/abs/2204.01960
FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering Deepfakes. (98%)
Paarth Neekhara; Shehzeen Hussain; Xinqiao Zhang; Ke Huang; Julian McAuley; Farinaz Koushanfar

http://arxiv.org/abs/2204.01090
Breaking the De-Pois Poisoning Defense. (98%)
Alaa Anani; Mohamed Ghanem; Lotfy Abdel Khaliq

http://arxiv.org/abs/2204.01099
Adversarially robust segmentation models learn perceptually-aligned gradients. (16%)
Pedro Sandoval-Segura

http://arxiv.org/abs/2204.01193
Detecting In-vehicle Intrusion via Semi-supervised Learning-based Convolutional Adversarial Autoencoders. (1%)
Thien-Nu Hoang; Daehee Kim

http://arxiv.org/abs/2204.00993
Improving Vision Transformers by Revisiting High-frequency Components. (1%)
Jiawang Bai; Li Yuan; Shu-Tao Xia; Shuicheng Yan; Zhifeng Li; Wei Liu

http://arxiv.org/abs/2204.00972
DST: Dynamic Substitute Training for Data-free Black-box Attack. (98%)
Wenxuan Wang; Xuelin Qian; Yanwei Fu; Xiangyang Xue

http://arxiv.org/abs/2204.00853
Adversarial Neon Beam: Robust Physical-World Adversarial Attack to DNNs. (98%)
Chengyin Hu; Kalibinuer Tiliwalidi

http://arxiv.org/abs/2204.00734
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning. (47%)
Nilaksh Das; Sheng-Yun Peng; Duen Horng Chau

http://arxiv.org/abs/2204.00487
Robust and Accurate -- Compositional Architectures for Randomized Smoothing. (31%)
Miklós Z. Horváth; Mark Niklas Müller; Marc Fischer; Martin Vechev

http://arxiv.org/abs/2204.00491
FrequencyLowCut Pooling -- Plug & Play against Catastrophic Overfitting. (16%)
Julia Grabinski; Steffen Jung; Janis Keuper; Margret Keuper

http://arxiv.org/abs/2204.00292
Preventing Distillation-based Attacks on Neural Network IP. (2%)
Mahdieh Grailoo; Zain Ul Abideen; Mairo Leier; Samuel Pagliarini

http://arxiv.org/abs/2204.01499
FedRecAttack: Model Poisoning Attack to Federated Recommendation. (1%)
Dazhong Rong; Shuai Ye; Ruoyan Zhao; Hon Ning Yuen; Jianhai Chen; Qinming He

http://arxiv.org/abs/2204.00008
Improving Adversarial Transferability via Neuron Attribution-Based Attacks. (99%)
Jianping Zhang; Weibin Wu; Jen-tse Huang; Yizhan Huang; Wenxuan Wang; Yuxin Su; Michael R. Lyu

http://arxiv.org/abs/2203.17209
Adversarial Examples in Random Neural Networks with General Activations. (98%)
Andrea Montanari; Yuchen Wu

http://arxiv.org/abs/2204.00103
Scalable Whitebox Attacks on Tree-based Models. (96%)
Giuseppe Castiglione; Gavin Ding; Masoud Hashemi; Christopher Srinivasa; Ga Wu

http://arxiv.org/abs/2203.16931
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond. (86%)
Yi Yu; Wenhan Yang; Yap-Peng Tan; Alex C. Kot

http://arxiv.org/abs/2204.00032
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. (81%)
Florian Tramèr; Reza Shokri; Ayrton San Joaquin; Hoang Le; Matthew Jagielski; Sanghyun Hong; Nicholas Carlini

http://arxiv.org/abs/2204.00089
Investigating Top-$k$ White-Box and Transferable Black-box Attack. (87%)
Chaoning Zhang; Philipp Benz; Adil Karjauv; Jae Won Cho; Kang Zhang; In So Kweon

http://arxiv.org/abs/2203.16130
Sensor Data Validation and Driving Safety in Autonomous Driving Systems. (83%)
Jindi Zhang

http://arxiv.org/abs/2203.16141
Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis. (56%)
Yi Chang; Zhao Ren; Thanh Tam Nguyen; Wolfgang Nejdl; Björn W. Schuller

http://arxiv.org/abs/2203.15283
Mel Frequency Spectral Domain Defenses against Adversarial Attacks on Speech Recognition Systems. (99%)
Nicholas Mehlman; Anirudh Sreeram; Raghuveer Peri; Shrikanth Narayanan

http://arxiv.org/abs/2203.15230
Zero-Query Transfer Attacks on Context-Aware Object Detectors. (99%)
Zikui Cai; Shantanu Rane; Alejandro E. Brito; Chengyu Song; Srikanth V. Krishnamurthy; Amit K. Roy-Chowdhury; M. Salman Asif

http://arxiv.org/abs/2203.15674
Exploring Frequency Adversarial Attacks for Face Forgery Detection. (99%)
Shuai Jia; Chao Ma; Taiping Yao; Bangjie Yin; Shouhong Ding; Xiaokang Yang

http://arxiv.org/abs/2203.16000
StyleFool: Fooling Video Classification Systems via Style Transfer. (99%)
Yuxin Cao; Xi Xiao; Ruoxi Sun; Derui Wang; Minhui Xue; Sheng Wen

http://arxiv.org/abs/2203.16536
Recent improvements of ASR models in the face of adversarial attacks. (98%)
Raphael Olivier; Bhiksha Raj

http://arxiv.org/abs/2203.15245
Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients. (83%)
Kaidong Li; Ziming Zhang; Cuncong Zhong; Guanghui Wang

http://arxiv.org/abs/2203.15529
Treatment Learning Causal Transformer for Noisy Image Classification. (26%)
Chao-Han Huck Yang; I-Te Danny Hung; Yi-Chieh Liu; Pin-Yu Chen

http://arxiv.org/abs/2203.15319
Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation. (11%)
Pietro Liguori; Cristina Improta; Vivo Simona De; Roberto Natella; Bojan Cukic; Domenico Cotroneo

http://arxiv.org/abs/2203.14607
Boosting Black-Box Adversarial Attacks with Meta Learning. (99%)
Junjie the State Key Lab of Intelligent Control and Decision of Complex Systems and the School of Automation, Beijing Institute of Technology, Beijing, China Beijing Institute of Technology Chongqing Innovation Center, Chongqing, China Fu; Jian the State Key Lab of Intelligent Control and Decision of Complex Systems and the School of Automation, Beijing Institute of Technology, Beijing, China Beijing Institute of Technology Chongqing Innovation Center, Chongqing, China Sun; Gang the State Key Lab of Intelligent Control and Decision of Complex Systems and the School of Automation, Beijing Institute of Technology, Beijing, China Beijing Institute of Technology Chongqing Innovation Center, Chongqing, China Wang

http://arxiv.org/abs/2204.00426
A Fast and Efficient Conditional Learning for Tunable Trade-Off between Accuracy and Robustness. (62%)
Souvik Kundu; Sairam Sundaresan; Massoud Pedram; Peter A. Beerel

http://arxiv.org/abs/2203.14533
Robust Unlearnable Examples: Protecting Data Against Adversarial Learning. (16%)
Shaopeng Fu; Fengxiang He; Yang Liu; Li Shen; Dacheng Tao

http://arxiv.org/abs/2203.15076
Neurosymbolic hybrid approach to driver collision warning. (15%)
Kyongsik Yun; Thomas Lu; Alexander Huyen; Patrick Hammer; Pei Wang

http://arxiv.org/abs/2203.15563
Attacker Attribution of Audio Deepfakes. (1%)
Nicolas M. Müller; Franziska Dieckmann; Jennifer Williams

http://arxiv.org/abs/2203.14207
Text Adversarial Purification as Defense against Adversarial Attacks. (99%)
Linyang Li; Demin Song; Xipeng Qiu

http://arxiv.org/abs/2203.14299
Adversarial Representation Sharing: A Quantitative and Secure Collaborative Learning Framework. (8%)
Jikun Chen; Feng Qiang; Na Ruan

http://arxiv.org/abs/2203.14195
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective. (99%)
Yimeng Zhang; Yuguang Yao; Jinghan Jia; Jinfeng Yi; Mingyi Hong; Shiyu Chang; Sijia Liu

http://arxiv.org/abs/2203.14046
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies. (99%)
Zhuang Qian; Kaizhu Huang; Qiu-Feng Wang; Xu-Yao Zhang

http://arxiv.org/abs/2203.14145
Reverse Engineering of Imperceptible Adversarial Image Perturbations. (99%)
Yifan Gong; Yuguang Yao; Yize Li; Yimeng Zhang; Xiaoming Liu; Xue Lin; Sijia Liu

http://arxiv.org/abs/2203.14141
Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding. (33%)
Zhilu Wang; Chao Huang; Qi Zhu

http://arxiv.org/abs/2203.14965
A Systematic Survey of Attack Detection and Prevention in Connected and Autonomous Vehicles. (1%)
Trupil Limbasiya; Ko Zheng Teng; Sudipta Chattopadhyay; Jianying Zhou

http://arxiv.org/abs/2203.14101
A Roadmap for Big Model. (1%)
Sha Yuan; Hanyu Zhao; Shuai Zhao; Jiahong Leng; Yangxiao Liang; Xiaozhi Wang; Jifan Yu; Xin Lv; Zhou Shao; Jiaao He; Yankai Lin; Xu Han; Zhenghao Liu; Ning Ding; Yongming Rao; Yizhao Gao; Liang Zhang; Ming Ding; Cong Fang; Yisen Wang; Mingsheng Long; Jing Zhang; Yinpeng Dong; Tianyu Pang; Peng Cui; Lingxiao Huang; Zheng Liang; Huawei Shen; Hui Zhang; Quanshi Zhang; Qingxiu Dong; Zhixing Tan; Mingxuan Wang; Shuo Wang; Long Zhou; Haoran Li; Junwei Bao; Yingwei Pan; Weinan Zhang; Zhou Yu; Rui Yan; Chence Shi; Minghao Xu; Zuobai Zhang; Guoqiang Wang; Xiang Pan; Mengjie Li; Xiaoyu Chu; Zijun Yao; Fangwei Zhu; Shulin Cao; Weicheng Xue; Zixuan Ma; Zhengyan Zhang; Shengding Hu; Yujia Qin; Chaojun Xiao; Zheni Zeng; Ganqu Cui; Weize Chen; Weilin Zhao; Yuan Yao; Peng Li; Wenzhao Zheng; Wenliang Zhao; Ziyi Wang; Borui Zhang; Nanyi Fei; Anwen Hu; Zenan Ling; Haoyang Li; Boxi Cao; Xianpei Han; Weidong Zhan; Baobao Chang; Hao Sun; Jiawen Deng; Chujie Zheng; Juanzi Li; Lei Hou; Xigang Cao; Jidong Zhai; Zhiyuan Liu; Maosong Sun; Jiwen Lu; Zhiwu Lu; Qin Jin; Ruihua Song; Ji-Rong Wen; Zhouchen Lin; Liwei Wang; Hang Su; Jun Zhu; Zhifang Sui; Jiajun Zhang; Yang Liu; Xiaodong He; Minlie Huang; Jian Tang; Jie Tang

http://arxiv.org/abs/2203.13479
Enhancing Transferability of Adversarial Examples with Spatial Momentum. (99%)
Guoqiu Wang; Huanqian Yan; Xingxing Wei

http://arxiv.org/abs/2203.13779
Origins of Low-dimensional Adversarial Perturbations. (98%)
Elvis Dohmatob; Chuan Guo; Morgane Goibert

http://arxiv.org/abs/2203.13639
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness. (89%)
Giulio Lovisotto; Nicole Finnie; Mauricio Munoz; Chaithanya Kumar Mummadi; Jan Hendrik Metzen

http://arxiv.org/abs/2203.13890
Improving Robustness of Jet Tagging Algorithms with Adversarial Training. (10%)
Annika Stein; Xavier Coubez; Spandan Mondal; Andrzej Novak; Alexander Schmidt

http://arxiv.org/abs/2203.13455
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training. (5%)
Yifei Wang; Yisen Wang; Jiansheng Yang; Zhouchen Lin

http://arxiv.org/abs/2203.13834
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration. (1%)
Ramya Hebbalaguppe; Jatin Prakash; Neelabh Madan; Chetan Arora

http://arxiv.org/abs/2203.15506
Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning. (99%)
Arezoo Rajabi; Bhaskar Ramasubramanian; Radha Poovendran

http://arxiv.org/abs/2203.13214
A Perturbation Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow. (99%)
Jenny Schmalfuss; Philipp Scholze; Andrés Bruhn

http://arxiv.org/abs/2203.12915
NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks. (93%)
Xiaofei Xie; Tianlin Li; Jian Wang; Lei Ma; Qing Guo; Felix Juefei-Xu; Yang Liu

http://arxiv.org/abs/2203.12980
MERLIN -- Malware Evasion with Reinforcement LearnINg. (56%)
Tony Quertier; Benjamin Marais; Stéphane Morucci; Bertrand Fournel

http://arxiv.org/abs/2203.13612
Repairing Group-Level Errors for DNNs Using Weighted Regularization. (13%)
Ziyuan Zhong; Yuchi Tian; Conor J. Sweeney; Vicente Ordonez-Roman; Baishakhi Ray

http://arxiv.org/abs/2203.13277
A Manifold View of Adversarial Risk. (11%)
Wenjia Zhang; Yikai Zhang; Xiaoling Hu; Mayank Goswami; Chao Chen; Dimitris Metaxas

http://arxiv.org/abs/2203.15498
Powerful Physical Adversarial Examples Against Practical Face Recognition Systems. (99%)
Inderjeet Singh; Toshinori Araki; Kazuya Kakizaki

http://arxiv.org/abs/2203.12709
Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation. (99%)
Hanjie Chen; Yangfeng Ji

http://arxiv.org/abs/2203.12298
Input-specific Attention Subnetworks for Adversarial Detection. (99%)
Emil Biju; Anirudh Sriram; Pratyush Kumar; Mitesh M Khapra

http://arxiv.org/abs/2203.12208
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection. (69%)
Liang Chen; Yong Zhang; Yibing Song; Lingqiao Liu; Jue Wang

http://arxiv.org/abs/2203.12249
Distort to Detect, not Affect: Detecting Stealthy Sensor Attacks with Micro-distortion. (3%)
Suman Sourav; Binbin Chen

http://arxiv.org/abs/2203.12387
On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations. (3%)
Philipp Terhörst; Florian Bierbaum; Marco Huber; Naser Damer; Florian Kirchbuchner; Kiran Raja; Arjan Kuijper

http://arxiv.org/abs/2203.11492
Exploring High-Order Structure for Robust Graph Structure Learning. (99%)
Guangqian Yang; Yibing Zhan; Jinlong Li; Baosheng Yu; Liu Liu; Fengxiang He

http://arxiv.org/abs/2203.12122
On Adversarial Robustness of Large-scale Audio Visual Learning. (93%)
Juncheng B Bernie Li; Shuhui Bernie Qu; Xinjian Bernie Li; Bernie Po-Yao; Huang; Florian Metze

http://arxiv.org/abs/2203.11864
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes. (86%)
Elvis Dohmatob; Alberto Bietti

http://arxiv.org/abs/2203.11633
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis. (78%)
Yuwei Sun; Hideya Ochiai; Jun Sakuma

http://arxiv.org/abs/2203.11849
A Girl Has A Name, And It's ... Adversarial Authorship Attribution for Deobfuscation. (2%)
Wanyue Zhai; Jonathan Rusert; Zubair Shafiq; Padmini Srinivasan

http://arxiv.org/abs/2203.11894
GradViT: Gradient Inversion of Vision Transformers. (1%)
Ali Hatamizadeh; Hongxu Yin; Holger Roth; Wenqi Li; Jan Kautz; Daguang Xu; Pavlo Molchanov

http://arxiv.org/abs/2203.11805
On Robust Classification using Contractive Hamiltonian Neural ODEs. (1%)
Muhammad Zakwan; Liang Xu; Giancarlo Ferrari-Trecate

http://arxiv.org/abs/2203.11433
Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack. (92%)
Chi Liu; Huajie Chen; Tianqing Zhu; Jun Zhang; Wanlei Zhou

http://arxiv.org/abs/2203.10902
Integrity Fingerprinting of DNN with Double Black-box Design and Verification. (10%)
Shuo Wang; Sidharth Agarwal; Sharif Abuadbba; Kristen Moore; Surya Nepal; Salil Kanhere

http://arxiv.org/abs/2203.11331
On The Robustness of Offensive Language Classifiers. (2%)
Jonathan Rusert; Zubair Shafiq; Padmini Srinivasan

http://arxiv.org/abs/2203.10734
Defending against Co-residence Attack in Energy-Efficient Cloud: An Optimization based Real-time Secure VM Allocation Strategy. (1%)
Lu Cao; Ruiwen Li; Xiaojun Ruan; Yuhong Liu

http://arxiv.org/abs/2203.10723
An Intermediate-level Attack Framework on The Basis of Linear Regression. (99%)
Yiwen Guo; Qizhang Li; Wangmeng Zuo; Hao Chen

http://arxiv.org/abs/2203.10714
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement. (99%)
Yuting Yang; Pei Huang; Juan Cao; Jintao Li; Yun Lin; Jin Song Dong; Feifei Ma; Jian Zhang

http://arxiv.org/abs/2203.10693
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition. (82%)
Aaron Reich; Jiaao Chen; Aastha Agrawal; Yanzhe Zhang; Diyi Yang

http://arxiv.org/abs/2203.10502
Adversarial Parameter Attack on Deep Neural Networks. (62%)
Lijia Yu; Yihan Wang; Xiao-Shan Gao

http://arxiv.org/abs/2203.10290
Adversarial Defense via Image Denoising with Chaotic Encryption. (99%)
Shi Hu; Eric Nalisnick; Max Welling

http://arxiv.org/abs/2203.10346
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense. (98%)
Thai Le; Jooyoung Lee; Kevin Yen; Yifan Hu; Dongwon Lee

http://arxiv.org/abs/2203.11199
Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model. (84%)
Jiayi Wang; Rongzhou Bao; Zhuosheng Zhang; Hai Zhao

http://arxiv.org/abs/2203.11201
Efficient Neural Network Analysis with Sum-of-Infeasibilities. (74%)
Haoze Wu; Aleksandar Zeljić; Guy Katz; Clark Barrett

http://arxiv.org/abs/2203.10366
Deep Learning Generalization, Extrapolation, and Over-parameterization. (68%)
Roozbeh Yousefzadeh

http://arxiv.org/abs/2203.10378
On Robust Prefix-Tuning for Text Classification. (10%)
Zonghan Yang; Yang Liu

http://arxiv.org/abs/2203.10166
Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike. (99%)
Johannes Schneider; Giovanni Apruzzese

http://arxiv.org/abs/2203.10183
Adversarial Attacks on Deep Learning-based Video Compression and Classification Systems. (99%)
Jung-Woo Chang; Mojan Javaheripi; Seira Hidano; Farinaz Koushanfar

http://arxiv.org/abs/2203.09849
Neural Predictor for Black-Box Adversarial Attacks on Speech Recognition. (99%)
Marie Biolková; Bac Nguyen

http://arxiv.org/abs/2203.09756
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack. (99%)
Jinqiao Li; Xiaotao Liu; Jian Zhao; Furao Shen

http://arxiv.org/abs/2203.09940
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC. (96%)
Anna Kuzina; Max Welling; Jakub M. Tomczak

http://arxiv.org/abs/2203.09831
DTA: Physical Camouflage Attacks using Differentiable Transformation Network. (83%)
Naufal Suryanto; Yongsu Kim; Hyoeun Kang; Harashta Tatimma Larasati; Youngyeo Yun; Thi-Thu-Huong Le; Hunmin Yang; Se-Yoon Oh; Howon Kim

http://arxiv.org/abs/2203.09792
AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networks. (78%)
Arman Pashamokhtari; Gustavo Batista; Hassan Habibi Gharakheili

http://arxiv.org/abs/2203.09790
Towards Robust 2D Convolution for Reliable Visual Recognition. (9%)
Lida Li; Shuai Li; Kun Wang; Xiangchu Feng; Lei Zhang

http://arxiv.org/abs/2203.09123
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input. (99%)
Junyoung Byun; Seungju Cho; Myung-Joon Kwon; Hee-Seon Kim; Changick Kim

http://arxiv.org/abs/2203.09678
Self-Ensemble Adversarial Training for Improved Robustness. (99%)
Hongjun Wang; Yisen Wang

http://arxiv.org/abs/2203.09566
Leveraging Adversarial Examples to Quantify Membership Information Leakage. (98%)
Grosso Ganesh Del; Hamid Jalalzai; Georg Pichler; Catuscia Palamidessi; Pablo Piantanida

http://arxiv.org/abs/2203.09243
On the Properties of Adversarially-Trained CNNs. (93%)
Mattia Carletti; Matteo Terzi; Gian Antonio Susto

http://arxiv.org/abs/2203.09289
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks. (89%)
Yue Wang; Wenqing Li; Esha Sarkar; Muhammad Shafique; Michail Maniatakos; Saif Eddin Jabari

http://arxiv.org/abs/2203.09681
HDLock: Exploiting Privileged Encoding to Protect Hyperdimensional Computing Models against IP Stealing. (1%)
Shijin Duan; Shaolei Ren; Xiaolin Xu

http://arxiv.org/abs/2203.08959
Robustness through Cognitive Dissociation Mitigation in Contrastive Adversarial Training. (99%)
Adir Rahamim; Itay Naeh

http://arxiv.org/abs/2203.08519
Towards Practical Certifiable Patch Defense with Vision Transformer. (98%)
Zhaoyu Chen; Bo Li; Jianghe Xu; Shuang Wu; Shouhong Ding; Wenqiang Zhang

http://arxiv.org/abs/2203.08392
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations? (97%)
Yonggan Fu; Shunyao Zhang; Shang Wu; Cheng Wan; Yingyan Lin

http://arxiv.org/abs/2203.08945
Provable Adversarial Robustness for Fractional Lp Threat Models. (87%)
Alexander Levine; Soheil Feizi

http://arxiv.org/abs/2203.08739
What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Study. (83%)
Binxiao Huang; Chaofan Tao; Rui Lin; Ngai Wong

http://arxiv.org/abs/2203.08398
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. (82%)
Fan Wu; Linyi Li; Chejian Xu; Huan Zhang; Bhavya Kailkhura; Krishnaram Kenthapadi; Ding Zhao; Bo Li

http://arxiv.org/abs/2203.08689
Sniper Backdoor: Single Client Targeted Backdoor Attack in Federated Learning. (70%)
Gorka Abad; Servio Paguada; Oguzhan Ersoy; Stjepan Picek; Víctor Julio Ramírez-Durán; Aitor Urbieta

http://arxiv.org/abs/2203.08390
Reducing Flipping Errors in Deep Neural Networks. (68%)
Xiang Deng; Yun Xiao; Bo Long; Zhongfei Zhang

http://arxiv.org/abs/2203.08725
Attacking deep networks with surrogate-based adversarial black-box methods is easy. (45%)
Nicholas A. Lord; Romain Mueller; Luca Bertinetto

http://arxiv.org/abs/2203.08961
On the Convergence of Certified Robust Training with Interval Bound Propagation. (15%)
Yihan Wang; Zhouxing Shi; Quanquan Gu; Cho-Jui Hsieh

http://arxiv.org/abs/2203.08669
MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients. (15%)
Xiaoyu Cao; Neil Zhenqiang Gong

http://arxiv.org/abs/2203.08822
Understanding robustness and generalization of artificial neural networks through Fourier masks. (2%)
Nikos Karantzas; Emma Besier; Josue Ortega Caro; Xaq Pitkow; Andreas S. Tolias; Ankit B. Patel; Fabio Anselmi

http://arxiv.org/abs/2203.07653
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness. (76%)
Tejas Gokhale; Swaroop Mishra; Man Luo; Bhavdeep Singh Sachdeva; Chitta Baral

http://arxiv.org/abs/2203.08302
Internet-based Social Engineering Attacks, Defenses and Psychology: A Survey. (13%)
Theodore Longtchi; Rosana Montañez Rodriguez; Laith Al-Shawaf; Adham Atyabi; Shouhuai Xu

http://arxiv.org/abs/2203.07670
Towards Adversarial Control Loops in Sensor Attacks: A Case Study to Control the Kinematics and Actuation of Embedded Systems. (10%)
Yazhou Tu; Sara Rampazzi; Xiali Hei

http://arxiv.org/abs/2203.07713
LDP: Learnable Dynamic Precision for Efficient Deep Neural Network Training and Inference. (1%)
Zhongzhi Yu; Yonggan Fu; Shang Wu; Mengquan Li; Haoran You; Yingyan Lin

http://arxiv.org/abs/2203.07815
Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification. (1%)
Tian Xia; Pedro Sanchez; Chen Qin; Sotirios A. Tsaftaris

http://arxiv.org/abs/2203.06898
Efficient universal shuffle attack for visual object tracking. (99%)
Siao Liu; Zhaoyu Chen; Wei Li; Jiwei Zhu; Jiafeng Wang; Wenqiang Zhang; Zhongxue Gan

http://arxiv.org/abs/2203.09487
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training. (99%)
Jiahao Shao; Shijia Geng; Zhaoji Fu; Weilun Xu; Tong Liu; Shenda Hong

http://arxiv.org/abs/2203.07596
Task-Agnostic Robust Representation Learning. (98%)
A. Tuan Nguyen; Ser Nam Lim; Philip Torr

http://arxiv.org/abs/2203.08147
Energy-Latency Attacks via Sponge Poisoning. (91%)
Antonio Emanuele Cinà; Ambra Demontis; Battista Biggio; Fabio Roli; Marcello Pelillo

http://arxiv.org/abs/2203.07138
Adversarial amplitude swap towards robust image classifiers. (83%)
Chun Yang Tan; Hiroshi Kera; Kazuhiko Kawamoto

http://arxiv.org/abs/2203.07159
On the benefits of knowledge distillation for adversarial robustness. (82%)
Javier Maroto; Guillermo Ortiz-Jiménez; Pascal Frossard

http://arxiv.org/abs/2203.08148
RES-HD: Resilient Intelligent Fault Diagnosis Against Adversarial Attacks Using Hyper-Dimensional Computing. (82%)
Onat Gungor; Tajana Rosing; Baris Aksanli

http://arxiv.org/abs/2203.07341
Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis. (54%)
Giulio Rossolini; Federico Nesti; Fabio Brau; Alessandro Biondi; Giorgio Buttazzo

http://arxiv.org/abs/2203.06616
LAS-AT: Adversarial Training with Learnable Attack Strategy. (99%)
Xiaojun Jia; Yong Zhang; Baoyuan Wu; Ke Ma; Jue Wang; Xiaochun Cao

http://arxiv.org/abs/2203.06694
Generating Practical Adversarial Network Traffic Flows Using NIDSGAN. (99%)
Bolor-Erdene Zolbayar; Ryan Sheatsley; Patrick McDaniel; Michael J. Weisman; Sencun Zhu; Shitong Zhu; Srikanth Krishnamurthy

http://arxiv.org/abs/2203.06570
Model Inversion Attack against Transfer Learning: Inverting a Model without Accessing It. (92%)
Dayong Ye; Huiqiang Chen; Shuai Zhou; Tianqing Zhu; Wanlei Zhou; Shouling Ji

http://arxiv.org/abs/2203.06580
One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy. (67%)
Dayong Ye; Sheng Shen; Tianqing Zhu; Bo Liu; Wanlei Zhou

http://arxiv.org/abs/2203.06587
Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model. (3%)
Jialian Li; Tongzheng Ren; Dong Yan; Hang Su; Jun Zhu

http://arxiv.org/abs/2203.06560
Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior. (99%)
Yinpeng Dong; Shuyu Cheng; Tianyu Pang; Hang Su; Jun Zhu

http://arxiv.org/abs/2203.06414
A Survey of Adversarial Defences and Robustness in NLP. (99%)
Shreya Goyal; Sumanth Doddapaneni; Mitesh M. Khapra; Balaraman Ravindran

http://arxiv.org/abs/2203.06555
Label-only Model Inversion Attack: The Attack that Requires the Least Information. (47%)
Dayong Ye; Tianqing Zhu; Shuai Zhou; Bo Liu; Wanlei Zhou

http://arxiv.org/abs/2203.05948
Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers. (99%)
Sahar Sadrizadeh; Ljiljana Dolamic; Pascal Frossard

http://arxiv.org/abs/2203.07027
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification. (98%)
Jianzhang Zheng; Fan Yang; Hao Shen; Xuan Tang; Mingsong Chen; Liang Song; Xian Wei

http://arxiv.org/abs/2203.10930
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks. (96%)
Anirudh Yadav; Ashutosh Upadhyay; S. Sharanya

http://arxiv.org/abs/2203.06020
Enhancing Adversarial Training with Second-Order Statistics of Weights. (38%)
Gaojie Jin; Xinping Yi; Wei Huang; Sven Schewe; Xiaowei Huang

http://arxiv.org/abs/2203.06060
ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI. (33%)
Lyndon Boone; Mahdi Biparva; Parisa Mojiri Forooshani; Joel Ramirez; Mario Masellis; Robert Bartha; Sean Symons; Stephen Strother; Sandra E. Black; Chris Heyn; Anne L. Martel; Richard H. Swartz; Maged Goubran

http://arxiv.org/abs/2203.06254
Perception Over Time: Temporal Dynamics for Robust Image Understanding. (16%)
Maryam Daniali; Edward Kim

http://arxiv.org/abs/2203.05774
Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation. (15%)
Yunhan Huang; Quanyan Zhu

http://arxiv.org/abs/2203.05323
Exploiting the Potential of Datasets: A Data-Centric Approach for Model Robustness. (92%)
Yiqi Zhong; Lei Wu; Xianming Liu; Junjun Jiang

http://arxiv.org/abs/2203.05212
Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation. (75%)
Saeed Ranjbar Alvar; Lanjun Wang; Jian Pei; Yong Zhang

http://arxiv.org/abs/2203.05653
Attack Analysis of Face Recognition Authentication Systems Using Fast Gradient Sign Method. (69%)
Arbena Musa; Kamer Vishi; Blerim Rexha

http://arxiv.org/abs/2203.05408
Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems. (64%)
Hadi Abdullah; Aditya Karlekar; Saurabh Prasad; Muhammad Sajidur Rahman; Logan Blue; Luke A. Bauer; Vincent Bindschaedler; Patrick Traynor

http://arxiv.org/abs/2203.05314
SoK: On the Semantic AI Security in Autonomous Driving. (10%)
Junjie Shen; Ningfei Wang; Ziwen Wan; Yunpeng Luo; Takami Sato; Zhisheng Hu; Xinyang Zhang; Shengjian Guo; Zhenyu Zhong; Kang Li; Ziming Zhao; Chunming Qiao; Qi Alfred Chen

http://arxiv.org/abs/2203.04607
Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation. (99%)
Qilong Zhang; Chaoning Zhang; Chaoqun Li; Jingkuan Song; Lianli Gao; Heng Tao Shen

http://arxiv.org/abs/2203.05154
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack. (99%)
Ye Liu; Yaya Cheng; Lianli Gao; Xianglong Liu; Qilong Zhang; Jingkuan Song

http://arxiv.org/abs/2203.05151
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity. (99%)
Cheng Luo; Qinliang Lin; Weicheng Xie; Bizhu Wu; Jinheng Xie; Linlin Shen

http://arxiv.org/abs/2203.04855
Binary Classification Under $\ell_0$ Attacks for General Noise Distribution. (98%)
Payam Delgosha; Hamed Hassani; Ramtin Pedarsani

http://arxiv.org/abs/2203.04623
Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition. (97%)
Xiao Yang; Yinpeng Dong; Tianyu Pang; Zihao Xiao; Hang Su; Jun Zhu

http://arxiv.org/abs/2203.04886
Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees. (92%)
Darshan Thaker; Paris Giampouras; René Vidal

http://arxiv.org/abs/2203.04713
Defending Black-box Skeleton-based Human Activity Classifiers. (92%)
He Wang; Yunfeng Diao; Zichang Tan; Guodong Guo

http://arxiv.org/abs/2203.04696
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition. (81%)
Yi Chang; Sofiane Laridi; Zhao Ren; Gregory Palmer; Björn W. Schuller; Marco Fisichella

http://arxiv.org/abs/2203.05103
Improving Neural ODEs via Knowledge Distillation. (80%)
Haoyu Chu; Shikui Wei; Qiming Lu; Yao Zhao

http://arxiv.org/abs/2203.06055
Physics-aware Complex-valued Adversarial Machine Learning in Reconfigurable Diffractive All-optical Neural Network. (22%)
Ruiyang Chen; Yingjie Li; Minhan Lou; Jichao Fan; Yingheng Tang; Berardi Sensale-Rodriguez; Cunxi Yu; Weilu Gao

http://arxiv.org/abs/2203.04946
On the surprising tradeoff between ImageNet accuracy and perceptual similarity. (1%)
Manoj Kumar; Neil Houlsby; Nal Kalchbrenner; Ekin D. Cubuk

http://arxiv.org/abs/2203.04234
Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust NIDS. (99%)
João Vitorino; Nuno Oliveira; Isabel Praça

http://arxiv.org/abs/2203.04041
Shape-invariant 3D Adversarial Point Clouds. (99%)
Qidong Huang; Xiaoyi Dong; Dongdong Chen; Hang Zhou; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2203.03888
ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation. (92%)
Robin Wang; Yibo Yang; Dacheng Tao

http://arxiv.org/abs/2203.04160
Robustly-reliable learners under poisoning attacks. (13%)
Maria-Florina Balcan; Avrim Blum; Steve Hanneke; Dravyansh Sharma

http://arxiv.org/abs/2203.04428
DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses. (2%)
Alexander Veicht; Cedric Renggli; Diogo Barradas

http://arxiv.org/abs/2203.06649
Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4. (1%)
William Berrios; Arturo Deza

http://arxiv.org/abs/2203.04420
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems. (1%)
Rahil Institute for Systems Research, University of Maryland Parikh; Ilya Google Inc Kavalerov; Carol Institute for Systems Research, University of Maryland Espy-Wilson; Shihab Institute for Systems Research, University of Maryland Shamma

http://arxiv.org/abs/2203.04412
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches. (99%)
Maura Pintor; Daniele Angioni; Angelo Sotgiu; Luca Demetrio; Ambra Demontis; Battista Biggio; Fabio Roli

http://arxiv.org/abs/2203.04405
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art. (99%)
Phoenix Williams; Ke Li

http://arxiv.org/abs/2203.03818
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon. (99%)
Yiqi Zhong; Xianming Liu; Deming Zhai; Junjun Jiang; Xiangyang Ji

http://arxiv.org/abs/2203.03373
Adversarial Texture for Fooling Person Detectors in the Physical World. (98%)
Zhanhao Hu; Siyuan Huang; Xiaopei Zhu; Xiaolin Hu; Fuchun Sun; Bo Zhang

http://arxiv.org/abs/2203.03762
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision. (83%)
Jun Zhuang; Mohammad Al Hasan

http://arxiv.org/abs/2203.03810
Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation. (31%)
Xiaogeng Liu; Haoyu Wang; Yechao Zhang; Fangzhou Wu; Shengshan Hu

http://arxiv.org/abs/2203.03128
$A^{3}D$: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks. (99%)
Jialiang Sun; Wen Yao; Tingsong Jiang; Chao Li; Xiaoqian Chen

http://arxiv.org/abs/2203.03121
Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer. (98%)
Shengshan Hu; Xiaogeng Liu; Yechao Zhang; Minghui Li; Leo Yu Zhang; Hai Jin; Libing Wu

http://arxiv.org/abs/2203.03048
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors. (45%)
Yibo Yang; Georgios Kissas; Paris Perdikaris

http://arxiv.org/abs/2203.02928
Evaluation of Interpretability Methods and Perturbation Artifacts in Deep Neural Networks. (2%)
Lennart Brocki; Neo Christopher Chung

http://arxiv.org/abs/2203.02735
aaeCAPTCHA: The Design and Implementation of Audio Adversarial CAPTCHA. (92%)
Md Imran Hossen; Xiali Hei

http://arxiv.org/abs/2203.03560
Targeted Data Poisoning Attack on News Recommendation System by Content Perturbation. (82%)
Xudong Zhang; Zan Wang; Jingke Zhao; Lanjun Wang

http://arxiv.org/abs/2203.02586
Concept-based Explanations for Out-Of-Distribution Detectors. (1%)
Jihye Choi; Jayaram Raghuram; Ryan Feng; Jiefeng Chen; Somesh Jha; Atul Prakash

http://arxiv.org/abs/2203.01516
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking. (99%)
Changhong Fu; Sihang Li; Xinnan Yuan; Junjie Ye; Ziang Cao; Fangqiang Ding

http://arxiv.org/abs/2203.01677
Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation. (98%)
KiYoon Yoo; Jangho Kim; Jiho Jang; Nojun Kwak

http://arxiv.org/abs/2203.02121
Adversarial Patterns: Building Robust Android Malware Classifiers. (98%)
Dipkamal Bhusal; Nidhi Rastogi

http://arxiv.org/abs/2203.01895
Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training. (84%)
Pervaiz Iqbal Khan; Shoaib Ahmed Siddiqui; Imran Razzak; Andreas Dengel; Sheraz Ahmed

http://arxiv.org/abs/2203.01925
Label-Only Model Inversion Attacks via Boundary Repulsion. (74%)
Mostafa Kahla; Si Chen; Hoang Anh Just; Ruoxi Jia

http://arxiv.org/abs/2203.01584
Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models. (56%)
Zhibo Wang; Xiaowei Dong; Henry Xue; Zhifei Zhang; Weifeng Chiu; Tao Wei; Kui Ren

http://arxiv.org/abs/2203.02006
Why adversarial training can hurt robust accuracy. (22%)
Jacob Clarysse; Julia Hörmann; Fanny Yang

http://arxiv.org/abs/2203.01881
Understanding Failure Modes of Self-Supervised Learning. (4%)
Neha Mukund Kalibhat; Kanika Narang; Liang Tan; Hamed Firooz; Maziar Sanjabi; Soheil Feizi

http://arxiv.org/abs/2203.01606
Ensemble Methods for Robust Support Vector Machines using Integer Programming. (2%)
Jannis Kurtz

http://arxiv.org/abs/2203.02050
Autonomous and Resilient Control for Optimal LEO Satellite Constellation Coverage Against Space Threats. (1%)
Yuhan Zhao; Quanyan Zhu

http://arxiv.org/abs/2203.01439
Enhancing Adversarial Robustness for Deep Metric Learning. (99%)
Mo Zhou; Vishal M. Patel

http://arxiv.org/abs/2203.00922
Canonical foliations of neural networks: application to robustness. (98%)
Eliot Tron; Nicolas Couellan; Stéphane Puechmorel

http://arxiv.org/abs/2203.01177
Detecting Adversarial Perturbations in Multi-Task Perception. (98%)
Marvin Klingner; Varun Ravi Kumar; Senthil Yogamani; Andreas Bär; Tim Fingscheidt

http://arxiv.org/abs/2203.07983
Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers. (69%)
Evan Crothers; Nathalie Japkowicz; Herna Viktor; Paula Branco

http://arxiv.org/abs/2203.00928
Video is All You Need: Attacking PPG-based Biometric Authentication. (13%)
Lin Li; Chao Chen; Lei Pan; Jun Zhang; Yang Xiang

http://arxiv.org/abs/2203.00915
MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members. (2%)
Ismat Jarin; Birhanu Eshete

http://arxiv.org/abs/2203.01212
A Quantitative Geometric Approach to Neural-Network Smoothness. (2%)
Zi Wang; Gautam Prakriya; Somesh Jha

http://arxiv.org/abs/2203.00302
Adversarial samples for deep monocular 6D object pose estimation. (99%)
Jinlai Zhang; Weiming Li; Shuang Liang; Hao Wang; Jihong Zhu

http://arxiv.org/abs/2203.00858
Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving. (87%)
Xingshuo Han; Guowen Xu; Yuan Zhou; Xuehuan Yang; Jiwei Li; Tianwei Zhang

http://arxiv.org/abs/2203.00553
Global-Local Regularization Via Distributional Robustness. (86%)
Hoang Phan; Trung Le; Trung Phung; Tuan Anh Bui; Nhat Ho; Dinh Phung

http://arxiv.org/abs/2203.01323
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation. (11%)
Wei Dai; Daniel Berleant

http://arxiv.org/abs/2203.00637
Signature Correction Attack on Dilithium Signature Scheme. (1%)
Saad Islam; Koksal Mus; Richa Singh; Patrick Schaumont; Berk Sunar

http://arxiv.org/abs/2202.13625
Enhance transferability of adversarial examples with model architecture. (99%)
Mingyuan Fan; Wenzhong Guo; Shengxing Yu; Zuobin Ying; Ximeng Liu

http://arxiv.org/abs/2202.13755
Towards Robust Stacked Capsule Autoencoder with Hybrid Adversarial Training. (99%)
Jiazhu Dai; Siwei Xiong

http://arxiv.org/abs/2202.13711
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses. (98%)
Francesco Croce; Sven Gowal; Thomas Brunner; Evan Shelhamer; Matthias Hein; Taylan Cemgil

http://arxiv.org/abs/2202.13922
MaMaDroid2.0 -- The Holes of Control Flow Graphs. (88%)
Harel Berger; Chen Hajaj; Enrico Mariconti; Amit Dvir

http://arxiv.org/abs/2202.13636
Improving Lexical Embeddings for Robust Question Answering. (67%)
Weiwen Xu; Bowei Zou; Wai Lam; Ai Ti Aw

http://arxiv.org/abs/2202.13817
Robust Textual Embedding against Word-level Adversarial Attacks. (26%)
Yichen Yang; Xiaosen Wang; Kun He

http://arxiv.org/abs/2202.14010
Artificial Intelligence for Cyber Security (AICS). (1%)
James Holt; Edward Raff; Ahmad Ridley; Dennis Ross; Arunesh Sinha; Diane Staheli; William Streilen; Milind Tambe; Yevgeniy Vorobeychik; Allan Wollaber

http://arxiv.org/abs/2203.00150
Explaining RADAR features for detecting spoofing attacks in Connected Autonomous Vehicles. (1%)
Nidhi Rastogi; Sara Rampazzi; Michael Clifford; Miriam Heller; Matthew Bishop; Karl Levitt

http://arxiv.org/abs/2202.13437
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training. (99%)
Tuan Anh Bui; Trung Le; Quan Tran; He Zhao; Dinh Phung

http://arxiv.org/abs/2202.13440
Robust Control of Partially Specified Boolean Networks. (1%)
Luboš Brim; Samuel Pastva; David Šafránek; Eva Šmijáková

http://arxiv.org/abs/2202.13216
Adversarial robustness of sparse local Lipschitz predictors. (87%)
Ramchandran Muthukumar; Jeremias Sulam

http://arxiv.org/abs/2202.13074
Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations. (45%)
Metehan Cekic; Can Bakiskan; Upamanyu Madhow

http://arxiv.org/abs/2202.13133
Automation of reversible steganographic coding with nonlinear discrete optimisation. (1%)
Ching-Chun Chang

http://arxiv.org/abs/2202.12860
ARIA: Adversarially Robust Image Attribution for Content Provenance. (99%)
Maksym Andriushchenko; Xiaoyang Rebecca Li; Geoffrey Oxholm; Thomas Gittings; Tu Bui; Nicolas Flammarion; John Collomosse

http://arxiv.org/abs/2202.12993
Projective Ranking-based GNN Evasion Attacks. (97%)
He Zhang; Xingliang Yuan; Chuan Zhou; Shirui Pan

http://arxiv.org/abs/2202.12506
On the Effectiveness of Dataset Watermarking in Adversarial Settings. (56%)
Buse Gul Atli Tekgul; N. Asokan

http://arxiv.org/abs/2202.12154
Towards Effective and Robust Neural Trojan Defenses via Input Filtering. (92%)
Kien Do; Haripriya Harikumar; Hung Le; Dung Nguyen; Truyen Tran; Santu Rana; Dang Nguyen; Willy Susilo; Svetha Venkatesh

http://arxiv.org/abs/2202.11910
Robust Probabilistic Time Series Forecasting. (76%)
TaeHo Yoon; Youngsuk Park; Ernest K. Ryu; Yuyang Wang

http://arxiv.org/abs/2202.12435
Understanding Adversarial Robustness from Feature Maps of Convolutional Layers. (70%)
Cong Xu; Min Yang

http://arxiv.org/abs/2202.12162
Measuring CLEVRness: Blackbox testing of Visual Reasoning Models. (16%)
Spyridon Mouselinos; Henryk Michalewski; Mateusz Malinowski

http://arxiv.org/abs/2202.12232
Bounding Membership Inference. (11%)
Anvith Thudi; Ilia Shumailov; Franziska Boenisch; Nicolas Papernot

http://arxiv.org/abs/2202.12412
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration. (3%)
Ryan Soklaski; Michael Yee; Theodoros Tsiligkaridis

http://arxiv.org/abs/2202.11919
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. (2%)
Chih-Kuan Yeh; Kuan-Yun Lee; Frederick Liu; Pradeep Ravikumar

http://arxiv.org/abs/2202.11915
Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning. (1%)
Xihong Yang; Xiaochang Hu; Sihang Zhou; Xinwang Liu; En Zhu

http://arxiv.org/abs/2202.11898
Improving Robustness of Convolutional Neural Networks Using Element-Wise Activation Scaling. (96%)
Zhi-Yuan Zhang; Di Liu

http://arxiv.org/abs/2202.11865
Using calibrator to improve robustness in Machine Reading Comprehension. (13%)
Jing Jin; Houfeng Wang

http://arxiv.org/abs/2202.11287
LPF-Defense: 3D Adversarial Defense based on Frequency Analysis. (99%)
Hanieh Naderi; Kimia Noorbakhsh; Arian Etemadi; Shohreh Kasaei

http://arxiv.org/abs/2202.10693
Universal adversarial perturbation for remote sensing images. (95%)
Zhaoxia Yin; Qingyu Wang; Jin Tang; Bin Luo

http://arxiv.org/abs/2202.10673
Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era. (84%)
Changjiang Li; Li Wang; Shouling Ji; Xuhong Zhang; Zhaohan Xi; Shanqing Guo; Ting Wang

http://arxiv.org/abs/2202.11202
Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning. (1%)
Hao He; Kaiwen Zha; Dina Katabi

http://arxiv.org/abs/2202.10594
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey. (99%)
Ngoc Dung Huynh; Mohamed Reda Bouadjenek; Imran Razzak; Kevin Lee; Chetan Arora; Ali Hassani; Arkady Zaslavsky

http://arxiv.org/abs/2202.10523
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness. (99%)
Beomsu Kim; Junghoon Seo

http://arxiv.org/abs/2202.10309
HoneyModels: Machine Learning Honeypots. (99%)
Ahmed Abdou; Ryan Sheatsley; Yohan Beugin; Tyler Shipp; Patrick McDaniel

http://arxiv.org/abs/2202.09994
Transferring Adversarial Robustness Through Robust Representation Matching. (99%)
Pratik Vaishnavi; Kevin Eykholt; Amir Rahmati

http://arxiv.org/abs/2202.10627
On the Effectiveness of Adversarial Training against Backdoor Attacks. (96%)
Yinghua Gao; Dongxian Wu; Jingfeng Zhang; Guanhao Gan; Shu-Tao Xia; Gang Niu; Masashi Sugiyama

http://arxiv.org/abs/2202.10276
Poisoning Attacks and Defenses on Artificial Intelligence: A Survey. (83%)
Miguel A. Ramirez; Song-Kyoo Kim; Hussam Al Hamadi; Ernesto Damiani; Young-Ji Byon; Tae-Yeon Kim; Chung-Suk Cho; Chan Yeob Yeun

http://arxiv.org/abs/2202.10377
A Tutorial on Adversarial Learning Attacks and Countermeasures. (75%)
Cato Pauling; Michael Gimson; Muhammed Qaid; Ahmad Kida; Basel Halak

http://arxiv.org/abs/2202.11196
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection. (41%)
Yein Kim; Huili Chen; Farinaz Koushanfar

http://arxiv.org/abs/2202.10546
Privacy Leakage of Adversarial Training Models in Federated Learning Systems. (38%)
Jingyang Zhang; Yiran Chen; Hai Li

http://arxiv.org/abs/2202.10103
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition. (11%)
Tianyu Pang; Min Lin; Xiao Yang; Jun Zhu; Shuicheng Yan

http://arxiv.org/abs/2202.10354
Cyber-Physical Defense in the Quantum Era. (2%)
Michel Barbeau; Joaquin Garcia-Alfaro

http://arxiv.org/abs/2202.11197
Real-time Over-the-air Adversarial Perturbations for Digital Communications using Deep Neural Networks. (93%)
Roman A. Sandler; Peter K. Relich; Cloud Cho; Sean Holloway

http://arxiv.org/abs/2202.09844
Sparsity Winning Twice: Better Robust Generaliztion from More Efficient Training. (26%)
Tianlong Chen; Zhenyu Zhang; Pengjun Wang; Santosh Balachandra; Haoyu Ma; Zehao Wang; Zhangyang Wang

http://arxiv.org/abs/2202.09735
Overparametrization improves robustness against adversarial attacks: A replication study. (3%)
Ali Borji

http://arxiv.org/abs/2202.09300
Exploring Adversarially Robust Training for Unsupervised Domain Adaptation. (99%)
Shao-Yuan Lo; Vishal M. Patel

http://arxiv.org/abs/2202.09446
Learning Representations Robust to Group Shifts and Adversarial Examples. (93%)
Ming-Chang Chiu; Xuezhe Ma

http://arxiv.org/abs/2202.09039
Critical Checkpoints for Evaluating Defence Models Against Adversarial Attack and Robustness. (92%)
Kanak Tekwani; Manojkumar Parmar

http://arxiv.org/abs/2202.10320
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches. (80%)
Reena Zelenkova; Jack Swallow; M. A. P. Chamikara; Dongxi Liu; Mohan Baruwal Chhetri; Seyit Camtepe; Marthie Grobler; Mahathir Almashor

http://arxiv.org/abs/2202.09483
Data-Driven Mitigation of Adversarial Text Perturbation. (75%)
Rasika Bhalerao; Mohammad Al-Rubaie; Anand Bhaskar; Igor Markov

http://arxiv.org/abs/2202.10582
Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias. (68%)
Shangxi Wu; Qiuyang He; Yi Zhang; Jitao Sang

http://arxiv.org/abs/2202.09248
Stochastic Perturbations of Tabular Features for Non-Deterministic Inference with Automunge. (38%)
Nicholas J. Teague

http://arxiv.org/abs/2202.11203
Label-Smoothed Backdoor Attack. (33%)
Minlong Peng; Zidi Xiong; Mingming Sun; Ping Li

http://arxiv.org/abs/2202.09389
Black-box Node Injection Attack for Graph Neural Networks. (33%)
Mingxuan Ju; Yujie Fan; Yanfang Ye; Liang Zhao

http://arxiv.org/abs/2202.09514
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training. (9%)
Peide Huang; Mengdi Xu; Fei Fang; Ding Zhao

http://arxiv.org/abs/2202.09465
Attacks, Defenses, And Tools: A Framework To Facilitate Robust AI/ML Systems. (4%)
Mohamad Fazelnia; Igor Khokhlov; Mehdi Mirakhorli

http://arxiv.org/abs/2202.09381
Synthetic Disinformation Attacks on Automated Fact Verification Systems. (1%)
Yibing Du; Antoine Bosselut; Christopher D. Manning

http://arxiv.org/abs/2202.08944
Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problem. (99%)
Abderrahmen Amich; Birhanu Eshete

http://arxiv.org/abs/2202.08532
Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition. (98%)
Chao-Han Huck Yang; Zeeshan Ahmed; Yile Gu; Joseph Szurley; Roger Ren; Linda Liu; Andreas Stolcke; Ivan Bulyko

http://arxiv.org/abs/2202.08892
Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies. (98%)
Chris Wise; Jo Plested

http://arxiv.org/abs/2202.08602
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations. (78%)
Zirui Peng; Shaofeng Li; Guoxing Chen; Cheng Zhang; Haojin Zhu; Minhui Xue

http://arxiv.org/abs/2202.08185
The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining. (99%)
Murat Kuzlu; Ferhat Ozgur Catak; Umit Cali; Evren Catak; Ozgur Guler

http://arxiv.org/abs/2202.08057
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. (10%)
Yongqiang Chen; Han Yang; Yonggang Zhang; Kaili Ma; Tongliang Liu; Bo Han; James Cheng

http://arxiv.org/abs/2202.10943
Gradient Based Activations for Accurate Bias-Free Learning. (1%)
Vinod K Kurmi; Rishabh Sharma; Yash Vardhan Sharma; Vinay P. Namboodiri

http://arxiv.org/abs/2202.07342
Unreasonable Effectiveness of Last Hidden Layer Activations. (99%)
Omer Faruk Tuna; Ferhat Ozgur Catak; M. Taner Eskil

http://arxiv.org/abs/2202.07261
Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks. (99%)
Qianjiang Hu; Daizong Liu; Wei Hu

http://arxiv.org/abs/2202.07568
StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection. (99%)
Aqib Rashid; Jose Such

http://arxiv.org/abs/2202.07453
Random Walks for Adversarial Meshes. (97%)
Amir Belder; Gal Yefet; Ran Ben Izhak; Ayellet Tal

http://arxiv.org/abs/2202.07802
Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing. (93%)
Zhiyan Chen; Burak Kantarci

http://arxiv.org/abs/2202.07815
Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars. (89%)
Aakash Kumar

http://arxiv.org/abs/2202.07562
Improving the repeatability of deep learning models with Monte Carlo dropout. (1%)
Andreanne Lemay; Katharina Hoebel; Christopher P. Bridge; Brian Befano; Sanjosé Silvia De; Diden Egemen; Ana Cecilia Rodriguez; Mark Schiffman; John Peter Campbell; Jayashree Kalpathy-Cramer

http://arxiv.org/abs/2202.07201
Holistic Adversarial Robustness of Deep Learning Models. (1%)
Pin-Yu Chen; Sijia Liu

http://arxiv.org/abs/2202.07679
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. (1%)
Zhen Lin; Shubhendu Trivedi; Jimeng Sun

http://arxiv.org/abs/2202.07054
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark. (99%)
Yonghao Xu; Pedram Ghamisi

http://arxiv.org/abs/2202.06488
Finding Dynamics Preserving Adversarial Winning Tickets. (86%)
Xupeng Shi; Pengfei Zheng; A. Adam Ding; Yuan Gao; Weizhong Zhang

http://arxiv.org/abs/2202.07114
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack. (83%)
Jintang Li; Bingzhe Wu; Chengbin Hou; Guoji Fu; Yatao Bian; Liang Chen; Junzhou Huang; Zibin Zheng

http://arxiv.org/abs/2202.06658
PFGE: Parsimonious Fast Geometric Ensembling of DNNs. (1%)
Hao Guo; Jiyong Jin; Bin Liu

http://arxiv.org/abs/2202.06701
UA-FedRec: Untargeted Attack on Federated News Recommendation. (1%)
Jingwei Yi; Fangzhao Wu; Bin Zhu; Jing Yao; Zhulin Tao; Guangzhong Sun; Xing Xie

http://arxiv.org/abs/2202.06312
Progressive Backdoor Erasing via connecting Backdoor and Adversarial Attacks. (99%)
Bingxu Mu; Zhenxing Niu; Le Wang; Xue Wang; Rong Jin; Gang Hua

http://arxiv.org/abs/2202.06382
Training with More Confidence: Mitigating Injected and Natural Backdoors During Training. (92%)
Zhenting Wang; Hailun Ding; Juan Zhai; Shiqing Ma

http://arxiv.org/abs/2202.06474
Extracting Label-specific Key Input Features for Neural Code Intelligence Models. (9%)
Md Rafiqul Islam Rabin

http://arxiv.org/abs/2202.06414
Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey. (2%)
Zhilin Wang; Qiao Kang; Xinyi Zhang; Qin Hu

http://arxiv.org/abs/2202.07471
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation. (1%)
Cong Guo; Yuxian Qiu; Jingwen Leng; Xiaotian Gao; Chen Zhang; Yunxin Liu; Fan Yang; Yuhao Zhu; Minyi Guo

http://arxiv.org/abs/2202.06043
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation. (98%)
Zhen Qian Li; Qian Guenevere; Chen; Chen Chen; Yayi Zou; Shouhuai Xu

http://arxiv.org/abs/2202.07464
Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons. (98%)
Haibo Jin; Ruoxi Chen; Haibin Zheng; Jinyin Chen; Yao Cheng; Yue Yu; Xianglong Liu

http://arxiv.org/abs/2202.07421
Adversarial Attacks and Defense Methods for Power Quality Recognition. (99%)
Jiwei Tian; Buhong Wang; Jing Li; Zhen Wang; Mete Ozay

http://arxiv.org/abs/2202.05687
Towards Adversarially Robust Deepfake Detection: An Ensemble Approach. (99%)
Ashish Hooda; Neal Mangaokar; Ryan Feng; Kassem Fawaz; Somesh Jha; Atul Prakash

http://arxiv.org/abs/2202.05953
Open-set Adversarial Defense with Clean-Adversarial Mutual Learning. (98%)
Rui Shao; Pramuditha Perera; Pong C. Yuen; Vishal M. Patel

http://arxiv.org/abs/2202.05758
Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment Analysis Models. (92%)
Abigail Swenor; Jugal Kalita

http://arxiv.org/abs/2202.05488
Fast Adversarial Training with Noise Augmentation: A Unified Perspective on RandStart and GradAlign. (74%)
Axi Niu; Kang Zhang; Chaoning Zhang; Chenshuang Zhang; In So Kweon; Chang D. Yoo; Yanning Zhang

http://arxiv.org/abs/2202.05834
Predicting Out-of-Distribution Error with the Projection Norm. (62%)
Yaodong Yu; Zitong Yang; Alexander Wei; Yi Ma; Jacob Steinhardt

http://arxiv.org/abs/2202.05470
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers. (62%)
Limin Yang; Zhi Chen; Jacopo Cortellazzi; Feargus Pendlebury; Kevin Tu; Fabio Pierazzi; Lorenzo Cavallaro; Gang Wang

http://arxiv.org/abs/2202.05778
White-Box Attacks on Hate-speech BERT Classifiers in German with Explicit and Implicit Character Level Defense. (12%)
Shahrukh Khan; Mahnoor Shahid; Navdeeppal Singh

http://arxiv.org/abs/2202.05725
On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systems. (10%)
Zesheng Chen

http://arxiv.org/abs/2202.05737
Improving Generalization via Uncertainty Driven Perturbations. (2%)
Matteo Pagliardini; Gilberto Manunza; Martin Jaggi; Michael I. Jordan; Tatjana Chavdarova

http://arxiv.org/abs/2202.05613
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning. (1%)
Jun Shu; Xiang Yuan; Deyu Meng; Zongben Xu

http://arxiv.org/abs/2202.05416
FAAG: Fast Adversarial Audio Generation through Interactive Attack Optimisation. (99%)
Yuantian Miao; Chao Chen; Lei Pan; Jun Zhang; Yang Xiang

http://arxiv.org/abs/2202.04978
Towards Assessing and Characterizing the Semantic Robustness of Face Recognition. (76%)
Juan C. Pérez; Motasem Alfarra; Ali Thabet; Pablo Arbeláez; Bernard Ghanem

http://arxiv.org/abs/2202.05068
Controlling the Complexity and Lipschitz Constant improves polynomial nets. (12%)
Zhenyu Zhu; Fabian Latorre; Grigorios G Chrysos; Volkan Cevher

http://arxiv.org/abs/2202.04975
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling. (8%)
Chuhan Wu; Fangzhao Wu; Tao Qi; Yongfeng Huang; Xing Xie

http://arxiv.org/abs/2202.05271
A Field of Experts Prior for Adapting Neural Networks at Test Time. (1%)
Neerav Karani; Georg Brunner; Ertunc Erdil; Simin Fei; Kerem Tezcan; Krishna Chaitanya; Ender Konukoglu

http://arxiv.org/abs/2202.04781
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios. (99%)
Jung Im Choi; Qing Tian

http://arxiv.org/abs/2202.04347
Gradient Methods Provably Converge to Non-Robust Networks. (82%)
Gal Vardi; Gilad Yehudai; Ohad Shamir

http://arxiv.org/abs/2202.04479
False Memory Formation in Continual Learners Through Imperceptible Backdoor Trigger. (22%)
Muhammad Umer; Robi Polikar

http://arxiv.org/abs/2202.04311
ARIBA: Towards Accurate and Robust Identification of Backdoor Attacks in Federated Learning. (10%)
Yuxi Mi; Jihong Guan; Shuigeng Zhou

http://arxiv.org/abs/2202.04291
L2B: Learning to Bootstrap Robust Models for Combating Label Noise. (2%)
Yuyin Zhou; Xianhang Li; Fengze Liu; Qingyue Wei; Xuxi Chen; Lequan Yu; Cihang Xie; Matthew P. Lungren; Lei Xing

http://arxiv.org/abs/2202.04392
Model Architecture Adaption for Bayesian Neural Networks. (1%)
Duo Wang; Yiren Zhao; Ilia Shumailov; Robert Mullins

http://arxiv.org/abs/2202.04235
Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations. (99%)
Lei Hsiung; Yun-Yun Tsai; Pin-Yu Chen; Tsung-Yi Ho

http://arxiv.org/abs/2202.03898
Verification-Aided Deep Ensemble Selection. (96%)
Guy Amir; Guy Katz; Michael Schapira

http://arxiv.org/abs/2202.04271
Adversarial Detection without Model Information. (87%)
Abhishek Moitra; Youngeun Kim; Priyadarshini Panda

http://arxiv.org/abs/2202.03861
Towards Making a Trojan-horse Attack on Text-to-Image Retrieval. (68%)
Fan Hu; Aozhu Chen; Xirong Li

http://arxiv.org/abs/2202.05395
Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks. (1%)
Alireza Sadeghi

http://arxiv.org/abs/2202.05877
Blind leads Blind: A Zero-Knowledge Attack on Federated Learning. (99%)
Jiyue Huang; Zilong Zhao; Lydia Y. Chen; Stefanie Roos

http://arxiv.org/abs/2202.03277
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks. (99%)
Salijona Dyrmishi; Salah Ghamizi; Thibault Simonetto; Yves Le Traon; Maxime Cordy

http://arxiv.org/abs/2202.03077
Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests. (98%)
Xilie Xu; Jingfeng Zhang; Feng Liu; Masashi Sugiyama; Mohan Kankanhalli

http://arxiv.org/abs/2202.03558
Evaluating Robustness of Cooperative MARL: A Model-based Approach. (98%)
Nhan H. Pham; Lam M. Nguyen; Jie Chen; Hoang Thanh Lam; Subhro Das; Tsui-Wei Weng

http://arxiv.org/abs/2202.03195
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks. (68%)
Jing Xu; Rui Wang; Kaitai Liang; Stjepan Picek

http://arxiv.org/abs/2202.03335
Membership Inference Attacks and Defenses in Neural Network Pruning. (50%)
Xiaoyong Yuan; Lan Zhang

http://arxiv.org/abs/2202.03104
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation. (4%)
Jun Xia; Lirong Wu; Jintao Chen; Bozhen Hu; Stan Z. Li

http://arxiv.org/abs/2202.03460
Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning. (3%)
Ji Gao; Sanjam Garg; Mohammad Mahmoody; Prashant Nalini Vasudevan

http://arxiv.org/abs/2202.02751
Tubes Among Us: Analog Attack on Automatic Speaker Identification. (99%)
Shimaa Ahmed; Yash Wani; Ali Shahin Shamsabadi; Mohammad Yaghini; Ilia Shumailov; Nicolas Papernot; Kassem Fawaz

http://arxiv.org/abs/2202.02902
Redactor: A Data-centric and Individualized Defense Against Inference Attacks. (8%)
Geon Heo; Steven Euijong Whang

http://arxiv.org/abs/2202.02626
Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) framework. (99%)
Mohammad Khalooei; Mohammad Mehdi Homayounpour; Maryam Amirmazlaghani

http://arxiv.org/abs/2202.02503
Adversarial Detector with Robust Classifier. (93%)
Takayuki Osakabe; Maungmaung Aprilpyone; Sayaka Shiota; Hitoshi Kiya

http://arxiv.org/abs/2202.02595
Memory Defense: More Robust Classification via a Memory-Masking Autoencoder. (76%)
Eashan Lehigh University Adhikarla; Dan Lehigh University Luo; Brian D. Lehigh University Davison

http://arxiv.org/abs/2202.02628
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation. (75%)
Wenxiao Wang; Alexander Levine; Soheil Feizi

http://arxiv.org/abs/2202.02236
Pixle: a fast and effective black-box attack based on rearranging pixels. (98%)
Jary Pomponi; Simone Scardapane; Aurelio Uncini

http://arxiv.org/abs/2202.03423
Backdoor Defense via Decoupling the Training Process. (80%)
Kunzhe Huang; Yiming Li; Baoyuan Wu; Zhan Qin; Kui Ren

http://arxiv.org/abs/2202.02278
LTU Attacker for Membership Inference. (67%)
Joseph Pedersen; Rafael Muñoz-Gómez; Jiangnan Huang; Haozhe Sun; Wei-Wei Tu; Isabelle Guyon

http://arxiv.org/abs/2202.02215
A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective. (1%)
Wenhao Ding; Chejian Xu; Mansur Arief; Haohong Lin; Bo Li; Ding Zhao

http://arxiv.org/abs/2202.01811
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking. (93%)
Chong Xiang; Alexander Valtchanov; Saeed Mahloujifar; Prateek Mittal

http://arxiv.org/abs/2202.01832
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization. (75%)
Xiaojun Xu; Jacky Yibo Zhang; Evelyn Ma; Danny Son; Oluwasanmi Koyejo; Bo Li

http://arxiv.org/abs/2202.01117
An Eye for an Eye: Defending against Gradient-based Attacks with Gradients. (99%)
Hanbin Hong; Yuan Hong; Yu Kong

http://arxiv.org/abs/2202.01186
Smoothed Embeddings for Certified Few-Shot Learning. (76%)
Mikhail Pautov; Olesya Kuznetsova; Nurislam Tursynbek; Aleksandr Petiushko; Ivan Oseledets

http://arxiv.org/abs/2202.01136
Probabilistically Robust Learning: Balancing Average- and Worst-case Performance. (75%)
Alexander Robey; Luiz F. O. Chamon; George J. Pappas; Hamed Hassani

http://arxiv.org/abs/2202.01181
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training. (70%)
Jorge Pau de; Adel Bibi; Riccardo Volpi; Amartya Sanyal; Philip H. S. Torr; Grégory Rogez; Puneet K. Dokania

http://arxiv.org/abs/2202.01341
Robust Binary Models by Pruning Randomly-initialized Networks. (10%)
Chen Liu; Ziqi Zhao; Sabine Süsstrunk; Mathieu Salzmann

http://arxiv.org/abs/2202.01263
NoisyMix: Boosting Robustness by Combining Data Augmentations, Stability Training, and Noise Injections. (10%)
N. Benjamin Erichson; Soon Hoe Lim; Francisco Utrera; Winnie Xu; Ziang Cao; Michael W. Mahoney

http://arxiv.org/abs/2202.00399
Language Dependencies in Adversarial Attacks on Speech Recognition Systems. (98%)
Karla Markert; Donika Mirdita; Konstantin Böttinger

http://arxiv.org/abs/2202.00838
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks. (80%)
Anne Harrington; Arturo Deza

http://arxiv.org/abs/2202.00673
Visualizing Automatic Speech Recognition -- Means for a Better Understanding? (64%)
Karla Markert; Romain Parracone; Mykhailo Kulakov; Philip Sperl; Ching-Yu Kao; Konstantin Böttinger

http://arxiv.org/abs/2202.00622
Datamodels: Predicting Predictions from Training Data. (2%)
Andrew Ilyas; Sung Min Park; Logan Engstrom; Guillaume Leclerc; Aleksander Madry

http://arxiv.org/abs/2201.12347
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons. (99%)
Chandresh Pravin; Ivan Martino; Giuseppe Nicosia; Varun Ojha

http://arxiv.org/abs/2201.13444
Boundary Defense Against Black-box Adversarial Attacks. (99%)
Manjushree B. Aithal; Xiaohua Li

http://arxiv.org/abs/2202.00091
Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models. (99%)
Viet Quoc Vo; Ehsan Abbasnejad; Damith C. Ranasinghe

http://arxiv.org/abs/2201.13329
Can Adversarial Training Be Manipulated By Non-Robust Features? (98%)
Lue Tao; Lei Feng; Hongxin Wei; Jinfeng Yi; Sheng-Jun Huang; Songcan Chen

http://arxiv.org/abs/2201.13102
GADoT: GAN-based Adversarial Training for Robust DDoS Attack Detection. (96%)
Maged Abdelaty; Sandra Scott-Hayward; Roberto Doriguzzi-Corin; Domenico Siracusa

http://arxiv.org/abs/2202.03133
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks? (93%)
Youngeun Kim; Hyoungseob Park; Abhishek Moitra; Abhiroop Bhattacharjee; Yeshwanth Venkatesha; Priyadarshini Panda

http://arxiv.org/abs/2202.01179
AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks. (89%)
Muhammad Usman; Youcheng Sun; Divya Gopinath; Corina S. Pasareanu

http://arxiv.org/abs/2201.13164
Imperceptible and Multi-channel Backdoor Attack against Deep Neural Networks. (81%)
Mingfu Xue; Shifeng Ni; Yinghao Wu; Yushu Zhang; Jian Wang; Weiqiang Liu

http://arxiv.org/abs/2201.13019
On the Robustness of Quality Measures for GANs. (80%)
Motasem Alfarra; Juan C. Pérez; Anna Frühstück; Philip H. S. Torr; Peter Wonka; Bernard Ghanem

http://arxiv.org/abs/2202.00008
MEGA: Model Stealing via Collaborative Generator-Substitute Networks. (76%)
Chi Hong; Jiyue Huang; Lydia Y. Chen

http://arxiv.org/abs/2201.13025
Learning Robust Representation through Graph Adversarial Contrastive Learning. (26%)
Jiayan Guo; Shangyang Li; Yue Zhao; Yan Zhang

http://arxiv.org/abs/2201.13279
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs. (16%)
Philipp Oberdiek; Gernot A. Fink; Matthias Rottmann

http://arxiv.org/abs/2201.13178
Few-Shot Backdoor Attacks on Visual Object Tracking. (10%)
Yiming Li; Haoxiang Zhong; Xingjun Ma; Yong Jiang; Shu-Tao Xia

http://arxiv.org/abs/2202.00137
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors. (5%)
Pedro Miguel Sánchez Sánchez; Alberto Huertas Celdrán; Timo Schenk; Adrian Lars Benjamin Iten; Gérôme Bovet; Gregorio Martínez Pérez; Burkhard Stiller

http://arxiv.org/abs/2201.13086
Securing Federated Sensitive Topic Classification against Poisoning Attacks. (1%)
Tianyue Chu; Alvaro Garcia-Recuero; Costas Iordanou; Georgios Smaragdakis; Nikolaos Laoutaris

http://arxiv.org/abs/2201.12765
Improving Corruption and Adversarial Robustness by Enhancing Weak Subnets. (92%)
Yong Guo; David Stutz; Bernt Schiele

http://arxiv.org/abs/2201.12741
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks. (84%)
Chenhui Deng; Xiuyu Li; Zhuo Feng; Zhiru Zhang

http://arxiv.org/abs/2201.12733
TPC: Transformation-Specific Smoothing for Point Cloud Models. (75%)
Wenda Chu; Linyi Li; Bo Li

http://arxiv.org/abs/2201.12527
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses. (99%)
Mengting Xu; Tao Zhang; Zhongnian Li; Daoqiang Zhang

http://arxiv.org/abs/2201.12686
Robustness of Deep Recommendation Systems to Untargeted Interaction Perturbations. (82%)
Sejoon Oh; Srijan Kumar

http://arxiv.org/abs/2201.12700
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms. (1%)
Jeongyeol Kwon; Yonathan Efroni; Constantine Caramanis; Shie Mannor

http://arxiv.org/abs/2201.12356
Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning. (87%)
Jie Zhang; Lei Zhang; Gang Li; Chao Wu

http://arxiv.org/abs/2201.12107
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods. (81%)
Raoul Schönhof; Artem Werner; Jannes Elstner; Boldizsar Zopcsak; Ramez Awad; Marco Huber

http://arxiv.org/abs/2201.12440
Certifying Model Accuracy under Distribution Shifts. (74%)
Aounon Kumar; Alexander Levine; Tom Goldstein; Soheil Feizi

http://arxiv.org/abs/2201.12296
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions. (13%)
Jiachen Sun; Qingzhao Zhang; Bhavya Kailkhura; Zhiding Yu; Chaowei Xiao; Z. Morley Mao

http://arxiv.org/abs/2201.12179
Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks. (8%)
Lukas Struppek; Dominik Hintersdorf; Antonio De Almeida Correia; Antonia Adler; Kristian Kersting

http://arxiv.org/abs/2201.12211
Backdoors Stuck At The Frontdoor: Multi-Agent Backdoor Attacks That Backfire. (3%)
Siddhartha Datta; Nigel Shadbolt

http://arxiv.org/abs/2201.12328
Toward Training at ImageNet Scale with Differential Privacy. (1%)
Alexey Kurakin; Shuang Song; Steve Chien; Roxana Geambasu; Andreas Terzis; Abhradeep Thakurta

http://arxiv.org/abs/2201.11528
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains. (99%)
Qilong Zhang; Xiaodan Li; Yuefeng Chen; Jingkuan Song; Lianli Gao; Yuan He; Hui Xue

http://arxiv.org/abs/2201.11674
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities. (10%)
Xin Du; Benedicte Legastelois; Bhargavi Ganesh; Ajitha Rajan; Hana Chockler; Vaishak Belle; Stuart Anderson; Subramanian Ramamoorthy

http://arxiv.org/abs/2201.11692
SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders. (2%)
Tianshuo Cong; Xinlei He; Yang Zhang

http://arxiv.org/abs/2201.11377
CacheFX: A Framework for Evaluating Cache Security. (1%)
Daniel Genkin; William Kosasih; Fangfei Liu; Anna Trikalinou; Thomas Unterluggauer; Yuval Yarom

http://arxiv.org/abs/2201.10937
Boosting 3D Adversarial Attacks with Attacking On Frequency. (98%)
Binbin Liu; Jinlai Zhang; Lyujie Chen; Jihong Zhu

http://arxiv.org/abs/2201.10972
How Robust are Discriminatively Trained Zero-Shot Learning Models? (98%)
Mehmet Kerim Yucel; Ramazan Gokberk Cinbis; Pinar Duygulu

http://arxiv.org/abs/2201.11148
Autonomous Cyber Defense Introduces Risk: Can We Manage the Risk? (2%)
Alexandre K. Ligo; Alexander Kott; Igor Linkov

http://arxiv.org/abs/2201.10833
Automatic detection of access control vulnerabilities via API specification processing. (1%)
Alexander Barabanov; Denis Dergunov; Denis Makrushin; Aleksey Teplov

http://arxiv.org/abs/2201.10675
Virtual Adversarial Training for Semi-supervised Breast Mass Classification. (3%)
Xuxin Chen; Ximin Wang; Ke Zhang; Kar-Ming Fung; Theresa C. Thai; Kathleen Moore; Robert S. Mannel; Hong Liu; Bin Zheng; Yuchen Qiu

http://arxiv.org/abs/2201.10737
Class-Aware Adversarial Transformers for Medical Image Segmentation. (1%)
Chenyu You; Ruihan Zhao; Fenglin Liu; Siyuan Dong; Sandeep Chinchali; Ufuk Topcu; Lawrence Staib; James S. Duncan

http://arxiv.org/abs/2201.10207
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training. (1%)
Wenyong Huang; Zhenhe Zhang; Yu Ting Yeung; Xin Jiang; Qun Liu

http://arxiv.org/abs/2201.09650
What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction. (99%)
Yijun Yang; Ruiyuan Gao; Yu Li; Qiuxia Lai; Qiang Xu

http://arxiv.org/abs/2201.10055
Identifying a Training-Set Attack's Target Using Renormalized Influence Estimation. (95%)
Zayd Hammoudeh; Daniel Lowd

http://arxiv.org/abs/2201.09967
Attacks and Defenses for Free-Riders in Multi-Discriminator GAN. (76%)
Zilong Zhao; Jiyue Huang; Stefanie Roos; Lydia Y. Chen

http://arxiv.org/abs/2201.09538
Backdoor Defense with Machine Unlearning. (33%)
Yang Liu; Mingyuan Fan; Cen Chen; Ximeng Liu; Zhuo Ma; Li Wang; Jianfeng Ma

http://arxiv.org/abs/2201.09631
On the Complexity of Attacking Elliptic Curve Based Authentication Chips. (1%)
Ievgen Kabin; Zoya Dyka; Dan Klann; Jan Schaeffner; Peter Langendoerfer

http://arxiv.org/abs/2201.09369
Efficient and Robust Classification for Sparse Attacks. (83%)
Mark Beliaev; Payam Delgosha; Hamed Hassani; Ramtin Pedarsani

http://arxiv.org/abs/2202.00469
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning. (78%)
Chenghao Fan; Ziao Li; Wei wei

http://arxiv.org/abs/2201.09370
Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models. (56%)
Shagufta Mehnaz; Sayanton V. Dibbo; Ehsanul Kabir; Ninghui Li; Elisa Bertino

http://arxiv.org/abs/2201.09243
Increasing the Cost of Model Extraction with Calibrated Proof of Work. (22%)
Adam Dziedzic; Muhammad Ahmad Kaleem; Yu Shen Lu; Nicolas Papernot

http://arxiv.org/abs/2201.08970
Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection. (99%)
Siyuan Liang; Baoyuan Wu; Yanbo Fan; Xingxing Wei; Xiaochun Cao

http://arxiv.org/abs/2201.09109
Robust Unpaired Single Image Super-Resolution of Faces. (98%)
Saurabh Goswami; Rajagopalan A. N

http://arxiv.org/abs/2201.09051
On the Robustness of Counterfactual Explanations to Adverse Perturbations. (10%)
Marco Virgolin; Saverio Fracaros

http://arxiv.org/abs/2201.08698
Natural Attack for Pre-trained Models of Code. (99%)
Zhou Yang; Jieke Shi; Junda He; David Lo

http://arxiv.org/abs/2201.08557
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective. (99%)
Jihong Wang; Minnan Luo; Jundong Li; Ziqi Liu; Jun Zhou; Qinghua Zheng

http://arxiv.org/abs/2201.08661
The Security of Deep Learning Defences for Medical Imaging. (80%)
Moshe Levy; Guy Amit; Yuval Elovici; Yisroel Mirsky

http://arxiv.org/abs/2201.08619
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World. (75%)
Hua Ma; Yinshan Li; Yansong Gao; Alsharif Abuadbba; Zhi Zhang; Anmin Fu; Hyoungshick Kim; Said F. Al-Sarawi; Nepal Surya; Derek Abbott

http://arxiv.org/abs/2201.08555
Identifying Adversarial Attacks on Text Classifiers. (73%)
Zhouhang Xie; Jonathan Brophy; Adam Noack; Wencong You; Kalyani Asthana; Carter Perkins; Sabrina Reis; Sameer Singh; Daniel Lowd

http://arxiv.org/abs/2201.08956
The Many Faces of Adversarial Risk. (47%)
Muni Sreenivas Pydi; Varun Jog

http://arxiv.org/abs/2201.08193
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack. (99%)
Zhen Yu; Xiaosen Wang; Wanxiang Che; Kun He

http://arxiv.org/abs/2201.08318
Cheating Automatic Short Answer Grading: On the Adversarial Usage of Adjectives and Adverbs. (95%)
Anna Filighera; Sebastian Ochs; Tim Steuer; Thomas Tregel

http://arxiv.org/abs/2201.08135
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges. (93%)
Nuria Rodríguez-Barroso; Daniel Jiménez López; M. Victoria Luzón; Francisco Herrera; Eugenio Martínez-Cámara

http://arxiv.org/abs/2201.08731
Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples. (83%)
Haidong Xie; Jia Tan; Xiaoying Zhang; Nan Ji; Haihua Liao; Zuguo Yu; Xueshuang Xiang; Naijin Liu

http://arxiv.org/abs/2201.08474
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios. (70%)
Zhen Xiang; David J. Miller; George Kesidis

http://arxiv.org/abs/2201.08052
Adversarial Jamming for a More Effective Constellation Attack. (56%)
Haidong Xie; Yizhou Xu; Yuanqing Chen; Nan Ji; Shuai Yuan; Naijin Liu; Xueshuang Xiang

http://arxiv.org/abs/2201.08388
Steerable Pyramid Transform Enables Robust Left Ventricle Quantification. (13%)
Xiangyang Zhu; Kede Ma; Wufeng Xue

http://arxiv.org/abs/2201.08531
Black-box Prompt Learning for Pre-trained Language Models. (13%)
Shizhe Diao; Zhichao Huang; Ruijia Xu; Xuechun Li; Yong Lin; Xiao Zhou; Tong Zhang

http://arxiv.org/abs/2201.08087
DeepGalaxy: Testing Neural Network Verifiers via Two-Dimensional Input Space Exploration. (1%)
Xuan Xie; Fuyuan Zhang

http://arxiv.org/abs/2201.07986
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation. (96%)
Sixiao Zhang; Hongxu Chen; Xiangguo Sun; Yicong Li; Guandong Xu

http://arxiv.org/abs/2201.07513
Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders. (8%)
Zeyang Sha; Xinlei He; Ning Yu; Michael Backes; Yang Zhang

http://arxiv.org/abs/2201.07391
MetaV: A Meta-Verifier Approach to Task-Agnostic Model Fingerprinting. (99%)
Xudong Pan; Yifan Yan; Mi Zhang; Min Yang

http://arxiv.org/abs/2201.07012
Adversarial vulnerability of powerful near out-of-distribution detection. (78%)
Stanislav Fort

http://arxiv.org/abs/2201.07063
How to Backdoor HyperNetwork in Personalized Federated Learning? (13%)
Phung Lai; NhatHai Phan; Issa Khalil; Abdallah Khreishah; Xintao Wu

http://arxiv.org/abs/2201.06937
Secure IoT Routing: Selective Forwarding Attacks and Trust-based Defenses in RPL Network. (2%)
Jun Jiang; Yuhong Liu

http://arxiv.org/abs/2201.07381
Unveiling Project-Specific Bias in Neural Code Models. (1%)
Zhiming Li; Yanzhou Li; Tianlin Li; Mengnan Du; Bozhi Wu; Yushi Cao; Junzhe Jiang; Yang Liu

http://arxiv.org/abs/2201.07344
Lung Swapping Autoencoder: Learning a Disentangled Structure-texture Representation of Chest Radiographs. (1%)
Lei Zhou; Joseph Bae; Huidong Liu; Gagandeep Singh; Jeremy Green; Amit Gupta; Dimitris Samaras; Prateek Prasanna

http://arxiv.org/abs/2201.06427
Masked Faces with Faced Masks. (81%)
Jiayi Zhu; Qing Guo; Felix Juefei-Xu; Yihao Huang; Yang Liu; Geguang Pu

http://arxiv.org/abs/2201.06384
Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations. (56%)
Chris Emmery; Ákos Kádár; Grzegorz Chrupała; Walter Daelemans

http://arxiv.org/abs/2201.06494
AugLy: Data Augmentations for Robustness. (3%)
Zoe Papakipos; Joanna Bitton

http://arxiv.org/abs/2201.06192
Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems. (99%)
Wei Jia; Zhaojun Lu; Haichun Zhang; Zhenglin Liu; Jie Wang; Gang Qu

http://arxiv.org/abs/2201.06070
ALA: Adversarial Lightness Attack via Naturalness-aware Regularizations. (99%)
Liangru Sun; Felix Juefei-Xu; Yihao Huang; Qing Guo; Jiayi Zhu; Jincao Feng; Yang Liu; Geguang Pu

http://arxiv.org/abs/2201.06093
Adversarial Machine Learning Threat Analysis in Open Radio Access Networks. (64%)
Ron Bitton; Dan Avraham; Eitan Klevansky; Dudu Mimran; Oleg Brodt; Heiko Lehmann; Yuval Elovici; Asaf Shabtai

http://arxiv.org/abs/2201.06202
Neighboring Backdoor Attacks on Graph Convolutional Network. (22%)
Liang Chen; Qibiao Peng; Jintang Li; Yang Liu; Jiawei Chen; Yong Li; Zibin Zheng

http://arxiv.org/abs/2201.05819
Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection. (26%)
Yuefei Lyu; Xiaoyu Yang; Jiaxin Liu; Philip S. Yu; Sihong Xie; Xi Zhang

http://arxiv.org/abs/2201.05889
StolenEncoder: Stealing Pre-trained Encoders. (13%)
Yupei Liu; Jinyuan Jia; Hongbin Liu; Neil Zhenqiang Gong

http://arxiv.org/abs/2201.05320
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification. (56%)
Alon Talmor; Ori Yoran; Ronan Le Bras; Chandra Bhagavatula; Yoav Goldberg; Yejin Choi; Jonathan Berant

http://arxiv.org/abs/2201.05326
Security Orchestration, Automation, and Response Engine for Deployment of Behavioural Honeypots. (1%)
Upendra Bartwal; Subhasis Mukhopadhyay; Rohit Negi; Sandeep Shukla

http://arxiv.org/abs/2201.05001
Evaluation of Four Black-box Adversarial Attacks and Some Query-efficient Improvement Analysis. (96%)
Rui Wang

http://arxiv.org/abs/2201.05149
The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression. (93%)
Hamed Hassani; Adel Javanmard

http://arxiv.org/abs/2201.05057
On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles. (83%)
Qingzhao Zhang; Shengtuo Hu; Jiachen Sun; Qi Alfred Chen; Z. Morley Mao

http://arxiv.org/abs/2201.04845
Reconstructing Training Data with Informed Adversaries. (54%)
Borja Balle; Giovanni Cherubin; Jamie Hayes

http://arxiv.org/abs/2201.05172
Jamming Attacks on Federated Learning in Wireless Networks. (2%)
Yi Shi; Yalin E. Sagduyu

http://arxiv.org/abs/2201.04733
Adversarially Robust Classification by Conditional Generative Model Inversion. (99%)
Mitra Alirezaei; Tolga Tasdizen

http://arxiv.org/abs/2201.04397
Towards Adversarially Robust Deep Image Denoising. (99%)
Hanshu Yan; Jingfeng Zhang; Jiashi Feng; Masashi Sugiyama; Vincent Y. F. Tan

http://arxiv.org/abs/2201.04569
Get your Foes Fooled: Proximal Gradient Split Learning for Defense against Model Inversion Attacks on IoMT data. (70%)
Sunder Ali Khowaja; Ik Hyun Lee; Kapal Dev; Muhammad Aslam Jarwar; Nawab Muhammad Faseeh Qureshi

http://arxiv.org/abs/2201.04736
Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges. (1%)
Huaming Chen; M. Ali Babar

http://arxiv.org/abs/2201.03829
Quantifying Robustness to Adversarial Word Substitutions. (99%)
Yuting Yang; Pei Huang; FeiFei Ma; Juan Cao; Meishan Zhang; Jian Zhang; Jintao Li

http://arxiv.org/abs/2201.04011
Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition. (99%)
Hanrui Wang; Shuo Wang; Zhe Jin; Yandan Wang; Cunjian Chen; Massimo Tistarell

http://arxiv.org/abs/2201.05071
Evaluation of Neural Networks Defenses and Attacks using NDCG and Reciprocal Rank Metrics. (98%)
Haya Brama; Lihi Dery; Tal Grinshpoun

http://arxiv.org/abs/2201.03281
IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification. (89%)
Tao Hou; Tao Wang; Zhuo Lu; Yao Liu; Yalin Sagduyu

http://arxiv.org/abs/2201.03777
Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task. (73%)
Himashi Peiris; Zhaolin Chen; Gary Egan; Mehrtash Harandi

http://arxiv.org/abs/2201.03353
GMFIM: A Generative Mask-guided Facial Image Manipulation Model for Privacy Preservation. (3%)
Mohammad Hossein Khojaste; Nastaran Moradzadeh Farid; Ahmad Nickabadi

http://arxiv.org/abs/2201.03668
Towards Group Robustness in the presence of Partial Group Labels. (1%)
Vishnu Suresh Lokhande; Kihyuk Sohn; Jinsung Yoon; Madeleine Udell; Chen-Yu Lee; Tomas Pfister

http://arxiv.org/abs/2201.02993
Rethink Stealthy Backdoor Attacks in Natural Language Processing. (89%)
Lingfeng Shen; Haiyun Jiang; Lemao Liu; Shuming Shi

http://arxiv.org/abs/2201.02986
A Retrospective and Futurespective of Rowhammer Attacks and Defenses on DRAM. (76%)
Zhi Zhang; Jiahao Qi; Yueqiang Cheng; Shijie Jiang; Yiyang Lin; Yansong Gao; Surya Nepal; Yi Zou

http://arxiv.org/abs/2201.03004
Privacy-aware Early Detection of COVID-19 through Adversarial Training. (10%)
Omid Rohanian; Samaneh Kouchaki; Andrew Soltan; Jenny Yang; Morteza Rohanian; Yang Yang; David Clifton

http://arxiv.org/abs/2201.02873
LoMar: A Local Defense Against Poisoning Attack on Federated Learning. (9%)
Xingyu Li; Zhe Qu; Shangqing Zhao; Bo Tang; Zhuo Lu; Yao Liu

http://arxiv.org/abs/2201.02863
PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++. (1%)
Jaewoo Song; Fangzhen Lin

http://arxiv.org/abs/2201.02331
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection. (93%)
Ramneet Kaur; Susmit Jha; Anirban Roy; Sangdon Park; Edgar Dobriban; Oleg Sokolsky; Insup Lee

http://arxiv.org/abs/2201.02351
Asymptotic Security using Bayesian Defense Mechanisms with Application to Cyber Deception. (11%)
Hampei Sasahara; Henrik Sandberg

http://arxiv.org/abs/2201.02445
Negative Evidence Matters in Interpretable Histology Image Classification. (1%)
Soufiane Belharbi; Marco Pedersoli; Ismail Ben Ayed; Luke McCaffrey; Eric Granger

http://arxiv.org/abs/2201.02009
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation. (98%)
Juncheng Wan; Jian Yang; Shuming Ma; Dongdong Zhang; Weinan Zhang; Yong Yu; Zhoujun Li

http://arxiv.org/abs/2201.02265
Learning to be adversarially robust and differentially private. (31%)
Jamie Hayes; Borja Balle; M. Pawan Kumar

http://arxiv.org/abs/2201.01965
Efficient Global Optimization of Two-layer ReLU Networks: Quadratic-time Algorithms and Adversarial Training. (2%)
Yatong Bai; Tanmay Gautam; Somayeh Sojoudi

http://arxiv.org/abs/2201.01850
On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving. (99%)
Giulio Rossolini; Federico Nesti; Gianluca D'Amico; Saasha Nair; Alessandro Biondi; Giorgio Buttazzo

http://arxiv.org/abs/2201.01621
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints. (99%)
Amira Guesmi; Khaled N. Khasawneh; Nael Abu-Ghazaleh; Ihsen Alouani

http://arxiv.org/abs/2201.01842
Adversarial Robustness in Cognitive Radio Networks. (1%)
Makan Zamanipour

http://arxiv.org/abs/2201.01102
Towards Transferable Unrestricted Adversarial Examples with Minimum Changes. (99%)
Fangcheng Liu; Chao Zhang; Hongyang Zhang

http://arxiv.org/abs/2201.01080
Towards Understanding and Harnessing the Effect of Image Transformation in Adversarial Detection. (99%)
Hui Liu; Bo Zhao; Yuefeng Peng; Weidong Li; Peng Liu

http://arxiv.org/abs/2201.01235
On the Minimal Adversarial Perturbation for Deep Neural Networks with Provable Estimation Error. (86%)
Fabio Brau; Giulio Rossolini; Alessandro Biondi; Giorgio Buttazzo

http://arxiv.org/abs/2201.01409
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness. (31%)
Amin Eslami Abyane; Derui Zhu; Roberto Souza; Lei Ma; Hadi Hemmati

http://arxiv.org/abs/2201.01399
Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness. (10%)
Hieu Le; Hans Walker; Dung Tran; Peter Chin

http://arxiv.org/abs/2201.00672
Compression-Resistant Backdoor Attack against Deep Neural Networks. (75%)
Mingfu Xue; Xin Wang; Shichang Sun; Yushu Zhang; Jian Wang; Weiqiang Liu

http://arxiv.org/abs/2201.00763
DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection. (68%)
Phillip Rieger; Thien Duc Nguyen; Markus Miettinen; Ahmad-Reza Sadeghi

http://arxiv.org/abs/2201.00801
Revisiting PGD Attacks for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Control. (11%)
Aaron Havens; Darioush Keivan; Peter Seiler; Geir Dullerud; Bin Hu

http://arxiv.org/abs/2201.00455
Actor-Critic Network for Q&A in an Adversarial Environment. (33%)
Bejan Sadeghian

http://arxiv.org/abs/2201.00318
On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations. (12%)
Aamir Miyajiwala; Arnav Ladkat; Samiksha Jagadale; Raviraj Joshi

http://arxiv.org/abs/2201.00148
Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness. (87%)
Hao Yang; Min Wang; Zhengfei Yu; Yun Zhou

http://arxiv.org/abs/2201.00191
Revisiting Neuron Coverage Metrics and Quality of Deep Neural Networks. (41%)
Zhou Yang; Jieke Shi; Muhammad Hilmi Asyrofi; David Lo

http://arxiv.org/abs/2201.00167
Generating Adversarial Samples For Training Wake-up Word Detection Systems Against Confusing Words. (1%)
Haoxu Wang; Yan Jia; Zeqing Zhao; Xuyang Wang; Junjie Wang; Ming Li

http://arxiv.org/abs/2201.00097
Adversarial Attack via Dual-Stage Network Erosion. (99%)
Yexin Duan; Junhua Zou; Xingyu Zhou; Wu Zhang; Jin Zhang; Zhisong Pan

http://arxiv.org/abs/2112.15329
On Distinctive Properties of Universal Perturbations. (83%)
Sung Min Park; Kuo-An Wei; Kai Xiao; Jerry Li; Aleksander Madry

http://arxiv.org/abs/2112.15250
Benign Overfitting in Adversarially Robust Linear Classification. (99%)
Jinghui Chen; Yuan Cao; Quanquan Gu

http://arxiv.org/abs/2112.15089
Causal Attention for Interpretable and Generalizable Graph Classification. (1%)
Yongduo Sui; Xiang Wang; Jiancan Wu; Min Lin; Xiangnan He; Tat-Seng Chua

http://arxiv.org/abs/2112.14420
Invertible Image Dataset Protection. (92%)
Kejiang Chen; Xianhan Zeng; Qichao Ying; Sheng Li; Zhenxing Qian; Xinpeng Zhang

http://arxiv.org/abs/2112.14468
Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning. (4%)
Junyu Shi; Wei Wan; Shengshan Hu; Jianrong Lu; Leo Yu Zhang

http://arxiv.org/abs/2112.14232
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks. (99%)
Weiran Lin; Keane Lucas; Lujo Bauer; Michael K. Reiter; Mahmood Sharif

http://arxiv.org/abs/2112.14337
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently. (99%)
Futa Waseda; Sosuke Nishikawa; Trung-Nghia Le; Huy H. Nguyen; Isao Echizen

http://arxiv.org/abs/2201.02504
Repairing Adversarial Texts through Perturbation. (99%)
Guoliang Dong; Jingyi Wang; Jun Sun; Sudipta Chattopadhyay; Xinyu Wang; Ting Dai; Jie Shi; Jin Song Dong

http://arxiv.org/abs/2112.14299
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification. (91%)
Aleksandra Ćiprijanović; Diana Kafkes; Gregory Snyder; F. Javier Sánchez; Gabriel Nathan Perdue; Kevin Pedro; Brian Nord; Sandeep Madireddy; Stefan M. Wild

http://arxiv.org/abs/2112.14340
Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge. (88%)
Kartikeya Bhardwaj; Dibakar Gope; James Ward; Paul Whatmough; Danny Loh

http://arxiv.org/abs/2201.00402
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs. (86%)
Han Lu; Zenan Li; Runzhong Wang; Qibing Ren; Junchi Yan; Xiaokang Yang

http://arxiv.org/abs/2112.14771
Gas Gauge: A Security Analysis Tool for Smart Contract Out-of-Gas Vulnerabilities. (1%)
Behkish Nassirzadeh; Huaiying Sun; Sebastian Banescu; Vijay Ganesh

http://arxiv.org/abs/2112.13534
Adversarial Attack for Asynchronous Event-based Data. (99%)
Wooju Lee; Hyun Myung

http://arxiv.org/abs/2112.13547
PRIME: A Few Primitives Can Boost Robustness to Common Corruptions. (81%)
Apostolos Modas; Rahul Rade; Guillermo Ortiz-Jiménez; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard

http://arxiv.org/abs/2112.13989
Associative Adversarial Learning Based on Selective Attack. (26%)
Runqi Wang; Xiaoyue Duan; Baochang Zhang; Song Xue; Wentao Zhu; David Doermann; Guodong Guo

http://arxiv.org/abs/2112.13551
Learning Robust and Lightweight Model through Separable Structured Transformations. (8%)
Yanhui Huang; Yangyu Xu; Xian Wei

http://arxiv.org/abs/2112.13408
Perlin Noise Improve Adversarial Robustness. (99%)
Chengjun Tang; Kun Zhang; Chunfang Xing; Yong Ding; Zengmin Xu

http://arxiv.org/abs/2112.13267
Task and Model Agnostic Adversarial Attack on Graph Neural Networks. (99%)
Kartik Sharma; Samidha Verma; Sourav Medya; Sayan Ranu; Arnab Bhattacharya

http://arxiv.org/abs/2112.13214
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification. (50%)
Haibin Zheng; Zhiqing Chen; Tianyu Du; Xuhong Zhang; Yao Cheng; Shouling Ji; Jingyi Wang; Yue Yu; Jinyin Chen

http://arxiv.org/abs/2112.13162
Stealthy Attack on Algorithmic-Protected DNNs via Smart Bit Flipping. (99%)
Behnam Ghavami; Seyd Movi; Zhenman Fang; Lesley Shannon

http://arxiv.org/abs/2112.13060
NIP: Neuron-level Inverse Perturbation Against Adversarial Attacks. (98%)
Ruoxi Chen; Haibo Jin; Jinyin Chen; Haibin Zheng; Yue Yu; Shouling Ji

http://arxiv.org/abs/2112.13064
CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing. (82%)
Haibo Jin; Ruoxi Chen; Jinyin Chen; Yao Cheng; Chong Fu; Ting Wang; Yue Yu; Zhaoyan Ming

http://arxiv.org/abs/2112.13144
SoK: A Study of the Security on Voice Processing Systems. (9%)
Robert Chang; Logan Kuo; Arthur Liu; Nader Sehatbakhsh

http://arxiv.org/abs/2112.12998
DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning. (1%)
Ismat Jarin; Birhanu Eshete

http://arxiv.org/abs/2112.13178
Gradient Leakage Attack Resilient Deep Learning. (1%)
Wenqi Wei; Ling Liu

http://arxiv.org/abs/2112.12431
Adaptive Modeling Against Adversarial Attacks. (99%)
Zhiwen Yan; Teck Khim Ng

http://arxiv.org/abs/2112.12376
Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization. (99%)
Yihua Zhang; Guanhua Zhang; Prashant Khanduri; Mingyi Hong; Shiyu Chang; Sijia Liu

http://arxiv.org/abs/2112.12920
Robust Secretary and Prophet Algorithms for Packing Integer Programs. (2%)
C. J. Argue; Anupam Gupta; Marco Molinaro; Sahil Singla

http://arxiv.org/abs/2112.12938
Counterfactual Memorization in Neural Language Models. (2%)
Chiyuan Zhang; Daphne Ippolito; Katherine Lee; Matthew Jagielski; Florian Tramèr; Nicholas Carlini

http://arxiv.org/abs/2112.12310
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art. (99%)
Xiang Ling; Lingfei Wu; Jiangyu Zhang; Zhenqing Qu; Wei Deng; Xiang Chen; Yaguan Qian; Chunming Wu; Shouling Ji; Tianyue Luo; Jingzheng Wu; Yanjun Wu

http://arxiv.org/abs/2112.11668
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness? (98%)
Xinhsuai Dong; Luu Anh Tuan; Min Lin; Shuicheng Yan; Hanwang Zhang

http://arxiv.org/abs/2112.12095
Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems. (98%)
Islam Debicha; Thibault Debatty; Jean-Michel Dricot; Wim Mees; Tayeb Kenaza

http://arxiv.org/abs/2112.11937
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies. (96%)
Aizaz Sharif; Dusica Marijan

http://arxiv.org/abs/2112.12792
Understanding and Measuring Robustness of Multimodal Learning. (69%)
Nishant Vishwamitra; Hongxin Hu; Ziming Zhao; Long Cheng; Feng Luo

http://arxiv.org/abs/2112.11947
Evaluating the Robustness of Deep Reinforcement Learning for Autonomous and Adversarial Policies in a Multi-agent Urban Driving Environment. (41%)
Aizaz Sharif; Dusica Marijan

http://arxiv.org/abs/2112.11018
A Theoretical View of Linear Backpropagation and Its Convergence. (99%)
Ziang Li; Yiwen Guo; Haodi Liu; Changshui Zhang

http://arxiv.org/abs/2112.11660
An Attention Score Based Attacker for Black-box NLP Classifier. (91%)
Yueyang Liu; Hunmin Lee; Zhipeng Cai

http://arxiv.org/abs/2112.11414
Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces. (81%)
Brian Kim; Tugba Erpek; Yalin E. Sagduyu; Sennur Ulukus

http://arxiv.org/abs/2112.11255
Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems. (76%)
Andrea Stocco; Brian Pulfer; Paolo Tonella

http://arxiv.org/abs/2112.12084
Input-Specific Robustness Certification for Randomized Smoothing. (68%)
Ruoxin Chen; Jie Li; Junchi Yan; Ping Li; Bin Sheng

http://arxiv.org/abs/2112.11235
Improving Robustness with Image Filtering. (68%)
Matteo Terzi; Mattia Carletti; Gian Antonio Susto

http://arxiv.org/abs/2112.11313
On the Adversarial Robustness of Causal Algorithmic Recourse. (10%)
Ricardo Dominguez-Olmedo; Amir-Hossein Karimi; Bernhard Schölkopf

http://arxiv.org/abs/2112.11542
MIA-Former: Efficient and Robust Vision Transformers via Multi-grained Input-Adaptation. (4%)
Zhongzhi Yu; Yonggan Fu; Sicheng Li; Chaojian Li; Yingyan Lin

http://arxiv.org/abs/2112.11643
Exploring Credibility Scoring Metrics of Perception Systems for Autonomous Driving. (2%)
Viren Khandal; Arth Vidyarthi

http://arxiv.org/abs/2112.11136
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction. (2%)
Kailun Wu; Zhangming Chan; Weijie Bian; Lejian Ren; Shiming Xiang; Shuguang Han; Hongbo Deng; Bo Zheng

http://arxiv.org/abs/2112.11289
Longitudinal Study of the Prevalence of Malware Evasive Techniques. (1%)
Lorenzo Maffia; Dario Nisi; Platon Kotzias; Giovanni Lagorio; Simone Aonzo; Davide Balzarotti

http://arxiv.org/abs/2112.10525
Certified Federated Adversarial Training. (98%)
Giulio Zizzo; Ambrish Rawat; Mathieu Sinn; Sergio Maffeis; Chris Hankin

http://arxiv.org/abs/2112.11226
Energy-bounded Learning for Robust Models of Code. (83%)
Nghi D. Q. Bui; Yijun Yu

http://arxiv.org/abs/2112.12591
Black-Box Testing of Deep Neural Networks through Test Case Diversity. (82%)
Zohreh Aghababaeyan; Manel Abdellatif; Lionel Briand; Ramesh S; Mojtaba Bagherzadeh

http://arxiv.org/abs/2112.10424
Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction. (80%)
Dongfang Li; Baotian Hu; Qingcai Chen; Tujie Xu; Jingcong Tao; Yunan Zhang

http://arxiv.org/abs/2112.10690
Adversarially Robust Stability Certificates can be Sample-Efficient. (2%)
Thomas T. C. K. Zhang; Stephen Tu; Nicholas M. Boffi; Jean-Jacques E. Slotine; Nikolai Matni

http://arxiv.org/abs/2112.10098
Initiative Defense against Facial Manipulation. (67%)
Qidong Huang; Jie Zhang; Wenbo Zhou; WeimingZhang; Nenghai Yu

http://arxiv.org/abs/2112.09968
Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks. (12%)
Simone Marullo; Matteo Tiezzi; Marco Gori; Stefano Melacci

http://arxiv.org/abs/2112.10038
Android-COCO: Android Malware Detection with Graph Neural Network for Byte- and Native-Code. (1%)
Peng Xu

http://arxiv.org/abs/2112.09658
Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering. (92%)
Jiayu Fudan University Ding; Siyuan Fudan University Wang; Qin East China Normal University Chen; Zhongyu Fudan University Wei

http://arxiv.org/abs/2112.09333
Deep Bayesian Learning for Car Hacking Detection. (81%)
Laha Ale; Scott A. King; Ning Zhang

http://arxiv.org/abs/2112.09669
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations. (81%)
Siddhant Arora; Danish Pruthi; Norman Sadeh; William W. Cohen; Zachary C. Lipton; Graham Neubig

http://arxiv.org/abs/2112.09428
Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network. (80%)
An Tao; Yueqi Duan; He Wang; Ziyi Wu; Pengliang Ji; Haowen Sun; Jie Zhou; Jiwen Lu

http://arxiv.org/abs/2112.09625
Provable Adversarial Robustness in the Quantum Model. (62%)
Khashayar Barooti; Grzegorz Głuch; Ruediger Urbanke

http://arxiv.org/abs/2112.09343
Domain Adaptation on Point Clouds via Geometry-Aware Implicits. (1%)
Yuefan Shen; Yanchao Yang; Mi Yan; He Wang; Youyi Zheng; Leonidas Guibas

http://arxiv.org/abs/2112.08862
Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives. (99%)
Arawinkumaar Selvakkumar; Shantanu Pal; Zahra Jadidi

http://arxiv.org/abs/2112.08691
Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning. (99%)
Tong Chen; Zhan Ma

http://arxiv.org/abs/2112.09219
All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines. (99%)
Yuxuan Zhang; Bo Dong; Felix Heide

http://arxiv.org/abs/2112.09279
Robust Upper Bounds for Adversarial Training. (75%)
Dimitris Bertsimas; Xavier Boix; Kimberly Villalobos Carballo; Dick den Hertog

http://arxiv.org/abs/2112.09151
TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations. (64%)
Shivangi Aneja; Lev Markhasin; Matthias Niessner

http://arxiv.org/abs/2112.08772
Sharpness-Aware Minimization with Dynamic Reweighting. (31%)
Wenxuan Zhou; Fangyu Liu; Huan Zhang; Muhao Chen

http://arxiv.org/abs/2112.09008
APTSHIELD: A Stable, Efficient and Real-time APT Detection System for Linux Hosts. (16%)
Tiantian Zhu; Jinkai Yu; Tieming Chen; Jiayu Wang; Jie Ying; Ye Tian; Mingqi Lv; Yan Chen; Yuan Fan; Ting Wang

http://arxiv.org/abs/2112.08806
Correlation inference attacks against machine learning models. (13%)
Ana-Maria Creţu; Florent Guépin; Montjoye Yves-Alexandre de

http://arxiv.org/abs/2112.09062
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants. (2%)
Max Bartolo; Tristan Thrush; Sebastian Riedel; Pontus Stenetorp; Robin Jia; Douwe Kiela

http://arxiv.org/abs/2112.08810
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images. (2%)
Shiran Zada; Itay Benou; Michal Irani

http://arxiv.org/abs/2112.08304
On the Convergence and Robustness of Adversarial Training. (99%)
Yisen Wang; Xingjun Ma; James Bailey; Jinfeng Yi; Bowen Zhou; Quanquan Gu

http://arxiv.org/abs/2112.07921
Temporal Shuffling for Defending Deep Action Recognition Models against Adversarial Attacks. (98%)
Jaehui Hwang; Huan Zhang; Jun-Ho Choi; Cho-Jui Hsieh; Jong-Seok Lee

http://arxiv.org/abs/2112.08609
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models. (75%)
Hongyu Zhu; Yan Chen; Jing Yan; Jing Liu; Yu Hong; Ying Chen; Hua Wu; Haifeng Wang

http://arxiv.org/abs/2112.08102
Robust Neural Network Classification via Double Regularization. (1%)
Olof Zetterqvist; Rebecka Jörnsten; Johan Jonasson

http://arxiv.org/abs/2112.07400
Robustifying automatic speech recognition by extracting slowly varying features. (99%)
Matias Pizarro; Dorothea Kolossa; Asja Fischer

http://arxiv.org/abs/2112.07512
Adversarial Examples for Extreme Multilabel Text Classification. (99%)
Mohammadreza Qaraei; Rohit Babbar

http://arxiv.org/abs/2112.07668
Dual-Key Multimodal Backdoors for Visual Question Answering. (81%)
Matthew Walmer; Karan Sikka; Indranil Sur; Abhinav Shrivastava; Susmit Jha

http://arxiv.org/abs/2112.07324
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training. (76%)
Chen Liu; Zhichao Huang; Mathieu Salzmann; Tong Zhang; Sabine Süsstrunk

http://arxiv.org/abs/2112.07178
MuxLink: Circumventing Learning-Resilient MUX-Locking Using Graph Neural Network-based Link Prediction. (4%)
Lilas Alrahis; Satwik Patnaik; Muhammad Shafique; Ozgur Sinanoglu

http://arxiv.org/abs/2112.06443
Detecting Audio Adversarial Examples with Logit Noising. (99%)
Namgyu Park; Sangwoo Ji; Jong Kim

http://arxiv.org/abs/2112.06569
Triangle Attack: A Query-efficient Decision-based Adversarial Attack. (99%)
Xiaosen Wang; Zeliang Zhang; Kangheng Tong; Dihong Gong; Kun He; Zhifeng Li; Wei Liu

http://arxiv.org/abs/2112.06323
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses. (98%)
Chun Pong Lau; Jiang Liu; Hossein Souri; Wei-An Lin; Soheil Feizi; Rama Chellappa

http://arxiv.org/abs/2112.06276
Quantifying and Understanding Adversarial Examples in Discrete Input Spaces. (91%)
Volodymyr Kuleshov; Evgenii Nikishin; Shantanu Thakoor; Tingfung Lau; Stefano Ermon

http://arxiv.org/abs/2112.06274
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. (91%)
Ashwinee Panda; Saeed Mahloujifar; Arjun N. Bhagoji; Supriyo Chakraborty; Prateek Mittal

http://arxiv.org/abs/2112.06384
WOOD: Wasserstein-based Out-of-Distribution Detection. (12%)
Yinan Wang; Wenbo Sun; Jionghua "Judy" Jin; Zhenyu "James" Kong; Xiaowei Yue

http://arxiv.org/abs/2112.06063
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare. (99%)
Muchao Ye; Junyu Luo; Guanjie Zheng; Cao Xiao; Ting Wang; Fenglong Ma

http://arxiv.org/abs/2112.06011
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting. (98%)
Junhua Zou; Zhisong Pan; Junyang Qiu; Xin Liu; Ting Rui; Wei Li

http://arxiv.org/abs/2112.06116
Stereoscopic Universal Perturbations across Different Architectures and Datasets. (98%)
Zachary Berger; Parth Agrawal; Tian Yu Liu; Stefano Soatto; Alex Wong

http://arxiv.org/abs/2112.06658
Learning to Learn Transferable Attack. (99%)
Shuman Fang; Jie Li; Xianming Lin; Rongrong Ji

http://arxiv.org/abs/2112.05379
Cross-Modal Transferable Adversarial Attacks from Images to Videos. (99%)
Zhipeng Wei; Jingjing Chen; Zuxuan Wu; Yu-Gang Jiang

http://arxiv.org/abs/2112.05871
Attacking Point Cloud Segmentation with Color-only Perturbation. (99%)
Jiacen Xu; Zhe Zhou; Boyuan Feng; Yufei Ding; Zhou Li

http://arxiv.org/abs/2112.05634
Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks. (92%)
Seungyong Moon; Gaon An; Hyun Oh Song

http://arxiv.org/abs/2112.05409
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning. (75%)
Yang Liu; Tianyuan Zou; Yan Kang; Wenhan Liu; Yuanqin He; Zhihao Yi; Qiang Yang

http://arxiv.org/abs/2112.05588
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models. (68%)
Jialuo Chen; Jingyi Wang; Tinglan Peng; Youcheng Sun; Peng Cheng; Shouling Ji; Xingjun Ma; Bo Li; Dawn Song

http://arxiv.org/abs/2112.05367
Efficient Action Poisoning Attacks on Linear Contextual Bandits. (67%)
Guanlin Liu; Lifeng Lai

http://arxiv.org/abs/2112.05495
How Private Is Your RL Policy? An Inverse RL Based Analysis Framework. (41%)
Kritika Prakash; Fiza Husain; Praveen Paruchuri; Sujit P. Gujar

http://arxiv.org/abs/2112.05423
SoK: On the Security & Privacy in Federated Learning. (5%)
Gorka Abad; Stjepan Picek; Aitor Urbieta

http://arxiv.org/abs/2112.04720
Amicable Aid: Turning Adversarial Attack to Benefit Classification. (99%)
Juyeop Kim; Jun-Ho Choi; Soobeom Jang; Jong-Seok Lee

http://arxiv.org/abs/2112.05005
Mutual Adversarial Training: Learning together is better than going alone. (99%)
Jiang Liu; Chun Pong Lau; Hossein Souri; Soheil Feizi; Rama Chellappa

http://arxiv.org/abs/2112.04948
PARL: Enhancing Diversity of Ensemble Networks to Resist Adversarial Attacks via Pairwise Adversarially Robust Loss Function. (99%)
Manaar Alam; Shubhajit Datta; Debdeep Mukhopadhyay; Arijit Mondal; Partha Pratim Chakrabarti

http://arxiv.org/abs/2112.05282
RamBoAttack: A Robust Query Efficient Deep Neural Network Decision Exploit. (99%)
Viet Quoc Vo; Ehsan Abbasnejad; Damith C. Ranasinghe

http://arxiv.org/abs/2112.05224
Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures. (69%)
Eugene Bagdasaryan; Vitaly Shmatikov

http://arxiv.org/abs/2112.05310
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach. (38%)
Saber Jafarpour; Matthew Abate; Alexander Davydov; Francesco Bullo; Samuel Coogan

http://arxiv.org/abs/2112.05135
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures. (10%)
Dan Hendrycks; Andy Zou; Mantas Mazeika; Leonard Tang; Dawn Song; Jacob Steinhardt

http://arxiv.org/abs/2112.05307
Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems. (2%)
Jiankai Jin; Eleanor McMurtry; Benjamin I. P. Rubinstein; Olga Ohrimenko

http://arxiv.org/abs/2112.04764
3D-VField: Learning to Adversarially Deform Point Clouds for Robust 3D Object Detection. (1%)
Alexander Lehner; Stefano Gasperini; Alvaro Marcos-Ramiro; Michael Schmidt; Mohammad-Ali Nikouei Mahani; Nassir Navab; Benjamin Busam; Federico Tombari

http://arxiv.org/abs/2112.04532
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection. (99%)
Jiang Liu; Alexander Levine; Chun Pong Lau; Rama Chellappa; Soheil Feizi

http://arxiv.org/abs/2112.04367
On visual self-supervision and its effect on model robustness. (99%)
Michal Kucer; Diane Oyen; Garrett Kenyon

http://arxiv.org/abs/2112.04154
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization. (93%)
Wenbo Gou; Wen Shi; Jian Lou; Lijie Huang; Pan Zhou; Ruixuan Li

http://arxiv.org/abs/2112.04468
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework. (8%)
Ching-Yun Ko; Jeet Mohapatra; Sijia Liu; Pin-Yu Chen; Luca Daniel; Lily Weng

http://arxiv.org/abs/2112.03615
Saliency Diversified Deep Ensemble for Robustness to Adversaries. (99%)
Alex Bogun; Dimche Kostadinov; Damian Borth

http://arxiv.org/abs/2112.03909
Vehicle trajectory prediction works, but not everywhere. (50%)
Mohammadhossein Bahari; Saeed Saadatnejad; Ahmad Rahimi; Mohammad Shaverdikondori; Mohammad Shahidzadeh; Seyed-Mohsen Moosavi-Dezfooli; Alexandre Alahi

http://arxiv.org/abs/2112.03662
Lightning: Striking the Secure Isolation on GPU Clouds with Transient Hardware Faults. (11%)
Rihui Sun; Pefei Qiu; Yongqiang Lyu; Donsheng Wang; Jiang Dong; Gang Qu

http://arxiv.org/abs/2112.03570
Membership Inference Attacks From First Principles. (2%)
Nicholas Carlini; Steve Chien; Milad Nasr; Shuang Song; Andreas Terzis; Florian Tramer

http://arxiv.org/abs/2112.03508
Training Deep Models to be Explained with Fewer Examples. (1%)
Tomoharu Iwata; Yuya Yoshikawa

http://arxiv.org/abs/2112.04038
Presentation Attack Detection Methods based on Gaze Tracking and Pupil Dynamic: A Comprehensive Survey. (1%)
Jalil Nourmohammadi Khiarak

http://arxiv.org/abs/2112.03315
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review. (99%)
Huda Ali Alatwi; Charles Morisset

http://arxiv.org/abs/2112.03492
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal. (84%)
Yucheng Shi; Yahong Han; Yu-an Tan; Xiaohui Kuang

http://arxiv.org/abs/2112.02797
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks. (82%)
Jing Lin; Long Dang; Mohamed Rahouti; Kaiqi Xiong

http://arxiv.org/abs/2112.03350
Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks. (82%)
Xi Li; Zhen Xiang; David J. Miller; George Kesidis

http://arxiv.org/abs/2112.02918
When the Curious Abandon Honesty: Federated Learning Is Not Private. (68%)
Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot

http://arxiv.org/abs/2112.03476
Defending against Model Stealing via Verifying Embedded External Features. (33%)
Yiming Li; Linghui Zhu; Xiaojun Jia; Yong Jiang; Shu-Tao Xia; Xiaochun Cao

http://arxiv.org/abs/2112.03223
Context-Aware Transfer Attacks for Object Detection. (1%)
Zikui Cai; Xinxin Xie; Shasha Li; Mingjun Yin; Chengyu Song; Srikanth V. Krishnamurthy; Amit K. Roy-Chowdhury; M. Salman Asif

http://arxiv.org/abs/2112.02542
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models. (96%)
Yuejun Guo; Qiang Hu; Maxime Cordy; Mike Papadakis; Yves Le Traon

http://arxiv.org/abs/2112.02671
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness. (88%)
Konstantinos P. Panousis; Sotirios Chatzis; Sergios Theodoridis

http://arxiv.org/abs/2112.02705
Beyond Robustness: Resilience Verification of Tree-Based Classifiers. (2%)
Stefano Calzavara; Lorenzo Cazzaro; Claudio Lucchese; Federico Marcuzzi; Salvatore Orlando

http://arxiv.org/abs/2112.02606
On Impact of Semantically Similar Apps in Android Malware Datasets. (1%)
Roopak Surendran

http://arxiv.org/abs/2112.02469
RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather. (10%)
Jialu Wang; Muhamad Risqi U. Saputra; Chris Xiaoxuan Lu; Niki Trigon; Andrew Markham

http://arxiv.org/abs/2112.01724
Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach. (99%)
James Lee Hu; Mohammadreza Ebrahimi; Hsinchun Chen

http://arxiv.org/abs/2112.02209
Generalized Likelihood Ratio Test for Adversarially Robust Hypothesis Testing. (99%)
Bhagyashree Puranik; Upamanyu Madhow; Ramtin Pedarsani

http://arxiv.org/abs/2112.01821
Blackbox Untargeted Adversarial Testing of Automatic Speech Recognition Systems. (98%)
Xiaoliang Wu; Ajitha Rajan

http://arxiv.org/abs/2112.01777
Attack-Centric Approach for Evaluating Transferability of Adversarial Samples in Machine Learning Models. (54%)
Tochukwu Idika; Ismail Akturk

http://arxiv.org/abs/2112.01723
Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector. (13%)
Andrew Du; Yee Wei Law; Michele Sasdelli; Bo Chen; Ken Clarke; Michael Brown; Tat-Jun Chin

http://arxiv.org/abs/2112.02223
A Game-Theoretic Approach for AI-based Botnet Attack Defence. (9%)
Hooman Alavizadeh; Julian Jang-Jaccard; Tansu Alpcan; Seyit A. Camtepe

http://arxiv.org/abs/2112.01156
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space. (99%)
Thibault Simonetto; Salijona Dyrmishi; Salah Ghamizi; Maxime Cordy; Yves Le Traon

http://arxiv.org/abs/2112.01555
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks? (93%)
Ayesha Siddique; Khaza Anuarul Hoque

http://arxiv.org/abs/2112.01601
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? (75%)
Peter Lorenz; Dominik Strassel; Margret Keuper; Janis Keuper

http://arxiv.org/abs/2112.01423
Training Efficiency and Robustness in Deep Learning. (41%)
Fartash Faghri

http://arxiv.org/abs/2112.01405
FedRAD: Federated Robust Adaptive Distillation. (10%)
Stefán Páll Sturluson; Samuel Trew; Luis Muñoz-González; Matei Grama; Jonathan Passerat-Palmbach; Daniel Rueckert; Amir Alansary

http://arxiv.org/abs/2112.01148
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis. (3%)
Yu Feng; Benteng Ma; Jing Zhang; Shanshan Zhao; Yong Xia; Dacheng Tao

http://arxiv.org/abs/2112.01694
On the Existence of the Adversarial Bayes Classifier (Extended Version). (2%)
Pranjal Awasthi; Natalie S. Frank; Mehryar Mohri

http://arxiv.org/abs/2112.01008
Editing a classifier by rewriting its prediction rules. (1%)
Shibani Santurkar; Dimitris Tsipras; Mahalaxmi Elango; David Bau; Antonio Torralba; Aleksander Madry

http://arxiv.org/abs/2112.00973
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems. (99%)
Siyu Wang; Yuanjiang Cao; Xiaocong Chen; Lina Yao; Xianzhi Wang; Quan Z. Sheng

http://arxiv.org/abs/2112.00323
Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness. (99%)
Jia-Li Yin; Lehui Xie; Wanqing Zhu; Ximeng Liu; Bo-Hao Chen

http://arxiv.org/abs/2112.00378
$\ell_\infty$-Robustness and Beyond: Unleashing Efficient Adversarial Training. (99%)
Hadi M. Dolatabadi; Sarah Erfani; Christopher Leckie

http://arxiv.org/abs/2112.00659
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines. (96%)
Jiachen Sun; Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Dan Hendrycks; Jihun Hamm; Z. Morley Mao

http://arxiv.org/abs/2112.00428
Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial Domain Adaptation. (95%)
Tianyue Zheng; Zhe Chen; Shuya Ding; Chao Cai; Jun Luo

http://arxiv.org/abs/2112.00639
Robustness in Deep Learning for Computer Vision: Mind the gap? (31%)
Nathan Drenkow; Numair Sani; Ilya Shpitser; Mathias Unberath

http://arxiv.org/abs/2112.00686
CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning. (1%)
Aidan Boyd; Patrick Tinsley; Kevin Bowyer; Adam Czajka

http://arxiv.org/abs/2111.15213
Using a GAN to Generate Adversarial Examples to Facial Image Recognition. (99%)
Andrew Merrigan; Alan F. Smeaton

http://arxiv.org/abs/2111.15160
Mitigating Adversarial Attacks by Distributing Different Copies to Different Users. (96%)
Jiyi Zhang; Wesley Joon-Wie Tann; Ee-Chien Chang

http://arxiv.org/abs/2111.15603
Human Imperceptible Attacks and Applications to Improve Fairness. (83%)
Xinru Hua; Huanzhong Xu; Jose Blanchet; Viet Nguyen

http://arxiv.org/abs/2112.00059
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. (81%)
Yangsibo Huang; Samyak Gupta; Zhao Song; Kai Li; Sanjeev Arora

http://arxiv.org/abs/2111.15487
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection. (78%)
Nikolaos Dionelis

http://arxiv.org/abs/2111.15276
COREATTACK: Breaking Up the Core Structure of Graphs. (78%)
Bo Zhou; Yuqian Lv; Jinhuan Wang; Jian Zhang; Qi Xuan

http://arxiv.org/abs/2112.00247
Adversarial Attacks Against Deep Generative Models on Data: A Survey. (12%)
Hui Sun; Tianqing Zhu; Zhiqiu Zhang; Dawei Jin. Ping Xiong; Wanlei Zhou

http://arxiv.org/abs/2111.15416
A Face Recognition System's Worst Morph Nightmare, Theoretically. (1%)
Una M. Kelly; Raymond Veldhuis; Luuk Spreeuwers

http://arxiv.org/abs/2111.15205
New Datasets for Dynamic Malware Classification. (1%)
Berkant Düzgün; Aykut Çayır; Ferhat Demirkıran; Ceyda Nur Kayha; Buket Gençaydın; Hasan Dağ

http://arxiv.org/abs/2112.00646
Reliability Assessment and Safety Arguments for Machine Learning Components in Assuring Learning-Enabled Autonomous Systems. (1%)
Xingyu Zhao; Wei Huang; Vibhav Bharti; Yi Dong; Victoria Cox; Alec Banks; Sen Wang; Sven Schewe; Xiaowei Huang

http://arxiv.org/abs/2111.14564
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack. (99%)
Mengting Xu; Tao Zhang; Daoqiang Zhang

http://arxiv.org/abs/2111.14833
Adversarial Attacks in Cooperative AI. (82%)
Ted Fujimoto; Arthur Paul Pedersen

http://arxiv.org/abs/2111.15039
Living-Off-The-Land Command Detection Using Active Learning. (10%)
Talha Ongun; Jack W. Stokes; Jonathan Bar Or; Ke Tian; Farid Tajaddodianfar; Joshua Neil; Christian Seifert; Alina Oprea; John C. Platt

http://arxiv.org/abs/2111.14726
Do Invariances in Deep Neural Networks Align with Human Perception? (9%)
Vedant Nanda; Ayan Majumdar; Camila Kolling; John P. Dickerson; Krishna P. Gummadi; Bradley C. Love; Adrian Weller

http://arxiv.org/abs/2111.14745
A Simple Long-Tailed Recognition Baseline via Vision-Language Model. (1%)
Teli Ma; Shijie Geng; Mengmeng Wang; Jing Shao; Jiasen Lu; Hongsheng Li; Peng Gao; Yu Qiao

http://arxiv.org/abs/2111.14341
ROBIN : A Benchmark for Robustness to Individual Nuisances in Real-World Out-of-Distribution Shifts. (1%)
Bingchen Zhao; Shaozuo Yu; Wufei Ma; Mingxin Yu; Shenxiao Mei; Angtian Wang; Ju He; Alan Yuille; Adam Kortylewski

http://arxiv.org/abs/2111.15121
Pyramid Adversarial Training Improves ViT Performance. (1%)
Charles Herrmann; Kyle Sargent; Lu Jiang; Ramin Zabih; Huiwen Chang; Ce Liu; Dilip Krishnan; Deqing Sun

http://arxiv.org/abs/2111.15518
Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional Variational AutoEncoders for Adversary Detection in the Presence of Noisy Images. (96%)
Dvij Kalaria; Aritra Hazra; Partha Pratim Chakrabarti

http://arxiv.org/abs/2111.14185
MALIGN: Explainable Static Raw-byte Based Malware Family Classification using Sequence Alignment. (68%)
Shoumik Saha; Sadia Afroz; Atif Rahman

http://arxiv.org/abs/2111.14255
Automated Runtime-Aware Scheduling for Multi-Tenant DNN Inference on GPU. (1%)
Fuxun Yu; Shawn Bray; Di Wang; Longfei Shangguan; Xulong Tang; Chenchen Liu; Xiang Chen

http://arxiv.org/abs/2111.14271
ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification. (1%)
Zhibo Zhang; Jongseong Jang; Chiheb Trabelsi; Ruiwen Li; Scott Sanner; Yeonjeong Jeong; Dongsub Shim

http://arxiv.org/abs/2111.13844
Adaptive Image Transformations for Transfer-based Adversarial Attack. (99%)
Zheng Yuan; Jie Zhang; Shiguang Shan

http://arxiv.org/abs/2111.13841
Adaptive Perturbation for Adversarial Attack. (99%)
Zheng Yuan; Jie Zhang; Zhaoyan Jiang; Liangliang Li; Shiguang Shan

http://arxiv.org/abs/2111.14037
Statically Detecting Adversarial Malware through Randomised Chaining. (98%)
Matthew Crawford; Wei Wang; Ruoxi Sun; Minhui Xue

http://arxiv.org/abs/2111.14035
Dissecting Malware in the Wild. (1%)
Hamish Spencer; Wei Wang; Ruoxi Sun; Minhui Xue

http://arxiv.org/abs/2111.13330
ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks. (50%)
Hua Qi; Zhijie Wang; Qing Guo; Jianlang Chen; Felix Juefei-Xu; Lei Ma; Jianjun Zhao

http://arxiv.org/abs/2111.12971
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network. (99%)
Yihao Huang; Felix Juefei-Xu; Qing Guo; Geguang Pu; Yang Liu

http://arxiv.org/abs/2111.12922
Clustering Effect of (Linearized) Adversarial Robust Models. (97%)
Yang Bai; Xin Yan; Yong Jiang; Shu-Tao Xia; Yisen Wang

http://arxiv.org/abs/2111.13301
Simple Contrastive Representation Adversarial Learning for NLP Tasks. (93%)
Deshui Miao; Jiaqi Zhang; Wenbo Xie; Jian Song; Xin Li; Lijuan Jia; Ning Guo

http://arxiv.org/abs/2111.13244
Going Grayscale: The Road to Understanding and Improving Unlearnable Examples. (92%)
Zhuoran Liu; Zhengyu Zhao; Alex Kolmus; Tijn Berns; Laarhoven Twan van; Tom Heskes; Martha Larson

http://arxiv.org/abs/2111.12965
Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks. (92%)
Xiangyu Qi; Tinghao Xie; Ruizhe Pan; Jifeng Zhu; Yong Yang; Kai Bu

http://arxiv.org/abs/2112.01299
Gradient Inversion Attack: Leaking Private Labels in Two-Party Split Learning. (3%)
Sanjay Kariyappa; Moinuddin K Qureshi

http://arxiv.org/abs/2111.13236
Joint inference and input optimization in equilibrium networks. (1%)
Swaminathan Gurumurthy; Shaojie Bai; Zachary Manchester; J. Zico Kolter

http://arxiv.org/abs/2111.12631
Unity is strength: Improving the Detection of Adversarial Examples with Ensemble Approaches. (99%)
Francesco Craighero; Fabrizio Angaroni; Fabio Stella; Chiara Damiani; Marco Antoniotti; Alex Graudenzi

http://arxiv.org/abs/2111.12305
Thundernna: a white box adversarial attack. (99%)
Linfeng Ye; Shayan Mohajer Hamidi

http://arxiv.org/abs/2111.12906
Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity. (92%)
Bernardo Aquino; Arash Rahnama; Peter Seiler; Lizhen Lin; Vijay Gupta

http://arxiv.org/abs/2111.12629
WFDefProxy: Modularly Implementing and Empirically Evaluating Website Fingerprinting Defenses. (15%)
Jiajun Gong; Wuqi Zhang; Charles Zhang; Tao Wang

http://arxiv.org/abs/2111.12273
Sharpness-aware Quantization for Deep Neural Networks. (10%)
Jing Liu; Jianfei Cai; Bohan Zhuang

http://arxiv.org/abs/2111.12896
SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation. (5%)
Yizhou Wang; Can Qin; Rongzhe Wei; Yi Xu; Yue Bai; Yun Fu

http://arxiv.org/abs/2111.12405
An Attack on Facial Soft-biometric Privacy Enhancement. (2%)
Dailé Osorio-Roig; Christian Rathgeb; Pawel Drozdowski; Philipp Terhörst; Vitomir Štruc; Christoph Busch

http://arxiv.org/abs/2111.12621
Accelerating Deep Learning with Dynamic Data Pruning. (1%)
Ravi S Raju; Kyle Daruwalla; Mikko Lipasti

http://arxiv.org/abs/2111.12034
Adversarial machine learning for protecting against online manipulation. (92%)
Stefano Cresci; Marinella Petrocchi; Angelo Spognardi; Stefano Tognazzi

http://arxiv.org/abs/2111.12197
Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS. (84%)
Witt Christian Schroeder de; Yongchao Huang; Philip H. S. Torr; Martin Strohmeier

http://arxiv.org/abs/2111.12229
Subspace Adversarial Training. (69%)
Tao Li; Yingwen Wu; Sizhe Chen; Kun Fang; Xiaolin Huang

http://arxiv.org/abs/2111.11986
HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization Performance. (1%)
Huanrui Yang; Xiaoxuan Yang; Neil Zhenqiang Gong; Yiran Chen

http://arxiv.org/abs/2111.11368
Adversarial Examples on Segmentation Models Can be Easy to Transfer. (99%)
Jindong Gu; Hengshuang Zhao; Volker Tresp; Philip Torr

http://arxiv.org/abs/2111.11056
Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes. (99%)
Utku Ozbulak; Maura Pintor; Messem Arnout Van; Neve Wesley De

http://arxiv.org/abs/2111.10990
Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification. (99%)
Daizong Liu; Wei Hu

http://arxiv.org/abs/2111.10991
Backdoor Attack through Frequency Domain. (92%)
Tong Wang; Yuan Yao; Feng Xu; Shengwei An; Hanghang Tong; Ting Wang

http://arxiv.org/abs/2111.11157
NTD: Non-Transferability Enabled Backdoor Detection. (69%)
Yinshan Li; Hua Ma; Zhi Zhang; Yansong Gao; Alsharif Abuadbba; Anmin Fu; Yifeng Zheng; Said F. Al-Sarawi; Derek Abbott

http://arxiv.org/abs/2111.11487
A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries. (33%)
Prithviraj Dasgupta; Zachariah Osman

http://arxiv.org/abs/2111.11534
Poisoning Attacks to Local Differential Privacy Protocols for Key-Value Data. (13%)
Yongji Wu; Xiaoyu Cao; Jinyuan Jia; Neil Zhenqiang Gong

http://arxiv.org/abs/2111.11581
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration. (1%)
Yifan Gong; Geng Yuan; Zheng Zhan; Wei Niu; Zhengang Li; Pu Zhao; Yuxuan Cai; Sijia Liu; Bin Ren; Xue Lin; Xulong Tang; Yanzhi Wang

http://arxiv.org/abs/2111.11317
Electric Vehicle Attack Impact on Power Grid Operation. (1%)
Mohammad Ali Sayed; Ribal Atallah; Chadi Assi; Mourad Debbabi

http://arxiv.org/abs/2111.10752
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability. (99%)
Yifeng Xiong; Jiadong Lin; Min Zhang; John E. Hopcroft; Kun He

http://arxiv.org/abs/2111.10759
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model. (99%)
Alon Zolfi; Shai Avidan; Yuval Elovici; Asaf Shabtai

http://arxiv.org/abs/2111.10969
Medical Aegis: Robust adversarial protectors for medical images. (99%)
Qingsong Yao; Zecheng He; S. Kevin Zhou

http://arxiv.org/abs/2111.10754
Local Linearity and Double Descent in Catastrophic Overfitting. (73%)
Varun Sivashankar; Nikil Selvam

http://arxiv.org/abs/2111.10844
Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks. (62%)
Kaiyuan Liu; Xingyu Li; Yi Zhou; Jisong Guan; Yurui Lai; Ge Zhang; Hang Su; Jiachen Wang; Chunxu Guo

http://arxiv.org/abs/2111.10659
Are Vision Transformers Robust to Patch Perturbations? (98%)
Jindong Gu; Volker Tresp; Yao Qin

http://arxiv.org/abs/2111.10055
Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method. (99%)
Tao Bai; Jun Zhao; Jinlin Zhu; Shoudong Han; Jiefeng Chen; Bo Li; Alex Kot

http://arxiv.org/abs/2111.10291
Meta Adversarial Perturbations. (99%)
Chia-Hung Yuan; Pin-Yu Chen; Chia-Mu Yu

http://arxiv.org/abs/2111.10272
Resilience from Diversity: Population-based approach to harden models against adversarial attacks. (99%)
Jasser Jasser; Ivan Garibay

http://arxiv.org/abs/2111.10075
Enhanced countering adversarial attacks via input denoising and feature restoring. (99%)
Yanni Li; Wenhui Zhang; Jiawei Liu; Xiaoli Kou; Hui Li; Jiangtao Cui

http://arxiv.org/abs/2111.10481
PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testing. (99%)
Yuheng Huang; Lei Ma; Yuanchun Li

http://arxiv.org/abs/2111.10130
Fooling Adversarial Training with Inducing Noise. (98%)
Zhirui Wang; Yifei Wang; Yisen Wang

http://arxiv.org/abs/2111.10085
Exposing Weaknesses of Malware Detectors with Explainability-Guided Evasion Attacks. (86%)
Wei Wang; Ruoxi Sun; Tian Dong; Shaofeng Li; Minhui Xue; Gareth Tyson; Haojin Zhu

http://arxiv.org/abs/2111.09999
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems. (99%)
Bao Gia Doan; Minhui Xue; Shiqing Ma; Ehsan Abbasnejad; Damith C. Ranasinghe

http://arxiv.org/abs/2111.09961
A Review of Adversarial Attack and Defense for Classification Methods. (99%)
Yao Li; Minhao Cheng; Cho-Jui Hsieh; Thomas C. M. Lee

http://arxiv.org/abs/2111.09571
Robust Person Re-identification with Multi-Modal Joint Defence. (98%)
Yunpeng Gong; Lifei Chen

http://arxiv.org/abs/2111.09626
Enhancing the Insertion of NOP Instructions to Obfuscate Malware via Deep Reinforcement Learning. (96%)
Daniel Gibert; Matt Fredrikson; Carles Mateu; Jordi Planes; Quan Le

http://arxiv.org/abs/2112.03007
How to Build Robust FAQ Chatbot with Controllable Question Generator? (80%)
Yan Pan; Mingyang Ma; Bernhard Pflugfelder; Georg Groh

http://arxiv.org/abs/2111.09561
Adversarial attacks on voter model dynamics in complex networks. (76%)
Katsumi Chiyomaru; Kazuhiro Takemoto

http://arxiv.org/abs/2111.09679
Enhanced Membership Inference Attacks against Machine Learning Models. (12%)
Jiayuan Ye; Aadyaa Maddi; Sasi Kumar Murakonda; Reza Shokri

http://arxiv.org/abs/2111.09779
Wiggling Weights to Improve the Robustness of Classifiers. (2%)
Sadaf Gulshad; Ivan Sosnovik; Arnold Smeulders

http://arxiv.org/abs/2111.09613
Improving Transferability of Representations via Augmentation-Aware Self-Supervision. (1%)
Hankook Lee; Kibok Lee; Kimin Lee; Honglak Lee; Jinwoo Shin

http://arxiv.org/abs/2111.08954
TraSw: Tracklet-Switch Adversarial Attacks against Multi-Object Tracking. (99%)
Delv Lin; Qi Chen; Chengyu Zhou; Kun He

http://arxiv.org/abs/2111.08973
Generating Unrestricted 3D Adversarial Point Clouds. (99%)
Xuelong Dai; Yanjie Li; Hua Dai; Bin Xiao

http://arxiv.org/abs/2111.09277
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness. (93%)
Jongheon Jeong; Sejun Park; Minkyu Kim; Heung-Chang Lee; Doguk Kim; Jinwoo Shin

http://arxiv.org/abs/2111.09488
Attacking Deep Learning AI Hardware with Universal Adversarial Perturbation. (92%)
Mehdi Sadi; B. M. S. Bahar Talukder; Kaniz Mishty; Md Tauhidur Rahman

http://arxiv.org/abs/2111.09076
Do Not Trust Prediction Scores for Membership Inference Attacks. (33%)
Dominik Hintersdorf; Lukas Struppek; Kristian Kersting

http://arxiv.org/abs/2111.08591
Robustness of Bayesian Neural Networks to White-Box Adversarial Attacks. (99%)
Adaku Uchendu; Daniel Campoy; Christopher Menart; Alexandra Hildenbrandt

http://arxiv.org/abs/2111.08529
Improving the robustness and accuracy of biomedical language models through adversarial training. (99%)
Milad Moradi; Matthias Samwald

http://arxiv.org/abs/2111.08785
Detecting AutoAttack Perturbations in the Frequency Domain. (99%)
Peter Lorenz; Paula Harder; Dominik Strassel; Margret Keuper; Janis Keuper

http://arxiv.org/abs/2111.08864
Adversarial Tradeoffs in Linear Inverse Problems and Robust StateEstimation. (92%)
Bruce D. Lee; Thomas T. C. K. Zhang; Hamed Hassani; Nikolai Matni

http://arxiv.org/abs/2111.08485
Consistent Semantic Attacks on Optical Flow. (81%)
Tom Koren; Lior Talker; Michael Dinerstein; Roy J Jevnisek

http://arxiv.org/abs/2111.08429
An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences. (54%)
Wei Guo; Benedetta Tondi; Mauro Barni

http://arxiv.org/abs/2111.08251
Enabling equivariance for arbitrary Lie groups. (1%)
Lachlan Ewen MacDonald; Sameera Ramasinghe; Simon Lucey

http://arxiv.org/abs/2111.08223
A Survey on Adversarial Attacks for Malware Analysis. (98%)
Kshitiz Aryal; Maanak Gupta; Mahmoud Abdelsalam

http://arxiv.org/abs/2111.07970
Triggerless Backdoor Attack for NLP Tasks with Clean Labels. (68%)
Leilei Gan; Jiwei Li; Tianwei Zhang; Xiaoya Li; Yuxian Meng; Fei Wu; Shangwei Guo; Chun Fan

http://arxiv.org/abs/2111.07608
Property Inference Attacks Against GANs. (67%)
Junhao Zhou; Yufei Chen; Chao Shen; Yang Zhang

http://arxiv.org/abs/2111.07424
Generating Band-Limited Adversarial Surfaces Using Neural Networks. (99%)
Roee Ben-Shlomo; Yevgeniy Men; Ido Imanuel

http://arxiv.org/abs/2111.07492
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks. (76%)
Chen Ma; Xiangyu Guo; Li Chen; Jun-Hai Yong; Yisen Wang

http://arxiv.org/abs/2111.07454
Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning. (75%)
Youxiang Zhu; Bang Tran; Xiaohui Liang; John A. Batsis; Robert M. Roth

http://arxiv.org/abs/2111.07439
Improving Compound Activity Classification via Deep Transfer and Representation Learning. (1%)
Vishal Dey; Raghu Machiraju; Xia Ning

http://arxiv.org/abs/2111.07239
Robust and Accurate Object Detection via Self-Knowledge Distillation. (62%)
Weipeng Xu; Pengzhi Chu; Renhao Xie; Xiongziyan Xiao; Hongcheng Huang

http://arxiv.org/abs/2111.07062
UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction. (2%)
Lilas Alrahis; Satwik Patnaik; Muhammad Abdullah Hanif; Muhammad Shafique; Ozgur Sinanoglu

http://arxiv.org/abs/2111.06979
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception. (99%)
Joel Dapello; Jenelle Feather; Hang Le; Tiago Marques; David D. Cox; Josh H. McDermott; James J. DiCarlo; SueYeon Chung

http://arxiv.org/abs/2111.07035
Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances. (98%)
Daniel Steinberg; Paul Munro

http://arxiv.org/abs/2111.06961
Adversarially Robust Learning for Security-Constrained Optimal Power Flow. (10%)
Priya L. Donti; Aayushya Agarwal; Neeraj Vijay Bedmutha; Larry Pileggi; J. Zico Kolter

http://arxiv.org/abs/2111.06719
On Transferability of Prompt Tuning for Natural Language Processing. (8%)
Yusheng Su; Xiaozhi Wang; Yujia Qin; Chi-Min Chan; Yankai Lin; Huadong Wang; Kaiyue Wen; Zhiyuan Liu; Peng Li; Juanzi Li; Lei Hou; Maosong Sun; Jie Zhou

http://arxiv.org/abs/2111.06682
A Bayesian Nash equilibrium-based moving target defense against stealthy sensor attacks. (1%)
David Umsonst; Serkan Sarıtaş; György Dán; Henrik Sandberg

http://arxiv.org/abs/2111.06776
Resilient Consensus-based Multi-agent Reinforcement Learning. (1%)
Martin Figura; Yixuan Lin; Ji Liu; Vijay Gupta

http://arxiv.org/abs/2111.06063
On the Equivalence between Neural Network and Support Vector Machine. (1%)
Yilan Chen; Wei Huang; Lam M. Nguyen; Tsui-Wei Weng

http://arxiv.org/abs/2111.05978
Trustworthy Medical Segmentation with Uncertainty Estimation. (93%)
Giuseppina Carannante; Dimah Dera; Nidhal C. Bouaynaya; Ghulam Rasool; Hassan M. Fathallah-Shaykh

http://arxiv.org/abs/2111.05953
Robust Learning via Ensemble Density Propagation in Deep Neural Networks. (2%)
Giuseppina Carannante; Dimah Dera; Ghulam Rasool; Nidhal C. Bouaynaya; Lyudmila Mihaylova

http://arxiv.org/abs/2111.05063
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search. (99%)
Pengfei Xia; Ziqiang Li; Bin Li

http://arxiv.org/abs/2111.05073
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps. (99%)
Muhammad Awais; Fengwei Zhou; Chuanlong Xie; Jiawei Li; Sung-Ho Bae; Zhenguo Li

http://arxiv.org/abs/2111.05468
Sparse Adversarial Video Attacks with Spatial Transformations. (98%)
Ronghui Mu; Wenjie Ruan; Leandro Soriano Marcolino; Qiang Ni

http://arxiv.org/abs/2111.05077
A Statistical Difference Reduction Method for Escaping Backdoor Detection. (97%)
Pengfei Xia; Hongjing Niu; Ziqiang Li; Bin Li

http://arxiv.org/abs/2111.05328
Data Augmentation Can Improve Robustness. (73%)
Sylvestre-Alvise Rebuffi; Sven Gowal; Dan A. Calian; Florian Stimberg; Olivia Wiles; Timothy Mann

http://arxiv.org/abs/2111.05464
Are Transformers More Robust Than CNNs? (67%)
Yutong Bai; Jieru Mei; Alan Yuille; Cihang Xie

http://arxiv.org/abs/2111.04371
Geometrically Adaptive Dictionary Attack on Face Recognition. (99%)
Junyoung Byun; Hyojun Go; Changick Kim

http://arxiv.org/abs/2111.04303
Defense Against Explanation Manipulation. (98%)
Ruixiang Tang; Ninghao Liu; Fan Yang; Na Zou; Xia Hu

http://arxiv.org/abs/2111.04625
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories. (98%)
Adnan Siraj Rakin; Md Hafizul Islam Chowdhuryy; Fan Yao; Deliang Fan

http://arxiv.org/abs/2111.04865
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods. (75%)
Paulina Stevia Nouwou Mindom; Amin Nikanjam; Foutse Khomh; John Mullins

http://arxiv.org/abs/2111.04394
Get a Model! Model Hijacking Attack Against Machine Learning Models. (69%)
Ahmed Salem; Michael Backes; Yang Zhang

http://arxiv.org/abs/2111.04404
Robust and Information-theoretically Safe Bias Classifier against Adversarial Attacks. (69%)
Lijia Yu; Xiao-Shan Gao

http://arxiv.org/abs/2111.04330
Characterizing the adversarial vulnerability of speech self-supervised learning. (68%)
Haibin Wu; Bo Zheng; Xu Li; Xixin Wu; Hung-yi Lee; Helen Meng

http://arxiv.org/abs/2111.04703
HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis. (67%)
Tajuddin Manhar Mohammed; Lakshmanan Nataraj; Satish Chikkagoudar; Shivkumar Chandrasekaran; B. S. Manjunath

http://arxiv.org/abs/2111.04314
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning. (67%)
Qinkai Zheng; Xu Zou; Yuxiao Dong; Yukuo Cen; Da Yin; Jiarong Xu; Yang Yang; Jie Tang

http://arxiv.org/abs/2111.04550
BARFED: Byzantine Attack-Resistant Federated Averaging Based on Outlier Elimination. (45%)
Ece Isik-Polat; Gorkem Polat; Altan Kocyigit

http://arxiv.org/abs/2111.04266
Generative Dynamic Patch Attack. (99%)
Xiang Li; Shihao Ji

http://arxiv.org/abs/2111.04204
Natural Adversarial Objects. (81%)
Felix Lau; Nishant Subramani; Sasha Harrison; Aerin Kim; Elliot Branson; Rosanne Liu

http://arxiv.org/abs/2111.05108
"How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector. (11%)
Zhi Lu; Vrizlynn L. L. Thing

http://arxiv.org/abs/2111.03536
A Unified Game-Theoretic Interpretation of Adversarial Robustness. (98%)
Jie Ren; Die Zhang; Yisen Wang; Lu Chen; Zhanpeng Zhou; Yiting Chen; Xu Cheng; Xin Wang; Meng Zhou; Jie Shi; Quanshi Zhang

http://arxiv.org/abs/2112.03000
Sequential Randomized Smoothing for Adversarially Robust Speech Recognition. (96%)
Raphael Olivier; Bhiksha Raj

http://arxiv.org/abs/2111.03363
Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups. (2%)
Aidmar Wainakh; Ephraim Zimmer; Sandeep Subedi; Jens Keim; Tim Grube; Shankar Karuppayah; Alejandro Sanchez Guinea; Max Mühlhäuser

http://arxiv.org/abs/2111.02840
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models. (99%)
Boxin Wang; Chejian Xu; Shuohang Wang; Zhe Gan; Yu Cheng; Jianfeng Gao; Ahmed Hassan Awadallah; Bo Li

http://arxiv.org/abs/2111.02842
Adversarial Attacks on Graph Classification via Bayesian Optimisation. (87%)
Xingchen Wan; Henry Kenlay; Binxin Ru; Arno Blaas; Michael A. Osborne; Xiaowen Dong

http://arxiv.org/abs/2111.03120
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods. (47%)
Peru Bhardwaj; John Kelleher; Luca Costabello; Declan O'Sullivan

http://arxiv.org/abs/2111.02845
Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles. (3%)
Ao Qu; Yihong Tang; Wei Ma

http://arxiv.org/abs/2111.02331
LTD: Low Temperature Distillation for Robust Adversarial Training. (88%)
Erh-Chung Chen; Che-Rung Lee

http://arxiv.org/abs/2111.02018
Multi-Glimpse Network: A Robust and Efficient Classification Architecture based on Recurrent Downsampled Attention. (41%)
Sia Huat Tan; Runpei Dong; Kaisheng Ma

http://arxiv.org/abs/2111.01528
Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization. (99%)
Shengcai Liu; Ning Lu; Wenjing Hong; Chao Qian; Ke Tang

http://arxiv.org/abs/2111.01714
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks. (96%)
Maksym Yatsura; Jan Hendrik Metzen; Matthias Hein

http://arxiv.org/abs/2111.01395
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds. (70%)
Yujia Huang; Huan Zhang; Yuanyuan Shi; J Zico Kolter; Anima Anandkumar

http://arxiv.org/abs/2111.01996
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness. (68%)
Ke Sun; Mingjie Li; Zhouchen Lin

http://arxiv.org/abs/2111.01363
Knowledge Cross-Distillation for Membership Privacy. (38%)
Rishav Chourasia; Batnyam Enkhtaivan; Kunihiro Ito; Junki Mori; Isamu Teranishi; Hikaru Tsuchida

http://arxiv.org/abs/2111.01965
Adversarially Perturbed Wavelet-based Morphed Face Generation. (9%)
Kelsey O'Haire; Sobhan Soleymani; Baaria Chaudhary; Poorya Aghdaie; Jeremy Dawson; Nasser M. Nasrabadi

http://arxiv.org/abs/2111.00684
Graph Structural Attack by Spectral Distance. (93%)
Lu Lin; Ethan Blaser; Hongning Wang

http://arxiv.org/abs/2111.00898
Availability Attacks Create Shortcuts. (89%)
Da Yu; Huishuai Zhang; Wei Chen; Jian Yin; Tie-Yan Liu

http://arxiv.org/abs/2111.00961
Robustness of deep learning algorithms in astronomy -- galaxy morphology studies. (83%)
A. Ćiprijanović; D. Kafkes; G. N. Perdue; K. Pedro; G. Snyder; F. J. Sánchez; S. Madireddy; S. Wild; B. Nord

http://arxiv.org/abs/2111.01124
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning? (69%)
Lijie Fan; Sijia Liu; Pin-Yu Chen; Gaoyuan Zhang; Chuang Gan

http://arxiv.org/abs/2111.01080
ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack. (9%)
Dahoon Park; Kon-Woo Kwon; Sunghoon Im; Jaeha Kung

http://arxiv.org/abs/2111.00435
An Actor-Critic Method for Simulation-Based Optimization. (56%)
Kuo Li; Qing-Shan Jia; Jiaqi Yan

http://arxiv.org/abs/2111.00295
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach. (97%)
Anindya Sarkar; Anirban Sarkar; Sowrya Gali; Vineeth N Balasubramanian

http://arxiv.org/abs/2111.00350
AdvCodeMix: Adversarial Attack on Code-Mixed Data. (93%)
Sourya Dipta Das; Ayan Basak; Soumil Mandal; Dipankar Das

http://arxiv.org/abs/2111.00197
Backdoor Pre-trained Models Can Transfer to All. (3%)
Lujia Shen; Shouling Ji; Xuhong Zhang; Jinfeng Li; Jing Chen; Jie Shi; Chengfang Fang; Jianwei Yin; Ting Wang

http://arxiv.org/abs/2111.00169
Trojan Source: Invisible Vulnerabilities. (1%)
Nicholas Boucher; Ross Anderson

http://arxiv.org/abs/2110.15629
Attacking Video Recognition Models with Bullet-Screen Comments. (99%)
Kai Chen; Zhipeng Wei; Jingjing Chen; Zuxuan Wu; Yu-Gang Jiang

http://arxiv.org/abs/2110.15767
Adversarial Robustness with Semi-Infinite Constrained Learning. (92%)
Alexander Robey; Luiz F. O. Chamon; George J. Pappas; Hamed Hassani; Alejandro Ribeiro

http://arxiv.org/abs/2110.15764
{\epsilon}-weakened Robustness of Deep Neural Networks. (62%)
Pei Huang; Yuting Yang; Minghao Liu; Fuqi Jia; Feifei Ma; Jian Zhang

http://arxiv.org/abs/2111.00162
You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership. (11%)
Xuxi Chen; Tianlong Chen; Zhenyu Zhang; Zhangyang Wang

http://arxiv.org/abs/2110.15317
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework. (99%)
Lifan Yuan; Yichi Zhang; Yangyi Chen; Wei Wei

http://arxiv.org/abs/2110.14880
AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis. (92%)
Junfeng Guo; Ang Li; Cong Liu

http://arxiv.org/abs/2110.15188
The magnitude vector of images. (1%)
Michael F. Adamer; Leslie O'Bray; Brouwer Edward De; Bastian Rieck; Karsten Borgwardt

http://arxiv.org/abs/2110.14735
Towards Evaluating the Robustness of Neural Networks Learned by Transduction. (98%)
Jiefeng Chen; Xi Wu; Yang Guo; Yingyu Liang; Somesh Jha

http://arxiv.org/abs/2110.14855
CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks. (98%)
Haotian Xue; Kaixiong Zhou; Tianlong Chen; Kai Guo; Xia Hu; Yi Chang; Xin Wang

http://arxiv.org/abs/2110.14693
Towards Robust Reasoning over Knowledge Graphs. (83%)
Zhaohan Xi; Ren Pang; Changjiang Li; Shouling Ji; Xiapu Luo; Xusheng Xiao; Ting Wang

http://arxiv.org/abs/2110.14357
Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge. (80%)
Deepsayan Sadhukhan; Nitin Priyadarshini Shankar; Nancy Nayak; Thulasi Tholeti; Sheetal Kalyani

http://arxiv.org/abs/2110.14871
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks. (74%)
Hassan Dbouk; Naresh R. Shanbhag

http://arxiv.org/abs/2110.14430
Adversarial Neuron Pruning Purifies Backdoored Deep Models. (15%)
Dongxian Wu; Yisen Wang

http://arxiv.org/abs/2110.14844
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems. (5%)
Yao Zhou; Haonan Wang; Jingrui He; Haixun Wang

http://arxiv.org/abs/2110.14189
Robust Contrastive Learning Using Negative Samples with Diminished Semantics. (1%)
Songwei Ge; Shlok Mishra; Haohan Wang; Chun-Liang Li; David Jacobs

http://arxiv.org/abs/2110.14188
RoMA: Robust Model Adaptation for Offline Model-based Optimization. (1%)
Sihyun Yu; Sungsoo Ahn; Le Song; Jinwoo Shin

http://arxiv.org/abs/2110.13950
Can't Fool Me: Adversarially Robust Transformer for Video Understanding. (99%)
Divya Choudhary; Palash Goyal; Saurabh Sahu

http://arxiv.org/abs/2110.13935
Frequency Centric Defense Mechanisms against Adversarial Examples. (99%)
Sanket B. Shah; Param Raval; Harin Khakhi; Mehul S. Raval

http://arxiv.org/abs/2110.14120
ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers. (99%)
Husheng Han; Kaidi Xu; Xing Hu; Xiaobing Chen; Ling Liang; Zidong Du; Qi Guo; Yanzhi Wang; Yunji Chen

http://arxiv.org/abs/2110.14068
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks. (99%)
Yonggan Fu; Qixuan Yu; Yang Zhang; Shang Wu; Xu Ouyang; David Cox; Yingyan Lin

http://arxiv.org/abs/2110.13864
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective. (98%)
Jingwei Sun; Ang Li; Louis DiValentin; Amin Hassanzadeh; Yiran Chen; Hai Li

http://arxiv.org/abs/2111.00861
A Frequency Perspective of Adversarial Robustness. (98%)
Shishira R Maiya; Max Ehrlich; Vatsal Agarwal; Ser-Nam Lim; Tom Goldstein; Abhinav Shrivastava

http://arxiv.org/abs/2110.13741
Disrupting Deep Uncertainty Estimation Without Harming Accuracy. (86%)
Ido Galil; Ran El-Yaniv

http://arxiv.org/abs/2110.14030
Improving Local Effectiveness for Global robust training. (83%)
Jingyue Lu; M. Pawan Kumar

http://arxiv.org/abs/2110.14038
Robustness of Graph Neural Networks at Scale. (76%)
Simon Geisler; Tobias Schmidt; Hakan Şirin; Daniel Zügner; Aleksandar Bojchevski; Stephan Günnemann

http://arxiv.org/abs/2110.13980
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions. (75%)
Izzat Alsmadi; Kashif Ahmad; Mahmoud Nazzal; Firoj Alam; Ala Al-Fuqaha; Abdallah Khreishah; Abdulelah Algosaibi

http://arxiv.org/abs/2110.15053
Adversarial Robustness in Multi-Task Learning: Promises and Illusions. (64%)
Salah Ghamizi; Maxime Cordy; Mike Papadakis; Yves Le Traon

http://arxiv.org/abs/2110.13771
AugMax: Adversarial Composition of Random Augmentations for Robust Training. (56%)
Haotao Wang; Chaowei Xiao; Jean Kossaifi; Zhiding Yu; Anima Anandkumar; Zhangyang Wang

http://arxiv.org/abs/2110.13541
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes. (50%)
Sanghyun Hong; Michael-Andrei Panaitescu-Liess; Yiğitcan Kaya; Tudor Dumitraş

http://arxiv.org/abs/2110.13414
Semantic Host-free Trojan Attack. (10%)
Haripriya Harikumar; Kien Do; Santu Rana; Sunil Gupta; Svetha Venkatesh

http://arxiv.org/abs/2110.15122
CAFE: Catastrophic Data Leakage in Vertical Federated Learning. (3%)
Xiao Jin; Pin-Yu Chen; Chia-Yi Hsu; Chia-Mu Yu; Tianyi Chen

http://arxiv.org/abs/2110.14032
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge. (1%)
Geng Yuan; Xiaolong Ma; Wei Niu; Zhengang Li; Zhenglun Kong; Ning Liu; Yifan Gong; Zheng Zhan; Chaoyang He; Qing Jin; Siyue Wang; Minghai Qin; Bin Ren; Yanzhi Wang; Sijia Liu; Xue Lin

http://arxiv.org/abs/2110.14019
Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection. (1%)
Chunjong Park; Anas Awadalla; Tadayoshi Kohno; Shwetak Patel

http://arxiv.org/abs/2110.13859
Defensive Tensorization. (1%)
Adrian Bulat; Jean Kossaifi; Sourav Bhattacharya; Yannis Panagakis; Timothy Hospedales; Georgios Tzimiropoulos; Nicholas D Lane; Maja Pantic

http://arxiv.org/abs/2110.13409
Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware. (1%)
Jinting Zhu; Julian Jang-Jaccard; Amardeep Singh; Paul A. Watters; Seyit Camtepe

http://arxiv.org/abs/2110.12976
Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks. (99%)
Qiyu Kang; Yang Song; Qinxu Ding; Wee Peng Tay

http://arxiv.org/abs/2110.12948
Generating Watermarked Adversarial Texts. (99%)
Mingjie Li; Hanzhou Wu; Xinpeng Zhang

http://arxiv.org/abs/2110.13250
Beyond $L_p$ clipping: Equalization-based Psychoacoustic Attacks against ASRs. (92%)
Hadi Abdullah; Muhammad Sajidur Rahman; Christian Peeters; Cassidy Gibson; Washington Garcia; Vincent Bindschaedler; Thomas Shrimpton; Patrick Traynor

http://arxiv.org/abs/2110.12734
Fast Gradient Non-sign Methods. (92%)
Yaya Cheng; Jingkuan Song; Xiaosu Zhu; Qilong Zhang; Lianli Gao; Heng Tao Shen

http://arxiv.org/abs/2110.14814
Ensemble Federated Adversarial Training with Non-IID data. (87%)
Shuang Luo; Didi Zhu; Zexi Li; Chao Wu

http://arxiv.org/abs/2110.13650
GANash -- A GAN approach to steganography. (81%)
Venkatesh Subramaniyan; Vignesh Sivakumar; A. K. Vagheesan; S. Sakthivelan; K. J. Jegadish Kumar; K. K. Nagarajan

http://arxiv.org/abs/2110.12690
A Dynamical System Perspective for Lipschitz Neural Networks. (81%)
Laurent Meunier; Blaise Delattre; Alexandre Araujo; Alexandre Allauzen

http://arxiv.org/abs/2110.12700
An Adaptive Structural Learning of Deep Belief Network for Image-based Crack Detection in Concrete Structures Using SDNET2018. (13%)
Shin Kamada; Takumi Ichimura; Takashi Iwasaki

http://arxiv.org/abs/2110.12357
Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples. (80%)
Yi Xiang Marcus Tan; Penny Chong; Jiamei Sun; Ngai-man Cheung; Yuval Elovici; Alexander Binder

http://arxiv.org/abs/2110.12321
ADC: Adversarial attacks against object Detection that evade Context consistency checks. (99%)
Mingjun Yin; Shasha Li; Chengyu Song; M. Salman Asif; Amit K. Roy-Chowdhury; Srikanth V. Krishnamurthy

http://arxiv.org/abs/2110.12308
A Layer-wise Adversarial-aware Quantization Optimization for Improving Robustness. (81%)
Chang Song; Riya Ranjan; Hai Li

http://arxiv.org/abs/2110.11987
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations. (99%)
Marek Galovic; Branislav Bosansky; Viliam Lisy

http://arxiv.org/abs/2110.12072
How and When Adversarial Robustness Transfers in Knowledge Distillation? (91%)
Rulin Shao; Jinfeng Yi; Pin-Yu Chen; Cho-Jui Hsieh

http://arxiv.org/abs/2110.12020
Fairness Degrading Adversarial Attacks Against Clustering Algorithms. (86%)
Anshuman Chhabra; Adish Singla; Prasant Mohapatra

http://arxiv.org/abs/2110.11950
Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff. (80%)
Adel Javanmard; Mohammad Mehrabi

http://arxiv.org/abs/2110.11578
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy. (15%)
Xiaolan Gu; Ming Li; Li Xiong

http://arxiv.org/abs/2110.11597
ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI. (15%)
Samuel Hess; Gregory Ditzler

http://arxiv.org/abs/2110.12923
Spoofing Detection on Hand Images Using Quality Assessment. (1%)
Asish Bera; Ratnadeep Dey; Debotosh Bhattacharjee; Mita Nasipuri; Hubert P. H. Shum

http://arxiv.org/abs/2110.11589
Text Counterfactuals via Latent Optimization and Shapley-Guided Search. (1%)
Quintin Pope; Xiaoli Z. Fern

http://arxiv.org/abs/2110.11891
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning. (1%)
Anvith Thudi; Hengrui Jia; Ilia Shumailov; Nicolas Papernot

http://arxiv.org/abs/2110.11736
MANDERA: Malicious Node Detection in Federated Learning via Ranking. (1%)
Wanchuang Zhu; Benjamin Zi Hao Zhao; Simon Luo; Tongliang Liu; Ke Deng

http://arxiv.org/abs/2110.11459
CAPTIVE: Constrained Adversarial Perturbations to Thwart IC Reverse Engineering. (98%)
Amir Hosein Afandizadeh Zargari; Marzieh AshrafiAmiri; Minjun Seo; Sai Manoj Pudukotai Dinakarrao; Mohammed E. Fouda; Fadi Kurdahi

http://arxiv.org/abs/2110.11411
PROVES: Establishing Image Provenance using Semantic Signatures. (93%)
Mingyang Xie; Manav Kulshrestha; Shaojie Wang; Jinghan Yang; Ayan Chakrabarti; Ning Zhang; Yevgeniy Vorobeychik

http://arxiv.org/abs/2110.11088
RoMA: a Method for Neural Network Robustness Measurement and Assessment. (92%)
Natan Levy; Guy Katz

http://arxiv.org/abs/2110.11571
Anti-Backdoor Learning: Training Clean Models on Poisoned Data. (83%)
Yige Li; Xixiang Lyu; Nodens Koren; Lingjuan Lyu; Bo Li; Xingjun Ma

http://arxiv.org/abs/2110.10926
PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion. (68%)
Shijie Zhang; Hongzhi Yin; Tong Chen; Zi Huang; Quoc Viet Hung Nguyen; Lizhen Cui

http://arxiv.org/abs/2110.11205
Robustness through Data Augmentation Loss Consistency. (61%)
Tianjian Huang; Shaunak Halbe; Chinnadhurai Sankar; Pooyan Amini; Satwik Kottur; Alborz Geramifard; Meisam Razaviyayn; Ahmad Beirami

http://arxiv.org/abs/2110.10942
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. (61%)
Simon Geisler; Johanna Sommer; Jan Schuchardt; Aleksandar Bojchevski; Stephan Günnemann

http://arxiv.org/abs/2110.11024
Watermarking Graph Neural Networks based on Backdoor Attacks. (31%)
Jing Xu; Stjepan Picek

http://arxiv.org/abs/2110.11290
Physical Side-Channel Attacks on Embedded Neural Networks: A Survey. (8%)
Maria Méndez Real; Rubén Salvador

http://arxiv.org/abs/2110.10655
Adversarial Socialbot Learning via Multi-Agent Deep Hierarchical Reinforcement Learning. (83%)
Thai Le; Long Tran-Thanh; Dongwon Lee

http://arxiv.org/abs/2110.10482
Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks. (62%)
Zihan Liul; Yun Luo; Zelin Zang; Stan Z. Li

http://arxiv.org/abs/2110.10444
Moir\'e Attack (MA): A New Potential Risk of Screen Photos. (56%)
Dantong Niu; Ruohao Guo; Yisen Wang

http://arxiv.org/abs/2110.10783
Adversarial attacks against Bayesian forecasting dynamic models. (13%)
Roi Naveiro

http://arxiv.org/abs/2110.12899
No One Representation to Rule Them All: Overlapping Features of Training Methods. (1%)
Raphael Gontijo-Lopes; Yann Dauphin; Ekin D. Cubuk

http://arxiv.org/abs/2110.10287
Multi-concept adversarial attacks. (99%)
Vibha Belavadi; Yan Zhou; Murat Kantarcioglu; Bhavani M. Thuraisingham

http://arxiv.org/abs/2110.09759
A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification. (96%)
Linhai Ma; Liang Liang

http://arxiv.org/abs/2110.10108
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks. (69%)
Atul Sharma; Wei Chen; Joshua Zhao; Qiang Qiu; Somali Chaterji; Saurabh Bagchi

http://arxiv.org/abs/2110.09902
Understanding Convolutional Neural Networks from Theoretical Perspective via Volterra Convolution. (61%)
Tenghui Li; Guoxu Zhou; Yuning Qiu; Qibin Zhao

http://arxiv.org/abs/2110.10354
Detecting Backdoor Attacks Against Point Cloud Classifiers. (26%)
Zhen Xiang; David J. Miller; Siheng Chen; Xi Li; George Kesidis

http://arxiv.org/abs/2110.09814
Speech Pattern based Black-box Model Watermarking for Automatic Speech Recognition. (13%)
Haozhe Chen; Weiming Zhang; Kunlin Liu; Kejiang Chen; Han Fang; Nenghai Yu

http://arxiv.org/abs/2110.10291
A Deeper Look into RowHammer`s Sensitivities: Experimental Analysis of Real DRAM Chips and Implications on Future Attacks and Defenses. (5%)
Lois Orosa; Abdullah Giray Yağlıkçı; Haocong Luo; Ataberk Olgun; Jisung Park; Hasan Hassan; Minesh Patel; Jeremie S. Kim; Onur Mutlu

http://arxiv.org/abs/2110.09075
Boosting the Transferability of Video Adversarial Examples via Temporal Translation. (99%)
Zhipeng Wei; Jingjing Chen; Zuxuan Wu; Yu-Gang Jiang

http://arxiv.org/abs/2110.09714
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information. (99%)
Baolin Zheng; Peipei Jiang; Qian Wang; Qi Li; Chao Shen; Cong Wang; Yunjie Ge; Qingyang Teng; Shenyi Zhang

http://arxiv.org/abs/2110.09468
Improving Robustness using Generated Data. (97%)
Sven Gowal; Sylvestre-Alvise Rebuffi; Olivia Wiles; Florian Stimberg; Dan Andrei Calian; Timothy Mann

http://arxiv.org/abs/2110.09506
MEMO: Test Time Robustness via Adaptation and Augmentation. (13%)
Marvin Zhang; Sergey Levine; Chelsea Finn

http://arxiv.org/abs/2110.09929
Minimal Multi-Layer Modifications of Deep Neural Networks. (4%)
Idan Refaeli; Guy Katz

http://arxiv.org/abs/2110.09903
Unrestricted Adversarial Attacks on ImageNet Competition. (99%)
Yuefeng Chen; Xiaofeng Mao; Yuan He; Hui Xue; Chao Li; Yinpeng Dong; Qi-An Fu; Xiao Yang; Wenzhao Xiang; Tianyu Pang; Hang Su; Jun Zhu; Fangcheng Liu; Chao Zhang; Hongyang Zhang; Yichi Zhang; Shilong Liu; Chang Liu; Wenzhao Xiang; Yajie Wang; Huipeng Zhou; Haoran Lyu; Yidan Xu; Zixuan Xu; Taoyu Zhu; Wenjun Li; Xianfeng Gao; Guoqiu Wang; Huanqian Yan; Ying Guo; Chaoning Zhang; Zheng Fang; Yang Wang; Bingyang Fu; Yunfei Zheng; Yekui Wang; Haorong Luo; Zhen Yang

http://arxiv.org/abs/2110.08956
Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training. (99%)
Alexander Daniel Pan; Daniel Yongkyun; Lee; Huan Zhang; Yize Chen; Yuanyuan Shi

http://arxiv.org/abs/2110.09983
ECG-ATK-GAN: Robustness against Adversarial Attacks on ECGs using Conditional Generative Adversarial Networks. (99%)
Khondker Fariha Hossain; Sharif Amit Kamran; Alireza Tavakkoli; Xingjun Ma

http://arxiv.org/abs/2110.08760
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications. (22%)
Bang Wu; Xiangwen Yang; Shirui Pan; Xingliang Yuan

http://arxiv.org/abs/2110.08932
Poisoning Attacks on Fair Machine Learning. (12%)
Minh-Hao Van; Wei Du; Xintao Wu; Aidong Lu

http://arxiv.org/abs/2110.08712
Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models. (99%)
Bibek Poudel; Weizi Li

http://arxiv.org/abs/2110.08514
Analyzing Dynamic Adversarial Training Data in the Limit. (82%)
Eric Wallace; Adina Williams; Robin Jia; Douwe Kiela

http://arxiv.org/abs/2110.08517
Characterizing Improper Input Validation Vulnerabilities of Mobile Crowdsourcing Services. (5%)
Sojhal Ismail Khan; Dominika Woszczyk; Chengzeng You; Soteris Demetriou; Muhammad Naveed

http://arxiv.org/abs/2110.08690
Tackling the Imbalance for GNNs. (4%)
Rui Wang; Weixuan Xiong; Qinghu Hou; Ou Wu

http://arxiv.org/abs/2110.08449
Adversarial Attacks on Gaussian Process Bandits. (99%)
Eric Han; Jonathan Scarlett

http://arxiv.org/abs/2110.08036
Generating Natural Language Adversarial Examples through An Improved Beam Search Algorithm. (99%)
Tengfei Zhao; Zhaocheng Ge; Hanping Hu; Dingmeng Shi

http://arxiv.org/abs/2110.08042
Adversarial Attacks on ML Defense Models Competition. (99%)
Yinpeng Dong; Qi-An Fu; Xiao Yang; Wenzhao Xiang; Tianyu Pang; Hang Su; Jun Zhu; Jiayu Tang; Yuefeng Chen; XiaoFeng Mao; Yuan He; Hui Xue; Chao Li; Ye Liu; Qilong Zhang; Lianli Gao; Yunrui Yu; Xitong Gao; Zhe Zhao; Daquan Lin; Jiadong Lin; Chuanbiao Song; Zihao Wang; Zhennan Wu; Yang Guo; Jiequan Cui; Xiaogang Xu; Pengguang Chen

http://arxiv.org/abs/2110.08324
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture. (76%)
Xinyu Tang; Saeed Mahloujifar; Liwei Song; Virat Shejwalkar; Milad Nasr; Amir Houmansadr; Prateek Mittal

http://arxiv.org/abs/2110.08322
Robustness of different loss functions and their impact on networks learning capability. (76%)
Vishal Rajput

http://arxiv.org/abs/2110.08139
Chunked-Cache: On-Demand and Scalable Cache Isolation for Security Architectures. (22%)
Ghada Dessouky; Alexander Gruler; Pouya Mahmoody; Ahmad-Reza Sadeghi; Emmanuel Stapf

http://arxiv.org/abs/2110.08247
Textual Backdoor Attacks Can Be More Harmful via Two Simple Tricks. (10%)
Yangyi Chen; Fanchao Qi; Zhiyuan Liu; Maosong Sun

http://arxiv.org/abs/2110.07858
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. (8%)
Yao Qin; Chiyuan Zhang; Ting Chen; Balaji Lakshminarayanan; Alex Beutel; Xuezhi Wang

http://arxiv.org/abs/2110.08113
Hand Me Your PIN! Inferring ATM PINs of Users Typing with a Covered Hand. (1%)
Matteo Cardaioli; Stefano Cecconello; Mauro Conti; Simone Milani; Stjepan Picek; Eugen Saraci

http://arxiv.org/abs/2110.07182
Adversarial examples by perturbing high-level features in intermediate decoder layers. (99%)
Vojtěch Čermák; Lukáš Adam

http://arxiv.org/abs/2110.07305
DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networks. (99%)
Yixiang Wang; Jiqiang Liu; Xiaolin Chang; Jianhua Wang; Ricardo J. Rodríguez

http://arxiv.org/abs/2110.07801
Adversarial Purification through Representation Disentanglement. (99%)
Tao Bai; Jun Zhao; Lanqing Guo; Bihan Wen

http://arxiv.org/abs/2110.07831
RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models. (93%)
Wenkai Yang; Yankai Lin; Peng Li; Jie Zhou; Xu Sun

http://arxiv.org/abs/2110.07683
An Optimization Perspective on Realizing Backdoor Injection Attacks on Deep Neural Networks in Hardware. (87%)
M. Caner Tol; Saad Islam; Berk Sunar; Ziming Zhang

http://arxiv.org/abs/2110.07667
Interactive Analysis of CNN Robustness. (80%)
Stefan Sietzen; Mathias Lechner; Judy Borowski; Ramin Hasani; Manuela Waldner

http://arxiv.org/abs/2110.07462
On Adversarial Vulnerability of PHM algorithms: An Initial Study. (69%)
Weizhong Yan; Zhaoyuan Yang; Jianwei Qiu

http://arxiv.org/abs/2110.07736
Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models. (61%)
Tianlu Wang; Diyi Yang; Xuezhi Wang

http://arxiv.org/abs/2110.07537
Toward Degradation-Robust Voice Conversion. (9%)
Chien-yu Huang; Kai-Wei Chang; Hung-yi Lee

http://arxiv.org/abs/2110.07159
Interpreting the Robustness of Neural NLP Models to Textual Perturbations. (9%)
Yunxiang Zhang; Liangming Pan; Samson Tan; Min-Yen Kan

http://arxiv.org/abs/2110.07596
Retrieval-guided Counterfactual Generation for QA. (2%)
Bhargavi Paranjape; Matthew Lamm; Ian Tenney

http://arxiv.org/abs/2110.08260
Effective Certification of Monotone Deep Equilibrium Models. (1%)
Mark Niklas Müller; Robin Staab; Marc Fischer; Martin Vechev

http://arxiv.org/abs/2110.06816
A Framework for Verification of Wasserstein Adversarial Robustness. (99%)
Tobias Wegel; Felix Assion; David Mickisch; Florens Greßner

http://arxiv.org/abs/2110.06802
Identification of Attack-Specific Signatures in Adversarial Examples. (99%)
Hossein Souri; Pirazh Khorramshahi; Chun Pong Lau; Micah Goldblum; Rama Chellappa

http://arxiv.org/abs/2110.08256
Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness. (99%)
Xiao Yang; Yinpeng Dong; Wenzhao Xiang; Tianyu Pang; Hang Su; Jun Zhu

http://arxiv.org/abs/2110.07139
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer. (98%)
Fanchao Qi; Yangyi Chen; Xurui Zhang; Mukai Li; Zhiyuan Liu; Maosong Sun

http://arxiv.org/abs/2110.07120
Brittle interpretations: The Vulnerability of TCAV and Other Concept-based Explainability Tools to Adversarial Attack. (93%)
Davis Brown; Henry Kvinge

http://arxiv.org/abs/2110.06904
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks. (92%)
Shawn Shan; Arjun Nitin Bhagoji; Haitao Zheng; Ben Y. Zhao

http://arxiv.org/abs/2110.06850
Boosting the Certified Robustness of L-infinity Distance Nets. (1%)
Bohang Zhang; Du Jiang; Di He; Liwei Wang

http://arxiv.org/abs/2110.06513
Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions. (1%)
Chenyu Yi; Siyuan Yang; Haoliang Li; Yap-peng Tan; Alex Kot

http://arxiv.org/abs/2110.07718
Adversarial Attack across Datasets. (99%)
Yunxiao Qin; Yuanhao Xiong; Jinfeng Yi; Cho-Jui Hsieh

http://arxiv.org/abs/2110.06468
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning. (99%)
Jinyin Chen; Guohan Huang; Haibin Zheng; Shanqing Yu; Wenrong Jiang; Chen Cui

http://arxiv.org/abs/2110.05748
SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial Text. (92%)
Hoang-Quoc Nguyen-Son; Seira Hidano; Kazuhide Fukushima; Shinsaku Kiyomoto

http://arxiv.org/abs/2110.06018
On the Security Risks of AutoML. (45%)
Ren Pang; Zhaohan Xi; Shouling Ji; Xiapu Luo; Ting Wang

http://arxiv.org/abs/2110.05797
Zero-bias Deep Neural Network for Quickest RF Signal Surveillance. (1%)
Yongxin Liu; Yingjie Chen; Jian Wang; Shuteng Niu; Dahai Liu; Houbing Song

http://arxiv.org/abs/2110.05007
Boosting Fast Adversarial Training with Learnable Adversarial Initialization. (99%)
Xiaojun Jia; Yong Zhang; Baoyuan Wu; Jue Wang; Xiaochun Cao

http://arxiv.org/abs/2110.05626
Parameterizing Activation Functions for Adversarial Robustness. (98%)
Sihui Dai; Saeed Mahloujifar; Prateek Mittal

http://arxiv.org/abs/2110.05059
Amicable examples for informed source separation. (86%)
Naoya Takahashi; Yuki Mitsufuji

http://arxiv.org/abs/2110.05691
Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation. (12%)
Weiting Tan; Shuoyang Ding; Huda Khayrallah; Philipp Koehn

http://arxiv.org/abs/2110.05365
Intriguing Properties of Input-dependent Randomized Smoothing. (1%)
Peter Súkeník; Aleksei Kuvshinov; Stephan Günnemann

http://arxiv.org/abs/2110.05689
Hiding Images into Images with Real-world Robustness. (1%)
Qichao Ying; Hang Zhou; Xianhan Zeng; Haisheng Xu; Zhenxing Qian; Xinpeng Zhang

http://arxiv.org/abs/2110.05054
Source Mixing and Separation Robust Audio Steganography. (1%)
Naoya Takahashi; Mayank Kumar Singh; Yuki Mitsufuji

http://arxiv.org/abs/2110.05290
Homogeneous Learning: Self-Attention Decentralized Deep Learning. (1%)
Yuwei Sun; Hideya Ochiai

http://arxiv.org/abs/2110.05679
Large Language Models Can Be Strong Differentially Private Learners. (1%)
Xuechen Li; Florian Tramèr; Percy Liang; Tatsunori Hashimoto

http://arxiv.org/abs/2110.05076
A Closer Look at Prototype Classifier for Few-shot Image Classification. (1%)
Mingcheng Hou; Issei Sato

http://arxiv.org/abs/2110.07719
Certified Patch Robustness via Smoothed Vision Transformers. (1%)
Hadi Salman; Saachi Jain; Eric Wong; Aleksander Mądry

http://arxiv.org/abs/2110.04887
Adversarial Attacks in a Multi-view Setting: An Empirical Study of the Adversarial Patches Inter-view Transferability. (98%)
Bilel Tarchoun; Ihsen Alouani; Anouar Ben Khalifa; Mohamed Ali Mahjoub

http://arxiv.org/abs/2110.04731
Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO System. (92%)
Pablo Millán Santos; B. R. Manoj; Meysam Sadeghi; Erik G. Larsson

http://arxiv.org/abs/2110.04488
Demystifying the Transferability of Adversarial Attacks in Computer Networks. (99%)
Ehsan Nowroozi; Yassine Mekdad; Mohammad Hajian Berenjestanaki; Mauro Conti; Abdeslam EL Fergougui

http://arxiv.org/abs/2110.04471
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning. (93%)
Guanlin Liu; Lifeng Lai

http://arxiv.org/abs/2110.04571
Widen The Backdoor To Let More Attackers In. (13%)
Siddhartha Datta; Giulio Lovisotto; Ivan Martinovic; Nigel Shadbolt

http://arxiv.org/abs/2110.04158
Explainability-Aware One Point Attack for Point Cloud Neural Networks. (99%)
Hanxiao Tan; Helena Kotthaus

http://arxiv.org/abs/2110.06166
Game Theory for Adversarial Attacks and Defenses. (98%)
Shorya Sharma

http://arxiv.org/abs/2110.03999
Graphs as Tools to Improve Deep Learning Methods. (10%)
Carlos Lassance; Myriam Bontonou; Mounia Hamidouche; Bastien Pasdeloup; Lucas Drumetz; Vincent Gripon

http://arxiv.org/abs/2110.04180
IHOP: Improved Statistical Query Recovery against Searchable Symmetric Encryption through Quadratic Optimization. (3%)
Simon Oya; Florian Kerschbaum

http://arxiv.org/abs/2110.04259
A Wireless Intrusion Detection System for 802.11 WPA3 Networks. (1%)
Neil Dalal; Nadeem Akhtar; Anubhav Gupta; Nikhil Karamchandani; Gaurav S. Kasbekar; Jatin Parekh

http://arxiv.org/abs/2110.04301
Salient ImageNet: How to discover spurious features in Deep Learning? (1%)
Sahil Singla; Soheil Feizi

http://arxiv.org/abs/2110.03605
Robust Feature-Level Adversaries are Interpretability Tools. (99%)
Stephen Casper; Max Nadeau; Dylan Hadfield-Menell; Gabriel Kreiman

http://arxiv.org/abs/2110.03301
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection. (99%)
Hamid Bostani; Veelasha Moonsamy

http://arxiv.org/abs/2110.03745
Adversarial Attack by Limited Point Cloud Surface Modifications. (98%)
Atrin Arya; Hanieh Naderi; Shohreh Kasaei

http://arxiv.org/abs/2110.03825
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks. (98%)
Hanxun Huang; Yisen Wang; Sarah Monazam Erfani; Quanquan Gu; James Bailey; Xingjun Ma

http://arxiv.org/abs/2110.03875
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction. (80%)
Jinyin Chen; Haiyang Xiong; Haibin Zheng; Jian Zhang; Guodong Jiang; Yi Liu

http://arxiv.org/abs/2110.03175
Fingerprinting Multi-exit Deep Neural Network Models via Inference Time. (62%)
Tian Dong; Han Qiu; Tianwei Zhang; Jiwei Li; Hewu Li; Jialiang Lu

http://arxiv.org/abs/2110.03735
Adversarial Unlearning of Backdoors via Implicit Hypergradient. (56%)
Yi Zeng; Si Chen; Won Park; Z. Morley Mao; Ming Jin; Ruoxi Jia

http://arxiv.org/abs/2110.03302
MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings. (1%)
Kailai Sun; Xiaoteng Ma; Peng Liu; Qianchuan Zhao

http://arxiv.org/abs/2110.11417
HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise. (99%)
Souvik Kundu; Massoud Pedram; Peter A. Beerel

http://arxiv.org/abs/2110.02700
Reversible adversarial examples against local visual perturbation. (99%)
Zhaoxia Yin; Li Chen; Shaowei Zhu

http://arxiv.org/abs/2110.02516
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attack. (99%)
Chin-Yuan Yeh; Hsi-Wen Chen; Hong-Han Shuai; De-Nian Yang; Ming-Syan Chen

http://arxiv.org/abs/2110.02797
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs. (99%)
Philipp Benz; Soomin Ham; Chaoning Zhang; Adil Karjauv; In So Kweon

http://arxiv.org/abs/2110.02498
Adversarial Attacks on Machinery Fault Diagnosis. (99%)
Jiahao Chen; Diqun Yan

http://arxiv.org/abs/2110.02929
Adversarial Attacks on Spiking Convolutional Networks for Event-based Vision. (98%)
Julian Büchel; Gregor Lenz; Yalun Hu; Sadique Sheik; Martino Sorbaro

http://arxiv.org/abs/2110.03092
A Uniform Framework for Anomaly Detection in Deep Neural Networks. (97%)
Fangzhen Zhao; Chenyi Zhang; Naipeng Dong; Zefeng You; Zhenxin Wu

http://arxiv.org/abs/2110.03135
Double Descent in Adversarial Training: An Implicit Label Noise Perspective. (88%)
Chengyu Dong; Liyuan Liu; Jingbo Shang

http://arxiv.org/abs/2110.03124
Improving Adversarial Robustness for Free with Snapshot Ensemble. (83%)
Yihao Wang

http://arxiv.org/abs/2110.03154
DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems. (45%)
Ce Michigan State University Zhou; Qiben Michigan State University Yan; Yan Michigan State University Shi; Lichao Lehigh University Sun

http://arxiv.org/abs/2110.02631
Inference Attacks Against Graph Neural Networks. (2%)
Zhikun Zhang; Min Chen; Michael Backes; Yun Shen; Yang Zhang

http://arxiv.org/abs/2110.03149
Data-driven behavioural biometrics for continuous and adaptive user verification using Smartphone and Smartwatch. (1%)
Akriti Verma; Valeh Moghaddam; Adnan Anwar

http://arxiv.org/abs/2110.03054
On The Vulnerability of Recurrent Neural Networks to Membership Inference Attacks. (1%)
Yunhao Yang; Parham Gohari; Ufuk Topcu

http://arxiv.org/abs/2110.03141
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks. (1%)
Jiawei Du; Hanshu Yan; Jiashi Feng; Joey Tianyi Zhou; Liangli Zhen; Rick Siow Mong Goh; Vincent Y. F. Tan

http://arxiv.org/abs/2110.02504
Stegomalware: A Systematic Survey of MalwareHiding and Detection in Images, Machine LearningModels and Research Challenges. (1%)
Rajasekhar Chaganti; Vinayakumar Ravi; Mamoun Alazab; Tuan D. Pham

http://arxiv.org/abs/2110.02863
Exploring the Common Principal Subspace of Deep Features in Neural Networks. (1%)
Haoran Liu; Haoyi Xiong; Yaqing Wang; Haozhe An; Dongrui Wu; Dejing Dou

http://arxiv.org/abs/2110.02718
Generalizing Neural Networks by Reflecting Deviating Data in Production. (1%)
Yan Xiao; Yun Lin; Ivan Beschastnikh; Changsheng Sun; David S. Rosenblum; Jin Song Dong

http://arxiv.org/abs/2110.02125
Adversarial Robustness Verification and Attack Synthesis in Stochastic Systems. (99%)
Lisa Oakley; Alina Oprea; Stavros Tripakis

http://arxiv.org/abs/2110.01823
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations. (99%)
Shasha Li; Abhishek Aich; Shitong Zhu; M. Salman Asif; Chengyu Song; Amit K. Roy-Chowdhury; Srikanth Krishnamurthy

http://arxiv.org/abs/2110.02364
Adversarial defenses via a mixture of generators. (99%)
Maciej Żelaszczyk; Jacek Mańdziuk

http://arxiv.org/abs/2110.01818
Neural Network Adversarial Attack Method Based on Improved Genetic Algorithm. (92%)
Dingming Yang; Yanrong Cui; Hongqiang Yuan

http://arxiv.org/abs/2110.02467
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models. (33%)
Kangjie Chen; Yuxian Meng; Xiaofei Sun; Shangwei Guo; Tianwei Zhang; Jiwei Li; Chun Fan

http://arxiv.org/abs/2110.02424
Spectral Bias in Practice: The Role of Function Frequency in Generalization. (1%)
Sara Fridovich-Keil; Raphael Gontijo-Lopes; Rebecca Roelofs

http://arxiv.org/abs/2110.02417
CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation. (1%)
Peng Liu; Charlie T. Tran; Bin Kong; Ruogu Fang

http://arxiv.org/abs/2110.02180
Noisy Feature Mixup. (1%)
Soon Hoe Lim; N. Benjamin Erichson; Francisco Utrera; Winnie Xu; Michael W. Mahoney

http://arxiv.org/abs/2110.01232
Benchmarking Safety Monitors for Image Classifiers with Machine Learning. (1%)
Raul Sena LAAS Ferreira; Jean LAAS Arlat; Jeremie LAAS Guiochet; Hélène LAAS Waeselynck

http://arxiv.org/abs/2110.01094
Adversarial Examples Generation for Reducing Implicit Gender Bias in Pre-trained Models. (82%)
Wenqian Ye; Fei Xu; Yaojia Huang; Cassie Huang; Ji A

http://arxiv.org/abs/2110.14597
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication. (98%)
Elliu Huang; Troia Fabio Di; Mark Stamp

http://arxiv.org/abs/2110.00899
Anti-aliasing Deep Image Classifiers using Novel Depth Adaptive Blurring and Activation Function. (13%)
Md Tahmid Hossain; Shyh Wei Teng; Ferdous Sohel; Guojun Lu

http://arxiv.org/abs/2110.00623
Calibrated Adversarial Training. (98%)
Tianjin Huang; Vlado Menkovski; Yulong Pei; Mykola Pechenizkiy

http://arxiv.org/abs/2110.00708
Universal Adversarial Spoofing Attacks against Face Recognition. (87%)
Takuma Amada; Seng Pei Liew; Kazuya Kakizaki; Toshinori Araki

http://arxiv.org/abs/2110.00473
Score-Based Generative Classifiers. (84%)
Roland S. Zimmermann; Lukas Schott; Yang Song; Benjamin A. Dunn; David A. Klindt

http://arxiv.org/abs/2110.05929
One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency. (1%)
Sayeed Shafayet Chowdhury; Nitin Rathi; Kaushik Roy

http://arxiv.org/abs/2109.15160
Mitigating Black-Box Adversarial Attacks via Output Noise Perturbation. (98%)
Manjushree B. Aithal; Xiaohua Li

http://arxiv.org/abs/2109.15177
You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors. (95%)
Zijian Zhu; Hang Su; Chang Liu; Wenzhao Xiang; Shibao Zheng

http://arxiv.org/abs/2109.15009
Adversarial Semantic Contour for Object Detection. (92%)
Yichi Zhang; Zijian Zhu; Xiao Yang; Jun Zhu

http://arxiv.org/abs/2109.14868
From Zero-Shot Machine Learning to Zero-Day Attack Detection. (10%)
Mohanad Sarhan; Siamak Layeghy; Marcus Gallagher; Marius Portmann

http://arxiv.org/abs/2109.14205
On Brightness Agnostic Adversarial Examples Against Face Recognition Systems. (99%)
Inderjeet Singh; Satoru Momiyama; Kazuya Kakizaki; Toshinori Araki

http://arxiv.org/abs/2109.15031
Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks. (70%)
Kaleel Mahmood; Rigel Mahmood; Ethan Rathbun; Dijk Marten van

http://arxiv.org/abs/2109.14707
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining. (41%)
Weizhe Hua; Yichi Zhang; Chuan Guo; Zhiru Zhang; G. Edward Suh

http://arxiv.org/abs/2109.14678
Mitigation of Adversarial Policy Imitation via Constrained Randomization of Policy (CRoP). (10%)
Nancirose Piazza; Vahid Behzadan

http://arxiv.org/abs/2109.14002
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks. (1%)
Elizabeth Newman; Julianne Chung; Matthias Chung; Lars Ruthotto

http://arxiv.org/abs/2109.12838
MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles. (99%)
Yuejun Guo; Qiang Hu; Maxime Cordy; Michail Papadakis; Yves Le Traon

http://arxiv.org/abs/2109.13069
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors. (99%)
Zhengyi Wang; Zhongkai Hao; Ziqiao Wang; Hang Su; Jun Zhu

http://arxiv.org/abs/2109.13215
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective. (98%)
Adhyyan Narang; Vidya Muthukumar; Anant Sahai

http://arxiv.org/abs/2109.13297
GANG-MAM: GAN based enGine for Modifying Android Malware. (64%)
Renjith G; Sonia Laudanna; Aji S; Corrado Aaron Visaggio; Vinod P

http://arxiv.org/abs/2109.12803
Distributionally Robust Multi-Output Regression Ranking. (3%)
Shahabeddin Sotudian; Ruidi Chen; Ioannis Paschalidis

http://arxiv.org/abs/2109.12851
Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing. (1%)
Kanil Patel; William Beluch; Kilian Rambach; Michael Pfeiffer; Bin Yang

http://arxiv.org/abs/2109.13012
Federated Deep Learning with Bayesian Privacy. (1%)
Hanlin Gu; Lixin Fan; Bowen Li; Yan Kang; Yuan Yao; Qiang Yang

http://arxiv.org/abs/2109.12772
Distributionally Robust Multiclass Classification and Applications in Deep CNN Image Classifiers. (11%)
Ruidi Chen; Boran Hao; Ioannis Paschalidis

http://arxiv.org/abs/2109.12459
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency. (99%)
Sohaib Kiani; Sana Awan; Chao Lan; Fengjun Li; Bo Luo

http://arxiv.org/abs/2109.13232
Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning. (98%)
Víctor Gallego

http://arxiv.org/abs/2109.12406
MINIMAL: Mining Models for Data Free Universal Adversarial Triggers. (93%)
Swapnil Parekh; Yaman Singla Kumar; Somesh Singh; Changyou Chen; Balaji Krishnamurthy; Rajiv Ratn Shah

http://arxiv.org/abs/2109.11803
Local Intrinsic Dimensionality Signals Adversarial Perturbations. (98%)
Sandamal Weerasinghe; Tansu Alpcan; Sarah M. Erfani; Christopher Leckie; Benjamin I. P. Rubinstein

http://arxiv.org/abs/2109.11308
Breaking BERT: Understanding its Vulnerabilities for Biomedical Named Entity Recognition through Adversarial Attack. (98%)
Anne Dirkson; Suzan Verberne; Wessel Kraaij

http://arxiv.org/abs/2109.11249
FooBaR: Fault Fooling Backdoor Attack on Neural Network Training. (88%)
Jakub Breier; Xiaolu Hou; Martín Ochoa; Jesus Solano

http://arxiv.org/abs/2109.11728
AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses. (68%)
Yaman Kumar Singla; Swapnil Parekh; Somesh Singh; Junyi Jessy Li; Rajiv Ratn Shah; Changyou Chen

http://arxiv.org/abs/2109.11495
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications. (1%)
Dongqi Han; Zhiliang Wang; Wenqi Chen; Ying Zhong; Su Wang; Han Zhang; Jiahai Yang; Xingang Shi; Xia Yin

http://arxiv.org/abs/2109.10770
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers. (99%)
Honggang Yu; Shihfeng Zeng; Teng Zhang; Ing-Chao Lin; Yier Jin

http://arxiv.org/abs/2109.10696
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks. (81%)
Mikhail Pautov; Nurislam Tursynbek; Marina Munkhoeva; Nikita Muravev; Aleksandr Petiushko; Ivan Oseledets

http://arxiv.org/abs/2109.11041
Security Analysis of Capsule Network Inference using Horizontal Collaboration. (69%)
Adewale Adeyemo; Faiq Khalid; Tolulope A. Odetola; Syed Rafay Hasan

http://arxiv.org/abs/2109.11125
Adversarial Transfer Attacks With Unknown Data and Class Overlap. (62%)
Luke E. Richards; André Nguyen; Ryan Capps; Steven Forsythe; Cynthia Matuszek; Edward Raff

http://arxiv.org/abs/2109.10859
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation. (1%)
Diptesh Kanojia; Marina Fomicheva; Tharindu Ranasinghe; Frédéric Blain; Constantin Orăsan; Lucia Specia

http://arxiv.org/abs/2109.10512
Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis. (1%)
Zeyuan Yin; Ye Yuan; Panfeng Guo; Pan Zhou

http://arxiv.org/abs/2109.10417
Attacks on Visualization-Based Malware Detection: Balancing Effectiveness and Executability. (99%)
Hadjer Benkraouda; Jingyu Qian; Hung Quoc Tran; Berkay Kaplan

http://arxiv.org/abs/2109.10161
3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation. (93%)
Mengxi Wu; Hao Huang; Yi Fang

http://arxiv.org/abs/2109.09955
DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learning. (76%)
Md Tamjid Hossain; Shafkat Islam; Shahriar Badsha; Haoting Shen

http://arxiv.org/abs/2109.09963
Privacy, Security, and Utility Analysis of Differentially Private CPES Data. (13%)
Md Tamjid Hossain; Shahriar Badsha; Haoting Shen

http://arxiv.org/abs/2109.09320
Robust Physical-World Attacks on Face Recognition. (99%)
Xin Zheng; Yanbo Fan; Baoyuan Wu; Yong Zhang; Jue Wang; Shirui Pan

http://arxiv.org/abs/2109.09901
Modeling Adversarial Noise for Adversarial Defense. (99%)
Dawei Zhou; Nannan Wang; Bo Han; Tongliang Liu

http://arxiv.org/abs/2109.09654
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection? (99%)
Deqiang Li; Tian Qiu; Shuo Chen; Qianmu Li; Shouhuai Xu

http://arxiv.org/abs/2109.09869
Robustness Analysis of Deep Learning Frameworks on Mobile Platforms. (10%)
Amin Eslami Abyane; Hadi Hemmati

http://arxiv.org/abs/2109.09598
"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World. (2%)
Emily Wenger; Max Bronckers; Christian Cianfarani; Jenna Cryan; Angela Sha; Haitao Zheng; Ben Y. Zhao

http://arxiv.org/abs/2109.09829
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework. (1%)
Muhammad Shafique; Alberto Marchisio; Rachmad Vidya Wicaksana Putra; Muhammad Abdullah Hanif

http://arxiv.org/abs/2109.09060
On the Noise Stability and Robustness of Adversarially Trained Networks on NVM Crossbars. (99%)
Deboleena Roy; Chun Tao; Indranil Chakraborty; Kaushik Roy

http://arxiv.org/abs/2109.09075
Adversarial Training with Contrastive Learning in NLP. (16%)
Daniela N. Rim; DongNyeong Heo; Heeyoul Choi

http://arxiv.org/abs/2109.08868
Clean-label Backdoor Attack against Deep Hashing based Retrieval. (98%)
Kuofeng Gao; Jiawang Bai; Bin Chen; Dongxian Wu; Shu-Tao Xia

http://arxiv.org/abs/2109.08465
Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects. (98%)
Enrico Meloni; Matteo Tiezzi; Luca Pasqualini; Marco Gori; Stefano Melacci

http://arxiv.org/abs/2109.08776
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations. (8%)
Ke Sun; Yingnan Zhao; Shangling Jui; Linglong Kong

http://arxiv.org/abs/2109.07986
Harnessing Perceptual Adversarial Patches for Crowd Counting. (99%)
Shunchang Liu; Jiakai Wang; Aishan Liu; Yingwei Li; Yijie Gao; Xianglong Liu; Dacheng Tao

http://arxiv.org/abs/2109.08191
KATANA: Simple Post-Training Robustness Using Test Time Augmentations. (98%)
Gilad Cohen; Raja Giryes

http://arxiv.org/abs/2109.07723
Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns. (96%)
Prasanth Buddareddygari; Travis Zhang; Yezhou Yang; Yi Ren

http://arxiv.org/abs/2109.08139
Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications. (95%)
Brian Kim; Yi Shi; Yalin E. Sagduyu; Tugba Erpek; Sennur Ulukus

http://arxiv.org/abs/2109.07926
Don't Search for a Search Method -- Simple Heuristics Suffice for Adversarial Text Attacks. (68%)
Nathaniel Berger; Stefan Riezler; Artem Sokolov; Sebastian Ebert

http://arxiv.org/abs/2109.08045
Membership Inference Attacks Against Recommender Systems. (3%)
Minxing Zhang; Zhaochun Ren; Zihan Wang; Pengjie Ren; Zhumin Chen; Pengfei Hu; Yang Zhang

http://arxiv.org/abs/2109.07142
Universal Adversarial Attack on Deep Learning Based Prognostics. (99%)
Arghya Basak; Pradeep Rathore; Sri Harsha Nistala; Sagar Srinivas; Venkataramana Runkana

http://arxiv.org/abs/2109.07171
Balancing detectability and performance of attacks on the control channel of Markov Decision Processes. (98%)
Alessio Russo; Alexandre Proutiere

http://arxiv.org/abs/2109.07193
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack. (95%)
DonghuaWang; Tingsong Jiang; Jialiang Sun; Weien Zhou; Xiaoya Zhang; Zhiqiang Gong; Wen Yao; Xiaoqian Chen

http://arxiv.org/abs/2109.07403
BERT is Robust! A Case Against Synonym-Based Adversarial Examples in Text Classification. (92%)
Jens Hauser; Zhao Meng; Damián Pascual; Roger Wattenhofer

http://arxiv.org/abs/2109.07177
Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup. (13%)
Guang Liu; Yuzhao Mao; Hailong Huang; Weiguo Gao; Xuan Li

http://arxiv.org/abs/2109.07395
Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channel. (10%)
Henrique Teles Maia; Chang Xiao; Dingzeyu Li; Eitan Grinspun; Changxi Zheng

http://arxiv.org/abs/2109.06634
A Novel Data Encryption Method Inspired by Adversarial Attacks. (99%)
Praveen Fernando; Jin Wei-Kocsis

http://arxiv.org/abs/2109.06536
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder. (99%)
Yao Qiu; Jinchao Zhang; Jie Zhou

http://arxiv.org/abs/2109.06777
PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models. (99%)
Bing He; Mustaque Ahamad; Srijan Kumar

http://arxiv.org/abs/2109.08026
EVAGAN: Evasion Generative Adversarial Network for Low Data Regimes. (76%)
Rizwan Hamid Randhawa; Nauman Aslam; Muhammad Alauthman; Husnain Rafiq; Muhammad Khalid

http://arxiv.org/abs/2109.06467
Dodging Attack Using Carefully Crafted Natural Makeup. (47%)
Nitzan Guetta; Asaf Shabtai; Inderjeet Singh; Satoru Momiyama; Yuval Elovici

http://arxiv.org/abs/2109.07028
Avengers Ensemble! Improving Transferability of Authorship Obfuscation. (12%)
Muhammad Haroon; Muhammad Fareed Zaffar; Padmini Srinivasan; Zubair Shafiq

http://arxiv.org/abs/2109.07048
ARCH: Efficient Adversarial Regularized Training with Caching. (8%)
Simiao Zuo; Chen Liang; Haoming Jiang; Pengcheng He; Xiaodong Liu; Jianfeng Gao; Weizhu Chen; Tuo Zhao

http://arxiv.org/abs/2109.05830
Adversarial Bone Length Attack on Action Recognition. (99%)
Nariki Tanaka; Hiroshi Kera; Kazuhiko Kawamoto

http://arxiv.org/abs/2109.05698
Randomized Substitution and Vote for Textual Adversarial Example Detection. (99%)
Xiaosen Wang; Yifeng Xiong; Kun He

http://arxiv.org/abs/2109.05820
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator. (99%)
Wenzhao Xiang; Hang Su; Chang Liu; Yandong Guo; Shibao Zheng

http://arxiv.org/abs/2109.05919
Evolving Architectures with Gradient Misalignment toward Low Adversarial Transferability. (98%)
Kevin Richard G. Operiano; Wanchalerm Pora; Hitoshi Iba; Hiroshi Kera

http://arxiv.org/abs/2109.06358
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems. (98%)
Moein Sabounchi; Jin Wei-Kocsis

http://arxiv.org/abs/2109.05925
Adversarial Examples for Evaluating Math Word Problem Solvers. (96%)
Vivek Kumar; Rishabh Maheshwary; Vikram Pudi

http://arxiv.org/abs/2109.05695
PAT: Pseudo-Adversarial Training For Detecting Adversarial Videos. (86%)
Nupur Thakur; Baoxin Li

http://arxiv.org/abs/2109.05872
Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering. (81%)
Jian Xu; Shao-Lun Huang; Linqi Song; Tian Lan

http://arxiv.org/abs/2109.06024
Formalizing and Estimating Distribution Inference Risks. (62%)
Anshuman Suri; David Evans

http://arxiv.org/abs/2109.05793
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models. (50%)
Kun Zhou; Wayne Xin Zhao; Sirui Wang; Fuzheng Zhang; Wei Wu; Ji-Rong Wen

http://arxiv.org/abs/2109.06363
Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models. (16%)
Won Park; Nan Li; Qi Alfred Chen; Z. Morley Mao

http://arxiv.org/abs/2109.05751
Adversarially Trained Object Detector for Unsupervised Domain Adaptation. (3%)
Kazuma Fujii; Hiroshi Kera; Kazuhiko Kawamoto

http://arxiv.org/abs/2109.05771
Perturbation CheckLists for Evaluating NLG Evaluation Metrics. (1%)
Ananya B. Sai; Tanay Dixit; Dev Yashpal Sheth; Sreyas Mohan; Mitesh M. Khapra

http://arxiv.org/abs/2109.05696
How to Select One Among All? An Extensive Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding. (1%)
Tianda Li; Ahmad Rashid; Aref Jafari; Pranav Sharma; Ali Ghodsi; Mehdi Rezagholizadeh

http://arxiv.org/abs/2109.06404
Detecting Safety Problems of Multi-Sensor Fusion in Autonomous Driving. (1%)
Ziyuan Zhong; Zhisheng Hu; Shengjian Guo; Xinyang Zhang; Zhenyu Zhong; Baishakhi Ray

http://arxiv.org/abs/2109.06176
TREATED:Towards Universal Defense against Textual Adversarial Attacks. (99%)
Bin Zhu; Zhaoquan Gu; Le Wang; Zhihong Tian

http://arxiv.org/abs/2109.05558
CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph. (98%)
Xugang Wu; Huijun Wu; Xu Zhou; Kai Lu

http://arxiv.org/abs/2109.05507
Check Your Other Door! Creating Backdoor Attacks in the Frequency Domain. (93%)
Hasan Abed Al Kader Hammoud; Bernard Ghanem

http://arxiv.org/abs/2109.05620
RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models. (84%)
Bill Yuchen Lin; Wenyang Gao; Jun Yan; Ryan Moreno; Xiang Ren

http://arxiv.org/abs/2109.05671
Shape-Biased Domain Generalization via Shock Graph Embeddings. (2%)
Maruthi Narayanan; Vickram Rajendran; Benjamin Kimia

http://arxiv.org/abs/2109.05659
Source Inference Attacks in Federated Learning. (1%)
Hongsheng Hu; Zoran Salcic; Lichao Sun; Gillian Dobbie; Xuyun Zhang

http://arxiv.org/abs/2109.05211
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques. (98%)
Shiyu Tang; Ruihao Gong; Yan Wang; Aishan Liu; Jiakai Wang; Xinyun Chen; Fengwei Yu; Xianglong Liu; Dawn Song; Alan Yuille; Philip H. S. Torr; Dacheng Tao

http://arxiv.org/abs/2109.05223
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency. (81%)
Yonggan Fu; Yang Zhao; Qixuan Yu; Chaojian Li; Yingyan Lin

http://arxiv.org/abs/2109.04775
A Strong Baseline for Query Efficient Attacks in a Black Box Setting. (99%)
Rishabh Maheshwary; Saket Maheshwary; Vikram Pudi

http://arxiv.org/abs/2109.04385
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification. (99%)
Maximilian Mozes; Max Bartolo; Pontus Stenetorp; Bennett Kleinberg; Lewis D. Griffin

http://arxiv.org/abs/2109.04300
Energy Attack: On Transferring Adversarial Examples. (99%)
Ruoxi Shi; Borui Yang; Yangzhou Jiang; Chenglong Zhao; Bingbing Ni

http://arxiv.org/abs/2109.04460
Protein Folding Neural Networks Are Not Robust. (99%)
Sumit Kumar Jha; Arvind Ramanathan; Rickard Ewetz; Alvaro Velasquez; Susmit Jha

http://arxiv.org/abs/2109.04176
Towards Transferable Adversarial Attacks on Vision Transformers. (99%)
Zhipeng Wei; Jingjing Chen; Micah Goldblum; Zuxuan Wu; Tom Goldstein; Yu-Gang Jiang

http://arxiv.org/abs/2109.04367
Multi-granularity Textual Adversarial Attack with Behavior Cloning. (98%)
Yangyi Chen; Jin Su; Wei Wei

http://arxiv.org/abs/2109.04608
Spatially Focused Attack against Spatiotemporal Graph Neural Networks. (81%)
Fuqiang Liu; Luis Miranda-Moreno; Lijun Sun

http://arxiv.org/abs/2109.04615
Differential Privacy in Personalized Pricing with Nonparametric Demand Models. (26%)
Xi Chen; Sentao Miao; Yining Wang

http://arxiv.org/abs/2109.04344
EvilModel 2.0: Bringing Neural Network Models into Malware Attacks. (5%)
Zhi Wang; Chaoge Liu; Xiang Cui; Jie Yin; Xutong Wang

http://arxiv.org/abs/2109.03975
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning. (89%)
Maziar Gomrokchi; Susan Amin; Hossein Aboutalebi; Alexander Wong; Doina Precup

http://arxiv.org/abs/2109.03857
Robust Optimal Classification Trees Against Adversarial Examples. (80%)
Daniël Vos; Sicco Verwer

http://arxiv.org/abs/2109.02889
Adversarial Parameter Defense by Multi-Step Risk Minimization. (98%)
Zhiyuan Zhang; Ruixuan Luo; Xuancheng Ren; Qi Su; Liangyou Li; Xu Sun

http://arxiv.org/abs/2109.02979
POW-HOW: An enduring timing side-channel to evade online malware sandboxes. (12%)
Antonio Nappa; Panagiotis Papadopoulos; Matteo Varvello; Daniel Aceituno Gomez; Juan Tapiador; Andrea Lanzi

http://arxiv.org/abs/2109.02973
Unpaired Adversarial Learning for Single Image Deraining with Rain-Space Contrastive Constraints. (1%)
Xiang Chen; Jinshan Pan; Kui Jiang; Yufeng Huang; Caihua Kong; Longgang Dai; Yufeng Li

http://arxiv.org/abs/2109.02765
Robustness and Generalization via Generative Adversarial Training. (82%)
Omid Poursaeed; Tianxing Jiang; Harry Yang; Serge Belongie; SerNam Lim

http://arxiv.org/abs/2109.02836
Trojan Signatures in DNN Weights. (33%)
Greg Fields; Mohammad Samragh; Mojan Javaheripi; Farinaz Koushanfar; Tara Javidi

http://arxiv.org/abs/2109.02532
Automated Robustness with Adversarial Training as a Post-Processing Step. (4%)
Ambrish Rawat; Mathieu Sinn; Beat Buesser

http://arxiv.org/abs/2109.02431
Exposing Length Divergence Bias of Textual Matching Models. (2%)
Lan Jiang; Tianshu Lyu; Chong Meng; Xiaoyong Lyu; Dawei Yin

http://arxiv.org/abs/2109.02229
Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack. (98%)
Shengcai Liu; Ning Lu; Cheng Chen; Ke Tang

http://arxiv.org/abs/2109.02018
Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learning. (2%)
Yusen Wu; Hao Chen; Xin Wang; Chao Liu; Phuong Nguyen; Yelena Yesha

http://arxiv.org/abs/2109.03326
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode. (1%)
Nadia Daoudi; Jordan Samhi; Abdoul Kader Kabore; Kevin Allix; Tegawendé F. Bissyandé; Jacques Klein

http://arxiv.org/abs/2109.03329
Real-World Adversarial Examples involving Makeup Application. (99%)
Chang-Sheng Lin; Chia-Yi Hsu; Pin-Yu Chen; Chia-Mu Yu

http://arxiv.org/abs/2109.01945
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness. (99%)
Uriya Pesso; Koby Bibas; Meir Feder

http://arxiv.org/abs/2109.01983
Training Meta-Surrogate Model for Transferable Adversarial Attack. (99%)
Yunxiao Qin; Yuanhao Xiong; Jinfeng Yi; Cho-Jui Hsieh

http://arxiv.org/abs/2109.01766
SEC4SR: A Security Analysis Platform for Speaker Recognition. (99%)
Guangke Chen; Zhe Zhao; Fu Song; Sen Chen; Lingling Fan; Yang Liu

http://arxiv.org/abs/2109.01553
Risk Assessment for Connected Vehicles under Stealthy Attacks on Vehicle-to-Vehicle Networks. (1%)
Tianci Yang; Carlos Murguia; Chen Lv

http://arxiv.org/abs/2109.01275
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples. (99%)
Guanxiong Liu; Issa Khalil; Abdallah Khreishah; NhatHai Phan

http://arxiv.org/abs/2109.00936
Impact of Attention on Adversarial Robustness of Image Classification Models. (99%)
Prachi Agrawal; Narinder Singh Punn; Sanjay Kumar Sonbhadra; Sonali Agarwal

http://arxiv.org/abs/2109.00946
Adversarial Robustness for Unsupervised Domain Adaptation. (98%)
Muhammad Awais; Fengwei Zhou; Hang Xu; Lanqing Hong; Ping Luo; Sung-Ho Bae; Zhenguo Li

http://arxiv.org/abs/2109.00864
Real World Robustness from Systematic Noise. (91%)
Yan Wang; Yuhang Li; Ruihao Gong

http://arxiv.org/abs/2109.00959
Building Compact and Robust Deep Neural Networks with Toeplitz Matrices. (61%)
Alexandre Araujo

http://arxiv.org/abs/2109.00544
Towards Improving Adversarial Training of NLP Models. (98%)
Jin Yong Yoo; Yanjun Qi

http://arxiv.org/abs/2109.00685
Excess Capacity and Backdoor Poisoning. (97%)
Naren Sarayu Manoj; Avrim Blum

http://arxiv.org/abs/2109.00678
Regional Adversarial Training for Better Robust Generalization. (96%)
Chuanbiao Song; Yanbo Fan; Yicheng Yang; Baoyuan Wu; Yiming Li; Zhifeng Li; Kun He

http://arxiv.org/abs/2109.00533
R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors. (86%)
Alberto Marchisio; Giacomo Pira; Maurizio Martina; Guido Masera; Muhammad Shafique

http://arxiv.org/abs/2109.00542
Proof Transfer for Neural Network Verification. (9%)
Christian Sprecher; Marc Fischer; Dimitar I. Dimitrov; Gagandeep Singh; Martin Vechev

http://arxiv.org/abs/2109.00187
Guarding Machine Learning Hardware Against Physical Side-Channel Attacks. (2%)
Anuj Dubey; Rosario Cammarota; Vikram Suresh; Aydin Aysu

http://arxiv.org/abs/2108.13930
EG-Booster: Explanation-Guided Booster of ML Evasion Attacks. (99%)
Abderrahmen Amich; Birhanu Eshete

http://arxiv.org/abs/2108.13952
Morphence: Moving Target Defense Against Adversarial Examples. (99%)
Abderrahmen Amich; Birhanu Eshete

http://arxiv.org/abs/2109.00124
DPA: Learning Robust Physical Adversarial Camouflages for Object Detectors. (93%)
Yexin Duan; Jialin Chen; Xingyu Zhou; Junhua Zou; Zhengyun He; Wu Zhang; Jin Zhang; Zhisong Pan

http://arxiv.org/abs/2109.01165
Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction. (83%)
Zhenrui Yue; Zhankui He; Huimin Zeng; Julian McAuley

http://arxiv.org/abs/2108.13617
Segmentation Fault: A Cheap Defense Against Adversarial Machine Learning. (75%)
Doha Al Bared; Mohamed Nassar

http://arxiv.org/abs/2108.13888
Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning. (4%)
Linyang Li; Demin Song; Xiaonan Li; Jiehang Zeng; Ruotian Ma; Xipeng Qiu

http://arxiv.org/abs/2108.13797
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings. (99%)
Mazda Moayeri; Soheil Feizi

http://arxiv.org/abs/2108.13093
Investigating Vulnerabilities of Deep Neural Policies. (99%)
Ezgi Korkmaz

http://arxiv.org/abs/2108.13562
Adversarial Example Devastation and Detection on Speech Recognition System by Adding Random Noise. (99%)
Mingyu Dong; Diqun Yan; Yongkang Gong; Rangding Wang

http://arxiv.org/abs/2108.13049
Single Node Injection Attack against Graph Neural Networks. (68%)
Shuchang Tao; Qi Cao; Huawei Shen; Junjie Huang; Yunfan Wu; Xueqi Cheng

http://arxiv.org/abs/2108.13446
Benchmarking the Accuracy and Robustness of Feedback Alignment Algorithms. (41%)
Albert Jiménez Sanfiz; Mohamed Akrout

http://arxiv.org/abs/2108.13239
Adaptive perturbation adversarial training: based on reinforcement learning. (41%)
Zhishen Nie; Ying Lin; Sp Ren; Lan Zhang

http://arxiv.org/abs/2108.13602
How Does Adversarial Fine-Tuning Benefit BERT? (33%)
Javid Ebrahimi; Hao Yang; Wei Zhang

http://arxiv.org/abs/2108.13373
ML-based IoT Malware Detection Under Adversarial Settings: A Systematic Evaluation. (26%)
Ahmed Abusnaina; Afsah Anwar; Sultan Alshamrani; Abdulrahman Alabduljabbar; RhongHo Jang; Daehun Nyang; David Mohaisen

http://arxiv.org/abs/2108.13140
DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation. (1%)
Lijie Wang; Hao Liu; Shuyuan Peng; Hongxuan Tang; Xinyan Xiao; Ying Chen; Hua Wu; Haifeng Wang

http://arxiv.org/abs/2108.12777
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution. (99%)
Zongyi Li; Jianhan Xu; Jiehang Zeng; Linyang Li; Xiaoqing Zheng; Qi Zhang; Kai-Wei Chang; Cho-Jui Hsieh

http://arxiv.org/abs/2108.13872
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models. (98%)
Zeyuan Wang; Chaofeng Sha; Su Yang

http://arxiv.org/abs/2108.12805
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks. (82%)
Shiwen Ni; Jiawen Li; Hung-Yu Kao

http://arxiv.org/abs/2110.00425
HAT4RD: Hierarchical Adversarial Training for Rumor Detection on Social Media. (81%)
Shiwen Ni; Jiawen Li; Hung-Yu Kao

http://arxiv.org/abs/2108.12473
Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights. (99%)
Omid Kargarnovin; Amir Mahdi Sadeghzadeh; Rasool Jalili

http://arxiv.org/abs/2108.12492
Disrupting Adversarial Transferability in Deep Neural Networks. (98%)
Christopher Wiedeman; Ge Wang

http://arxiv.org/abs/2108.12237
Evaluating the Robustness of Neural Language Models to Input Perturbations. (16%)
Milad Moradi; Matthias Samwald

http://arxiv.org/abs/2108.12242
Deep learning models are not robust against noise in clinical text. (1%)
Milad Moradi; Kathrin Blagec; Matthias Samwald

http://arxiv.org/abs/2108.12001
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks. (99%)
Landan Seguin; Anthony Ndirango; Neeli Mishra; SueYeon Chung; Tyler Lee

http://arxiv.org/abs/2108.11785
A Hierarchical Assessment of Adversarial Severity. (98%)
Guillaume Jeanneret; Juan C Perez; Pablo Arbelaez

http://arxiv.org/abs/2108.11765
Physical Adversarial Attacks on an Aerial Imagery Object Detector. (96%)
Andrew Du; Bo Chen; Tat-Jun Chin; Yee Wei Law; Michele Sasdelli; Ramesh Rajasegaran; Dillon Campbell

http://arxiv.org/abs/2108.11673
Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference. (80%)
Yang Zheng; Xiaoyi Feng; Zhaoqiang Xia; Xiaoyue Jiang; Ambra Demontis; Maura Pintor; Battista Biggio; Fabio Roli

http://arxiv.org/abs/2108.12081
Detection and Continual Learning of Novel Face Presentation Attacks. (2%)
Mohammad Rostami; Leonidas Spinoulas; Mohamed Hussein; Joe Mathai; Wael Abd-Almageed

http://arxiv.org/abs/2108.11168
Adversarially Robust One-class Novelty Detection. (99%)
Shao-Yuan Lo; Poojan Oza; Vishal M. Patel

http://arxiv.org/abs/2108.11299
Certifiers Make Neural Networks Vulnerable to Availability Attacks. (99%)
Tobias Lorenz; Marta Kwiatkowska; Mario Fritz

http://arxiv.org/abs/2108.11135
Bridged Adversarial Training. (93%)
Hoki Kim; Woojin Lee; Sungyoon Lee; Jaewook Lee

http://arxiv.org/abs/2108.11505
Generalized Real-World Super-Resolution through Adversarial Robustness. (93%)
Angela Castillo; María Escobar; Juan C. Pérez; Andrés Romero; Radu Timofte; Gool Luc Van; Pablo Arbeláez

http://arxiv.org/abs/2108.11032
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE. (99%)
Wenzhao Xiang; Chang Liu; Shibao Zheng

http://arxiv.org/abs/2108.10879
Are socially-aware trajectory prediction models really socially-aware? (92%)
Saeed Saadatnejad; Mohammadhossein Bahari; Pedram Khorsandi; Mohammad Saneian; Seyed-Mohsen Moosavi-Dezfooli; Alexandre Alahi

http://arxiv.org/abs/2108.10992
OOWL500: Overcoming Dataset Collection Bias in the Wild. (76%)
Brandon Leung; Chih-Hui Ho; Amir Persekian; David Orozco; Yen Chang; Erik Sandstrom; Bo Liu; Nuno Vasconcelos

http://arxiv.org/abs/2108.10549
StyleAugment: Learning Texture De-biased Representations by Style Augmentation without Pre-defined Textures. (1%)
Sanghyuk Chun; Song Park

http://arxiv.org/abs/2108.10451
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications. (99%)
Wenjie Ruan; Xinping Yi; Xiaowei Huang

http://arxiv.org/abs/2108.10015
Semantic-Preserving Adversarial Text Attacks. (99%)
Xinghao Yang; Weifeng Liu; James Bailey; Tianqing Zhu; Dacheng Tao; Wei Liu

http://arxiv.org/abs/2108.10217
Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks. (99%)
Nima Mirnateghi; Syed Afaq Ali Shah; Mohammed Bennamoun

http://arxiv.org/abs/2108.10251
Kryptonite: An Adversarial Attack Using Regional Focus. (99%)
Yogesh Kulkarni; Krisha Bhambani

http://arxiv.org/abs/2108.10241
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning. (73%)
Virat Shejwalkar; Amir Houmansadr; Peter Kairouz; Daniel Ramage

http://arxiv.org/abs/2108.09929
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness. (4%)
Md Amirul Islam; Matthew Kowal; Konstantinos G. Derpanis; Neil D. B. Bruce

http://arxiv.org/abs/2108.09713
Robustness-via-Synthesis: Robust Training with Generative Adversarial Perturbations. (99%)
Inci M. Baytas; Debayan Deb

http://arxiv.org/abs/2108.09891
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency. (98%)
Xueping Wang; Shasha Li; Min Liu; Yaonan Wang; Amit K. Roy-Chowdhury

http://arxiv.org/abs/2108.09768
Relating CNNs with brain: Challenges and findings. (10%)
Reem Abdel-Salam

http://arxiv.org/abs/2108.09513
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks. (99%)
Jiaming Mu; Binghui Wang; Qi Li; Kun Sun; Mingwei Xu; Zhuotao Liu

http://arxiv.org/abs/2108.09454
"Adversarial Examples" for Proof-of-Learning. (98%)
Rui Zhang; Jian Liu; Yuan Ding; Qingbiao Wu; Kui Ren

http://arxiv.org/abs/2108.13551
Regularizing Instabilities in Image Reconstruction Arising from Learned Denoisers. (2%)
Abinash Nayak

http://arxiv.org/abs/2108.09034
AdvDrop: Adversarial Attack to DNNs by Dropping Information. (99%)
Ranjie Duan; Yuefeng Chen; Dantong Niu; Yun Yang; A. K. Qin; Yuan He

http://arxiv.org/abs/2108.09135
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier. (99%)
Chong Xiang; Saeed Mahloujifar; Prateek Mittal

http://arxiv.org/abs/2108.09413
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks. (98%)
Haowen Lin; Jian Lou; Li Xiong; Cyrus Shahabi

http://arxiv.org/abs/2108.09093
Towards Understanding the Generative Capability of Adversarially Robust Classifiers. (98%)
Yao Zhu; Jiacheng Ma; Jiacheng Sun; Zewei Chen; Rongxin Jiang; Zhenguo Li

http://arxiv.org/abs/2108.09383
Detecting and Segmenting Adversarial Graphics Patterns from Images. (93%)
Xiangyu Purdue University Qu; Stanley H. Purdue University Chan

http://arxiv.org/abs/2108.09033
UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning. (1%)
Ege Erdogan; Alptekin Kupcu; A. Ercument Cicek

http://arxiv.org/abs/2108.09343
Early-exit deep neural networks for distorted images: providing an efficient edge offloading. (1%)
Roberto G. Pacheco; Fernanda D. V. R. Oliveira; Rodrigo S. Couto

http://arxiv.org/abs/2108.08972
Application of Adversarial Examples to Physical ECG Signals. (99%)
Taiga Waseda University Ono; Takeshi The University of Electro-Communications Sugawara; Jun University of Tsukuba Sakuma; Tatsuya Waseda University RIKEN AIP Mori

http://arxiv.org/abs/2108.08560
Pruning in the Face of Adversaries. (99%)
Florian Merkle; Maximilian Samsinger; Pascal Schöttle

http://arxiv.org/abs/2108.08976
ASAT: Adaptively Scaled Adversarial Training in Time Series. (98%)
Zhiyuan Zhang; Wei Li; Ruihan Bao; Keiko Harimoto; Yunfang Wu; Xu Sun

http://arxiv.org/abs/2108.08487
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain. (80%)
Guangyao Chen; Peixi Peng; Li Ma; Jia Li; Lin Du; Yonghong Tian

http://arxiv.org/abs/2108.07969
Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better. (99%)
Bojia Zi; Shihao Zhao; Xingjun Ma; Yu-Gang Jiang

http://arxiv.org/abs/2108.08421
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes. (98%)
Mingjun Yin; Shasha Li; Zikui Cai; Chengyu Song; M. Salman Asif; Amit K. Roy-Chowdhury; Srikanth V. Krishnamurthy

http://arxiv.org/abs/2108.08211
MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression. (45%)
Zhaoyang Jia; Han Fang; Weiming Zhang

http://arxiv.org/abs/2108.08476
Proceedings of the 1st International Workshop on Adaptive Cyber Defense. (1%)
Damian Marriott; Kimberly Ferguson-Walter; Sunny Fugate; Marco Carvalho

http://arxiv.org/abs/2108.07602
When Should You Defend Your Classifier -- A Game-theoretical Analysis of Countermeasures against Adversarial Examples. (98%)
Maximilian Samsinger; Florian Merkle; Pascal Schöttle; Tomas Pevny

http://arxiv.org/abs/2108.07920
Adversarial Relighting Against Face Recognition. (98%)
Qian Zhang; Qing Guo; Ruijun Gao; Felix Juefei-Xu; Hongkai Yu; Wei Feng

http://arxiv.org/abs/2108.07958
Semantic Perturbations with Normalizing Flows for Improved Generalization. (13%)
Oguz Kaan Yuksel; Sebastian U. Stich; Martin Jaggi; Tatjana Chavdarova

http://arxiv.org/abs/2108.07594
Coalesced Multi-Output Tsetlin Machines with Clause Sharing. (1%)
Sondre Glimsdal; Ole-Christoffer Granmo

http://arxiv.org/abs/2108.07779
Appearance Based Deep Domain Adaptation for the Classification of Aerial Images. (1%)
Dennis Wittich; Franz Rottensteiner

http://arxiv.org/abs/2108.07033
Exploring Transferable and Robust Adversarial Perturbation Generation from the Perspective of Network Hierarchy. (99%)
Ruikui Wang; Yuanfang Guo; Ruijie Yang; Yunhong Wang

http://arxiv.org/abs/2108.06895
Interpreting Attributions and Interactions of Adversarial Attacks. (83%)
Xin Wang; Shuyun Lin; Hao Zhang; Yufei Zhu; Quanshi Zhang

http://arxiv.org/abs/2108.07229
Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose? (62%)
Max Lennon; Nathan Drenkow; Philippe Burlina

http://arxiv.org/abs/2108.07256
NeuraCrypt is not private. (10%)
Nicholas Carlini; Sanjam Garg; Somesh Jha; Saeed Mahloujifar; Mohammad Mahmoody; Florian Tramer

http://arxiv.org/abs/2108.07083
Identifying and Exploiting Structures for Reliable Deep Learning. (2%)
Amartya Sanyal

http://arxiv.org/abs/2108.07258
On the Opportunities and Risks of Foundation Models. (2%)
Rishi Bommasani; Drew A. Hudson; Ehsan Adeli; Russ Altman; Simran Arora; Arx Sydney von; Michael S. Bernstein; Jeannette Bohg; Antoine Bosselut; Emma Brunskill; Erik Brynjolfsson; Shyamal Buch; Dallas Card; Rodrigo Castellon; Niladri Chatterji; Annie Chen; Kathleen Creel; Jared Quincy Davis; Dora Demszky; Chris Donahue; Moussa Doumbouya; Esin Durmus; Stefano Ermon; John Etchemendy; Kawin Ethayarajh; Li Fei-Fei; Chelsea Finn; Trevor Gale; Lauren Gillespie; Karan Goel; Noah Goodman; Shelby Grossman; Neel Guha; Tatsunori Hashimoto; Peter Henderson; John Hewitt; Daniel E. Ho; Jenny Hong; Kyle Hsu; Jing Huang; Thomas Icard; Saahil Jain; Dan Jurafsky; Pratyusha Kalluri; Siddharth Karamcheti; Geoff Keeling; Fereshte Khani; Omar Khattab; Pang Wei Koh; Mark Krass; Ranjay Krishna; Rohith Kuditipudi; Ananya Kumar; Faisal Ladhak; Mina Lee; Tony Lee; Jure Leskovec; Isabelle Levent; Xiang Lisa Li; Xuechen Li; Tengyu Ma; Ali Malik; Christopher D. Manning; Suvir Mirchandani; Eric Mitchell; Zanele Munyikwa; Suraj Nair; Avanika Narayan; Deepak Narayanan; Ben Newman; Allen Nie; Juan Carlos Niebles; Hamed Nilforoshan; Julian Nyarko; Giray Ogut; Laurel Orr; Isabel Papadimitriou; Joon Sung Park; Chris Piech; Eva Portelance; Christopher Potts; Aditi Raghunathan; Rob Reich; Hongyu Ren; Frieda Rong; Yusuf Roohani; Camilo Ruiz; Jack Ryan; Christopher Ré; Dorsa Sadigh; Shiori Sagawa; Keshav Santhanam; Andy Shih; Krishnan Srinivasan; Alex Tamkin; Rohan Taori; Armin W. Thomas; Florian Tramèr; Rose E. Wang; William Wang; Bohan Wu; Jiajun Wu; Yuhuai Wu; Sang Michael Xie; Michihiro Yasunaga; Jiaxuan You; Matei Zaharia; Michael Zhang; Tianyi Zhang; Xikun Zhang; Yuhui Zhang; Lucia Zheng; Kaitlyn Zhou; Percy Liang

http://arxiv.org/abs/2108.06885
Neural Architecture Dilation for Adversarial Robustness. (81%)
Yanxi Li; Zhaohui Yang; Yunhe Wang; Chang Xu

http://arxiv.org/abs/2108.06797
Deep Adversarially-Enhanced k-Nearest Neighbors. (74%)
Ren Wang; Tianqi Chen

http://arxiv.org/abs/2108.06871
IADA: Iterative Adversarial Data Augmentation Using Formal Verification and Expert Guidance. (1%)
Ruixuan Liu; Changliu Liu

http://arxiv.org/abs/2108.06504
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis. (1%)
Fan Wu; Yunhui Long; Ce Zhang; Bo Li

http://arxiv.org/abs/2108.06179
Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks. (99%)
Federico Nesti; Giulio Rossolini; Saasha Nair; Alessandro Biondi; Giorgio Buttazzo

http://arxiv.org/abs/2108.06247
Optical Adversarial Attack. (98%)
Abhiram Gnanasambandam; Alex M. Sherman; Stanley H. Chan

http://arxiv.org/abs/2108.06280
Understanding Structural Vulnerability in Graph Convolutional Networks. (96%)
Liang Chen; Jintang Li; Qibiao Peng; Yang Liu; Zibin Zheng; Carl Yang

http://arxiv.org/abs/2108.06131
The Forgotten Threat of Voltage Glitching: A Case Study on Nvidia Tegra X2 SoCs. (1%)
Otto Bittner; Thilo Krachenfels; Andreas Galauner; Jean-Pierre Seifert

http://arxiv.org/abs/2108.06017
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning. (99%)
Hong Wang; Yuefan Deng; Shinjae Yoo; Haibin Ling; Yuewei Lin

http://arxiv.org/abs/2108.05948
Deep adversarial attack on target detection systems. (99%)
Uche M. Osahor; Nasser M. Nasrabadi

http://arxiv.org/abs/2108.05921
Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hate. (69%)
Hannah Rose Kirk; Bertram Vidgen; Paul Röttger; Tristan Thrush; Scott A. Hale

http://arxiv.org/abs/2108.05075
Turning Your Strength against You: Detecting and Mitigating Robust and Universal Adversarial Patch Attacks. (99%)
Zitao Chen; Pritam Dash; Karthik Pattabiraman

http://arxiv.org/abs/2108.05490
Attacks against Ranking Algorithms with Text Embeddings: a Case Study on Recruitment Algorithms. (78%)
Anahita Samadi; Debapriya Banerjee; Shirin Nilizadeh

http://arxiv.org/abs/2108.05018
Are Neural Ranking Models Robust? (4%)
Chen Wu; Ruqing Zhang; Jiafeng Guo; Yixing Fan; Xueqi Cheng

http://arxiv.org/abs/2108.05149
Logic Explained Networks. (1%)
Gabriele Ciravegna; Pietro Barbiero; Francesco Giannini; Marco Gori; Pietro Lió; Marco Maggini; Stefano Melacci

http://arxiv.org/abs/2108.04979
Simple black-box universal adversarial attacks on medical image classification based on deep neural networks. (99%)
Kazuki Koga; Kazuhiro Takemoto

http://arxiv.org/abs/2108.04890
On the Effect of Pruning on Adversarial Robustness. (81%)
Artur Jordao; Helio Pedrini

http://arxiv.org/abs/2108.04974
SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version). (68%)
Nils Lukas; Edward Jiang; Xinda Li; Florian Kerschbaum

http://arxiv.org/abs/2108.04990
Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing. (64%)
Sanchit Sinha; Hanjie Chen; Arshdeep Sekhon; Yangfeng Ji; Yanjun Qi

http://arxiv.org/abs/2108.04584
UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks. (2%)
NareshKumar Gurulingan; Elahe Arani; Bahram Zonooz

http://arxiv.org/abs/2108.04547
Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation. (1%)
Weilun Wang; Wengang Zhou; Jianmin Bao; Dong Chen; Houqiang Li

http://arxiv.org/abs/2108.04204
Meta Gradient Adversarial Attack. (99%)
Zheng Yuan; Jie Zhang; Yunpei Jia; Chuanqi Tan; Tao Xue; Shiguang Shan

http://arxiv.org/abs/2108.04409
On Procedural Adversarial Noise Attack And Defense. (99%)
Jun Yan; Xiaoyang Deng; Huilin Yin; Wancheng Ge

http://arxiv.org/abs/2108.04430
Enhancing Knowledge Tracing via Adversarial Training. (98%)
Xiaopeng Guo; Zhijie Huang; Jie Gao; Mingyu Shang; Maojing Shu; Jun Sun

http://arxiv.org/abs/2108.04214
Neural Network Repair with Reachability Analysis. (96%)
Xiaodong Yang; Tom Yamaguchi; Hoang-Dung Tran; Bardh Hoxha; Taylor T Johnson; Danil Prokhorov

http://arxiv.org/abs/2108.04206
Classification Auto-Encoder based Detector against Diverse Data Poisoning Attacks. (92%)
Fereshteh Razmi; Li Xiong

http://arxiv.org/abs/2108.03803
Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning. (82%)
Wanqi Xue; Wei Qiu; Bo An; Zinovi Rabinovich; Svetlana Obraztsova; Chai Kiat Yeo

http://arxiv.org/abs/2108.04417
Privacy-Preserving Machine Learning: Methods, Challenges and Directions. (16%)
Runhua Xu; Nathalie Baracaldo; James Joshi

http://arxiv.org/abs/2108.04345
Explainable AI and susceptibility to adversarial attacks: a case study in classification of breast ultrasound images. (15%)
Hamza Rasaee; Hassan Rivaz

http://arxiv.org/abs/2108.03388
Jointly Attacking Graph Neural Network and its Explanations. (96%)
Wenqi Fan; Wei Jin; Xiaorui Liu; Han Xu; Xianfeng Tang; Suhang Wang; Qing Li; Jiliang Tang; Jianping Wang; Charu Aggarwal

http://arxiv.org/abs/2108.03506
Membership Inference Attacks on Lottery Ticket Networks. (33%)
Aadesh Bagmar; Shishira R Maiya; Shruti Bidwalka; Amol Deshpande

http://arxiv.org/abs/2108.03418
Information Bottleneck Approach to Spatial Attention Learning. (1%)
Qiuxia Lai; Yu Li; Ailing Zeng; Minhao Liu; Hanqiu Sun; Qiang Xu

http://arxiv.org/abs/2108.02940
Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles. (80%)
Jindi Zhang; Yang Lou; Jianping Wang; Kui Wu; Kejie Lu; Xiaohua Jia

http://arxiv.org/abs/2108.03288
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration Data. (2%)
Atik Faysal; Ngui Wai Keng; M. H. Lim

http://arxiv.org/abs/2108.02756
BOSS: Bidirectional One-Shot Synthesis of Adversarial Examples. (99%)
Ismail Alkhouri; Alvaro Velasquez; George Atia

http://arxiv.org/abs/2108.02488
Poison Ink: Robust and Invisible Backdoor Attack. (99%)
Jie Zhang; Dongdong Chen; Jing Liao; Qidong Huang; Gang Hua; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2108.02502
Imperceptible Adversarial Examples by Spatial Chroma-Shift. (99%)
Ayberk Aydin; Deniz Sen; Berat Tuna Karli; Oguz Hanoglu; Alptekin Temizel

http://arxiv.org/abs/2108.04062
Householder Activations for Provable Robustness against Adversarial Attacks. (83%)
Sahil Singla; Surbhi Singla; Soheil Feizi

http://arxiv.org/abs/2108.02707
Fairness Properties of Face Recognition and Obfuscation Systems. (68%)
Harrison Rosenberg; Brian Tang; Kassem Fawaz; Somesh Jha

http://arxiv.org/abs/2108.02360
Exploring Structure Consistency for Deep Model Watermarking. (10%)
Jie Zhang; Dongdong Chen; Jing Liao; Han Fang; Zehua Ma; Weiming Zhang; Gang Hua; Nenghai Yu

http://arxiv.org/abs/2108.02501
Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection. (1%)
Tungyu Wu; Youting Wang

http://arxiv.org/abs/2108.02340
Robust Transfer Learning with Pretrained Language Models through Adapters. (82%)
Wenjuan Han; Bo Pang; Yingnian Wu

http://arxiv.org/abs/2108.01852
Semi-supervised Conditional GAN for Simultaneous Generation and Detection of Phishing URLs: A Game theoretic Perspective. (31%)
Sharif Amit Kamran; Shamik Sengupta; Alireza Tavakkoli

http://arxiv.org/abs/2108.01807
On the Robustness of Domain Adaption to Adversarial Attacks. (99%)
Liyuan Zhang; Yuhang Zhou; Lei Zhang

http://arxiv.org/abs/2108.02010
On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples. (99%)
Adelin Travers; Lorna Licollari; Guanghan Wang; Varun Chandrasekaran; Adam Dziedzic; David Lie; Nicolas Papernot

http://arxiv.org/abs/2108.01289
AdvRush: Searching for Adversarially Robust Neural Architectures. (99%)
Jisoo Mok; Byunggook Na; Hyeokjun Choe; Sungroh Yoon

http://arxiv.org/abs/2108.01644
The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models. (88%)
Ambrish Rawat; Killian Levacher; Mathieu Sinn

http://arxiv.org/abs/2108.01281
DeepFreeze: Cold Boot Attacks and High Fidelity Model Recovery on Commercial EdgeML Device. (69%)
Yoo-Seung Won; Soham Chatterjee; Dirmanto Jap; Arindam Basu; Shivam Bhasin

http://arxiv.org/abs/2108.01734
Tutorials on Testing Neural Networks. (1%)
Nicolas Berthier; Youcheng Sun; Wei Huang; Yanghao Zhang; Wenjie Ruan; Xiaowei Huang

http://arxiv.org/abs/2108.01125
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack. (98%)
Reek Majumder; Sakib Mahmud Khan; Fahim Ahmed; Zadid Khan; Frank Ngeni; Gurcan Comert; Judith Mwakalonge; Dimitra Michalaka; Mashrur Chowdhury

http://arxiv.org/abs/2108.00833
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles. (10%)
Anum Talpur; Mohan Gurusamy

http://arxiv.org/abs/2108.00701
Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks. (9%)
Yuwei Sun; Ng Chong; Hideya Ochiai

http://arxiv.org/abs/2108.01124
Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles. (1%)
Sakib Mahmud Khan; Gurcan Comert; Mashrur Chowdhury

http://arxiv.org/abs/2108.00401
Advances in adversarial attacks and defenses in computer vision: A survey. (92%)
Naveed Akhtar; Ajmal Mian; Navid Kardan; Mubarak Shah

http://arxiv.org/abs/2108.00491
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders. (80%)
Huimin Zeng; Jiahao Su; Furong Huang

http://arxiv.org/abs/2108.00422
An Effective and Robust Detector for Logo Detection. (70%)
Xiaojun Jia; Huanqian Yan; Yonglin Wu; Xingxing Wei; Xiaochun Cao; Yong Zhang

http://arxiv.org/abs/2108.00402
Style Curriculum Learning for Robust Medical Image Segmentation. (2%)
Zhendong Liu; Van Manh; Xin Yang; Xiaoqiong Huang; Karim Lekadir; Víctor Campello; Nishant Ravikumar; Alejandro F Frangi; Dong Ni

http://arxiv.org/abs/2108.00180
Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework. (99%)
Li Ding; Yongwei Wang; Xin Ding; Kaiwen Yuan; Ping Wang; Hua Huang; Z. Jane Wang

http://arxiv.org/abs/2108.00213
Adversarial Robustness of Deep Code Comment Generation. (99%)
Yu Zhou; Xiaoqing Zhang; Juanjuan Shen; Tingting Han; Taolue Chen; Harald Gall

http://arxiv.org/abs/2108.00335
Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones. (93%)
Kelvin Cheng; Christopher Healey; Tianfu Wu

http://arxiv.org/abs/2108.00146
T$_k$ML-AP: Adversarial Attacks to Top-$k$ Multi-Label Learning. (81%)
Shu Hu; Lipeng Ke; Xin Wang; Siwei Lyu

http://arxiv.org/abs/2108.00352
BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning. (67%)
Jinyuan Jia; Yupei Liu; Neil Zhenqiang Gong

http://arxiv.org/abs/2108.00295
Fair Representation Learning using Interpolation Enabled Disentanglement. (1%)
Akshita Jha; Bhanukiran Vinzamuri; Chandan K. Reddy

http://arxiv.org/abs/2107.14601
Who's Afraid of Thomas Bayes? (92%)
Erick Galinkin

http://arxiv.org/abs/2107.14642
Practical Attacks on Voice Spoofing Countermeasures. (86%)
Andre Kassis; Urs Hengartner

http://arxiv.org/abs/2107.14569
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers. (50%)
Stefanos Koffas; Jing Xu; Mauro Conti; Stjepan Picek

http://arxiv.org/abs/2107.14756
Unveiling the potential of Graph Neural Networks for robust Intrusion Detection. (13%)
David Pujol-Perich; José Suárez-Varela; Albert Cabellos-Aparicio; Pere Barlet-Ros

http://arxiv.org/abs/2107.14185
Feature Importance-aware Transferable Adversarial Attacks. (99%)
Zhibo Wang; Hengchang Guo; Zhifei Zhang; Wenxin Liu; Zhan Qin; Kui Ren

http://arxiv.org/abs/2107.14110
Enhancing Adversarial Robustness via Test-time Transformation Ensembling. (98%)
Juan C. Pérez; Motasem Alfarra; Guillaume Jeanneret; Laura Rueda; Ali Thabet; Bernard Ghanem; Pablo Arbeláez

http://arxiv.org/abs/2107.13962
The Robustness of Graph k-shell Structure under Adversarial Attacks. (93%)
B. Zhou; Y. Q. Lv; Y. C. Mao; J. H. Wang; S. Q. Yu; Q. Xuan

http://arxiv.org/abs/2107.13876
Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality. (31%)
Vito Walter Anelli; Yashar Deldjoo; Noia Tommaso Di; Felice Antonio Merra

http://arxiv.org/abs/2107.14344
Towards robust vision by multi-task learning on monkey visual cortex. (3%)
Shahd Safarani; Arne Nix; Konstantin Willeke; Santiago A. Cadena; Kelli Restivo; George Denfield; Andreas S. Tolias; Fabian H. Sinz

http://arxiv.org/abs/2107.13639
Imbalanced Adversarial Training with Reweighting. (86%)
Wentao Wang; Han Xu; Xiaorui Liu; Yaxin Li; Bhavani Thuraisingham; Jiliang Tang

http://arxiv.org/abs/2107.13541
Towards Robustness Against Natural Language Word Substitutions. (73%)
Xinshuai Dong; Anh Tuan Luu; Rongrong Ji; Hong Liu

http://arxiv.org/abs/2107.13491
Models of Computational Profiles to Study the Likelihood of DNN Metamorphic Test Cases. (67%)
Ettore Merlo; Mira Marhaba; Foutse Khomh; Houssem Ben Braiek; Giuliano Antoniol

http://arxiv.org/abs/2107.13335
WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification. (15%)
Qiufu Li; Linlin Shen; Sheng Guo; Zhihui Lai

http://arxiv.org/abs/2107.13190
TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized Tabular Data Releasing. (2%)
Aoting Hu; Renjie Xie; Zhigang Lu; Aiqun Hu; Minhui Xue

http://arxiv.org/abs/2107.12732
Towards Black-box Attacks on Deep Learning Apps. (89%)
Hongchen Cao; Shuai Li; Yuming Zhou; Ming Fan; Xuejiao Zhao; Yutian Tang

http://arxiv.org/abs/2107.12612
Poisoning Online Learning Filters: DDoS Attacks and Countermeasures. (50%)
Wesley Joon-Wie Tann; Ee-Chien Chang

http://arxiv.org/abs/2107.12873
PDF-Malware: An Overview on Threats, Detection and Evasion Attacks. (8%)
Nicolas Fleury; Theo Dubrunquez; Ihsen Alouani

http://arxiv.org/abs/2107.11986
Benign Adversarial Attack: Tricking Models for Goodness. (99%)
Jitao Sang; Xian Zhao; Jiaming Zhang; Zhiyu Lin

http://arxiv.org/abs/2107.12085
Learning to Adversarially Blur Visual Object Tracking. (98%)
Qing Guo; Ziyi Cheng; Felix Juefei-Xu; Lei Ma; Xiaofei Xie; Yang Liu; Jianjun Zhao

http://arxiv.org/abs/2107.12473
Adversarial Attacks with Time-Scale Representations. (96%)
Alberto Santamaria-Pang; Jianwei Qiu; Aritra Chowdhury; James Kubricht; Peter Tu; Iyer Naresh; Nurali Virani

http://arxiv.org/abs/2107.11671
Adversarial training may be a double-edged sword. (99%)
Ali Rahmati; Seyed-Mohsen Moosavi-Dezfooli; Huaiyu Dai

http://arxiv.org/abs/2107.11630
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them. (98%)
Florian Tramèr

http://arxiv.org/abs/2107.11652
Stress Test Evaluation of Biomedical Word Embeddings. (73%)
Vladimir Araujo; Andrés Carvallo; Carlos Aspillaga; Camilo Thorne; Denis Parra

http://arxiv.org/abs/2107.11576
X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering. (1%)
Jingjing Jiang; Ziyi Liu; Yifan Liu; Zhixiong Nan; Nanning Zheng

http://arxiv.org/abs/2107.11275
A Differentiable Language Model Adversarial Attack on Text Classifiers. (99%)
Ivan Fursov; Alexey Zaytsev; Pavel Burnyshev; Ekaterina Dmitrieva; Nikita Klyuchnikov; Andrey Kravchenko; Ekaterina Artemova; Evgeny Burnaev

http://arxiv.org/abs/2107.11327
Structack: Structure-based Adversarial Attacks on Graph Neural Networks. (86%)
Hussain Hussain; Tomislav Duricic; Elisabeth Lex; Denis Helic; Markus Strohmaier; Roman Kern

http://arxiv.org/abs/2107.11252
Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation. (45%)
Bingqian Lin; Yi Zhu; Yanxin Long; Xiaodan Liang; Qixiang Ye; Liang Lin

http://arxiv.org/abs/2107.11472
Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers. (8%)
Yunhui Guo; Xudong Wang; Yubei Chen; Stella X. Yu

http://arxiv.org/abs/2107.10873
On the Certified Robustness for Ensemble Models and Beyond. (99%)
Zhuolin Yang; Linyi Li; Xiaojun Xu; Bhavya Kailkhura; Tao Xie; Bo Li

http://arxiv.org/abs/2107.10480
Unsupervised Detection of Adversarial Examples with Model Explanations. (99%)
Gihyuk Ko; Gyumin Lim

http://arxiv.org/abs/2107.12173
Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning. (83%)
Yi Shi; Yalin E. Sagduyu

http://arxiv.org/abs/2107.10599
Towards Explaining Adversarial Examples Phenomenon in Artificial Neural Networks. (75%)
Ramin Barati; Reza Safabakhsh; Mohammad Rahmati

http://arxiv.org/abs/2107.10989
Estimating Predictive Uncertainty Under Program Data Distribution Shift. (1%)
Yufei Li; Simin Chen; Wei Yang

http://arxiv.org/abs/2107.10457
Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack. (1%)
Fan Wu; Min Gao; Junliang Yu; Zongwei Wang; Kecheng Liu; Xu Wange

http://arxiv.org/abs/2107.09937
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients. (98%)
Huimin Wu; Zhengmian Hu; Bin Gu

http://arxiv.org/abs/2107.10137
Improved Text Classification via Contrastive Adversarial Training. (84%)
Lin Pan; Chung-Wei Hang; Avirup Sil; Saloni Potdar

http://arxiv.org/abs/2107.10174
Black-box Probe for Unsupervised Domain Adaptation without Model Transferring. (81%)
Kunhong Wu; Yucheng Shi; Yahong Han; Yunfeng Shao; Bingshuai Li

http://arxiv.org/abs/2107.09898
Defending against Reconstruction Attack in Vertical Federated Learning. (10%)
Jiankai Sun; Yuanshun Yao; Weihao Gao; Junyuan Xie; Chong Wang

http://arxiv.org/abs/2107.10139
Generative Models for Security: Attacks, Defenses, and Opportunities. (10%)
Luke A. Bauer; Vincent Bindschaedler

http://arxiv.org/abs/2107.10045
A Tandem Framework Balancing Privacy and Security for Voice User Interfaces. (5%)
Ranya Aloufi; Hamed Haddadi; David Boyle

http://arxiv.org/abs/2107.10443
Spinning Sequence-to-Sequence Models with Meta-Backdoors. (4%)
Eugene Bagdasaryan; Vitaly Shmatikov

http://arxiv.org/abs/2107.10110
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms. (2%)
Shuyu Cheng; Guoqiang Wu; Jun Zhu

http://arxiv.org/abs/2107.09804
Using Undervolting as an On-Device Defense Against Adversarial Machine Learning Attacks. (99%)
Saikat Majumdar; Mohammad Hossein Samavatian; Kristin Barber; Radu Teodorescu

http://arxiv.org/abs/2107.09258
A Markov Game Model for AI-based Cyber Security Attack Mitigation. (10%)
Hooman Alavizadeh; Julian Jang-Jaccard; Tansu Alpcan; Seyit A. Camtepe

http://arxiv.org/abs/2107.09833
Leaking Secrets through Modern Branch Predictor in the Speculative World. (1%)
Md Hafizul Islam Chowdhuryy; Fan Yao

http://arxiv.org/abs/2107.09225
Discriminator-Free Generative Adversarial Attack. (99%)
Shaohao Lu; Yuqiao Xian; Ke Yan; Yi Hu; Xing Sun; Xiaowei Guo; Feiyue Huang; Wei-Shi Zheng

http://arxiv.org/abs/2107.09502
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features. (99%)
Hui Liu; Bo Zhao; Yuefeng Peng; Jiabao Guo; Peng Liu

http://arxiv.org/abs/2107.09126
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition. (98%)
Benjamin Spetter-Goldstein; Nataniel Ruiz; Sarah Adel Bargal

http://arxiv.org/abs/2107.09045
On the Veracity of Local, Model-agnostic Explanations in Audio Classification: Targeted Investigations with Adversarial Examples. (80%)
Verena Praher; Katharina Prinz; Arthur Flexer; Gerhard Widmer

http://arxiv.org/abs/2107.08909
MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI. (33%)
Takayuki Miura; Satoshi Hasegawa; Toshiki Shibahara

http://arxiv.org/abs/2107.08688
Structural Watermarking to Deep Neural Networks via Network Channel Pruning. (11%)
Xiangyu Zhao; Yinzhe Yao; Hanzhou Wu; Xinpeng Zhang

http://arxiv.org/abs/2108.04328
Generative Adversarial Neural Cellular Automata. (1%)
Maximilian Otte; Quentin Delfosse; Johannes Czech; Kristian Kersting

http://arxiv.org/abs/2107.08767
Improving Interpretability of Deep Neural Networks in Medical Diagnosis by Investigating the Individual Units. (1%)
Woo-Jeoung Nam; Seong-Whan Lee

http://arxiv.org/abs/2107.09044
Just Train Twice: Improving Group Robustness without Training Group Information. (1%)
Evan Zheran Liu; Behzad Haghgoo; Annie S. Chen; Aditi Raghunathan; Pang Wei Koh; Shiori Sagawa; Percy Liang; Chelsea Finn

http://arxiv.org/abs/2107.08402
RobustFed: A Truth Inference Approach for Robust Federated Learning. (1%)
Farnaz Tahmasebian; Jian Lou; Li Xiong

http://arxiv.org/abs/2107.08189
BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmark. (9%)
Anand Avati; Martin Seneviratne; Emily Xue; Zhen Xu; Balaji Lakshminarayanan; Andrew M. Dai

http://arxiv.org/abs/2107.07737
EGC2: Enhanced Graph Classification with Easy Graph Compression. (89%)
Jinyin Chen; Haiyang Xiong; Haibin Zhenga; Dunjie Zhang; Jian Zhang; Mingwei Jia; Yi Liu

http://arxiv.org/abs/2107.08821
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. (1%)
Quanshi Zhang; Tian Han; Lixin Fan; Zhanxing Zhu; Hang Su; Ying Nian Wu; Jie Ren; Hao Zhang

http://arxiv.org/abs/2107.07610
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks. (99%)
Zhao Meng; Yihan Dong; Mrinmaya Sachan; Roger Wattenhofer

http://arxiv.org/abs/2107.07449
Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving. (98%)
Ibrahim Sobh; Ahmed Hamed; Varun Ravi Kumar; Senthil Yogamani

http://arxiv.org/abs/2107.07677
ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks. (92%)
Khondker Fariha Hossain; Sharif Amit Kamran; Alireza Tavakkoli; Lei Pan; Xingjun Ma; Sutharshan Rajasegarar; Chandan Karmaker

http://arxiv.org/abs/2107.07618
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks. (80%)
Ismail Alarab; Simant Prakoonwit

http://arxiv.org/abs/2107.07240
Subnet Replacement: Deployment-stage backdoor attack against deep neural networks in gray-box setting. (16%)
Xiangyu Qi; Jifeng Zhu; Chulin Xie; Yong Yang

http://arxiv.org/abs/2107.07150
Tailor: Generating and Perturbing Text with Semantic Controls. (3%)
Alexis Ross; Tongshuang Wu; Hao Peng; Matthew E. Peters; Matt Gardner

http://arxiv.org/abs/2107.07455
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. (1%)
Andrey Malinin; Neil Band; Ganshin; Alexander; German Chesnokov; Yarin Gal; Mark J. F. Gales; Alexey Noskov; Andrey Ploskonosov; Liudmila Prokhorenkova; Ivan Provilkov; Vatsal Raina; Vyas Raina; Roginskiy; Denis; Mariya Shmatova; Panos Tigas; Boris Yangel

http://arxiv.org/abs/2107.06501
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning. (99%)
Yihao Huang; Qing Guo; Felix Juefei-Xu; Lei Ma; Weikai Miao; Yang Liu; Geguang Pu

http://arxiv.org/abs/2107.06882
Conservative Objective Models for Effective Offline Model-Based Optimization. (67%)
Brandon Trabucco; Aviral Kumar; Xinyang Geng; Sergey Levine

http://arxiv.org/abs/2107.06456
AID-Purifier: A Light Auxiliary Network for Boosting Adversarial Defense. (88%)
Duhun Hwang; Eunjung Lee; Wonjong Rhee

http://arxiv.org/abs/2107.06400
Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam Detection. (69%)
Sergio Rojas-Galeano

http://arxiv.org/abs/2107.06158
Correlation Analysis between the Robustness of Sparse Neural Networks and their Random Hidden Structural Priors. (41%)
M. Ben Amor; J. Stier; M. Granitzer

http://arxiv.org/abs/2107.06217
What classifiers know what they don't? (1%)
Mohamed Ishmael Belghazi; David Lopez-Paz

http://arxiv.org/abs/2107.05754
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks. (99%)
Andrei Ilie; Marius Popescu; Alin Stefanescu

http://arxiv.org/abs/2107.05780
Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive Denoising. (99%)
Anouar Kherchouche; Sid Ahmed Fezza; Wassim Hamidouche

http://arxiv.org/abs/2107.05222
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems. (96%)
Anirudh Sreeram; Nicholas Mehlman; Raghuveer Peri; Dillon Knox; Shrikanth Narayanan

http://arxiv.org/abs/2107.05243
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning. (81%)
Jun Wang; Chang Xu; Francisco Guzman; Ahmed El-Kishky; Yuqing Tang; Benjamin I. P. Rubinstein; Trevor Cohn

http://arxiv.org/abs/2107.05712
A Closer Look at the Adversarial Robustness of Information Bottleneck Models. (70%)
Iryna Korshunova; David Stutz; Alexander A. Alemi; Olivia Wiles; Sven Gowal

http://arxiv.org/abs/2107.05747
SoftHebb: Bayesian inference in unsupervised Hebbian soft winner-take-all networks. (56%)
Timoleon Moraitis; Dmitry Toichkin; Yansong Chua; Qinghai Guo

http://arxiv.org/abs/2107.10302
Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks. (76%)
Kendra Albert; Maggie Delano; Bogdan Kulynych; Ram Shankar Siva Kumar

http://arxiv.org/abs/2107.05166
Stateful Detection of Model Extraction Attacks. (2%)
Soham Pal; Yash Gupta; Aditya Kanade; Shirish Shevade

http://arxiv.org/abs/2107.05127
Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning. (1%)
Muhammad Azmi Umer; Chuadhry Mujeeb Ahmed; Muhammad Taha Jilani; Aditya P. Mathur

http://arxiv.org/abs/2107.04764
Hack The Box: Fooling Deep Learning Abstraction-Based Monitors. (91%)
Sara Hajj Ibrahim; Mohamed Nassar

http://arxiv.org/abs/2107.04863
HOMRS: High Order Metamorphic Relations Selector for Deep Neural Networks. (88%)
Florian Tambon; Giulio Antoniol; Foutse Khomh

http://arxiv.org/abs/2107.04827
Identifying Layers Susceptible to Adversarial Attacks. (83%)
Shoaib Ahmed Siddiqui; Thomas Breuel

http://arxiv.org/abs/2107.04882
Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis. (22%)
Anisie Uwimana1; Ransalu Senanayake

http://arxiv.org/abs/2107.04910
Cyber-Security Challenges in Aviation Industry: A Review of Current and Future Trends. (1%)
Elochukwu Ukwandu; Mohamed Amine Ben Farah; Hanan Hindy; Miroslav Bures; Robert Atkinson; Christos Tachtatzis; Xavier Bellekens

http://arxiv.org/abs/2107.04435
Learning to Detect Adversarial Examples Based on Class Scores. (99%)
Tobias Uelwer; Felix Michels; Candido Oliver De

http://arxiv.org/abs/2107.04749
Resilience of Autonomous Vehicle Object Category Detection to Universal Adversarial Perturbations. (99%)
Mohammad Nayeem Teli; Seungwon Oh

http://arxiv.org/abs/2107.04284
Universal 3-Dimensional Perturbations for Black-Box Attacks on Video Recognition Systems. (99%)
Shangyu Xie; Han Wang; Yu Kong; Yuan Hong

http://arxiv.org/abs/2107.07043
GGT: Graph-Guided Testing for Adversarial Sample Detection of Deep Neural Network. (98%)
Zuohui Chen; Renxuan Wang; Jingyang Xiang; Yue Yu; Xin Xia; Shouling Ji; Qi Xuan; Xiaoniu Yang

http://arxiv.org/abs/2107.04263
Towards Robust General Medical Image Segmentation. (83%)
Laura Daza; Juan C. Pérez; Pablo Arbeláez

http://arxiv.org/abs/2107.04487
ARC: Adversarially Robust Control Policies for Autonomous Vehicles. (38%)
Sampo Kuutti; Saber Fallah; Richard Bowden

http://arxiv.org/abs/2107.03806
Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models. (99%)
Daniel Park; Haidar Khan; Azer Khan; Alex Gittens; Bülent Yener

http://arxiv.org/abs/2107.04401
Improving Model Robustness with Latent Distribution Locally and Globally. (99%)
Zhuang Qian; Shufei Zhang; Kaizhu Huang; Qiufeng Wang; Rui Zhang; Xinping Yi

http://arxiv.org/abs/2107.03759
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks. (50%)
Luong-Ha Nguyen; James-A. Goulet

http://arxiv.org/abs/2107.03919
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. (33%)
Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Jihun Hamm

http://arxiv.org/abs/2107.03050
Controlled Caption Generation for Images Through Adversarial Attacks. (99%)
Nayyer Aafaq; Naveed Akhtar; Wei Liu; Mubarak Shah; Ajmal Mian

http://arxiv.org/abs/2107.03250
Incorporating Label Uncertainty in Understanding Adversarial Robustness. (38%)
Xiao Zhang; David Evans

http://arxiv.org/abs/2107.03311
RoFL: Attestable Robustness for Secure Federated Learning. (2%)
Lukas Burkhalter; Hidde Lycklama; Alexander Viand; Nicolas Küchler; Anwar Hithnawi

http://arxiv.org/abs/2107.02425
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization. (99%)
Sungyoon Lee; Hoki Kim; Jaewook Lee

http://arxiv.org/abs/2107.02434
Self-Adversarial Training incorporating Forgery Attention for Image Forgery Localization. (95%)
Long Zhuo; Shunquan Tan; Bin Li; Jiwu Huang

http://arxiv.org/abs/2108.04217
ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients. (76%)
Alessandro Cappelli; Julien Launay; Laurent Meunier; Ruben Ohana; Iacopo Poli

http://arxiv.org/abs/2107.02658
On Generalization of Graph Autoencoders with Adversarial Training. (12%)
Tianjin huang; Yulong Pei; Vlado Menkovski; Mykola Pechenizkiy

http://arxiv.org/abs/2107.02488
On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving. (1%)
Takami Sato; Qi Alfred Chen

http://arxiv.org/abs/2107.01943
When and How to Fool Explainable Models (and Humans) with Adversarial Examples. (99%)
Jon Vadillo; Roberto Santana; Jose A. Lozano

http://arxiv.org/abs/2107.01809
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks. (99%)
Xiao Yang; Yinpeng Dong; Tianyu Pang; Hang Su; Jun Zhu

http://arxiv.org/abs/2107.01936
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction. (87%)
Xi Chen; Bo Kang; Jefrey Lijffijt; Bie Tijl De

http://arxiv.org/abs/2107.02052
Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning. (69%)
Mathias Löwe; Jennifer Villareale; Evan Freed; Aleksanteri Sladek; Jichen Zhu; Sebastian Risi

http://arxiv.org/abs/2107.02045
Understanding the Security of Deepfake Detection. (33%)
Xiaoyu Cao; Neil Zhenqiang Gong

http://arxiv.org/abs/2107.01806
Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems. (15%)
Ron Bitton; Nadav Maman; Inderjeet Singh; Satoru Momiyama; Yuval Elovici; Asaf Shabtai

http://arxiv.org/abs/2107.01854
Poisoning Attack against Estimating from Pairwise Comparisons. (15%)
Ke Ma; Qianqian Xu; Jinshan Zeng; Xiaochun Cao; Qingming Huang

http://arxiv.org/abs/2107.06993
Confidence Conditioned Knowledge Distillation. (10%)
Sourav Mishra; Suresh Sundaram

http://arxiv.org/abs/2107.01561
Certifiably Robust Interpretation via Renyi Differential Privacy. (67%)
Ao Liu; Xiaoyu Chen; Sijia Liu; Lirong Xia; Chuang Gan

http://arxiv.org/abs/2107.01709
Mirror Mirror on the Wall: Next-Generation Wireless Jamming Attacks Based on Software-Controlled Surfaces. (1%)
Paul Staat; Harald Elders-Boll; Christian Zenger; Christof Paar

http://arxiv.org/abs/2107.01396
Demiguise Attack: Crafting Invisible Semantic Adversarial Perturbations with Perceptual Similarity. (99%)
Yajie Wang; Shangbo Wu; Wenyi Jiang; Shengang Hao; Yu-an Tan; Quanxin Zhang

http://arxiv.org/abs/2107.00561
Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples. (99%)
Nelson Manohar-Alers; Ryan Feng; Sahib Singh; Jiguo Song; Atul Prakash

http://arxiv.org/abs/2107.00415
DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks. (99%)
Alberto Marchisio; Giacomo Pira; Maurizio Martina; Guido Masera; Muhammad Shafique

http://arxiv.org/abs/2107.00440
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding. (68%)
Dong Wang; Ning Ding; Piji Li; Hai-Tao Zheng

http://arxiv.org/abs/2107.00309
Adversarial Sample Detection for Speaker Verification by Neural Vocoders. (41%)
Haibin Wu; Po-chun Hsu; Ji Gao; Shanshan Zhang; Shen Huang; Jian Kang; Zhiyong Wu; Helen Meng; Hung-yi Lee

http://arxiv.org/abs/2107.00247
The Interplay between Distribution Parameters and the Accuracy-Robustness Tradeoff in Classification. (16%)
Alireza Mousavi Hosseini; Amir Mohammad Abouei; Mohammad Hossein Rohban

http://arxiv.org/abs/2107.00783
Reinforcement Learning for Feedback-Enabled Cyber Resilience. (10%)
Yunhan Huang; Linan Huang; Quanyan Zhu

http://arxiv.org/abs/2106.15998
Single-Step Adversarial Training for Semantic Segmentation. (96%)
Daniel Wiens; Barbara Hammer

http://arxiv.org/abs/2106.16198
Adversarial examples within the training distribution: A widespread challenge. (93%)
Spandan Madan; Tomotake Sasaki; Hanspeter Pfister; Tzu-Mao Li; Xavier Boix

http://arxiv.org/abs/2106.15860
Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning. (84%)
You Qiaoben; Chengyang Ying; Xinning Zhou; Hang Su; Jun Zhu; Bo Zhang

http://arxiv.org/abs/2106.15820
Explanation-Guided Diagnosis of Machine Learning Evasion Attacks. (82%)
Abderrahmen Amich; Birhanu Eshete

http://arxiv.org/abs/2107.02897
Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes. (8%)
Mustain Billah; Adnan Anwar; Ziaur Rahman; Syed Md. Galib

http://arxiv.org/abs/2106.15850
Exploring Robustness of Neural Networks through Graph Measures. (8%)
Asim Rowan University Waqas; Ghulam Rowan University Rasool; Hamza University of Minnesota Farooq; Nidhal C. Rowan University Bouaynaya

http://arxiv.org/abs/2106.15890
A Context-Aware Information-Based Clone Node Attack Detection Scheme in Internet of Things. (1%)
Khizar Hameed; Saurabh Garg; Muhammad Bilal Amin; Byeong Kang; Abid Khan

http://arxiv.org/abs/2106.15853
Understanding and Improving Early Stopping for Learning with Noisy Labels. (1%)
Yingbin Bai; Erkun Yang; Bo Han; Yanhua Yang; Jiatong Li; Yinian Mao; Gang Niu; Tongliang Liu

http://arxiv.org/abs/2107.02894
Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challenges. (99%)
Bowei Xi

http://arxiv.org/abs/2107.00003
Understanding Adversarial Examples Through Deep Neural Network's Response Surface and Uncertainty Regions. (99%)
Juan Shu; Bowei Xi; Charles Kamhoua

http://arxiv.org/abs/2106.15360
Attack Transferability Characterization for Adversarially Robust Multi-label Classification. (99%)
Zhuo Yang; Yufei Han; Xiangliang Zhang

http://arxiv.org/abs/2106.15202
Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices. (99%)
Tao Bai; Jinqi Luo; Jun Zhao

http://arxiv.org/abs/2107.02895
Bio-Inspired Adversarial Attack Against Deep Neural Networks. (98%)
Bowei Xi; Yujie Chen; Fan Fei; Zhan Tu; Xinyan Deng

http://arxiv.org/abs/2106.15130
Do Not Deceive Your Employer with a Virtual Background: A Video Conferencing Manipulation-Detection System. (62%)
Mauro Conti; Simone Milani; Ehsan Nowroozi; Gabriele Orazi

http://arxiv.org/abs/2106.15764
The Threat of Offensive AI to Organizations. (54%)
Yisroel Mirsky; Ambra Demontis; Jaidip Kotak; Ram Shankar; Deng Gelei; Liu Yang; Xiangyu Zhang; Wenke Lee; Yuval Elovici; Battista Biggio

http://arxiv.org/abs/2106.15776
Local Reweighting for Adversarial Training. (22%)
Ruize Gao; Feng Liu; Kaiwen Zhou; Gang Niu; Bo Han; James Cheng

http://arxiv.org/abs/2106.15355
On the Interaction of Belief Bias and Explanations. (15%)
Ana Valeria Gonzalez; Anna Rogers; Anders Søgaard

http://arxiv.org/abs/2106.14815
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack. (99%)
Gilad Gressel; Niranjan Hegde; Archana Sreekumar; Michael Darling

http://arxiv.org/abs/2106.15023
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent. (99%)
Oliver Bryniarski; Nabeel Hingun; Pedro Pachuca; Vincent Wang; Nicholas Carlini

http://arxiv.org/abs/2106.15058
Improving Transferability of Adversarial Patches on Face Recognition with Generative Models. (99%)
Zihao Xiao; Xianfeng Gao; Chilin Fu; Yinpeng Dong; Wei Gao; Xiaolu Zhang; Jun Zhou; Jun Zhu

http://arxiv.org/abs/2106.14851
Data Poisoning Won't Save You From Facial Recognition. (97%)
Evani Radiya-Dixit; Florian Tramèr

http://arxiv.org/abs/2106.14952
Adversarial Robustness of Streaming Algorithms through Importance Sampling. (61%)
Vladimir Braverman; Avinatan Hassidim; Yossi Matias; Mariano Schain; Sandeep Silwal; Samson Zhou

http://arxiv.org/abs/2106.14999
Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation. (2%)
Chaithanya Kumar Mummadi; Robin Hutmacher; Kilian Rambach; Evgeny Levinkov; Thomas Brox; Jan Hendrik Metzen

http://arxiv.org/abs/2106.14432
Certified Robustness via Randomized Smoothing over Multiplicative Parameters. (1%)
Nikita Muravev; Aleksandr Petiushko

http://arxiv.org/abs/2106.14707
Realtime Robust Malicious Traffic Detection via Frequency Domain Analysis. (1%)
Chuanpu Fu; Qi Li; Meng Shen; Ke Xu

http://arxiv.org/abs/2107.02840
RAILS: A Robust Adversarial Immune-inspired Learning System. (98%)
Ren Wang; Tianqi Chen; Stephen Lindsly; Cooper Stansbury; Alnawaz Rehemtulla; Indika Rajapakse; Alfred Hero

http://arxiv.org/abs/2106.14152
Who is Responsible for Adversarial Defense? (93%)
Kishor Datta Gupta; Dipankar Dasgupta

http://arxiv.org/abs/2106.14300
ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense. (82%)
Ren Wang; Tianqi Chen; Philip Yao; Sijia Liu; Indika Rajapakse; Alfred Hero

http://arxiv.org/abs/2107.02842
Immuno-mimetic Deep Neural Networks (Immuno-Net). (64%)
Ren Wang; Tianqi Chen; Stephen Lindsly; Cooper Stansbury; Indika Rajapakse; Alfred Hero

http://arxiv.org/abs/2106.14342
Stabilizing Equilibrium Models by Jacobian Regularization. (1%)
Shaojie Bai; Vladlen Koltun; J. Zico Kolter

http://arxiv.org/abs/2106.15357
Multi-stage Optimization based Adversarial Training. (99%)
Xiaosen Wang; Chuanbiao Song; Liwei Wang; Kun He

http://arxiv.org/abs/2106.13997
The Feasibility and Inevitability of Stealth Attacks. (69%)
Ivan Y. Tyukin; Desmond J. Higham; Eliyas Woldegeorgis; Alexander N. Gorban

http://arxiv.org/abs/2106.13326
On the (Un-)Avoidability of Adversarial Examples. (99%)
Sadia Chowdhury; Ruth Urner

http://arxiv.org/abs/2106.13394
Countering Adversarial Examples: Combining Input Transformation and Noisy Training. (99%)
Cheng Zhang; Pan Gao

http://arxiv.org/abs/2106.13123
Break it, Fix it: Attack and Defense for "Add-on'' Access Control Solutions in Distributed Data Analytics Platforms. (8%)
Fahad Data Security Technologies Shaon; Sazzadur University of Arizona Rahaman; Murat Data Security Technologies Kantarcioglu

http://arxiv.org/abs/2106.12611
Adversarial Examples in Multi-Layer Random ReLU Networks. (81%)
Peter L. Bartlett; Sébastien Bubeck; Yeshwanth Cherapanamjeri

http://arxiv.org/abs/2106.12478
Teacher Model Fingerprinting Attacks Against Transfer Learning. (2%)
Yufei Chen; Chao Shen; Cong Wang; Yang Zhang

http://arxiv.org/abs/2106.12723
Meaningfully Explaining Model Mistakes Using Conceptual Counterfactuals. (1%)
Abubakar Abid; Mert Yuksekgonul; James Zou

http://arxiv.org/abs/2106.12563
Feature Attributions and Counterfactual Explanations Can Be Manipulated. (1%)
Dylan Slack; Sophie Hilgard; Sameer Singh; Himabindu Lakkaraju

http://arxiv.org/abs/2106.12021
DetectX -- Adversarial Input Detection using Current Signatures in Memristive XBar Arrays. (99%)
Abhishek Moitra; Priyadarshini Panda

http://arxiv.org/abs/2106.11644
Self-Supervised Iterative Contextual Smoothing for Efficient Adversarial Defense against Gray- and Black-Box Attack. (99%)
Sungmin Cha; Naeun Ko; Youngjoon Yoo; Taesup Moon

http://arxiv.org/abs/2106.12900
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks. (83%)
Fan Liu; Shuyu Zhao; Xuelong Dai; Bin Xiao

http://arxiv.org/abs/2106.11629
On Adversarial Robustness of Synthetic Code Generation. (81%)
Mrinal Anand; Pratik Kayal; Mayank Singh

http://arxiv.org/abs/2106.11865
NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data. (67%)
I-Chung Hsieh; Cheng-Te Li

http://arxiv.org/abs/2106.11732
FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data. (1%)
Eugenia Iofinova; Nikola Konstantinov; Christoph H. Lampert

http://arxiv.org/abs/2106.11420
Policy Smoothing for Provably Robust Reinforcement Learning. (99%)
Aounon Kumar; Alexander Levine; Soheil Feizi

http://arxiv.org/abs/2106.10996
Delving into the pixels of adversarial samples. (98%)
Blerta Lindqvist

http://arxiv.org/abs/2106.11424
HODA: Hardness-Oriented Detection of Model Extraction Attacks. (98%)
Amir Mahdi Sadeghzadeh; Amir Mohammad Sobhanian; Faezeh Dehghan; Rasool Jalili

http://arxiv.org/abs/2106.10974
Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier. (91%)
Simone Marullo; Matteo Tiezzi; Marco Gori; Stefano Melacci

http://arxiv.org/abs/2106.11384
Membership Inference on Word Embedding and Beyond. (38%)
Saeed Mahloujifar; Huseyin A. Inan; Melissa Chase; Esha Ghosh; Marcello Hasegawa

http://arxiv.org/abs/2106.11478
An Alternative Auxiliary Task for Enhancing Image Classification. (11%)
Chen Liu

http://arxiv.org/abs/2106.14647
Zero-shot learning approach to adaptive Cybersecurity using Explainable AI. (1%)
Dattaraj Rao; Shraddha Mane

http://arxiv.org/abs/2106.10807
Adversarial Examples Make Strong Poisons. (98%)
Liam Fowl; Micah Goldblum; Ping-yeh Chiang; Jonas Geiping; Wojtek Czaja; Tom Goldstein

http://arxiv.org/abs/2106.10785
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem. (95%)
Jiaqi Ma; Junwei Deng; Qiaozhu Mei

http://arxiv.org/abs/2106.10696
Generative Model Adversarial Training for Deep Compressed Sensing. (8%)
Ashkan Esmaeili

http://arxiv.org/abs/2106.10606
Attack to Fool and Explain Deep Networks. (99%)
Naveed Akhtar; Muhammad A. A. K. Jalwana; Mohammed Bennamoun; Ajmal Mian

http://arxiv.org/abs/2106.11760
A Stealthy and Robust Fingerprinting Scheme for Generative Models. (47%)
Li Guanlin; Guo Shangwei; Wang Run; Xu Guowen; Zhang Tianwei

http://arxiv.org/abs/2106.10212
Residual Error: a New Performance Measure for Adversarial Robustness. (99%)
Hossein Aboutalebi; Mohammad Javad Shafiee; Michelle Karg; Christian Scharfenberger; Alexander Wong

http://arxiv.org/abs/2106.09947
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples. (99%)
Maura Pintor; Luca Demetrio; Angelo Sotgiu; Ambra Demontis; Nicholas Carlini; Battista Biggio; Fabio Roli

http://arxiv.org/abs/2106.10151
The Dimpled Manifold Model of Adversarial Examples in Machine Learning. (99%)
Adi Shamir; Odelia Melamed; Oriel BenShmuel

http://arxiv.org/abs/2106.09992
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. (99%)
Martin Pawelczyk; Chirag Agarwal; Shalmali Joshi; Sohini Upadhyay; Himabindu Lakkaraju

http://arxiv.org/abs/2106.09908
Light Lies: Optical Adversarial Attack. (92%)
Kyulim Kim; JeongSoo Kim; Seungri Song; Jun-Ho Choi; Chulmin Joo; Jong-Seok Lee

http://arxiv.org/abs/2106.09989
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection. (82%)
Yulin Zhu; Yuni Lai; Kaifa Zhao; Xiapu Luo; Mingquan Yuan; Jian Ren; Kai Zhou

http://arxiv.org/abs/2106.10252
Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks. (80%)
Emre Ozfatura; Muhammad Zaid Hameed; Kerem Ozfatura; Deniz Gunduz

http://arxiv.org/abs/2106.10324
Group-Structured Adversarial Training. (68%)
Farzan Farnia; Amirali Aghazadeh; James Zou; David Tse

http://arxiv.org/abs/2106.09993
Accumulative Poisoning Attacks on Real-time Data. (45%)
Tianyu Pang; Xiao Yang; Yinpeng Dong; Hang Su; Jun Zhu

http://arxiv.org/abs/2106.10147
Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks. (45%)
Suyoung Lee; Wonho Song; Suman Jana; Meeyoung Cha; Sooel Son

http://arxiv.org/abs/2106.10196
Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning. (5%)
Junyuan Hong; Haotao Wang; Zhangyang Wang; Jiayu Zhou

http://arxiv.org/abs/2106.09872
Analyzing Adversarial Robustness of Deep Neural Networks in Pixel Space: a Semantic Perspective. (99%)
Lina Wang; Xingshu Chen; Yulong Wang; Yawei Yue; Yi Zhu; Xuemei Zeng; Wei Wang

http://arxiv.org/abs/2106.09898
Bad Characters: Imperceptible NLP Attacks. (99%)
Nicholas Boucher; Ilia Shumailov; Ross Anderson; Nicolas Papernot

http://arxiv.org/abs/2106.09501
DeepInsight: Interpretability Assisting Detection of Adversarial Samples on Graphs. (99%)
Junhao Zhu; Yalu Shan; Jinhuan Wang; Shanqing Yu; Guanrong Chen; Qi Xuan

http://arxiv.org/abs/2106.09534
Adversarial Visual Robustness by Causal Intervention. (99%)
Kaihua Tang; Mingyuan Tao; Hanwang Zhang

http://arxiv.org/abs/2106.09820
Adversarial Detection Avoidance Attacks: Evaluating the robustness of perceptual hashing-based client-side scanning. (92%)
Shubham Jain; Ana-Maria Cretu; Montjoye Yves-Alexandre de

http://arxiv.org/abs/2106.09249
Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks. (91%)
Yulong *co-first authors Cao*; Ningfei *co-first authors Wang*; Chaowei *co-first authors Xiao*; Dawei *co-first authors Yang*; Jin *co-first authors Fang; Ruigang *co-first authors Yang; Qi Alfred *co-first authors Chen; Mingyan *co-first authors Liu; Bo *co-first authors Li

http://arxiv.org/abs/2106.09380
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems. (82%)
Giovanni Apruzzese; Mauro Andreolini; Luca Ferretti; Mirco Marchetti; Michele Colajanni

http://arxiv.org/abs/2106.09667
Poisoning and Backdooring Contrastive Learning. (70%)
Nicholas Carlini; Andreas Terzis

http://arxiv.org/abs/2106.09292
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing. (69%)
Fan Wu; Linyi Li; Zijian Huang; Yevgeniy Vorobeychik; Ding Zhao; Bo Li

http://arxiv.org/abs/2106.09242
CoCoFuzzing: Testing Neural Code Models with Coverage-Guided Fuzzing. (64%)
Moshi Wei; Yuchao Huang; Jinqiu Yang; Junjie Wang; Song Wang

http://arxiv.org/abs/2106.09385
On Deep Neural Network Calibration by Regularization and its Impact on Refinement. (3%)
Aditya Singh; Alessandro Bay; Biswa Sengupta; Andrea Mirabile

http://arxiv.org/abs/2106.09857
Effective Model Sparsification by Scheduled Grow-and-Prune Methods. (1%)
Xiaolong Ma; Minghai Qin; Fei Sun; Zejiang Hou; Kun Yuan; Yi Xu; Yanzhi Wang; Yen-Kuang Chen; Rong Jin; Yuan Xie

http://arxiv.org/abs/2106.08746
Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses. (99%)
Buse G. A. Tekgul; Shelly Wang; Samuel Marchal; N. Asokan

http://arxiv.org/abs/2106.09222
Localized Uncertainty Attacks. (99%)
Ousmane Amadou Dia; Theofanis Karaletsos; Caner Hazirbas; Cristian Canton Ferrer; Ilknur Kaynar Kabul; Erik Meijer

http://arxiv.org/abs/2106.09223
Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks. (67%)
Yutian Pang; Sheng Cheng; Jueming Hu; Yongming Liu

http://arxiv.org/abs/2106.08970
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch. (38%)
Hossein Souri; Liam Fowl; Rama Chellappa; Micah Goldblum; Tom Goldstein

http://arxiv.org/abs/2106.09106
Explainable AI for Natural Adversarial Images. (13%)
Tomas Folke; ZhaoBin Li; Ravi B. Sojitra; Scott Cheng-Hsin Yang; Patrick Shafto

http://arxiv.org/abs/2106.09129
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness. (2%)
James Diffenderfer; Brian R. Bartoldson; Shreya Chaganti; Jize Zhang; Bhavya Kailkhura

http://arxiv.org/abs/2106.09121
Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework. (1%)
Jiahao Su; Wonmin Byeon; Furong Huang

http://arxiv.org/abs/2106.08913
Loki: Hardening Code Obfuscation Against Automated Attacks. (1%)
Moritz Schloegel; Tim Blazytko; Moritz Contag; Cornelius Aschermann; Julius Basler; Thorsten Holz; Ali Abbasi

http://arxiv.org/abs/2106.08361
Adversarial Attacks on Deep Models for Financial Transaction Records. (99%)
Ivan Fursov; Matvey Morozov; Nina Kaploukhaya; Elizaveta Kovtun; Rodrigo Rivera-Castro; Gleb Gusev; Dmitry Babaev; Ivan Kireev; Alexey Zaytsev; Evgeny Burnaev

http://arxiv.org/abs/2106.08299
Model Extraction and Adversarial Attacks on Neural Networks using Switching Power Information. (99%)
Tommy Li; Cory Merkel

http://arxiv.org/abs/2106.08387
Towards Adversarial Robustness via Transductive Learning. (80%)
Jiefeng Chen; Yang Guo; Xi Wu; Tianqi Li; Qicheng Lao; Yingyu Liang; Somesh Jha

http://arxiv.org/abs/2106.07868
Voting for the right answer: Adversarial defense for speaker verification. (78%)
Haibin Wu; Yang Zhang; Zhiyong Wu; Dong Wang; Hung-yi Lee

http://arxiv.org/abs/2106.08104
Detect and remove watermark in deep neural networks via generative adversarial networks. (68%)
Haoqi Wang; Mingfu Xue; Shichang Sun; Yushu Zhang; Jian Wang; Weiqiang Liu

http://arxiv.org/abs/2106.08283
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks. (13%)
Chulin Xie; Minghao Chen; Pin-Yu Chen; Bo Li

http://arxiv.org/abs/2106.08013
Securing Face Liveness Detection Using Unforgeable Lip Motion Patterns. (12%)
Man Senior Member, IEEE Zhou; Qian Senior Member, IEEE Wang; Qi Senior Member, IEEE Li; Peipei Senior Member, IEEE Jiang; Jingxiao Senior Member, IEEE Yang; Chao Senior Member, IEEE Shen; Cong Fellow, IEEE Wang; Shouhong Ding

http://arxiv.org/abs/2106.07904
Probabilistic Margins for Instance Reweighting in Adversarial Training. (8%)
Qizhou Wang; Feng Liu; Bo Han; Tongliang Liu; Chen Gong; Gang Niu; Mingyuan Zhou; Masashi Sugiyama

http://arxiv.org/abs/2106.07895
CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an In-Vehicle CAN Bus Based on Deep Features of Voltage Signals. (1%)
Efrat Levy; Asaf Shabtai; Bogdan Groza; Pal-Stefan Murvay; Yuval Elovici

http://arxiv.org/abs/2106.07445
PopSkipJump: Decision-Based Attack for Probabilistic Classifiers. (99%)
Carl-Johann Simon-Gabriel; Noman Ahmed Sheikh; Andreas Krause

http://arxiv.org/abs/2106.08153
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology. (99%)
Alex Foote; Amina Asif; Ayesha Azam; Tim Marshall-Cox; Nasir Rajpoot; Fayyaz Minhas

http://arxiv.org/abs/2106.07428
Audio Attacks and Defenses against AED Systems -- A Practical Study. (99%)
Rodrigo dos Santos; Shirin Nilizadeh

http://arxiv.org/abs/2106.07214
Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions. (92%)
Antonio Emanuele Cinà; Kathrin Grosse; Sebastiano Vascon; Ambra Demontis; Battista Biggio; Fabio Roli; Marcello Pelillo

http://arxiv.org/abs/2106.07860
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery. (81%)
John Boutsikas; Maksim E. Eren; Charles Varga; Edward Raff; Cynthia Matuszek; Charles Nicholas

http://arxiv.org/abs/2106.07767
On the Relationship between Heterophily and Robustness of Graph Neural Networks. (81%)
Jiong Zhu; Junchen Jin; Donald Loveland; Michael T. Schaub; Danai Koutra

http://arxiv.org/abs/2106.07411
Partial success in closing the gap between human and machine vision. (15%)
Robert Geirhos; Kantharaju Narayanappa; Benjamin Mitzkus; Tizian Thieringer; Matthias Bethge; Felix A. Wichmann; Wieland Brendel

http://arxiv.org/abs/2106.07704
Text Generation with Efficient (Soft) Q-Learning. (2%)
Han Guo; Bowen Tan; Zhengzhong Liu; Eric P. Xing; Zhiting Hu

http://arxiv.org/abs/2106.07541
Resilient Control of Platooning Networked Robitic Systems via Dynamic Watermarking. (1%)
Matthew Porter; Arnav Joshi; Sidhartha Dey; Qirui Wu; Pedro Hespanhol; Anil Aswani; Matthew Johnson-Roberson; Ram Vasudevan

http://arxiv.org/abs/2106.07165
Self-training Guided Adversarial Domain Adaptation For Thermal Imagery. (1%)
Ibrahim Batuhan Akkaya; Fazil Altinel; Ugur Halici

http://arxiv.org/abs/2106.07851
Code Integrity Attestation for PLCs using Black Box Neural Network Predictions. (1%)
Yuqi Chen; Christopher M. Poskitt; Jun Sun

http://arxiv.org/abs/2106.07047
Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models. (99%)
Jatin Chauhan; Karan Bhukar; Manohar Kaul

http://arxiv.org/abs/2106.07141
Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks. (99%)
Utku Ozbulak; Esla Timothy Anzaku; Neve Wesley De; Messem Arnout Van

http://arxiv.org/abs/2106.06917
ATRAS: Adversarially Trained Robust Architecture Search. (96%)
Yigit Alparslan; Edward Kim

http://arxiv.org/abs/2106.07098
Security Analysis of Camera-LiDAR Semantic-Level Fusion Against Black-Box Attacks on Autonomous Vehicles. (64%)
R. Spencer Hallyburton; Yupei Liu; Miroslav Pajic

http://arxiv.org/abs/2106.07049
Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis. (1%)
Kangning Liu; Yiqiu Shen; Nan Wu; Jakub Chłędowski; Carlos Fernandez-Granda; Krzysztof J. Geras

http://arxiv.org/abs/2106.07068
HistoTransfer: Understanding Transfer Learning for Histopathology. (1%)
Yash Sharma; Lubaina Ehsan; Sana Syed; Donald E. Brown

http://arxiv.org/abs/2106.06685
Adversarial Robustness via Fisher-Rao Regularization. (67%)
Marine Picot; Francisco Messina; Malik Boudiaf; Fabrice Labeau; Ismail Ben Ayed; Pablo Piantanida

http://arxiv.org/abs/2106.06770
What can linearized neural networks actually say about generalization? (31%)
Guillermo Ortiz-Jiménez; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard

http://arxiv.org/abs/2106.06895
FeSHI: Feature Map Based Stealthy Hardware Intrinsic Attack. (2%)
Tolulope Odetola; Faiq Khalid; Travis Sandefur; Hawzhin Mohammed; Syed Rafay Hasan

http://arxiv.org/abs/2106.06196
CausalAdv: Adversarial Robustness through the Lens of Causality. (99%)
Yonggang Zhang; Mingming Gong; Tongliang Liu; Gang Niu; Xinmei Tian; Bo Han; Bernhard Schölkopf; Kun Zhang

http://arxiv.org/abs/2106.06235
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks. (99%)
Nezihe Merve Gürel; Xiangyu Qi; Luka Rimanic; Ce Zhang; Bo Li

http://arxiv.org/abs/2106.06041
Adversarial purification with Score-based generative models. (89%)
Jongmin Yoon; Sung Ju Hwang; Juho Lee

http://arxiv.org/abs/2106.06624
Relaxing Local Robustness. (80%)
Klas Leino; Matt Fredrikson

http://arxiv.org/abs/2106.06663
TDGIA:Effective Injection Attacks on Graph Neural Networks. (76%)
Xu Zou; Qinkai Zheng; Yuxiao Dong; Xinyu Guan; Evgeny Kharlamov; Jialiang Lu; Jie Tang

http://arxiv.org/abs/2106.06361
Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution. (56%)
Fanchao Qi; Yuan Yao; Sophia Xu; Zhiyuan Liu; Maosong Sun

http://arxiv.org/abs/2106.06667
CARTL: Cooperative Adversarially-Robust Transfer Learning. (8%)
Dian Chen; Hongxin Hu; Qian Wang; Yinli Li; Cong Wang; Chao Shen; Qi Li

http://arxiv.org/abs/2106.06603
A Shuffling Framework for Local Differential Privacy. (1%)
Casey Meehan; Amrita Roy Chowdhury; Kamalika Chaudhuri; Somesh Jha

http://arxiv.org/abs/2106.06027
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm. (99%)
Mingkang Zhu; Tianlong Chen; Zhangyang Wang

http://arxiv.org/abs/2106.05657
Deep neural network loses attention to adversarial images. (99%)
Shashank Kotyan; Danilo Vasconcellos Vargas

http://arxiv.org/abs/2106.05997
Verifying Quantized Neural Networks using SMT-Based Model Checking. (92%)
Luiz Sena; Xidan Song; Erickson Alves; Iury Bessa; Edoardo Manino; Lucas Cordeiro; Eddie de Lima Filho

http://arxiv.org/abs/2106.06056
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation. (80%)
Jiawei Zhang; Linyi Li; Huichen Li; Xiaolu Zhang; Shuang Yang; Bo Li

http://arxiv.org/abs/2106.05996
An Ensemble Approach Towards Adversarial Robustness. (41%)
Haifeng Qian

http://arxiv.org/abs/2106.05625
Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learning. (1%)
Nicola Loi; Claudio Borile; Daniele Ucci

http://arxiv.org/abs/2106.05964
Fair Classification with Adversarial Perturbations. (1%)
L. Elisa Celis; Anay Mehrotra; Nisheeth K. Vishnoi

http://arxiv.org/abs/2106.05825
HASI: Hardware-Accelerated Stochastic Inference, A Defense Against Adversarial Machine Learning Attacks. (99%)
Mohammad Hossein Samavatian; Saikat Majumdar; Kristin Barber; Radu Teodorescu

http://arxiv.org/abs/2106.05036
Towards Defending against Adversarial Examples via Attack-Invariant Features. (99%)
Dawei Zhou; Tongliang Liu; Bo Han; Nannan Wang; Chunlei Peng; Xinbo Gao

http://arxiv.org/abs/2106.04938
Attacking Adversarial Attacks as A Defense. (99%)
Boxi Wu; Heng Pan; Li Shen; Jindong Gu; Shuai Zhao; Zhifeng Li; Deng Cai; Xiaofei He; Wei Liu

http://arxiv.org/abs/2106.05453
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training. (99%)
Dawei Zhou; Nannan Wang; Xinbo Gao; Bo Han; Jun Yu; Xiaoyu Wang; Tongliang Liu

http://arxiv.org/abs/2106.05261
We Can Always Catch You: Detecting Adversarial Patched Objects WITH or WITHOUT Signature. (98%)
Bin Liang; Jiachun Li; Jianjun Huang

http://arxiv.org/abs/2106.05087
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL. (97%)
Yanchao Sun; Ruijie Zheng; Yongyuan Liang; Furong Huang

http://arxiv.org/abs/2106.05256
URLTran: Improving Phishing URL Detection Using Transformers. (10%)
Pranav Maneriker; Jack W. Stokes; Edir Garcia Lazo; Diana Carutasu; Farid Tajaddodianfar; Arun Gururajan

http://arxiv.org/abs/2106.05325
ZoPE: A Fast Optimizer for ReLU Networks with Low-Dimensional Inputs. (5%)
Christopher A. Strong; Sydney M. Katz; Anthony L. Corso; Mykel J. Kochenderfer

http://arxiv.org/abs/2106.04823
Practical Machine Learning Safety: A Survey and Primer. (4%)
Sina Mohseni; Haotao Wang; Zhiding Yu; Chaowei Xiao; Zhangyang Wang; Jay Yadawa

http://arxiv.org/abs/2106.05009
Network insensitivity to parameter noise via adversarial regularization. (2%)
Julian Büchel; Fynn Faber; Dylan R. Muir

http://arxiv.org/abs/2106.04169
On Improving Adversarial Transferability of Vision Transformers. (99%)
Muzammal Naseer; Kanchana Ranasinghe; Salman Khan; Fahad Shahbaz Khan; Fatih Porikli

http://arxiv.org/abs/2106.04569
Simulated Adversarial Testing of Face Recognition Models. (99%)
Nataniel Ruiz; Adam Kortylewski; Weichao Qiu; Cihang Xie; Sarah Adel Bargal; Alan Yuille; Stan Sclaroff

http://arxiv.org/abs/2106.04794
Towards the Memorization Effect of Neural Networks in Adversarial Training. (93%)
Han Xu; Xiaorui Liu; Wentao Wang; Wenbiao Ding; Zhongqin Wu; Zitao Liu; Anil Jain; Jiliang Tang

http://arxiv.org/abs/2106.04690
Handcrafted Backdoors in Deep Neural Networks. (92%)
Sanghyun Hong; Nicholas Carlini; Alexey Kurakin

http://arxiv.org/abs/2106.04435
Enhancing Robustness of Neural Networks through Fourier Stabilization. (73%)
Netanel Raviv; Aidan Kelley; Michael Guo; Yevgeny Vorobeychik

http://arxiv.org/abs/2106.04260
Provably Robust Detection of Out-of-distribution Data (almost) for free. (26%)
Alexander Meinke; Julian Bitterwolf; Matthias Hein

http://arxiv.org/abs/2106.03614
Adversarial Attack and Defense in Deep Ranking. (99%)
Mo Zhou; Le Wang; Zhenxing Niu; Qilin Zhang; Nanning Zheng; Gang Hua

http://arxiv.org/abs/2106.03734
Reveal of Vision Transformers Robustness against Adversarial Attacks. (99%)
Ahmed Aldahdooh; Wassim Hamidouche; Olivier Deforges

http://arxiv.org/abs/2106.03518
Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction. (89%)
Hanqi Yan; Lin Gui; Gabriele Pergola; Yulan He

http://arxiv.org/abs/2106.03805
3DB: A Framework for Debugging Computer Vision Models. (45%)
Guillaume Leclerc; Hadi Salman; Andrew Ilyas; Sai Vemprala; Logan Engstrom; Vibhav Vineet; Kai Xiao; Pengchuan Zhang; Shibani Santurkar; Greg Yang; Ashish Kapoor; Aleksander Madry

http://arxiv.org/abs/2106.03613
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models. (11%)
Xin Guo; Jianlei Yang; Haoyi Zhou; Xucheng Ye; Jianxin Li

http://arxiv.org/abs/2106.04066
Semantically Adversarial Scenario Generation with Explicit Knowledge Guidance. (1%)
Wenhao Ding; Haohong Lin; Bo Li; Ding Zhao

http://arxiv.org/abs/2106.03099
A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification. (98%)
Kevin Roth

http://arxiv.org/abs/2106.03310
Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model. (4%)
Zi Wang

http://arxiv.org/abs/2106.02867
Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness. (92%)
Renjue Li; Hanwei Zhang; Pengfei Yang; Cheng-Chao Huang; Aimin Zhou; Bai Xue; Lijun Zhang

http://arxiv.org/abs/2106.02978
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. (69%)
Qin Ding; Cho-Jui Hsieh; James Sharpnack

http://arxiv.org/abs/2106.02874
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking. (2%)
Jiaxing Huang; Dayan Guan; Aoran Xiao; Shijian Lu

http://arxiv.org/abs/2106.02734
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness. (99%)
Zifeng Wang; Tong Jian; Aria Masoomi; Stratis Ioannidis; Jennifer Dy

http://arxiv.org/abs/2106.02732
BO-DBA: Query-Efficient Decision-Based Adversarial Attacks via Bayesian Optimization. (99%)
Zhuosheng Zhang; Shucheng Yu

http://arxiv.org/abs/2106.02280
Human-Adversarial Visual Question Answering. (31%)
Sasha Sheng; Amanpreet Singh; Vedanuj Goswami; Jose Alberto Lopez Magana; Wojciech Galuba; Devi Parikh; Douwe Kiela

http://arxiv.org/abs/2106.02749
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics. (15%)
Bhavin Choksi; Milad Mozafari; Callum Biggs O'May; Benjamin Ador; Andrea Alamia; Rufin VanRullen

http://arxiv.org/abs/2106.02395
DOCTOR: A Simple Method for Detecting Misclassification Errors. (1%)
Federica Granese; Marco Romanelli; Daniele Gorla; Catuscia Palamidessi; Pablo Piantanida

http://arxiv.org/abs/2106.02443
Teaching keyword spotters to spot new keywords with limited examples. (1%)
Abhijeet Awasthi; Kevin Kilgour; Hassan Rom

http://arxiv.org/abs/2106.01617
Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout. (99%)
Pengfei Xie; Linyuan Wang; Ruoxi Qin; Kai Qiao; Shuhao Shi; Guoen Hu; Bin Yan

http://arxiv.org/abs/2106.01615
Imperceptible Adversarial Examples for Fake Image Detection. (99%)
Quanyu Liao; Yuezun Li; Xin Wang; Bin Kong; Bin Zhu; Siwei Lyu; Youbing Yin; Qi Song; Xi Wu

http://arxiv.org/abs/2106.02105
A Little Robustness Goes a Long Way: Leveraging Universal Features for Targeted Transfer Attacks. (99%)
Jacob M. Springer; Melanie Mitchell; Garrett T. Kenyon

http://arxiv.org/abs/2106.01618
Transferable Adversarial Examples for Anchor Free Object Detection. (99%)
Quanyu Liao; Xin Wang; Bin Kong; Siwei Lyu; Bin Zhu; Youbing Yin; Qi Song; Xi Wu

http://arxiv.org/abs/2106.01606
Exploring Memorization in Adversarial Training. (98%)
Yinpeng Dong; Ke Xu; Xiao Yang; Tianyu Pang; Zhijie Deng; Hang Su; Jun Zhu

http://arxiv.org/abs/2106.02078
Improving Neural Network Robustness via Persistency of Excitation. (68%)
Kaustubh Sridhar; Oleg Sokolsky; Insup Lee; James Weimer

http://arxiv.org/abs/2106.01810
Defending against Backdoor Attacks in Natural Language Generation. (38%)
Chun Fan; Xiaoya Li; Yuxian Meng; Xiaofei Sun; Xiang Ao; Fei Wu; Jiwei Li; Tianwei Zhang

http://arxiv.org/abs/2106.02240
Sneak Attack against Mobile Robotic Networks under Formation Control. (1%)
Yushan Li; Jianping He; Xuda Ding; Lin Cai; Xinping Guan

http://arxiv.org/abs/2106.01538
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack. (99%)
Alexander Matyasko; Lap-Pui Chau

http://arxiv.org/abs/2106.01065
Towards Robustness of Text-to-SQL Models against Synonym Substitution. (75%)
Yujian Gan; Xinyun Chen; Qiuping Huang; Matthew Purver; John R. Woodward; Jinxia Xie; Pengsheng Huang

http://arxiv.org/abs/2106.01452
BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks. (62%)
Yannik Keller; Jan Mackensen; Steffen Eger

http://arxiv.org/abs/2106.00273
Adversarial Defense for Automatic Speaker Verification by Self-Supervised Learning. (99%)
Haibin Wu; Xu Li; Andy T. Liu; Zhiyong Wu; Helen Meng; Hung-yi Lee

http://arxiv.org/abs/2106.00769
Improving Compositionality of Neural Networks by Decoding Representations to Inputs. (68%)
Mike Wu; Noah Goodman; Stefano Ermon

http://arxiv.org/abs/2106.00660
Markpainting: Adversarial Machine Learning meets Inpainting. (12%)
David Khachaturov; Ilia Shumailov; Yiren Zhao; Nicolas Papernot; Ross Anderson

http://arxiv.org/abs/2106.00872
On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study. (9%)
Divyansh Kaushik; Douwe Kiela; Zachary C. Lipton; Wen-tau Yih

http://arxiv.org/abs/2106.00245
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models. (5%)
Linjie Li; Jie Lei; Zhe Gan; Jingjing Liu

http://arxiv.org/abs/2106.01440
Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Models. (1%)
Rosa Biagio La; Roberto Capobianco; Daniele Nardi

http://arxiv.org/abs/2106.00221
Concurrent Adversarial Learning for Large-Batch Training. (1%)
Yong Liu; Xiangning Chen; Minhao Cheng; Cho-Jui Hsieh; Yang You

http://arxiv.org/abs/2105.15157
Adaptive Feature Alignment for Adversarial Training. (99%)
Tao Wang; Ruixin Zhang; Xingyu Chen; Kai Zhao; Xiaolin Huang; Yuge Huang; Shaoxin Li; Jilin Li; Feiyue Huang

http://arxiv.org/abs/2105.15010
QueryNet: An Efficient Attack Framework with Surrogates Carrying Multiple Identities. (99%)
Sizhe Chen; Zhehao Huang; Qinghua Tao; Xiaolin Huang

http://arxiv.org/abs/2105.14727
Transferable Sparse Adversarial Attack. (99%)
Ziwen He; Wei Wang; Jing Dong; Tieniu Tan

http://arxiv.org/abs/2105.14785
Adversarial Training with Rectified Rejection. (99%)
Tianyu Pang; Huishuai Zhang; Di He; Yinpeng Dong; Hang Su; Wei Chen; Jun Zhu; Tie-Yan Liu

http://arxiv.org/abs/2105.14710
Robustifying $\ell_\infty$ Adversarial Training to the Union of Perturbation Models. (82%)
Ameya D. Patil; Michael Tuttle; Alexander G. Schwing; Naresh R. Shanbhag

http://arxiv.org/abs/2105.15057
Dominant Patterns: Critical Features Hidden in Deep Neural Networks. (80%)
Zhixing Ye; Shaofei Qin; Sizhe Chen; Xiaolin Huang

http://arxiv.org/abs/2105.14813
Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models. (75%)
Chong Li; Cenyuan Zhang; Xiaoqing Zheng; Xuanjing Huang

http://arxiv.org/abs/2105.14803
Gradient-based Data Subversion Attack Against Binary Classifiers. (73%)
Rosni K Vasu; Sanjay Seetharaman; Shubham Malaviya; Manish Shukla; Sachin Lodha

http://arxiv.org/abs/2105.15164
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals. (1%)
Asma Ghandeharioun; Been Kim; Chun-Liang Li; Brendan Jou; Brian Eoff; Rosalind W. Picard

http://arxiv.org/abs/2105.14944
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores. (1%)
Giang Nguyen; Daeyoung Kim; Anh Nguyen

http://arxiv.org/abs/2105.14644
Generating Adversarial Examples with Graph Neural Networks. (99%)
Florian Jaeckle; M. Pawan Kumar

http://arxiv.org/abs/2105.14553
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice. (98%)
Rongzhou Bao; Jiayi Wang; Hai Zhao

http://arxiv.org/abs/2105.14676
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? (62%)
Jingfeng Zhang; Xilie Xu; Bo Han; Tongliang Liu; Gang Niu; Lizhen Cui; Masashi Sugiyama

http://arxiv.org/abs/2105.14564
Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks. (62%)
Ramy Maarouf; Danish Sattar; Ashraf Matrawy

http://arxiv.org/abs/2105.14638
DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows. (12%)
Baußnern Samuel von; Johannes Otterbach; Adrian Loy; Mathieu Salzmann; Thomas Wollmann

http://arxiv.org/abs/2107.09507
EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network. (1%)
Jian Cui; Zirui Lan; Olga Sourina; Wolfgang Müller-Wittig

http://arxiv.org/abs/2105.14259
Detecting Backdoor in Deep Neural Networks via Intentional Adversarial Perturbations. (99%)
Mingfu Xue; Yinghao Wu; Zhiyu Wu; Jian Wang; Yushu Zhang; Weiqiang Liu

http://arxiv.org/abs/2105.14240
Analysis and Applications of Class-wise Robustness in Adversarial Training. (99%)
Qi Tian; Kun Kuang; Kelu Jiang; Fei Wu; Yisen Wang

http://arxiv.org/abs/2105.14298
A Measurement Study on the (In)security of End-of-Life (EoL) Embedded Devices. (2%)
Dingding Wang; Muhui Jiang; Rui Chang; Yajin Zhou; Baolei Hou; Xiapu Luo; Lei Wu; Kui Ren

http://arxiv.org/abs/2105.13902
Demotivate adversarial defense in remote sensing. (99%)
Adrien Chan-Hon-Tong; Gaston Lenczner; Aurelien Plyer

http://arxiv.org/abs/2105.13697
AdvParams: An Active DNN Intellectual Property Protection Technique via Adversarial Perturbation Based Parameter Encryption. (92%)
Mingfu Xue; Zhiyu Wu; Jian Wang; Yushu Zhang; Weiqiang Liu

http://arxiv.org/abs/2105.13745
Robust Regularization with Adversarial Labelling of Perturbed Samples. (83%)
Xiaohui Guo; Richong Zhang; Yaowei Zheng; Yongyi Mao

http://arxiv.org/abs/2105.13746
SafeAMC: Adversarial training for robust modulation recognition models. (83%)
Javier Maroto; Gérôme Bovet; Pascal Frossard

http://arxiv.org/abs/2105.14119
Towards optimally abstaining from prediction. (81%)
Adam Tauman Kalai; Varun Kanade

http://arxiv.org/abs/2105.14083
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness. (76%)
Glenn Dawson; Robi Polikar

http://arxiv.org/abs/2105.14116
Visualizing Representations of Adversarially Perturbed Inputs. (68%)
Daniel Steinberg; Paul Munro

http://arxiv.org/abs/2105.13771
Chromatic and spatial analysis of one-pixel attacks against an image classifier. (15%)
Janne Alatalo; Joni Korpihalkola; Tuomo Sipola; Tero Kokkonen

http://arxiv.org/abs/2105.14173
FoveaTer: Foveated Transformer for Image Classification. (10%)
Aditya Jonnalagadda; William Yang Wang; B. S. Manjunath; Miguel P. Eckstein

http://arxiv.org/abs/2105.14035
DeepMoM: Robust Deep Learning With Median-of-Means. (1%)
Shih-Ting Huang; Johannes Lederer

http://arxiv.org/abs/2105.13530
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers. (84%)
Xi Li; David J. Miller; Zhen Xiang; George Kesidis

http://arxiv.org/abs/2105.12427
Deep Repulsive Prototypes for Adversarial Robustness. (99%)
Alex Serban; Erik Poll; Joost Visser

http://arxiv.org/abs/2105.12419
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge. (98%)
Heng Chang; Yu Rong; Tingyang Xu; Wenbing Huang; Honglei Zhang; Peng Cui; Xin Wang; Wenwu Zhu; Junzhou Huang

http://arxiv.org/abs/2105.12508
Adversarial robustness against multiple $l_p$-threat models at the price of one and how to quickly fine-tune robust models to another threat model. (93%)
Francesco Croce; Matthias Hein

http://arxiv.org/abs/2105.12697
Can Linear Programs Have Adversarial Examples? A Causal Perspective. (83%)
Matej Zečević; Devendra Singh Dhami; Kristian Kersting

http://arxiv.org/abs/2105.12400
Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger. (61%)
Fanchao Qi; Mukai Li; Yangyi Chen; Zhengyan Zhang; Zhiyuan Liu; Yasheng Wang; Maosong Sun

http://arxiv.org/abs/2105.12837
Fooling Partial Dependence via Data Poisoning. (13%)
Hubert Baniecki; Wojciech Kretowicz; Przemyslaw Biecek

http://arxiv.org/abs/2105.12237
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training. (98%)
Yatong Bai; Tanmay Gautam; Yu Gai; Somayeh Sojoudi

http://arxiv.org/abs/2105.12106
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation. (98%)
Mst. Tasnim Pervin; Linmi Tao; Aminul Huq; Zuoxiang He; Li Huo

http://arxiv.org/abs/2105.12049
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs. (67%)
Mohammad Malekzadeh; Anastasia Borovykh; Deniz Gündüz

http://arxiv.org/abs/2105.12189
Robust Value Iteration for Continuous Control Tasks. (9%)
Michael Lutter; Shie Mannor; Jan Peters; Dieter Fox; Animesh Garg

http://arxiv.org/abs/2105.11593
OFEI: A Semi-black-box Android Adversarial Sample Attack Framework Against DLaaS. (99%)
Guangquan Xu; GuoHua Xin; Litao Jiao; Jian Liu; Shaoying Liu; Meiqi Feng; Xi Zheng

http://arxiv.org/abs/2105.11363
Learning Security Classifiers with Verified Global Robustness Properties. (92%)
Yizheng Chen; Shiqi Wang; Yue Qin; Xiaojing Liao; Suman Jana; David Wagner

http://arxiv.org/abs/2105.11645
Feature Space Targeted Attacks by Statistic Alignment. (82%)
Lianli Gao; Yaya Cheng; Qilong Zhang; Xing Xu; Jingkuan Song

http://arxiv.org/abs/2105.11144
Improved OOD Generalization via Adversarial Training and Pre-training. (12%)
Mingyang Yi; Lu Hou; Jiacheng Sun; Lifeng Shang; Xin Jiang; Qun Liu; Zhi-Ming Ma

http://arxiv.org/abs/2105.11160
Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning. (5%)
Hannah Kim; Girmaw Abebe Tadesse; Celia Cintas; Skyler Speakman; Kush Varshney

http://arxiv.org/abs/2105.11166
AirNet: Neural Network Transmission over the Air. (1%)
Mikolaj Jankowski; Deniz Gunduz; Krystian Mikolajczyk

http://arxiv.org/abs/2105.11172
Every Byte Matters: Traffic Analysis of Bluetooth Wearable Devices. (1%)
Ludovic Barman; Alexandre Dumur; Apostolos Pyrgelis; Jean-Pierre Hubaux

http://arxiv.org/abs/2105.11136
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models. (1%)
Jieyu Lin; Jiajie Zou; Nai Ding

http://arxiv.org/abs/2105.11103
Dissecting Click Fraud Autonomy in the Wild. (1%)
Tong Zhu; Yan Meng; Haotian Hu; Xiaokuan Zhang; Minhui Xue; Haojin Zhu

http://arxiv.org/abs/2105.10909
Killing Two Birds with One Stone: Stealing Model and Inferring Attribute from BERT-based APIs. (99%)
Lingjuan Lyu; Xuanli He; Fangzhao Wu; Lichao Sun

http://arxiv.org/abs/2105.10872
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes. (92%)
Hao Huang; Yongtao Wang; Zhaoyu Chen; Yuheng Li; Zhi Tang; Wei Chu; Jingdong Chen; Weisi Lin; Kai-Kuang Ma

http://arxiv.org/abs/2105.10948
Regularization Can Help Mitigate Poisoning Attacks... with the Right Hyperparameters. (12%)
Javier Carnerero-Cano; Luis Muñoz-González; Phillippa Spencer; Emil C. Lupu

http://arxiv.org/abs/2105.10707
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems. (99%)
Yifan Jia; Jingyi Wang; Christopher M. Poskitt; Sudipta Chattopadhyay; Jun Sun; Yuqi Chen

http://arxiv.org/abs/2105.10843
Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation. (98%)
Jinyu Yang; Chunyuan Li; Weizhi An; Hehuan Ma; Yuzhi Guo; Yu Rong; Peilin Zhao; Junzhou Huang

http://arxiv.org/abs/2105.10663
Securing Optical Networks using Quantum-secured Blockchain: An Overview. (1%)
Purva Sharma; Vimal Bhatia; Shashi Prakash

http://arxiv.org/abs/2105.10393
ReLUSyn: Synthesizing Stealthy Attacks for Deep Neural Network Based Cyber-Physical Systems. (81%)
Aarti Kashyap; Syed Mubashir Iqbal; Karthik Pattabiraman; Margo Seltzer

http://arxiv.org/abs/2105.10304
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks. (76%)
Leo Schwinn; René Raab; An Nguyen; Dario Zanca; Bjoern Eskofier

http://arxiv.org/abs/2105.10123
Backdoor Attacks on Self-Supervised Learning. (68%)
Aniruddha Saha; Ajinkya Tejankar; Soroush Abbasi Koohpayegani; Hamed Pirsiavash

http://arxiv.org/abs/2105.10497
Intriguing Properties of Vision Transformers. (8%)
Muzammal Naseer; Kanchana Ranasinghe; Salman Khan; Munawar Hayat; Fahad Shahbaz Khan; Ming-Hsuan Yang

http://arxiv.org/abs/2105.13843
Explainable Enterprise Credit Rating via Deep Feature Crossing Network. (1%)
Weiyu Guo; Zhijiang Yang; Shu Wu; Fu Chen

http://arxiv.org/abs/2105.09685
Simple Transparent Adversarial Examples. (99%)
Jaydeep Borkar; Pin-Yu Chen

http://arxiv.org/abs/2105.10101
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks. (99%)
Hang Wang; David J. Miller; George Kesidis

http://arxiv.org/abs/2105.10051
Preventing Machine Learning Poisoning Attacks Using Authentication and Provenance. (11%)
Jack W. Stokes; Paul England; Kevin Kane

http://arxiv.org/abs/2105.10113
TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks. (1%)
Yu Li; Min Li; Qiuxia Lai; Yannan Liu; Qiang Xu

http://arxiv.org/abs/2105.09022
Attack on practical speaker verification system using universal adversarial perturbations. (99%)
Weiyi Zhang; Shuning Zhao; Le Liu; Jianmin Li; Xingliang Cheng; Thomas Fang Zheng; Xiaolin Hu

http://arxiv.org/abs/2105.09090
Local Aggressive Adversarial Attacks on 3D Point Cloud. (99%)
Yiming Sun; Feng Chen; Zhiyu Chen; Mingjie Wang

http://arxiv.org/abs/2105.09109
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks. (76%)
Cong Xu; Xiang Li; Min Yang

http://arxiv.org/abs/2105.09394
Balancing Robustness and Sensitivity using Feature Contrastive Learning. (15%)
Seungyeon Kim; Daniel Glasner; Srikumar Ramalingam; Cho-Jui Hsieh; Kishore Papineni; Sanjiv Kumar

http://arxiv.org/abs/2105.09453
DeepStrike: Remotely-Guided Fault Injection Attacks on DNN Accelerator in Cloud-FPGA. (1%)
Yukui Luo; Cheng Gongye; Yunsi Fei; Xiaolin Xu

http://arxiv.org/abs/2105.09369
User Label Leakage from Gradients in Federated Learning. (1%)
Aidmar Wainakh; Fabrizio Ventola; Till Müßig; Jens Keim; Carlos Garcia Cordero; Ephraim Zimmer; Tim Grube; Kristian Kersting; Max Mühlhäuser

http://arxiv.org/abs/2105.09157
Hunter in the Dark: Deep Ensemble Networks for Discovering Anomalous Activity from Smart Networks. (1%)
Shiyi Yang; Nour Moustafa; Hui Guo

http://arxiv.org/abs/2105.08269
Sparta: Spatially Attentive and Adversarially Robust Activation. (99%)
Qing Guo; Felix Juefei-Xu; Changqing Zhou; Wei Feng; Yang Liu; Song Wang

http://arxiv.org/abs/2105.08620
Detecting Adversarial Examples with Bayesian Neural Network. (99%)
Yao Li; Tongyi Tang; Cho-Jui Hsieh; Thomas C. M. Lee

http://arxiv.org/abs/2105.08714
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks. (98%)
Dequan Wang; An Ju; Evan Shelhamer; David Wagner; Trevor Darrell

http://arxiv.org/abs/2105.08619
On the Robustness of Domain Constraints. (98%)
Ryan Sheatsley; Blaine Hoak; Eric Pauley; Yohan Beugin; Michael J. Weisman; Patrick McDaniel

http://arxiv.org/abs/2105.08709
Learning and Certification under Instance-targeted Poisoning. (82%)
Ji Gao; Amin Karbasi; Mohammad Mahmoody

http://arxiv.org/abs/2105.07926
Towards Robust Vision Transformer. (95%)
Xiaofeng Mao; Gege Qi; Yuefeng Chen; Xiaodan Li; Ranjie Duan; Shaokai Ye; Yuan He; Hui Xue

http://arxiv.org/abs/2105.07985
Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning. (93%)
Franziska Boenisch; Philip Sperl; Konstantin Böttinger

http://arxiv.org/abs/2105.08037
An SDE Framework for Adversarial Training, with Convergence and Robustness Analysis. (69%)
Haotian Gu; Xin Guo

http://arxiv.org/abs/2105.07754
A Fusion-Denoising Attack on InstaHide with Data Augmentation. (1%)
Xinjian Luo; Xiaokui Xiao; Yuncheng Wu; Juncheng Liu; Beng Chin Ooi

http://arxiv.org/abs/2105.07581
Vision Transformers are Robust Learners. (99%)
Sayak Paul; Pin-Yu Chen

http://arxiv.org/abs/2105.07553
Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing. (99%)
Xunguang Wang; Zheng Zhang; Baoyuan Wu; Fumin Shen; Guangming Lu

http://arxiv.org/abs/2105.07574
SoundFence: Securing Ultrasonic Sensors in Vehicles Using Physical-Layer Defense. (2%)
Jianzhi Lou; Qiben Yan; Qing Hui; Huacheng Zeng

http://arxiv.org/abs/2105.07334
Real-time Detection of Practical Universal Adversarial Perturbations. (99%)
Kenneth T. Co; Luis Muñoz-González; Leslie Kanthan; Emil C. Lupu

http://arxiv.org/abs/2105.06807
Salient Feature Extractor for Adversarial Defense on Deep Neural Networks. (99%)
Jinyin Chen; Ruoxi Chen; Haibin Zheng; Zhaoyan Ming; Wenrong Jiang; Chen Cui

http://arxiv.org/abs/2105.07078
High-Robustness, Low-Transferability Fingerprinting of Neural Networks. (9%)
Siyue Wang; Xiao Wang; Pin-Yu Chen; Pu Zhao; Xue Lin

http://arxiv.org/abs/2105.06956
Information-theoretic Evolution of Model Agnostic Global Explanations. (1%)
Sukriti Verma; Nikaash Puri; Piyush Gupta; Balaji Krishnamurthy

http://arxiv.org/abs/2105.07080
Iterative Algorithms for Assessing Network Resilience Against Structured Perturbations. (1%)
Shenyu Liu; Sonia Martinez; Jorge Cortes

http://arxiv.org/abs/2105.06512
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs. (98%)
Lorena Qendro; Sangwon Ha; Jong René de; Partha Maji

http://arxiv.org/abs/2105.06152
When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks. (5%)
Jiahang Wang; Sheng Jin; Wentao Liu; Weizhong Liu; Chen Qian; Ping Luo

http://arxiv.org/abs/2105.06209
DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks. (1%)
Yingzhe He; Guozhu Meng; Kai Chen; Jinwen He; Xingbo Hu

http://arxiv.org/abs/2105.06625
Biometrics: Trust, but Verify. (1%)
Anil K. Jain; Debayan Deb; Joshua J. Engelsma

http://arxiv.org/abs/2105.05558
AVA: Adversarial Vignetting Attack against Visual Recognition. (99%)
Binyu Tian; Felix Juefei-Xu; Qing Guo; Xiaofei Xie; Xiaohong Li; Yang Liu

http://arxiv.org/abs/2105.05601
OutFlip: Generating Out-of-Domain Samples for Unknown Intent Detection with Natural Language Attack. (70%)
DongHyun Choi; Myeong Cheol Shin; EungGyun Kim; Dong Ryeol Shin

http://arxiv.org/abs/2105.05817
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control. (2%)
Feng Wang; M. Cenk Gursoy; Senem Velipasalar

http://arxiv.org/abs/2105.05610
A Statistical Threshold for Adversarial Classification in Laplace Mechanisms. (1%)
Ayşe Ünsal; Melek Önen

http://arxiv.org/abs/2105.04839
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds. (99%)
Guiyu Tian; Wenhao Jiang; Wei Liu; Yadong Mu

http://arxiv.org/abs/2105.04834
Improving Adversarial Transferability with Gradient Refining. (99%)
Guoqiu Wang; Huanqian Yan; Ying Guo; Xingxing Wei

http://arxiv.org/abs/2105.05381
Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective. (16%)
Shahbaz Rezaei; Zubair Shafiq; Xin Liu

http://arxiv.org/abs/2105.05029
Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum. (99%)
Tiangang Li

http://arxiv.org/abs/2105.04128
Examining and Mitigating Kernel Saturation in Convolutional Neural Networks using Negative Images. (1%)
Nidhi Gowdra; Roopak Sinha; Stephen MacDonell

http://arxiv.org/abs/2105.03931
Automated Decision-based Adversarial Attacks. (99%)
Qi-An Fu; Yinpeng Dong; Hang Su; Jun Zhu

http://arxiv.org/abs/2105.04003
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks. (88%)
Abhiroop Bhattacharjee; Abhishek Moitra; Priyadarshini Panda

http://arxiv.org/abs/2105.03905
Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction. (81%)
Ferhat Ozgur Catak; Evren Catak; Murat Kuzlu; Umit Cali

http://arxiv.org/abs/2105.04070
Robust Training Using Natural Transformation. (13%)
Shuo Wang; Lingjuan Lyu; Surya Nepal; Carsten Rudolph; Marthie Grobler; Kristen Moore

http://arxiv.org/abs/2105.03834
Learning Image Attacks toward Vision Guided Autonomous Vehicles. (4%)
Hyung-Jin Yoon; Hamidreza Jafarnejadsani; Petros Voulgaris

http://arxiv.org/abs/2105.03917
Combining Time-Dependent Force Perturbations in Robot-Assisted Surgery Training. (1%)
Yarden Sharon; Daniel Naftalovich; Lidor Bahar; Yael Refaely; Ilana Nisky

http://arxiv.org/abs/2105.03689
Self-Supervised Adversarial Example Detection by Disentangled Representation. (99%)
Zhaoxi Zhang; Leo Yu Zhang; Xufei Zheng; Shengshan Hu; Jinyu Tian; Jiantao Zhou

http://arxiv.org/abs/2105.03592
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks. (96%)
Jian Chen; Xuxin Zhang; Rui Zhang; Chen Wang; Ling Liu

http://arxiv.org/abs/2105.03743
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]. (93%)
Jiehang Zeng; Xiaoqing Zheng; Jianhan Xu; Linyang Li; Liping Yuan; Xuanjing Huang

http://arxiv.org/abs/2105.03692
Provable Guarantees against Data Poisoning Using Self-Expansion and Compatibility. (81%)
Charles Jin; Melinda Sun; Martin Rinard

http://arxiv.org/abs/2105.03726
Mental Models of Adversarial Machine Learning. (16%)
Lukas Bieringer; Kathrin Grosse; Michael Backes; Battista Biggio; Katharina Krombholz

http://arxiv.org/abs/2105.03162
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition. (99%)
Bangjie Yin; Wenxuan Wang; Taiping Yao; Junfeng Guo; Zelun Kong; Shouhong Ding; Jilin Li; Cong Liu

http://arxiv.org/abs/2105.03491
Uniform Convergence, Adversarial Spheres and a Simple Remedy. (15%)
Gregor Bachmann; Seyed-Mohsen Moosavi-Dezfooli; Thomas Hofmann

http://arxiv.org/abs/2105.02803
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model. (99%)
Ruoxi Qin; Linyuan Wang; Xingyuan Chen; Xuehui Du; Bin Yan

http://arxiv.org/abs/2105.02480
A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking. (99%)
Zhenbang Li; Yaya Shi; Jin Gao; Shaoru Wang; Bing Li; Pengpeng Liang; Weiming Hu

http://arxiv.org/abs/2105.02942
Understanding Catastrophic Overfitting in Adversarial Training. (92%)
Peilin Kang; Seyed-Mohsen Moosavi-Dezfooli

http://arxiv.org/abs/2105.02435
Attestation Waves: Platform Trust via Remote Power Analysis. (1%)
Ignacio M. Delgado-Lozano; Macarena C. Martínez-Rodríguez; Alexandros Bakas; Billy Bob Brumley; Antonis Michalas

http://arxiv.org/abs/2105.01959
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning. (99%)
Matthew Durham University, Durham, UK Watson; Noura Al Durham University, Durham, UK Moubayed

http://arxiv.org/abs/2105.03251
Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks. (97%)
Faiq Khalid; Muhammad Abdullah Hanif; Muhammad Shafique

http://arxiv.org/abs/2105.02001
Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation. (1%)
Robert A. Marsden; Alexander Bartler; Mario Döbler; Bin Yang

http://arxiv.org/abs/2105.01867
A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs. (1%)
Devansh Bisla; Apoorva Nandini Saridena; Anna Choromanska

http://arxiv.org/abs/2105.01622
Poisoning the Unlabeled Dataset of Semi-Supervised Learning. (92%)
Nicholas Carlini

http://arxiv.org/abs/2105.01560
Broadly Applicable Targeted Data Sample Omission Attacks. (68%)
Guy Barash; Eitan Farchi; Sarit Kraus; Onn Shehory

http://arxiv.org/abs/2105.01403
An Overview of Laser Injection against Embedded Neural Network Models. (2%)
Mathieu Dumont; Pierre-Alain Moellic; Raphael Viera; Jean-Max Dutertre; Rémi Bernhard

http://arxiv.org/abs/2105.00622
Physical world assistive signals for deep neural network classifiers -- neither defense nor attack. (83%)
Camilo Pestana; Wei Liu; David Glance; Robyn Owens; Ajmal Mian

http://arxiv.org/abs/2105.00623
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack. (73%)
Yixu Wang; Jie Li; Hong Liu; Yan Wang; Yongjian Wu; Feiyue Huang; Rongrong Ji

http://arxiv.org/abs/2105.00495
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability. (99%)
Xinglong Chang; Katharina Dost; Kaiqi Zhao; Ambra Demontis; Fabio Roli; Gill Dobbie; Jörg Wicker

http://arxiv.org/abs/2105.00433
Who's Afraid of Adversarial Transferability? (99%)
Ziv Katzir; Yuval Elovici

http://arxiv.org/abs/2105.00389
Multi-Robot Coordination and Planning in Uncertain and Adversarial Environments. (10%)
Lifeng Zhou; Pratap Tokekar

http://arxiv.org/abs/2105.00529
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning. (2%)
Hanchi Ren; Jingjing Deng; Xianghua Xie

http://arxiv.org/abs/2105.00391
Spinner: Automated Dynamic Command Subsystem Perturbation. (1%)
Meng Wang; Chijung Jung; Ali Ahad; Yonghwi Kwon

http://arxiv.org/abs/2105.00203
Adversarial Example Detection for DNN Models: A Review and Experimental Comparison. (99%)
Ahmed Aldahdooh; Wassim Hamidouche; Sid Ahmed Fezza; Olivier Deforges

http://arxiv.org/abs/2105.00278
A Perceptual Distortion Reduction Framework: Towards Generating Adversarial Examples with High Perceptual Quality and Attack Success Rate. (98%)
Ruijie Yang; Yunhong Wang; Ruikui Wang; Yuanfang Guo

http://arxiv.org/abs/2105.00227
On the Adversarial Robustness of Quantized Neural Networks. (75%)
Micah Gorsline; James Smith; Cory Merkel

http://arxiv.org/abs/2105.00164
Hidden Backdoors in Human-Centric Language Models. (73%)
Shaofeng Li; Hui Liu; Tian Dong; Benjamin Zi Hao Zhao; Minhui Xue; Haojin Zhu; Jialiang Lu

http://arxiv.org/abs/2105.00187
One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework. (62%)
Shahroz Tariq; Sangyup Lee; Simon S. Woo

http://arxiv.org/abs/2105.00249
A Master Key Backdoor for Universal Impersonation Attack against DNN-based Face Verification. (62%)
Wei Guo; Benedetta Tondi; Mauro Barni

http://arxiv.org/abs/2105.00350
Load Oscillating Attacks of Smart Grids: Demand Strategies and Vulnerability Analysis. (2%)
Falah Alanazi; Jinsub Kim; Eduardo Cotilla-Sanchez

http://arxiv.org/abs/2105.00303
RATT: Leveraging Unlabeled Data to Guarantee Generalization. (1%)
Saurabh Garg; Sivaraman Balakrishnan; J. Zico Kolter; Zachary C. Lipton

http://arxiv.org/abs/2104.15022
Deep Image Destruction: A Comprehensive Study on Vulnerability of Deep Image-to-Image Models against Adversarial Attacks. (99%)
Jun-Ho Choi; Huan Zhang; Jun-Hyuk Kim; Cho-Jui Hsieh; Jong-Seok Lee

http://arxiv.org/abs/2104.15061
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense. (99%)
Haoxi Zhan; Xiaobing Pei

http://arxiv.org/abs/2104.15064
Black-box adversarial attacks using Evolution Strategies. (98%)
Hao Qiu; Leonardo Lucio Custode; Giovanni Iacca

http://arxiv.org/abs/2105.00113
IPatch: A Remote Adversarial Patch. (97%)
Yisroel Mirsky

http://arxiv.org/abs/2104.15068
DeFiRanger: Detecting Price Manipulation Attacks on DeFi Applications. (10%)
Siwei Wu; Dabao Wang; Jianting He; Yajin Zhou; Lei Wu; Xingliang Yuan; Qinming He; Kui Ren

http://arxiv.org/abs/2104.14993
FIPAC: Thwarting Fault- and Software-Induced Control-Flow Attacks with ARM Pointer Authentication. (2%)
Robert Schilling; Pascal Nasahl; Stefan Mangard

http://arxiv.org/abs/2104.14528
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathology Image Classification. (67%)
Haoyuan Chen; Chen Li; Xiaoyan Li; Ge Wang; Weiming Hu; Yixin Li; Wanli Liu; Changhao Sun; Yudong Yao; Yueyang Teng; Marcin Grzegorzek

http://arxiv.org/abs/2104.14372
A neural anisotropic view of underspecification in deep learning. (26%)
Guillermo Ortiz-Jimenez; Itamar Franco Salazar-Reque; Apostolos Modas; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard

http://arxiv.org/abs/2104.14672
Analytical bounds on the local Lipschitz constants of ReLU networks. (12%)
Trevor Avant; Kristi A. Morgansen

http://arxiv.org/abs/2104.14379
Learning Robust Variational Information Bottleneck with Reference. (5%)
Weizhu Qian; Bowei Chen; Xiaowei Huang

http://arxiv.org/abs/2104.13673
AdvHaze: Adversarial Haze Attack. (99%)
Ruijun Gao; Qing Guo; Felix Juefei-Xu; Hongkai Yu; Wei Feng

http://arxiv.org/abs/2104.13484
Improved and Efficient Text Adversarial Attacks using Target Information. (97%)
Mahmoud Hossam; Trung Le; He Zhao; Viet Huynh; Dinh Phung

http://arxiv.org/abs/2104.13295
Metamorphic Detection of Repackaged Malware. (91%)
Shirish Singh; Gail Kaiser

http://arxiv.org/abs/2104.13012
Structure-Aware Hierarchical Graph Pooling using Information Bottleneck. (2%)
Kashob Kumar Roy; Amit Roy; A K M Mahbubur Rahman; M Ashraful Amin; Amin Ahsan Ali

http://arxiv.org/abs/2104.13061
Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity. (1%)
Mathias P. M. Parisot; Balazs Pejo; Dayana Spagnuelo

http://arxiv.org/abs/2104.12426
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT. (99%)
Pavlos Papadopoulos; Essen Oliver Thornewill von; Nikolaos Pitropakis; Christos Chrysoulas; Alexios Mylonas; William J. Buchanan

http://arxiv.org/abs/2104.12378
Delving into Data: Effectively Substitute Training for Black-box Attack. (99%)
Wenxuan Wang; Bangjie Yin; Taiping Yao; Li Zhang; Yanwei Fu; Shouhong Ding; Jilin Li; Feiyue Huang; Xiangyang Xue

http://arxiv.org/abs/2104.12848
secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Python. (99%)
Luca Demetrio; Battista Biggio

http://arxiv.org/abs/2104.12623
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Generative Adversarial Networks. (98%)
Sebastian Szyller; Vasisht Duddu; Tommi Gröndahl; N. Asokan

http://arxiv.org/abs/2104.12679
Impact of Spatial Frequency Based Constraints on Adversarial Robustness. (98%)
Rémi Bernhard; Pierre-Alain Moellic; Martial Mermillod; Yannick Bourrier; Romain Cohendet; Miguel Solinas; Marina Reyboz

http://arxiv.org/abs/2104.12609
PatchGuard++: Efficient Provable Attack Detection against Adversarial Patches. (87%)
Chong Xiang; Prateek Mittal

http://arxiv.org/abs/2104.12146
3D Adversarial Attacks Beyond Point Cloud. (99%)
Jinlai Zhang; Lyujie Chen; Binbin Liu; Bo Ouyang; Qizhi Xie; Jihong Zhu; Weiming Li; Yanmei Meng

http://arxiv.org/abs/2104.12069
Making Generated Images Hard To Spot: A Transferable Attack On Synthetic Image Detectors. (81%)
Xinwei Zhao; Matthew C. Stamm

http://arxiv.org/abs/2104.13230
Influence Based Defense Against Data Poisoning Attacks in Online Learning. (99%)
Sanjay Seetharaman; Shubham Malaviya; Rosni KV; Manish Shukla; Sachin Lodha

http://arxiv.org/abs/2104.11470
Theoretical Study of Random Noise Defense against Query-Based Black-Box Attacks. (98%)
Zeyu Qin; Yanbo Fan; Hongyuan Zha; Baoyuan Wu

http://arxiv.org/abs/2104.11729
Evaluating Deception Detection Model Robustness To Linguistic Variation. (82%)
Maria Glenski; Ellyn Ayton; Robin Cosbey; Dustin Arendt; Svitlana Volkova

http://arxiv.org/abs/2104.11408
Lightweight Detection of Out-of-Distribution and Adversarial Samples via Channel Mean Discrepancy. (3%)
Xin Dong; Junfeng Guo; Wei-Te Ting; H. T. Kung

http://arxiv.org/abs/2104.11601
Improving Neural Silent Speech Interface Models by Adversarial Training. (1%)
Amin Honarmandi Shandiz; László Tóth; Gábor Gosztolya; Alexandra Markó; Tamás Gábor Csapó

http://arxiv.org/abs/2104.10868
Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting. (99%)
Qiming Wu; Zhikang Zou; Pan Zhou; Xiaoqing Ye; Binghui Wang; Ang Li

http://arxiv.org/abs/2104.11101
Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors. (98%)
Arman Maesumi; Mingkang Zhu; Yi Wang; Tianlong Chen; Zhangyang Wang; Chandrajit Bajaj

http://arxiv.org/abs/2104.11103
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions. (86%)
Jing Wu; Mingyi Zhou; Ce Zhu; Yipeng Liu; Mehrtash Harandi; Li Li

http://arxiv.org/abs/2104.11294
Operator Shifting for General Noisy Matrix Systems. (56%)
Philip Etter; Lexing Ying

http://arxiv.org/abs/2104.11315
SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics. (22%)
Jonathan Hayase; Weihao Kong; Raghav Somani; Sewoong Oh

http://arxiv.org/abs/2104.10377
Dual Head Adversarial Training. (99%)
Yujing Jiang; Xingjun Ma; Sarah Monazam Erfani; James Bailey

http://arxiv.org/abs/2104.10586
Mixture of Robust Experts (MoRE): A Flexible Defense Against Multiple Perturbations. (99%)
Kaidi Xu; Chenan Wang; Xue Lin; Bhavya Kailkhura; Ryan Goldhahn

http://arxiv.org/abs/2104.10837
Robust Certification for Laplace Learning on Geometric Graphs. (96%)
Matthew Thorpe; Bao Wang

http://arxiv.org/abs/2104.10459
Jacobian Regularization for Mitigating Universal Adversarial Perturbations. (95%)
Kenneth T. Co; David Martinez Rego; Emil C. Lupu

http://arxiv.org/abs/2104.10706
Dataset Inference: Ownership Resolution in Machine Learning. (83%)
Pratyush Maini; Mohammad Yaghini; Nicolas Papernot

http://arxiv.org/abs/2104.09852
Adversarial Training for Deep Learning-based Intrusion Detection Systems. (99%)
Islam Debicha; Thibault Debatty; Jean-Michel Dricot; Wim Mees

http://arxiv.org/abs/2104.10076
MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis. (99%)
Yijun Yang; Ruiyuan Gao; Yu Li; Qiuxia Lai; Qiang Xu

http://arxiv.org/abs/2104.10336
MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training. (64%)
Jian Ma; Shuyi Xie; Haiqin Yang; Lianxin Jiang; Mengyuan Zhou; Xiaoyi Ruan; Yang Mo

http://arxiv.org/abs/2104.09789
Does enhanced shape bias improve neural network robustness to common corruptions? (26%)
Chaithanya Kumar Mummadi; Ranjitha Subramaniam; Robin Hutmacher; Julien Vitay; Volker Fischer; Jan Hendrik Metzen

http://arxiv.org/abs/2104.09872
Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles. (9%)
Jiwei Guan; Xi Zheng; Chen Wang; Yipeng Zhou; Alireza Jolfa

http://arxiv.org/abs/2104.10262
Network Defense is Not a Game. (1%)
Andres Molina-Markham; Ransom K. Winder; Ahmad Ridley

http://arxiv.org/abs/2104.09722
Staircase Sign Method for Boosting Adversarial Attacks. (99%)
Qilong Zhang; Xiaosu Zhu; Jingkuan Song; Lianli Gao; Heng Tao Shen

http://arxiv.org/abs/2104.09425
Improving Adversarial Robustness Using Proxy Distributions. (99%)
Vikash Sehwag; Saeed Mahloujifar; Tinashe Handina; Sihui Dai; Chong Xiang; Mung Chiang; Prateek Mittal

http://arxiv.org/abs/2104.09369
Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models. (99%)
Lyuyi Zhu; Kairui Feng; Ziyuan Pu; Wei Ma

http://arxiv.org/abs/2104.09284
LAFEAT: Piercing Through Adversarial Defenses with Latent Features. (99%)
Yunrui Yu; Xitong Gao; Cheng-Zhong Xu

http://arxiv.org/abs/2104.09197
Removing Adversarial Noise in Class Activation Feature Space. (99%)
Dawei Zhou; Nannan Wang; Chunlei Peng; Xinbo Gao; Xiaoyu Wang; Jun Yu; Tongliang Liu

http://arxiv.org/abs/2104.09172
Direction-Aggregated Attack for Transferable Adversarial Examples. (99%)
Tianjin Huang; Vlado Menkovski; Yulong Pei; YuHao Wang; Mykola Pechenizkiy

http://arxiv.org/abs/2104.09667
Manipulating SGD with Data Ordering Attacks. (95%)
Ilia Shumailov; Zakhar Shumaylov; Dmitry Kazhdan; Yiren Zhao; Nicolas Papernot; Murat A. Erdogdu; Ross Anderson

http://arxiv.org/abs/2104.09437
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. (22%)
Difan Zou; Spencer Frei; Quanquan Gu

http://arxiv.org/abs/2104.09203
Protecting the Intellectual Properties of Deep Neural Networks with an Additional Class and Steganographic Images. (11%)
Shichang Sun; Mingfu Xue; Jian Wang; Weiqiang Liu

http://arxiv.org/abs/2104.09136
Semi-Supervised Domain Adaptation with Prototypical Alignment and Consistency Learning. (1%)
Kai Li; Chang Liu; Handong Zhao; Yulun Zhang; Yun Fu

http://arxiv.org/abs/2104.08806
Best Practices for Noise-Based Augmentation to Improve the Performance of Emotion Recognition "In the Wild". (83%)
Mimansa Jaiswal; Emily Mower Provost

http://arxiv.org/abs/2104.08763
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training. (68%)
Shunsuke Kitada; Hitoshi Iyatomi

http://arxiv.org/abs/2104.08782
On the Sensitivity and Stability of Model Interpretations in NLP. (1%)
Fan Yin; Zhouxing Shi; Cho-Jui Hsieh; Kai-Wei Chang

http://arxiv.org/abs/2104.08453
Attacking Text Classifiers via Sentence Rewriting Sampler. (99%)
Lei Xu; Kalyan Veeramachaneni

http://arxiv.org/abs/2104.08690
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems. (99%)
Yue Gao; Ilia Shumailov; Kassem Fawaz

http://arxiv.org/abs/2104.08678
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation. (98%)
Max Bartolo; Tristan Thrush; Robin Jia; Sebastian Riedel; Pontus Stenetorp; Douwe Kiela

http://arxiv.org/abs/2104.08645
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training. (87%)
Kuan-Hao Huang; Wasi Uddin Ahmad; Nanyun Peng; Kai-Wei Chang

http://arxiv.org/abs/2104.08639
AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples. (15%)
Qianchu Liu; Edoardo M. Ponti; Diana McCarthy; Ivan Vulić; Anna Korhonen

http://arxiv.org/abs/2104.08422
Fashion-Guided Adversarial Attack on Person Segmentation. (99%)
Marc Treu; Trung-Nghia Le; Huy H. Nguyen; Junichi Yamagishi; Isao Echizen

http://arxiv.org/abs/2104.08139
Towards Variable-Length Textual Adversarial Attacks. (99%)
Junliang Guo; Zhirui Zhang; Linlin Zhang; Linli Xu; Boxing Chen; Enhong Chen; Weihua Luo

http://arxiv.org/abs/2104.08231
An Adversarially-Learned Turing Test for Dialog Generation Models. (96%)
Xiang Gao; Yizhe Zhang; Michel Galley; Bill Dolan

http://arxiv.org/abs/2104.08323
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators. (83%)
David Stutz; Nandhini Chandramoorthy; Matthias Hein; Bernt Schiele

http://arxiv.org/abs/2104.08382
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. (2%)
Arjun Nitin Bhagoji; Daniel Cullina; Vikash Sehwag; Prateek Mittal

http://arxiv.org/abs/2104.13733
Gradient-based Adversarial Attacks against Text Transformers. (99%)
Chuan Guo; Alexandre Sablayrolles; Hervé Jégou; Douwe Kiela

http://arxiv.org/abs/2104.07395
Robust Backdoor Attacks against Deep Neural Networks in Real Physical World. (86%)
Mingfu Xue; Can He; Shichang Sun; Jian Wang; Weiqiang Liu

http://arxiv.org/abs/2104.07646
Are Multilingual BERT models robust? A Case Study on Adversarial Attacks for Multilingual Question Answering. (12%)
Sara Rosenthal; Mihaela Bornea; Avirup Sil

http://arxiv.org/abs/2104.09994
Federated Learning for Malware Detection in IoT Devices. (10%)
Valerian Rey; Pedro Miguel Sánchez Sánchez; Alberto Huertas Celdrán; Gérôme Bovet; Martin Jaggi

http://arxiv.org/abs/2104.06728
Meaningful Adversarial Stickers for Face Recognition in Physical World. (98%)
Ying Guo; Xingxing Wei; Guoqiu Wang; Bo Zhang

http://arxiv.org/abs/2104.07167
Orthogonalizing Convolutional Layers with the Cayley Transform. (80%)
Asher Trockman; J. Zico Kolter

http://arxiv.org/abs/2104.06744
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks. (38%)
Nicolas M. Müller; Simon Roschmann; Konstantin Böttinger

http://arxiv.org/abs/2104.06718
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition. (1%)
Palma Alessandro De; Rudy Bunel; Alban Desmaison; Krishnamurthy Dvijotham; Pushmeet Kohli; Philip H. S. Torr; M. Pawan Kumar

http://arxiv.org/abs/2104.06377
Mitigating Adversarial Attack for Compute-in-Memory Accelerator Utilizing On-chip Finetune. (99%)
Shanshi Huang; Hongwu Jiang; Shimeng Yu

http://arxiv.org/abs/2104.06015
Detecting Operational Adversarial Examples for Reliable Deep Learning. (82%)
Xingyu Zhao; Wei Huang; Sven Schewe; Yi Dong; Xiaowei Huang

http://arxiv.org/abs/2104.05996
Fall of Giants: How popular text-based MLaaS fall against a simple evasion attack. (75%)
Luca Pajola; Mauro Conti

http://arxiv.org/abs/2104.05353
Sparse Coding Frontend for Robust Neural Networks. (99%)
Can Bakiskan; Metehan Cekic; Ahmet Dundar Sezer; Upamanyu Madhow

http://arxiv.org/abs/2104.05808
A Backdoor Attack against 3D Point Cloud Classifiers. (96%)
Zhen Xiang; David J. Miller; Siheng Chen; Xi Li; George Kesidis

http://arxiv.org/abs/2104.05801
Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation. (56%)
Sarik Ghazarian; Zixi Liu; Akash SM; Ralph Weischedel; Aram Galstyan; Nanyun Peng

http://arxiv.org/abs/2104.05232
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation. (50%)
Chong Zhang; Jieyu Zhao; Huan Zhang; Kai-Wei Chang; Cho-Jui Hsieh

http://arxiv.org/abs/2104.05921
Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack. (1%)
Xinyi Zhang; Chengfang Fang; Jie Shi

http://arxiv.org/abs/2104.05062
Achieving Model Robustness through Discrete Adversarial Training. (99%)
Maor Ivgi; Jonathan Berant

http://arxiv.org/abs/2104.05097
Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks. (1%)
Louis Béthune; Thibaut Boissin; Mathieu Serrurier; Franck Mamalet; Corentin Friedrich; Alberto González-Sanz

http://arxiv.org/abs/2104.04680
Distributed Estimation over Directed Graphs Resilient to Sensor Spoofing. (69%)
Shamik Bhattacharyya; Kiran Rokade; Rachel Kalpana Kalaimani

http://arxiv.org/abs/2104.04725
Fool Me Twice: Entailment from Wikipedia Gamification. (61%)
Julian Martin Eisenschlos; Bhuwan Dhingra; Jannis Bulian; Benjamin Börschinger; Jordan Boyd-Graber

http://arxiv.org/abs/2104.04886
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach. (15%)
Simiao Zuo; Chen Liang; Haoming Jiang; Xiaodong Liu; Pengcheng He; Jianfeng Gao; Weizhu Chen; Tuo Zhao

http://arxiv.org/abs/2104.04907
Disentangled Contrastive Learning for Learning Robust Textual Representations. (11%)
Xiang Chen; Xin Xie; Zhen Bi; Hongbin Ye; Shumin Deng; Ningyu Zhang; Huajun Chen

http://arxiv.org/abs/2104.04448
Relating Adversarially Robust Generalization to Flat Minima. (99%)
David Stutz; Matthias Hein; Bernt Schiele

http://arxiv.org/abs/2104.04553
SPoTKD: A Protocol for Symmetric Key Distribution over Public Channels Using Self-Powered Timekeeping Devices. (1%)
Mustafizur Rahman; Liang Zhou; Shantanu Chakrabartty

http://arxiv.org/abs/2104.04268
Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication. (1%)
Xiquan Guan; Huamin Feng; Weiming Zhang; Hang Zhou; Jie Zhang; Nenghai Yu

http://arxiv.org/abs/2104.04405
Learning Sampling Policy for Faster Derivative Free Optimization. (1%)
Zhou Zhai; Bin Gu; Heng Huang

http://arxiv.org/abs/2104.04107
FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems. (98%)
Liang Tong; Zhengzhang Chen; Jingchao Ni; Wei Cheng; Dongjin Song; Haifeng Chen; Yevgeniy Vorobeychik

http://arxiv.org/abs/2104.03674
Explainability-based Backdoor Attacks Against Graph Neural Networks. (15%)
Jing Jason Xu; Jason Minhui; Xue; Stjepan Picek

http://arxiv.org/abs/2104.03863
A single gradient step finds adversarial examples on random two-layers neural networks. (10%)
Sébastien Bubeck; Yeshwanth Cherapanamjeri; Gauthier Gidel; Rémi Tachet des Combes

http://arxiv.org/abs/2104.04054
Adversarial Learning Inspired Emerging Side-Channel Attacks and Defenses. (8%)
Abhijitt Dhavlle

http://arxiv.org/abs/2104.03000
Universal Adversarial Training with Class-Wise Perturbations. (99%)
Philipp Benz; Chaoning Zhang; Adil Karjauv; In So Kweon

http://arxiv.org/abs/2104.02963
The art of defense: letting networks fool the attacker. (98%)
Jinlai Zhang; Yinpeng Dong; Binbin Liu; Bo Ouyang; Jihong Zhu; Minchi Kuang; Houqing Wang; Yanmei Meng

http://arxiv.org/abs/2104.03356
Universal Spectral Adversarial Attacks for Deformable Shapes. (81%)
Arianna Rampini; Franco Pestarini; Luca Cosmo; Simone Melzi; Emanuele Rodolà

http://arxiv.org/abs/2104.03180
Adversarial Robustness Guarantees for Gaussian Processes. (68%)
Andrea Patane; Arno Blaas; Luca Laurenti; Luca Cardelli; Stephen Roberts; Marta Kwiatkowska

http://arxiv.org/abs/2104.03413
Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective. (61%)
Yi Zeng; Won Park; Z. Morley Mao; Ruoxi Jia

http://arxiv.org/abs/2104.03154
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attacks based on Critic Networks. (10%)
Lucas Schott; Manon Césaire; Hatem Hajri; Sylvain Lamprier

http://arxiv.org/abs/2104.02922
Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features. (3%)
Suryabhan Singh Hada; Miguel Á. Carreira-Perpiñán; Arman Zharmagambetov

http://arxiv.org/abs/2104.03366
An Object Detection based Solver for Google's Image reCAPTCHA v2. (1%)
Md Imran Hossen; Yazhou Tu; Md Fazle Rabby; Md Nazmul Islam; Hui Cao; Xiali Hei

http://arxiv.org/abs/2104.02757
Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models. (93%)
Zhiyun Lu; Wei Han; Yu Zhang; Liangliang Cao

http://arxiv.org/abs/2104.02703
Adversarial Robustness under Long-Tailed Distribution. (89%)
Tong Wu; Ziwei Liu; Qingqiu Huang; Yu Wang; Dahua Lin

http://arxiv.org/abs/2104.02334
Robust Adversarial Classification via Abstaining. (75%)
Abed AlRahman Al Makdah; Vaibhav Katewa; Fabio Pasqualetti

http://arxiv.org/abs/2104.02361
Backdoor Attack in the Physical World. (2%)
Yiming Li; Tongqing Zhai; Yong Jiang; Zhifeng Li; Shu-Tao Xia

http://arxiv.org/abs/2104.02189
Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model. (99%)
Payam Delgosha; Hamed Hassani; Ramtin Pedarsani

http://arxiv.org/abs/2104.02155
Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification. (98%)
Samuel Henrique Silva; Arun Das; Ian Scarff; Peyman Najafirad

http://arxiv.org/abs/2104.01782
BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification. (96%)
Ishani Mondal

http://arxiv.org/abs/2104.01789
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses. (74%)
Yao Deng; Tiehua Zhang; Guannan Lou; Xi Zheng; Jiong Jin; Qing-Long Han

http://arxiv.org/abs/2104.02000
Can audio-visual integration strengthen robustness under multimodal attacks? (68%)
Yapeng Tian; Chenliang Xu

http://arxiv.org/abs/2104.02107
Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models. (33%)
Neal Mangaokar; Jiameng Pu; Parantapa Bhattacharya; Chandan K. Reddy; Bimal Viswanath

http://arxiv.org/abs/2104.02156
Unified Detection of Digital and Physical Face Attacks. (8%)
Debayan Deb; Xiaoming Liu; Anil K. Jain

http://arxiv.org/abs/2104.02226
Beyond Categorical Label Representations for Image Classification. (2%)
Boyuan Chen; Yu Li; Sunand Raghupathi; Hod Lipson

http://arxiv.org/abs/2104.01853
Rethinking Perturbations in Encoder-Decoders for Fast Training. (1%)
Sho Takase; Shun Kiyono

http://arxiv.org/abs/2104.01732
Semantically Stealthy Adversarial Attacks against Segmentation Models. (99%)
Zhenhua Chen; Chuhua Wang; David J. Crandall

http://arxiv.org/abs/2104.01575
Reliably fast adversarial training via latent adversarial perturbation. (93%)
Geon Yeong Park; Sang Wan Lee

http://arxiv.org/abs/2104.01494
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression. (99%)
Rehana Mahfuz; Rajeev Sahay; Aly El Gamal

http://arxiv.org/abs/2104.06375
Gradient-based Adversarial Deep Modulation Classification with Data-driven Subsampling. (93%)
Jinho Yi; Aly El Gamal

http://arxiv.org/abs/2104.01396
Property-driven Training: All You (N)Ever Wanted to Know About. (38%)
Marco Casadio; Matthew Daggitt; Ekaterina Komendantskaya; Wen Kokke; Daniel Kienitz; Rob Stewart

http://arxiv.org/abs/2104.01086
Defending Against Image Corruptions Through Adversarial Augmentations. (92%)
Dan A. Calian; Florian Stimberg; Olivia Wiles; Sylvestre-Alvise Rebuffi; Andras Gyorgy; Timothy Mann; Sven Gowal

http://arxiv.org/abs/2104.01026
RABA: A Robust Avatar Backdoor Attack on Deep Neural Network. (83%)
Ying He; Zhili Shen; Chang Xia; Jingyu Hua; Wei Tong; Sheng Zhong

http://arxiv.org/abs/2104.01231
Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration under Noise Domain Shifts. (2%)
Athanasios Tsiligkaridis; Theodoros Tsiligkaridis

http://arxiv.org/abs/2104.00919
Fast-adapting and Privacy-preserving Federated Recommender System. (1%)
Qinyong Wang; Hongzhi Yin; Tong Chen; Junliang Yu; Alexander Zhou; Xiangliang Zhang

http://arxiv.org/abs/2104.00671
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness. (99%)
Zhuolin Yang; Linyi Li; Xiaojun Xu; Shiliang Zuo; Qian Chen; Benjamin Rubinstein; Pan Zhou; Ce Zhang; Bo Li

http://arxiv.org/abs/2104.00322
Domain Invariant Adversarial Learning. (98%)
Matan Levi; Idan Attias; Aryeh Kontorovich

http://arxiv.org/abs/2104.00312
Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction. (93%)
Luoqiu Li; Xiang Chen; Ningyu Zhang; Shumin Deng; Xin Xie; Chuanqi Tan; Mosha Chen; Fei Huang; Huajun Chen

http://arxiv.org/abs/2104.00447
Towards Evaluating and Training Verifiably Robust Neural Networks. (45%)
Zhaoyang Lyu; Minghao Guo; Tong Wu; Guodong Xu; Kehuan Zhang; Dahua Lin

http://arxiv.org/abs/2104.00460
Augmenting Zero Trust Architecture to Endpoints Using Blockchain: A Systematic Review. (3%)
Lampis Alevizos; Vinh Thong Ta; Max Hashem Eiza

http://arxiv.org/abs/2104.02570
Learning from Noisy Labels via Dynamic Loss Thresholding. (1%)
Hao Yang; Youzhi Jin; Ziyin Li; Deng-Bao Wang; Lei Miao; Xin Geng; Min-Ling Zhang

http://arxiv.org/abs/2104.00139
Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images. (99%)
Gerda Bortsova; Florian Dubost; Laurens Hogeweg; Ioannis Katramados; Bruijne Marleen de

http://arxiv.org/abs/2103.17122
Adversarial Attacks and Defenses for Speech Recognition Systems. (99%)
Piotr Żelasko; Sonal Joshi; Yiwen Shao; Jesus Villalba; Jan Trmal; Najim Dehak; Sanjeev Khudanpur

http://arxiv.org/abs/2103.17268
Fast Certified Robust Training with Short Warmup. (86%)
Zhouxing Shi; Yihan Wang; Huan Zhang; Jinfeng Yi; Cho-Jui Hsieh

http://arxiv.org/abs/2104.00219
Fast Jacobian-Vector Product for Deep Networks. (22%)
Randall Balestriero; Richard Baraniuk

http://arxiv.org/abs/2104.00236
Too Expensive to Attack: A Joint Defense Framework to Mitigate Distributed Attacks for the Internet of Things Grid. (2%)
Jianhua Li; Ximeng Liu; Jiong Jin; Shui Yu

http://arxiv.org/abs/2103.17028
Digital Forensics vs. Anti-Digital Forensics: Techniques, Limitations and Recommendations. (1%)
Jean-Paul A. Yaacoub; Hassan N. Noura; Ola Salman; Ali Chehab

http://arxiv.org/abs/2104.02610
On the Robustness of Vision Transformers to Adversarial Examples. (99%)
Kaleel Mahmood; Rigel Mahmood; Dijk Marten van

http://arxiv.org/abs/2103.16148
Class-Aware Robust Adversarial Training for Object Detection. (96%)
Pin-Chun Chen; Bo-Han Kung; Jun-Cheng Chen

http://arxiv.org/abs/2103.16074
PointBA: Towards Backdoor Attacks in 3D Point Cloud. (92%)
Xinke Li; Zhiru Chen; Yue Zhao; Zekun Tong; Yabang Zhao; Andrew Lim; Joey Tianyi Zhou

http://arxiv.org/abs/2103.16255
What Causes Optical Flow Networks to be Vulnerable to Physical Adversarial Attacks. (91%)
Simon Schrodi; Tonmoy Saikia; Thomas Brox

http://arxiv.org/abs/2103.16714
Statistical inference for individual fairness. (67%)
Subha Maity; Songkai Xue; Mikhail Yurochkin; Yuekai Sun

http://arxiv.org/abs/2103.16629
Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness. (47%)
Abed AlRahman Al Makdah; Vishaal Krishnan; Fabio Pasqualetti

http://arxiv.org/abs/2103.16241
Improving robustness against common corruptions with frequency biased models. (1%)
Tonmoy Saikia; Cordelia Schmid; Thomas Brox

http://arxiv.org/abs/2103.15385
Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization in Adversarial Training. (99%)
Mohammad Azizmalayeri; Mohammad Hossein Rohban

http://arxiv.org/abs/2103.15571
Enhancing the Transferability of Adversarial Attacks through Variance Tuning. (99%)
Xiaosen Wang; Kun He

http://arxiv.org/abs/2103.15670
On the Adversarial Robustness of Vision Transformers. (99%)
Rulin Shao; Zhouxing Shi; Jinfeng Yi; Pin-Yu Chen; Cho-Jui Hsieh

http://arxiv.org/abs/2103.15476
ZeroGrad : Mitigating and Explaining Catastrophic Overfitting in FGSM Adversarial Training. (95%)
Zeinab Golgooni; Mehrdad Saberi; Masih Eskandar; Mohammad Hossein Rohban

http://arxiv.org/abs/2103.16031
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. (93%)
Cheng Chen; Bhavya Kailkhura; Ryan Goldhahn; Yi Zhou

http://arxiv.org/abs/2103.15326
Fooling LiDAR Perception via Adversarial Trajectory Perturbation. (83%)
Yiming Li; Congcong Wen; Felix Juefei-Xu; Chen Feng

http://arxiv.org/abs/2103.15370
Robust Reinforcement Learning under model misspecification. (31%)
Lebin Yu; Jian Wang; Xudong Zhang

http://arxiv.org/abs/2103.15897
Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models. (16%)
Josh Kalin; David Noever; Matthew Ciolino; Dominick Hambrick; Gerry Dozier

http://arxiv.org/abs/2103.15918
MISA: Online Defense of Trojaned Models using Misattributions. (10%)
Panagiota Kiourti; Wenchao Li; Anirban Roy; Karan Sikka; Susmit Jha

http://arxiv.org/abs/2103.15543
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models. (9%)
Wenkai Yang; Lei Li; Zhiyuan Zhang; Xuancheng Ren; Xu Sun; Bin He

http://arxiv.org/abs/2103.15383
Selective Output Smoothing Regularization: Regularize Neural Networks by Softening Output Distributions. (1%)
Xuan Cheng; Tianshu Xie; Xiaomin Wang; Qifeng Weng; Minghui Liu; Jiali Deng; Ming Liu

http://arxiv.org/abs/2103.15089
Improved Autoregressive Modeling with Distribution Smoothing. (86%)
Chenlin Meng; Jiaming Song; Yang Song; Shengjia Zhao; Stefano Ermon

http://arxiv.org/abs/2103.14977
On the benefits of robust models in modulation recognition. (99%)
Javier Maroto; Gérôme Bovet; Pascal Frossard

http://arxiv.org/abs/2103.14938
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking. (99%)
Shuai Jia; Yibing Song; Chao Ma; Xiaokang Yang

http://arxiv.org/abs/2103.14835
LiBRe: A Practical Bayesian Approach to Adversarial Detection. (99%)
Zhijie Deng; Xiao Yang; Shizhen Xu; Hang Su; Jun Zhu

http://arxiv.org/abs/2103.14717
Cyclic Defense GAN Against Speech Adversarial Attacks. (99%)
Mohammad Esmaeilpour; Patrick Cardinal; Alessandro Lameiras Koerich

http://arxiv.org/abs/2103.14347
Combating Adversaries with Anti-Adversaries. (93%)
Motasem Alfarra; Juan C. Pérez; Ali Thabet; Adel Bibi; Philip H. S. Torr; Bernard Ghanem

http://arxiv.org/abs/2103.14641
On Generating Transferable Targeted Perturbations. (93%)
Muzammal Naseer; Salman Khan; Munawar Hayat; Fahad Shahbaz Khan; Fatih Porikli

http://arxiv.org/abs/2103.14332
Building Reliable Explanations of Unreliable Neural Networks: Locally Smoothing Perspective of Model Interpretation. (86%)
Dohun Lim; Hyeonseok Lee; Sungchan Kim

http://arxiv.org/abs/2103.14795
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness. (83%)
Yi Cai; Xuefei Ning; Huazhong Yang; Yu Wang

http://arxiv.org/abs/2103.14441
Visual Explanations from Spiking Neural Networks using Interspike Intervals. (62%)
Youngeun Kim; Priyadarshini Panda

http://arxiv.org/abs/2103.14577
Unsupervised Robust Domain Adaptation without Source Data. (13%)
Peshal Agarwal; Danda Pani Paudel; Jan-Nico Zaech; Gool Luc Van

http://arxiv.org/abs/2103.14222
Adversarial Attacks are Reversible with Natural Supervision. (99%)
Chengzhi Mao; Mia Chiquier; Hao Wang; Junfeng Yang; Carl Vondrick

http://arxiv.org/abs/2103.13989
Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond. (98%)
Brian Kim; Yalin E. Sagduyu; Tugba Erpek; Sennur Ulukus

http://arxiv.org/abs/2103.14211
MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes. (81%)
Zhikai Chen; Lingxi Xie; Shanmin Pang; Yong He; Bo Zhang

http://arxiv.org/abs/2103.14172
Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems. (70%)
Matthew Burruss; Shreyas Ramakrishna; Abhishek Dubey

http://arxiv.org/abs/2103.14021
Orthogonal Projection Loss. (45%)
Kanchana Ranasinghe; Muzammal Naseer; Munawar Hayat; Salman Khan; Fahad Shahbaz Khan

http://arxiv.org/abs/2103.13612
THAT: Two Head Adversarial Training for Improving Robustness at Scale. (26%)
Zuxuan Wu; Tom Goldstein; Larry S. Davis; Ser-Nam Lim

http://arxiv.org/abs/2103.14244
A Survey of Microarchitectural Side-channel Vulnerabilities, Attacks and Defenses in Cryptography. (11%)
Xiaoxuan Lou; Tianwei Zhang; Jun Jiang; Yinqian Zhang

http://arxiv.org/abs/2103.13628
HufuNet: Embedding the Left Piece as Watermark and Keeping the Right Piece for Ownership Verification in Deep Neural Networks. (10%)
Peizhuo Lv; Pan Li; Shengzhi Zhang; Kai Chen; Ruigang Liang; Yue Zhao; Yingjiu Li

http://arxiv.org/abs/2103.14108
The Geometry of Over-parameterized Regression and Adversarial Perturbations. (2%)
Jason W. Rocks; Pankaj Mehta

http://arxiv.org/abs/2103.14212
Synthesize-It-Classifier: Learning a Generative Classifier through RecurrentSelf-analysis. (1%)
Arghya Pal; Rapha Phan; KokSheik Wong

http://arxiv.org/abs/2103.13733
Spirit Distillation: Precise Real-time Prediction with Insufficient Data. (1%)
Zhiyuan Wu; Hong Qi; Yu Jiang; Chupeng Cui; Zongmin Yang; Xinhui Xue

http://arxiv.org/abs/2103.13598
Recent Advances in Large Margin Learning. (1%)
Yiwen Guo; Changshui Zhang

http://arxiv.org/abs/2103.13124
Towards Both Accurate and Robust Neural Networks without Extra Data. (99%)
Faqiang Liu; Rong Zhao

http://arxiv.org/abs/2103.13134
Vulnerability of Appearance-based Gaze Estimation. (97%)
Mingjie Xu; Haofei Wang; Yunfei Liu; Feng Lu

http://arxiv.org/abs/2103.13127
Black-box Detection of Backdoor Attacks with Limited Information and Data. (96%)
Yinpeng Dong; Xiao Yang; Zhijie Deng; Tianyu Pang; Zihao Xiao; Hang Su; Jun Zhu

http://arxiv.org/abs/2103.13567
Deepfake Forensics via An Adversarial Game. (10%)
Zhi Wang; Yiwen Guo; Wangmeng Zuo

http://arxiv.org/abs/2103.13886
Robust and Accurate Object Detection via Adversarial Learning. (98%)
Xiangning Chen; Cihang Xie; Mingxing Tan; Li Zhang; Cho-Jui Hsieh; Boqing Gong

http://arxiv.org/abs/2103.12531
CLIP: Cheap Lipschitz Training of Neural Networks. (96%)
Leon Bungert; René Raab; Tim Roith; Leo Schwinn; Daniel Tenbrinck

http://arxiv.org/abs/2103.12399
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers? (92%)
Antonio Emanuele Cinà; Sebastiano Vascon; Ambra Demontis; Battista Biggio; Fabio Roli; Marcello Pelillo

http://arxiv.org/abs/2103.12719
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations. (87%)
Chaitanya K. Ryali; David J. Schwab; Ari S. Morcos

http://arxiv.org/abs/2103.12469
RPATTACK: Refined Patch Attack on General Object Detectors. (76%)
Hao Huang; Yongtao Wang; Zhaoyu Chen; Zhi Tang; Wenqiang Zhang; Kai-Kuang Ma

http://arxiv.org/abs/2103.12535
NNrepair: Constraint-based Repair of Neural Network Classifiers. (50%)
Muhammad Usman; Divya Gopinath; Youcheng Sun; Yannic Noller; Corina Pasareanu

http://arxiv.org/abs/2103.12628
Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs. (31%)
Ramneet Kaur; Susmit Jha; Anirban Roy; Oleg Sokolsky; Insup Lee

http://arxiv.org/abs/2103.12913
Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces. (22%)
Jack Prescott; Xiao Zhang; David Evans

http://arxiv.org/abs/2103.12607
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning. (1%)
Oliver Lutz; Huili Chen; Hossein Fereidooni; Christoph Sendner; Alexandra Dmitrienko; Ahmad Reza Sadeghi; Farinaz Koushanfar

http://arxiv.org/abs/2103.11576
Grey-box Adversarial Attack And Defence For Sentiment Classification. (99%)
Ying Xu; Xu Zhong; Antonio Jimeno Yepes; Jey Han Lau

http://arxiv.org/abs/2103.13815
Fast Approximate Spectral Normalization for Robust Deep Neural Networks. (98%)
Zhixin Pan; Prabhat Mishra

http://arxiv.org/abs/2103.12256
Spatio-Temporal Sparsification for General Robust Graph Convolution Networks. (87%)
Mingming Lu; Ya Zhang

http://arxiv.org/abs/2103.13813
RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy. (75%)
Adnan Siraj Rakin; Li Yang; Jingtao Li; Fan Yao; Chaitali Chakrabarti; Yu Cao; Jae-sun Seo; Deliang Fan

http://arxiv.org/abs/2103.12171
Adversarial Feature Augmentation and Normalization for Visual Recognition. (13%)
Tianlong Chen; Yu Cheng; Zhe Gan; Jianfeng Wang; Lijuan Wang; Zhangyang Wang; Jingjing Liu

http://arxiv.org/abs/2103.11589
Adversarially Optimized Mixup for Robust Classification. (13%)
Jason Bunk; Srinjoy Chattopadhyay; B. S. Manjunath; Shivkumar Chandrasekaran

http://arxiv.org/abs/2103.11526
ExAD: An Ensemble Approach for Explanation-based Adversarial Detection. (99%)
Raj Vardhan; Ninghao Liu; Phakpoom Chinprutthiwong; Weijie Fu; Zhenyu Hu; Xia Ben Hu; Guofei Gu

http://arxiv.org/abs/2103.11441
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing. (75%)
Tao Gui; Xiao Wang; Qi Zhang; Qin Liu; Yicheng Zou; Xin Zhou; Rui Zheng; Chong Zhang; Qinzhuo Wu; Jiacheng Ye; Zexiong Pang; Yongxin Zhang; Zhengyan Li; Ruotian Ma; Zichu Fei; Ruijian Cai; Jun Zhao; Xinwu Hu; Zhiheng Yan; Yiding Tan; Yuan Hu; Qiyuan Bian; Zhihua Liu; Bolin Zhu; Shan Qin; Xiaoyu Xing; Jinlan Fu; Yue Zhang; Minlong Peng; Xiaoqing Zheng; Yaqian Zhou; Zhongyu Wei; Xipeng Qiu; Xuanjing Huang

http://arxiv.org/abs/2103.11372
Natural Perturbed Training for General Robustness of Neural Network Classifiers. (38%)
Sadaf Gulshad; Arnold Smeulders

http://arxiv.org/abs/2103.11362
Self adversarial attack as an augmentation method for immunohistochemical stainings. (33%)
Jelica Vasiljević; Friedrich Feuerhake; Cédric Wemmert; Thomas Lampert

http://arxiv.org/abs/2103.11257
Robust Models Are More Interpretable Because Attributions Look Normal. (15%)
Zifan Wang; Matt Fredrikson; Anupam Datta

http://arxiv.org/abs/2103.10787
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack. (99%)
Ashkan Esmaeili; Marzieh Edraki; Nazanin Rahnavard; Mubarak Shah; Ajmal Mian

http://arxiv.org/abs/2103.10651
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems. (93%)
Yuxuan Chen; Jiangshan Zhang; Xuejing Yuan; Shengzhi Zhang; Kai Chen; Xiaofeng Wang; Shanqing Guo

http://arxiv.org/abs/2103.11002
Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptions. (86%)
Michael Goebel; Jason Bunk; Srinjoy Chattopadhyay; Lakshmanan Nataraj; Shivkumar Chandrasekaran; B. S. Manjunath

http://arxiv.org/abs/2103.10689
Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond. (2%)
Xuhong Li; Haoyi Xiong; Xingjian Li; Xuanyu Wu; Xiao Zhang; Ji Liu; Jiang Bian; Dejing Dou

http://arxiv.org/abs/2103.11882
Generating Adversarial Computer Programs using Optimized Obfuscations. (99%)
Shashank Srikant; Sijia Liu; Tamara Mitrovska; Shiyu Chang; Quanfu Fan; Gaoyuan Zhang; Una-May O'Reilly

http://arxiv.org/abs/2103.10609
Boosting Adversarial Transferability through Enhanced Momentum. (99%)
Xiaosen Wang; Jiadong Lin; Han Hu; Jingdong Wang; Kun He

http://arxiv.org/abs/2103.10229
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles. (98%)
Gabriel D. Cantareira; Rodrigo F. Mello; Fernando V. Paulovich

http://arxiv.org/abs/2103.10043
Enhancing Transformer for Video Understanding Using Gated Multi-Level Attention and Temporal Adversarial Training. (76%)
Saurabh Sahu; Palash Goyal

http://arxiv.org/abs/2103.10013
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable! (69%)
Xuanli He; Lingjuan Lyu; Qiongkai Xu; Lichao Sun

http://arxiv.org/abs/2103.10274
TOP: Backdoor Detection in Neural Networks via Transferability of Perturbation. (61%)
Todd Huster; Emmanuel Ekwedike

http://arxiv.org/abs/2103.10603
Noise Modulation: Let Your Model Interpret Itself. (54%)
Haoyang Li; Xinggang Wang

http://arxiv.org/abs/2103.10094
KoDF: A Large-scale Korean DeepFake Detection Dataset. (16%)
Patrick Kwon; Jaeseong You; Gyuhyeon Nam; Sungwoo Park; Gyeongsu Chae

http://arxiv.org/abs/2103.10480
Reading Isn't Believing: Adversarial Attacks On Multi-Modal Neurons. (9%)
David A. Noever; Samantha E. Miller Noever

http://arxiv.org/abs/2103.09916
Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap? (99%)
Nathan Inkawhich; Kevin J Liang; Jingyang Zhang; Huanrui Yang; Hai Li; Yiran Chen

http://arxiv.org/abs/2103.09448
Adversarial Attacks on Camera-LiDAR Models for 3D Car Detection. (98%)
Mazen Abdelfattah; Kaiwen Yuan; Z. Jane Wang; Rabab Ward

http://arxiv.org/abs/2103.10834
Improved, Deterministic Smoothing for L1 Certified Robustness. (82%)
Alexander Levine; Soheil Feizi

http://arxiv.org/abs/2103.09947
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition. (41%)
Yaodong Yu; Zitong Yang; Edgar Dobriban; Jacob Steinhardt; Yi Ma

http://arxiv.org/abs/2103.09593
Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots. (38%)
Samson Tan; Shafiq Joty

http://arxiv.org/abs/2103.09713
Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss. (13%)
Boxiang Wendy Dong; Wendy Hui; Wang; Aparna S. Varde; Dawei Li; Bharath K. Samanthula; Weifeng Sun; Liang Zhao

http://arxiv.org/abs/2103.09151
Adversarial Driving: Attacking End-to-End Autonomous Driving. (93%)
Han Wu; Syed Yunas; Sareh Rowlands; Wenjie Ruan; Johan Wahlstrom

http://arxiv.org/abs/2103.08860
Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches. (92%)
Nan Ji; YanFei Feng; Haidong Xie; Xueshuang Xiang; Naijin Liu

http://arxiv.org/abs/2103.08896
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation. (75%)
Jungbeom Lee; Eunji Kim; Sungroh Yoon

http://arxiv.org/abs/2103.09265
Bio-inspired Robustness: A Review. (70%)
Harshitha Machiraju; Oh-Hyeon Choung; Pascal Frossard; Michael. H Herzog

http://arxiv.org/abs/2103.08265
Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy. (83%)
Bronya Roni Chernyak; Bhiksha Raj; Tamir Hazan; Joseph Keshet

http://arxiv.org/abs/2103.08187
Adversarial Training is Not Ready for Robot Learning. (67%)
Mathias Lechner; Ramin Hasani; Radu Grosu; Daniela Rus; Thomas A. Henzinger

http://arxiv.org/abs/2103.08668
HDTest: Differential Fuzz Testing of Brain-Inspired Hyperdimensional Computing. (64%)
Dongning Ma; Jianmin Guo; Yu Jiang; Xun Jiao

http://arxiv.org/abs/2103.07470
Understanding invariance via feedforward inversion of discriminatively trained classifiers. (10%)
Piotr Teterwak; Chiyuan Zhang; Dilip Krishnan; Michael C. Mozer

http://arxiv.org/abs/2103.08561
Meta-Solver for Neural Ordinary Differential Equations. (2%)
Julia Gusak; Alexandr Katrutsa; Talgat Daulbaev; Andrzej Cichocki; Ivan Oseledets

http://arxiv.org/abs/2103.08095
Towards Robust Speech-to-Text Adversarial Attack. (99%)
Mohammad Esmaeilpour; Patrick Cardinal; Alessandro Lameiras Koerich

http://arxiv.org/abs/2103.08031
BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks. (98%)
Manoj Rohit Vemparala; Alexander Frickenstein; Nael Fasfous; Lukas Frickenstein; Qi Zhao; Sabine Kuhn; Daniel Ehrhardt; Yuankai Wu; Christian Unger; Naveen Shankar Nagaraja; Walter Stechele

http://arxiv.org/abs/2103.08086
Multi-Discriminator Sobolev Defense-GAN Against Adversarial Attacks for End-to-End Speech Systems. (82%)
Mohammad Esmaeilpour; Patrick Cardinal; Alessandro Lameiras Koerich

http://arxiv.org/abs/2103.07853
Membership Inference Attacks on Machine Learning: A Survey. (68%)
Hongsheng Hu; Zoran Salcic; Lichao Sun; Gillian Dobbie; Philip S. Yu; Xuyun Zhang

http://arxiv.org/abs/2103.07633
Attack as Defense: Characterizing Adversarial Examples using Robustness. (99%)
Zhe Zhao; Guangke Chen; Jingyi Wang; Yiwei Yang; Fu Song; Jun Sun

http://arxiv.org/abs/2103.07640
Generating Unrestricted Adversarial Examples via Three Parameters. (99%)
Hanieh Naderi; Leili Goli; Shohreh Kasaei

http://arxiv.org/abs/2103.07704
Simeon -- Secure Federated Machine Learning Through Iterative Filtering. (12%)
Nicholas Malecki; Hye-young Paik; Aleksandar Ignjatovic; Alan Blair; Elisa Bertino

http://arxiv.org/abs/2103.07595
Learning Defense Transformers for Counterattacking Adversarial Examples. (99%)
Jincheng Li; Jiezhang Cao; Yifan Zhang; Jian Chen; Mingkui Tan

http://arxiv.org/abs/2103.07598
Internal Wasserstein Distance for Adversarial Attack and Defense. (99%)
Mingkui Tan; Shuhai Zhang; Jiezhang Cao; Jincheng Li; Yanwu Xu

http://arxiv.org/abs/2103.07364
A Unified Game-Theoretic Interpretation of Adversarial Robustness. (98%)
Jie Ren; Die Zhang; Yisen Wang; Lu Chen; Zhanpeng Zhou; Yiting Chen; Xu Cheng; Xin Wang; Meng Zhou; Jie Shi; Quanshi Zhang

http://arxiv.org/abs/2103.07268
Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case. (82%)
Evren Catak; Ferhat Ozgur Catak; Arild Moldsvor

http://arxiv.org/abs/2103.07583
Network Environment Design for Autonomous Cyberdefense. (1%)
Andres Molina-Markham; Cory Miniter; Becky Powell; Ahmad Ridley

http://arxiv.org/abs/2103.06936
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling. (99%)
Md Shohidul Islam; Ihsen Alouani; Khaled N. Khasawneh

http://arxiv.org/abs/2103.06624
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification. (99%)
Shiqi Wang; Huan Zhang; Kaidi Xu; Xue Lin; Suman Jana; Cho-Jui Hsieh; J. Zico Kolter

http://arxiv.org/abs/2103.06504
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink. (99%)
Ranjie Duan; Xiaofeng Mao; A. K. Qin; Yun Yang; Yuefeng Chen; Shaokai Ye; Yuan He

http://arxiv.org/abs/2103.06487
DAFAR: Detecting Adversaries by Feedback-Autoencoder Reconstruction. (99%)
Haowen Liu; Ping Yi; Hsiao-Ying Lin; Jie Shi

http://arxiv.org/abs/2103.08306
ReinforceBug: A Framework to Generate Adversarial Textual Examples. (97%)
Bushra Sabir; M. Ali Babar; Raj Gaire

http://arxiv.org/abs/2103.06473
Multi-Task Federated Reinforcement Learning with Adversaries. (15%)
Aqeel Anwar; Arijit Raychowdhury

http://arxiv.org/abs/2103.06797
BODAME: Bilevel Optimization for Defense Against Model Extraction. (8%)
Yuto Mori; Atsushi Nitanda; Akiko Takeda

http://arxiv.org/abs/2103.08307
Improving Adversarial Robustness via Channel-wise Activation Suppressing. (99%)
Yang Bai; Yuyuan Zeng; Yong Jiang; Shu-Tao Xia; Xingjun Ma; Yisen Wang

http://arxiv.org/abs/2103.06297
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack. (92%)
Yam Sharon; David Berend; Yang Liu; Asaf Shabtai; Yuval Elovici

http://arxiv.org/abs/2103.05905
VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples. (67%)
Tian Pan; Yibing Song; Tianyu Yang; Wenhao Jiang; Wei Liu

http://arxiv.org/abs/2103.13329
Fine-tuning of Pre-trained End-to-end Speech Recognition with Generative Adversarial Networks. (1%)
Md Akmal Haidar; Mehdi Rezagholizadeh

http://arxiv.org/abs/2103.05232
Stabilized Medical Image Attacks. (99%)
Gege Qi; Lijun Gong; Yibing Song; Kai Ma; Yefeng Zheng

http://arxiv.org/abs/2103.05354
Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection. (99%)
Ahmed Aldahdooh; Wassim Hamidouche; Olivier Déforges

http://arxiv.org/abs/2103.05248
Practical Relative Order Attack in Deep Ranking. (99%)
Mo Zhou; Le Wang; Zhenxing Niu; Qilin Zhang; Yinghui Xu; Nanning Zheng; Gang Hua

http://arxiv.org/abs/2103.05266
BASAR:Black-box Attack on Skeletal Action Recognition. (99%)
Yunfeng Diao; Tianjia Shao; Yong-Liang Yang; Kun Zhou; He Wang

http://arxiv.org/abs/2103.05347
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack. (98%)
He Wang; Feixiang He; Zhexi Peng; Tianjia Shao; Yong-Liang Yang; Kun Zhou; David Hogg

http://arxiv.org/abs/2103.05292
Deep Learning for Android Malware Defenses: a Systematic Literature Review. (11%)
Yue Liu; Chakkrit Tantithamthavorn; Li Li; Yepang Liu

http://arxiv.org/abs/2103.05590
Robust Black-box Watermarking for Deep NeuralNetwork using Inverse Document Frequency. (10%)
Mohammad Mehdi Yadollahi; Farzaneh Shoeleh; Sajjad Dadkhah; Ali A. Ghorbani

http://arxiv.org/abs/2103.05833
Towards Strengthening Deep Learning-based Side Channel Attacks with Mixup. (2%)
Zhimin Luo; Mengce Zheng; Ping Wang; Minhui Jin; Jiajia Zhang; Honggang Hu; Nenghai Yu

http://arxiv.org/abs/2103.04794
Packet-Level Adversarial Network Traffic Crafting using Sequence Generative Adversarial Networks. (99%)
Qiumei Cheng; Shiying Zhou; Yi Shen; Dezhang Kong; Chunming Wu

http://arxiv.org/abs/2103.04565
Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations. (99%)
Haimin Zhang; Min Xu

http://arxiv.org/abs/2103.05137
Contemplating real-world object classification. (81%)
Ali Borji

http://arxiv.org/abs/2103.04623
Consistency Regularization for Adversarial Robustness. (50%)
Jihoon Tack; Sihyun Yu; Jongheon Jeong; Minseon Kim; Sung Ju Hwang; Jinwoo Shin

http://arxiv.org/abs/2103.04952
Prime+Probe 1, JavaScript 0: Overcoming Browser-based Side-Channel Defenses. (2%)
Anatoly Shusterman; Ayush Agarwal; Sioli O'Connell; Daniel Genkin; Yossi Oren; Yuval Yarom

http://arxiv.org/abs/2103.04814
Deeply Unsupervised Patch Re-Identification for Pre-training Object Detectors. (1%)
Jian Ding; Enze Xie; Hang Xu; Chenhan Jiang; Zhenguo Li; Ping Luo; Gui-Song Xia

http://arxiv.org/abs/2103.04980
Deep Model Intellectual Property Protection via Deep Watermarking. (1%)
Jie Zhang; Dongdong Chen; Jing Liao; Weiming Zhang; Huamin Feng; Gang Hua; Nenghai Yu

http://arxiv.org/abs/2103.05469
Universal Adversarial Perturbations and Image Spam Classifiers. (99%)
Andy Phung; Mark Stamp

http://arxiv.org/abs/2103.04302
Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain. (99%)
Jinyu Tian; Jiantao Zhou; Yuanman Li; Jia Duan

http://arxiv.org/abs/2103.04513
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN. (99%)
Desheng School of Electrical Engineering, Southwest Jiaotong University, Chengdu, P. R. China Wang; Weidong School of Electrical Engineering, Southwest Jiaotong University, Chengdu, P. R. China Jin; Yunpu School of Electrical Engineering, Southwest Jiaotong University, Chengdu, P. R. China Wu; Aamir School of Electrical Engineering, Southwest Jiaotong University, Chengdu, P. R. China Khan

http://arxiv.org/abs/2103.04436
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy. (76%)
Chen Chen; Kezhi Kong; Peihong Yu; Juan Luque; Tom Goldstein; Furong Huang

http://arxiv.org/abs/2103.04264
T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification. (98%)
Ahmadreza Azizi; Ibrahim Asadullah Tahmid; Asim Waheed; Neal Mangaokar; Jiameng Pu; Mobin Javed; Chandan K. Reddy; Bimal Viswanath

http://arxiv.org/abs/2103.04038
Hidden Backdoor Attack against Semantic Segmentation Models. (93%)
Yiming Li; Yanjie Li; Yalei Lv; Yong Jiang; Shu-Tao Xia

http://arxiv.org/abs/2103.03530
Cyber Threat Intelligence Model: An Evaluation of Taxonomies, Sharing Standards, and Ontologies within Cyber Threat Intelligence. (13%)
Vasileios Mavroeidis; Siri Bromander

http://arxiv.org/abs/2103.03701
Don't Forget to Sign the Gradients! (10%)
Omid Aramoon; Pin-Yu Chen; Gang Qu

http://arxiv.org/abs/2103.03831
Tor circuit fingerprinting defenses using adaptive padding. (1%)
George Kadianakis; Theodoros Polyzos; Mike Perry; Kostas Chatzikokolakis

http://arxiv.org/abs/2103.03325
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples. (99%)
Washington Garcia; Pin-Yu Chen; Somesh Jha; Scott Clouse; Kevin R. B. Butler

http://arxiv.org/abs/2103.03344
WaveGuard: Understanding and Mitigating Audio Adversarial Examples. (99%)
Shehzeen Hussain; Paarth Neekhara; Shlomo Dubnov; Julian McAuley; Farinaz Koushanfar

http://arxiv.org/abs/2103.03438
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack. (99%)
Mengting Xu; Tao Zhang; Zhongnian Li; Mingxia Liu; Daoqiang Zhang

http://arxiv.org/abs/2103.02927
QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval. (99%)
Xiaodan Li; Jinfeng Li; Yuefeng Chen; Shaokai Ye; Yuan He; Shuhui Wang; Hang Su; Hui Xue

http://arxiv.org/abs/2103.03000
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain. (99%)
Paula Harder; Franz-Josef Pfreundt; Margret Keuper; Janis Keuper

http://arxiv.org/abs/2103.03076
Gradient-Guided Dynamic Efficient Adversarial Training. (96%)
Fu Wang; Yanghao Zhang; Yanbin Zheng; Wenjie Ruan

http://arxiv.org/abs/2103.03046
PointGuard: Provably Robust 3D Point Cloud Classification. (92%)
Hongbin Liu; Jinyuan Jia; Neil Zhenqiang Gong

http://arxiv.org/abs/2103.03078
Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods. (12%)
William Paul; Yinzhi Cao; Miaomiao Zhang; Phil Burlina

http://arxiv.org/abs/2103.03472
A Novel Framework for Threat Analysis of Machine Learning-based Smart Healthcare Systems. (1%)
Nur Imtiazul Haque; Mohammad Ashiqur Rahman; Md Hasan Shahriar; Alvi Ataur Khalil; Selcuk Uluagac

http://arxiv.org/abs/2103.02895
On the privacy-utility trade-off in differentially private hierarchical text classification. (1%)
Dominik Wunderlich; Daniel Bernau; Francesco Aldà; Javier Parra-Arnau; Thorsten Strufe

http://arxiv.org/abs/2103.02781
Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks. (99%)
Zhiqun Zhao; Hengyou Wang; Hao Sun; Zhihai He

http://arxiv.org/abs/2103.02718
A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models. (89%)
Josh Kalin; David Noever; Matthew Ciolino

http://arxiv.org/abs/2103.02695
Shift Invariance Can Reduce Adversarial Robustness. (87%)
Songwei Ge; Vasu Singla; Ronen Basri; David Jacobs

http://arxiv.org/abs/2103.02654
A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization Ability Against Adversarial Attacks. (81%)
Yudi Dong; Huaxia Wang; Yu-Dong Yao

http://arxiv.org/abs/2103.02325
On the effectiveness of adversarial training against common corruptions. (67%)
Klim Kireev; Maksym Andriushchenko; Nicolas Flammarion

http://arxiv.org/abs/2103.02200
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations. (64%)
Yu-Lin Tsai; Chia-Yi Hsu; Chia-Mu Yu; Pin-Yu Chen

http://arxiv.org/abs/2103.01914
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training. (99%)
Dorjan Hitaj; Giulio Pagnotta; Iacopo Masi; Luigi V. Mancini

http://arxiv.org/abs/2103.01498
A Survey On Universal Adversarial Attack. (99%)
Chaoning Zhang; Philipp Benz; Chenguo Lin; Adil Karjauv; Jing Wu; In So Kweon

http://arxiv.org/abs/2103.02014
Online Adversarial Attacks. (99%)
Andjela Mladenovic; Avishek Joey Bose; Hugo Berard; William L. Hamilton; Simon Lacoste-Julien; Pascal Vincent; Gauthier Gidel

http://arxiv.org/abs/2103.01895
Adversarial Examples for Unsupervised Machine Learning Models. (98%)
Chia-Yi Hsu; Pin-Yu Chen; Songtao Lu; Sijia Liu; Chia-Mu Yu

http://arxiv.org/abs/2103.01629
DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers. (97%)
Colin Paterson; Haoze Wu; John Grese; Radu Calinescu; Corina S. Pasareanu; Clark Barrett

http://arxiv.org/abs/2103.01527
ActiveGuard: An Active DNN IP Protection Technique via Adversarial Examples. (97%)
Mingfu Xue; Shichang Sun; Can He; Yushu Zhang; Jian Wang; Weiqiang Liu

http://arxiv.org/abs/2103.01946
Fixing Data Augmentation to Improve Adversarial Robustness. (69%)
Sylvestre-Alvise Rebuffi; Sven Gowal; Dan A. Calian; Florian Stimberg; Olivia Wiles; Timothy Mann

http://arxiv.org/abs/2103.01607
A Brief Survey on Deep Learning Based Data Hiding. (54%)
Chaoning Zhang; Chenguo Lin; Philipp Benz; Kejiang Chen; Weiming Zhang; In So Kweon

http://arxiv.org/abs/2103.02152
Group-wise Inhibition based Feature Regularization for Robust Classification. (16%)
Haozhe Liu; Haoqian Wu; Weicheng Xie; Feng Liu; Linlin Shen

http://arxiv.org/abs/2103.02079
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations. (1%)
Eitan Borgnia; Jonas Geiping; Valeriia Cherepanova; Liam Fowl; Arjun Gupta; Amin Ghiasi; Furong Huang; Micah Goldblum; Tom Goldstein

http://arxiv.org/abs/2103.01050
Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World. (99%)
Jiakai Wang; Aishan Liu; Zixin Yin; Shunchang Liu; Shiyu Tang; Xianglong Liu

http://arxiv.org/abs/2103.01359
Brain Programming is Immune to Adversarial Attacks: Towards Accurate and Robust Image Classification using Symbolic Learning. (99%)
Gerardo Ibarra-Vazquez; Gustavo Olague; Mariana Chan-Ley; Cesar Puente; Carlos Soubervielle-Montalvo

http://arxiv.org/abs/2103.01400
Smoothness Analysis of Adversarial Training. (98%)
Sekitoshi Kanai; Masanori Yamada; Hiroshi Takahashi; Yuki Yamanaka; Yasutoshi Ida

http://arxiv.org/abs/2103.00778
Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis. (96%)
Mahsa Paknezhad; Cuong Phuc Ngo; Amadeus Aristo Winarto; Alistair Cheong; Beh Chuen Yang; Wu Jiayang; Lee Hwee Kuan

http://arxiv.org/abs/2103.01208
Mind the box: $l_1$-APGD for sparse adversarial attacks on image classifiers. (93%)
Francesco Croce; Matthias Hein

http://arxiv.org/abs/2103.01319
Adversarial training in communication constrained federated learning. (87%)
Devansh Shah; Parijat Dube; Supriyo Chakraborty; Ashish Verma

http://arxiv.org/abs/2103.01096
Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms. (82%)
Miguel Á. Carreira-Perpiñán; Suryabhan Singh Hada

http://arxiv.org/abs/2103.00847
Am I a Real or Fake Celebrity? Measuring Commercial Face Recognition Web APIs under Deepfake Impersonation Attack. (70%)
Shahroz Tariq; Sowon Jeon; Simon S. Woo

http://arxiv.org/abs/2103.01276
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness. (64%)
Jacob Abernethy; Pranjal Awasthi; Satyen Kale

http://arxiv.org/abs/2103.03102
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation. (62%)
Wei Dai; Daniel Berleant

http://arxiv.org/abs/2103.00663
Model-Agnostic Defense for Lane Detection against Adversarial Attack. (98%)
Henry Xu; An Ju; David Wagner

http://arxiv.org/abs/2103.00671
Robust learning under clean-label attack. (22%)
Avrim Blum; Steve Hanneke; Jian Qian; Han Shao

http://arxiv.org/abs/2103.00250
Effective Universal Unrestricted Adversarial Attacks using a MOE Approach. (98%)
A. E. Baia; Bari G. Di; V. Poggioni

http://arxiv.org/abs/2103.00363
Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search. (93%)
Guoyang Xie; Jinbao Wang; Guo Yu; Feng Zheng; Yaochu Jin

http://arxiv.org/abs/2103.00345
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering. (73%)
Ruochen Jiao; Hengyi Liang; Takami Sato; Junjie Shen; Qi Alfred Chen; Qi Zhu

http://arxiv.org/abs/2103.00381
Adversarial Information Bottleneck. (33%)
Pemhlong Zhai; Shihua Zhang

http://arxiv.org/abs/2103.00229
Neuron Coverage-Guided Domain Generalization. (2%)
Chris Xing Tian; Haoliang Li; Xiaofei Xie; Yang Liu; Shiqi Wang

http://arxiv.org/abs/2102.13624
What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors.
Jonas Geiping; Liam Fowl; Gowthami Somepalli; Micah Goldblum; Michael Moeller; Tom Goldstein

http://arxiv.org/abs/2103.00124
NEUROSPF: A tool for the Symbolic Analysis of Neural Networks. (68%)
Muhammad Usman; Yannic Noller; Corina Pasareanu; Youcheng Sun; Divya Gopinath

http://arxiv.org/abs/2102.13066
On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations.
Chi Zhang; Jinghan Jia; Burhaneddin Yaman; Steen Moeller; Sijia Liu; Mingyi Hong; Mehmet Akçakaya

http://arxiv.org/abs/2102.12781
Do Input Gradients Highlight Discriminative Features?
Harshay Shah; Prateek Jain; Praneeth Netrapalli

http://arxiv.org/abs/2102.13184
Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks.
Huichen Li; Linyi Li; Xiaojun Xu; Xiaolu Zhang; Shuang Yang; Bo Li

http://arxiv.org/abs/2102.13170
Understanding Robustness in Teacher-Student Setting: A New Perspective.
Zhuolin Yang; Zhaoxi Chen; Tiffany Cai; Xinyun Chen; Bo Li; Yuandong Tian

http://arxiv.org/abs/2102.12827
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints.
Maura Pintor; Fabio Roli; Wieland Brendel; Battista Biggio

http://arxiv.org/abs/2102.13256
Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems.
Ranwa Al Mallah; Godwin Badu-Marfo; Bilal Farooq

http://arxiv.org/abs/2102.12967
A statistical framework for efficient out of distribution detection in deep neural networks. (1%)
Matan Haroush; Tzviel Frostig; Ruth Heller; Daniel Soudry

http://arxiv.org/abs/2102.12680
Confidence Calibration with Bounded Error Using Transformations.
Sooyong Jang; Radoslav Ivanov; Insup lee; James Weimer

http://arxiv.org/abs/2102.12567
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks.
Apoorva Sharma; Navid Azizan; Marco Pavone

http://arxiv.org/abs/2102.12555
Robust SleepNets.
Yigit Alparslan; Edward Kim

http://arxiv.org/abs/2102.12192
Multiplicative Reweighting for Robust Neural Network Optimization.
Noga Bar; Tomer Koren; Raja Giryes

http://arxiv.org/abs/2102.12196
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis.
Leo Schwinn; An Nguyen; René Raab; Leon Bungert; Daniel Tenbrinck; Dario Zanca; Martin Burger; Bjoern Eskofier

http://arxiv.org/abs/2102.12284
Graphfool: Targeted Label Adversarial Attack on Graph Embedding.
Jinyin Chen; Xiang Lin; Dunjie Zhang; Wenrong Jiang; Guohan Huang; Hui Xiong; Yun Xiang

http://arxiv.org/abs/2102.11917
The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations.
Jeremiah Duncan; Fabian Fallas; Chris Gropp; Emily Herron; Maria Mahbub; Paula Olaya; Eduardo Ponce; Tabitha K. Samuel; Daniel Schultz; Sudarshan Srinivasan; Maofeng Tang; Viktor Zenkov; Quan Zhou; Edmon Begoli

http://arxiv.org/abs/2102.11731
Rethinking Natural Adversarial Examples for Classification Models.
Xiao Li; Jianmin Li; Ting Dai; Jie Shi; Jun Zhu; Xiaolin Hu

http://arxiv.org/abs/2102.11860
Automated Discovery of Adaptive Attacks on Adversarial Defenses.
Chengyuan Yao; Pavol Bielik; Petar Tsankov; Martin Vechev

http://arxiv.org/abs/2102.12002
Adversarial Robustness with Non-uniform Perturbations.
Ecenaz Erdemir; Jeffrey Bickford; Luca Melis; Sergul Aydore

http://arxiv.org/abs/2102.11935
Non-Singular Adversarial Robustness of Neural Networks.
Yu-Lin Tsai; Chia-Yi Hsu; Chia-Mu Yu; Pin-Yu Chen

http://arxiv.org/abs/2102.11584
Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representation.
Jinfeng Li; Tianyu Du; Xiangyu Liu; Rong Zhang; Hui Xue; Shouling Ji

http://arxiv.org/abs/2102.11586
Adversarial Examples Detection beyond Image Space.
Kejiang Chen; Yuefeng Chen; Hang Zhou; Chuan Qin; Xiaofeng Mao; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/2102.11502
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models.
Liuqiao Chen; Hu Wang; Benjamin Zi Hao Zhao; Minhui Xue; Haifeng Qian

http://arxiv.org/abs/2102.10875
On the robustness of randomized classifiers to adversarial examples.
Rafael Pinot; Laurent Meunier; Florian Yger; Cédric Gouy-Pailler; Yann Chevaleyre; Jamal Atif

http://arxiv.org/abs/2102.11010
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks.
Ginevra Carbone; Guido Sanguinetti; Luca Bortolussi

http://arxiv.org/abs/2102.11455
Man-in-The-Middle Attacks and Defense in a Power System Cyber-Physical Testbed.
Patrick Wlazlo; Abhijeet Sahu; Zeyu Mao; Hao Huang; Ana Goulart; Katherine Davis; Saman Zonouz

http://arxiv.org/abs/2102.11382
Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity.
Xinyu Gong; Wuyang Chen; Tianlong Chen; Zhangyang Wang

http://arxiv.org/abs/2102.10534
The Effects of Image Distribution and Task on Adversarial Robustness.
Owen Kunhardt; Arturo Deza; Tomaso Poggio

http://arxiv.org/abs/2102.10707
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization.
HanQin Cai; Yuchen Lou; Daniel McKenzie; Wotao Yin

http://arxiv.org/abs/2102.12894
Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes. (1%)
Sara Sangalli; Ertunc Erdil; Andreas Hoetker; Olivio Donati; Ender Konukoglu

http://arxiv.org/abs/2102.10454
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning.
Ren Wang; Kaidi Xu; Sijia Liu; Pin-Yu Chen; Tsui-Wei Weng; Chuang Gan; Meng Wang

http://arxiv.org/abs/2102.10343
Measuring $\ell_\infty$ Attacks by the $\ell_2$ Norm.
Sizhe Chen; Qinghua Tao; Zhixing Ye; Xiaolin Huang

http://arxiv.org/abs/2102.11069
A PAC-Bayes Analysis of Adversarial Robustness.
Guillaume IRIT Vidot; Paul LHC Viallard; Amaury LHC Habrard; Emilie LHC Morvant

http://arxiv.org/abs/2102.10055
Effective and Efficient Vote Attack on Capsule Networks.
Jindong Gu; Baoyuan Wu; Volker Tresp

http://arxiv.org/abs/2102.09230
Random Projections for Improved Adversarial Robustness.
Ginevra Carbone; Guido Sanguinetti; Luca Bortolussi

http://arxiv.org/abs/2102.09695
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks.
Matthew Ciolino; Josh Kalin; David Noever

http://arxiv.org/abs/2102.09479
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications.
Leonard Berrada; Sumanth Dathathri; Krishnamurthy Dvijotham; Robert Stanforth; Rudy Bunel; Jonathan Uesato; Sven Gowal; M. Pawan Kumar

http://arxiv.org/abs/2102.09701
Center Smoothing: Provable Robustness for Functions with Metric-Space Outputs.
Aounon Kumar; Tom Goldstein

http://arxiv.org/abs/2102.09012
Improving Hierarchical Adversarial Robustness of Deep Neural Networks.
Avery Ma; Aladin Virmaux; Kevin Scaman; Juwei Lu

http://arxiv.org/abs/2102.09086
Consistent Non-Parametric Methods for Maximizing Robustness.
Robi Bhattacharjee; Kamalika Chaudhuri

http://arxiv.org/abs/2102.08868
Bridging the Gap Between Adversarial Robustness and Optimization Bias.
Fartash Faghri; Sven Gowal; Cristina Vasconcelos; David J. Fleet; Fabian Pedregosa; Nicolas Le Roux

http://arxiv.org/abs/2102.09057
Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids.
Jiangnan Li; Yingyuan Yang; Jinyuan Stella Sun; Kevin Tomsovic; Hairong Qi

http://arxiv.org/abs/2102.08452
Globally-Robust Neural Networks.
Klas Leino; Zifan Wang; Matt Fredrikson

http://arxiv.org/abs/2102.08093
A Law of Robustness for Weight-bounded Neural Networks.
Hisham Husain; Borja Balle

http://arxiv.org/abs/2102.08079
Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation.
Adil Kaan Akan; Emre Akbas; Fatos T. Yarman Vural

http://arxiv.org/abs/2102.07437
Data Profiling for Adversarial Training: On the Ruin of Problematic Data.
Chengyu Dong; Liyuan Liu; Jingbo Shang

http://arxiv.org/abs/2102.07818
Certified Robustness to Programmable Transformations in LSTMs.
Yuhao Zhang; Aws Albarghouthi; Loris D'Antoni

http://arxiv.org/abs/2102.07360
Generating Structured Adversarial Attacks Using Frank-Wolfe Method.
Ehsan Kazemi; Thomas Kerdreux; Liquang Wang

http://arxiv.org/abs/2102.07788
Universal Adversarial Examples and Perturbations for Quantum Classifiers.
Weiyuan Gong; Dong-Ling Deng

http://arxiv.org/abs/2102.07861
Low Curvature Activations Reduce Overfitting in Adversarial Training.
Vasu Singla; Sahil Singla; David Jacobs; Soheil Feizi

http://arxiv.org/abs/2102.07389
And/or trade-off in artificial neurons: impact on adversarial robustness.
Alessandro Fontana

http://arxiv.org/abs/2102.07559
Certifiably Robust Variational Autoencoders.
Ben Barrett; Alexander Camuto; Matthew Willetts; Tom Rainforth

http://arxiv.org/abs/2102.07327
Guided Interpolation for Adversarial Training.
Chen Chen; Jingfeng Zhang; Xilie Xu; Tianlei Hu; Gang Niu; Gang Chen; Masashi Sugiyama

http://arxiv.org/abs/2102.07244
Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS.
Felix Olowononi; Danda B. Rawat; Chunmei Liu

http://arxiv.org/abs/2102.07265
Exploring Adversarial Robustness of Deep Metric Learning.
Thomas Kobber Panum; Zi Wang; Pengyu Kan; Earlence Fernandes; Somesh Jha

http://arxiv.org/abs/2102.07164
Adversarial Attack on Network Embeddings via Supervised Network Poisoning.
Viresh Gupta; Tanmoy Chakraborty

http://arxiv.org/abs/2102.07140
Perceptually Constrained Adversarial Attacks.
Muhammad Zaid Hameed; Andras Gyorgy

http://arxiv.org/abs/2102.07304
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification.
Mingu Kang; Trung Quang Tran; Seungju Cho; Daeyoung Kim

http://arxiv.org/abs/2102.07325
Cross-modal Adversarial Reprogramming.
Paarth Neekhara; Shehzeen Hussain; Jinglong Du; Shlomo Dubnov; Farinaz Koushanfar; Julian McAuley

http://arxiv.org/abs/2102.06905
Mixed Nash Equilibria in the Adversarial Examples Game.
Laurent Meunier; Meyer Scetbon; Rafael Pinot; Jamal Atif; Yann Chevaleyre

http://arxiv.org/abs/2102.07047
Adversarial defense for automatic speaker verification by cascaded self-supervised learning models.
Haibin Wu; Xu Li; Andy T. Liu; Zhiyong Wu; Helen Meng; Hung-yi Lee

http://arxiv.org/abs/2102.06638
UAVs Path Deviation Attacks: Survey and Research Challenges.
Francesco Betti Sorbelli; Mauro Conti; Cristina M. Pinotti; Giulio Rigoni

http://arxiv.org/abs/2102.06479
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective.
Chaoning Zhang; Philipp Benz; Adil Karjauv; In So Kweon

http://arxiv.org/abs/2102.06747
Universal Adversarial Perturbations for Malware.
Raphael Labaca-Castro; Luis Muñoz-González; Feargus Pendlebury; Gabi Dreo Rodosek; Fabio Pierazzi; Lorenzo Cavallaro

http://arxiv.org/abs/2102.06700
On the Paradox of Certified Training. (13%)
Nikola Jovanović; Mislav Balunović; Maximilian Baader; Martin Vechev

http://arxiv.org/abs/2102.05950
Adversarially robust deepfake media detection using fused convolutional neural network predictions.
Sohail Ahmed Khan; Alessandro Artusi; Hang Dai

http://arxiv.org/abs/2102.06162
Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy.
Dylan Slack; Nathalie Rauschmayr; Krishnaram Kenthapadi

http://arxiv.org/abs/2102.05913
RobOT: Robustness-Oriented Testing for Deep Learning Systems.
Jingyi Wang; Jialuo Chen; Youcheng Sun; Xingjun Ma; Dongxia Wang; Jun Sun; Peng Cheng

http://arxiv.org/abs/2102.05561
Meta Federated Learning.
Omid Aramoon; Pin-Yu Chen; Gang Qu; Yuan Tian

http://arxiv.org/abs/2102.05475
Adversarial Robustness: What fools you makes you stronger.
Grzegorz Głuch; Rüdiger Urbanke

http://arxiv.org/abs/2102.05311
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection.
Hanshu Yan; Jingfeng Zhang; Gang Niu; Jiashi Feng; Vincent Y. F. Tan; Masashi Sugiyama

http://arxiv.org/abs/2102.05431
Dompteur: Taming Audio Adversarial Examples.
Thorsten Eisenhofer; Lea Schönherr; Joel Frank; Lars Speckemeier; Dorothea Kolossa; Thorsten Holz

http://arxiv.org/abs/2102.05334
Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenes.
Yael Mathov; Lior Rokach; Yuval Elovici

http://arxiv.org/abs/2102.05363
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons.
Bohang Zhang; Tianle Cai; Zhou Lu; Di He; Liwei Wang

http://arxiv.org/abs/2102.05368
RoBIC: A benchmark suite for assessing classifiers robustness.
Thibault Maho; Benoît Bonnet; Teddy Furon; Erwan Le Merrer

http://arxiv.org/abs/2102.05289
Bayesian Inference with Certifiable Adversarial Robustness.
Matthew Wicker; Luca Laurenti; Andrea Patane; Zhoutong Chen; Zheng Zhang; Marta Kwiatkowska

http://arxiv.org/abs/2102.04836
Target Training Does Adversarial Training Without Adversarial Samples.
Blerta Lindqvist

http://arxiv.org/abs/2102.04661
Security and Privacy for Artificial Intelligence: Opportunities and Challenges.
Ayodeji Oseni; Nour Moustafa; Helge Janicke; Peng Liu; Zahir Tari; Athanasios Vasilakos

http://arxiv.org/abs/2102.05104
"What's in the box?!": Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint Models.
Sahar Abdelnabi; Mario Fritz

http://arxiv.org/abs/2102.05110
Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers.
Jacob M. Springer; Melanie Mitchell; Garrett T. Kenyon

http://arxiv.org/abs/2102.05241
Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis.
Fengting Li; Xuankai Liu; Xiaoli Zhang; Qi Li; Kun Sun; Kang Li

http://arxiv.org/abs/2102.06020
Making Paper Reviewing Robust to Bid Manipulation Attacks.
Ruihan Wu; Chuan Guo; Felix Wu; Rahul Kidambi; der Maaten Laurens van; Kilian Q. Weinberger

http://arxiv.org/abs/2102.05096
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples.
Jay Nandy; Sudipan Saha; Wynne Hsu; Mong Li Lee; Xiao Xiang Zhu

http://arxiv.org/abs/2102.04154
Efficient Certified Defenses Against Patch Attacks on Image Classifiers.
Jan Hendrik Metzen; Maksym Yatsura

http://arxiv.org/abs/2102.04291
A Real-time Defense against Website Fingerprinting Attacks.
Shawn Shan; Arjun Nitin Bhagoji; Haitao Zheng; Ben Y. Zhao

http://arxiv.org/abs/2102.04615
Benford's law: what does it say on adversarial images?
João G. Zago; Fabio L. Baldissera; Eric A. Antonelo; Rodrigo T. Saad

http://arxiv.org/abs/2102.04150
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples.
Omer Faruk Tuna; Ferhat Ozgur Catak; M. Taner Eskil

http://arxiv.org/abs/2102.03726
Adversarial example generation with AdaBelief Optimizer and Crop Invariance.
Bo Yang; Hengwei Zhang; Yuchen Zhang; Kaiyong Xu; Jindong Wang

http://arxiv.org/abs/2102.03728
Adversarial Imaging Pipelines.
Buu Phan; Fahim Mannan; Felix Heide

http://arxiv.org/abs/2102.03716
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation.
Wuxinlin Cheng; Chenhui Deng; Zhiqiang Zhao; Yaohui Cai; Zhiru Zhang; Zhuo Feng

http://arxiv.org/abs/2102.03483
Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles.
Haowei Sun; Shuo Feng; Xintao Yan; Henry X. Liu

http://arxiv.org/abs/2102.03016
Model Agnostic Answer Reranking System for Adversarial Question Answering.
Sagnik Majumder; Chinmoy Samant; Greg Durrett

http://arxiv.org/abs/2102.03381
Robust Single-step Adversarial Training with Regularizer.
Lehui Xie; Yaopeng Wang; Jia-Li Yin; Ximeng Liu

http://arxiv.org/abs/2102.03482
Understanding the Interaction of Adversarial Training with Noisy Labels.
Jianing Zhu; Jingfeng Zhang; Bo Han; Tongliang Liu; Gang Niu; Hongxia Yang; Mohan Kankanhalli; Masashi Sugiyama

http://arxiv.org/abs/2102.03156
Optimal Transport as a Defense Against Adversarial Attacks.
Quentin Bouniot; Romaric Audigier; Angélique Loesch

http://arxiv.org/abs/2102.02956
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks.
Chong Xiang; Prateek Mittal

http://arxiv.org/abs/2102.02950
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression.
Masanori Yamada; Sekitoshi Kanai; Tomoharu Iwata; Tomokatsu Takahashi; Yuki Yamanaka; Hiroshi Takahashi; Atsutoshi Kumagai

http://arxiv.org/abs/2102.02885
Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR images.
Jiasong Chen; Linchen Qian; Timur Urakov; Weiyong Gu; Liang Liang

http://arxiv.org/abs/2102.02923
PredCoin: Defense against Query-based Hard-label Attack.
Junfeng Guo; Yaswanth Yadlapalli; Thiele Lothar; Ang Li; Cong Liu

http://arxiv.org/abs/2102.02729
Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review.
Dongrui Wu; Weili Fang; Yi Zhang; Liuqing Yang; Hanbin Luo; Lieyun Ding; Xiaodong Xu; Xiang Yu

http://arxiv.org/abs/2102.02417
Audio Adversarial Examples: Attacks Using Vocal Masks.
Lynnette Ng; Kai Yuan Tay; Wei Han Chua; Lucerne Loke; Danqi Ye; Melissa Chua

http://arxiv.org/abs/2102.02551
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models.
Yugeng Liu; Rui Wen; Xinlei He; Ahmed Salem; Zhikun Zhang; Michael Backes; Cristofaro Emiliano De; Mario Fritz; Yang Zhang

http://arxiv.org/abs/2102.02145
Adversarially Robust Learning with Unknown Perturbation Sets.
Omar Montasser; Steve Hanneke; Nathan Srebro

http://arxiv.org/abs/2102.02128
IWA: Integrated Gradient based White-box Attacks for Fooling Deep Neural Networks.
Yixiang Wang; Jiqiang Liu; Xiaolin Chang; Jelena Mišić; Vojislav B. Mišić

http://arxiv.org/abs/2102.01563
On Robustness of Neural Semantic Parsers.
Shuo Huang; Zhuang Li; Lizhen Qu; Lei Pan

http://arxiv.org/abs/2102.01862
Towards Robust Neural Networks via Close-loop Control.
Zhuotong Chen; Qianxiao Li; Zheng Zhang

http://arxiv.org/abs/2102.01356
Recent Advances in Adversarial Training for Adversarial Robustness.
Tao Bai; Jinqi Luo; Jun Zhao; Bihan Wen; Qian Wang

http://arxiv.org/abs/2102.01336
Probabilistic Trust Intervals for Out of Distribution Detection. (2%)
Gagandeep Singh; Deepak Mishra

http://arxiv.org/abs/2102.01208
Fast Training of Provably Robust Neural Networks by SingleProp.
Akhilan Boopathy; Tsui-Wei Weng; Sijia Liu; Pin-Yu Chen; Gaoyuan Zhang; Luca Daniel

http://arxiv.org/abs/2102.00662
Towards Speeding up Adversarial Training in Latent Spaces.
Yaguan Qian; Qiqi Shao; Tengteng Yao; Bin Wang; Shaoning Zeng; Zhaoquan Gu; Wassim Swaileh

http://arxiv.org/abs/2102.00918
Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems.
Alireza Bahramali; Milad Nasr; Amir Houmansadr; Dennis Goeckel; Don Towsley

http://arxiv.org/abs/2102.00533
Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional.
Xi Yu; Shujian Yu; Jose C. Principe

http://arxiv.org/abs/2102.00449
Towards Imperceptible Query-limited Adversarial Attacks with Perceptual Feature Fidelity Loss.
Pengrui Quan; Ruiming Guo; Mani Srivastava

http://arxiv.org/abs/2102.00436
Admix: Enhancing the Transferability of Adversarial Attacks.
Xiaosen Wang; Xuanran He; Jingdong Wang; Kun He

http://arxiv.org/abs/2102.00313
Cortical Features for Defense Against Adversarial Audio Attacks.
Ilya Kavalerov; Ruijie Zheng; Wojciech Czaja; Rama Chellappa

http://arxiv.org/abs/2102.00029
You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries.
Devin Willmott; Anit Kumar Sahu; Fatemeh Sheikholeslami; Filipe Condessa; Zico Kolter

http://arxiv.org/abs/2101.12097
Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities.
Hamidreza Habibollahi Najaf Abadi

http://arxiv.org/abs/2101.12090
Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network.
B. R. Manoj; Meysam Sadeghi; Erik G. Larsson

http://arxiv.org/abs/2101.12100
Increasing the Confidence of Deep Neural Networks by Coverage Analysis.
Giulio Rossolini; Alessandro Biondi; Giorgio Carlo Buttazzo

http://arxiv.org/abs/2101.12372
Adversarial Learning with Cost-Sensitive Classes.
Haojing Shen; Sihong Chen; Ran Wang; Xizhao Wang

http://arxiv.org/abs/2101.12031
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning.
Hemant Rathore; Sanjay K. Sahay; Piyush Nikam; Mohit Sewak

http://arxiv.org/abs/2101.11443
Adversaries in Online Learning Revisited: with applications in Robust Optimization and Adversarial training.
Sebastian Pokutta; Huan Xu

http://arxiv.org/abs/2101.11310
Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling.
Chris Emmery; Ákos Kádár; Grzegorz Chrupała

http://arxiv.org/abs/2101.11453
Meta Adversarial Training against Universal Patches.
Jan Hendrik Metzen; Nicole Finnie; Robin Hutmacher

http://arxiv.org/abs/2101.11466
Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting.
Federico Nesti; Alessandro Biondi; Giorgio Buttazzo

http://arxiv.org/abs/2101.11766
Improving Neural Network Robustness through Neighborhood Preserving Layers.
Bingyuan Liu; Christopher Malon; Lingzhou Xue; Erik Kruus

http://arxiv.org/abs/2101.10876
Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders.
Rasika Karkare; Randy Paffenroth; Gunjan Mahindre

http://arxiv.org/abs/2101.11073
Property Inference From Poisoning.
Melissa Chase; Esha Ghosh; Saeed Mahloujifar

http://arxiv.org/abs/2101.10792
Adversarial Vulnerability of Active Transfer Learning.
Nicolas M. Müller; Konstantin Böttinger

http://arxiv.org/abs/2101.10586
SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models.
Haekyu Park; Zijie J. Wang; Nilaksh Das; Anindya S. Paul; Pruthvi Perumalla; Zhiyan Zhou; Duen Horng Chau

http://arxiv.org/abs/2101.11081
The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs.
Xinwei Zhao; Matthew C. Stamm

http://arxiv.org/abs/2101.11060
Defenses Against Multi-Sticker Physical Domain Attacks on Classifiers.
Xinwei Zhao; Matthew C. Stamm

http://arxiv.org/abs/2101.10562
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems.
Utku Ozbulak; Baptist Vandersmissen; Azarakhsh Jalalvand; Ivo Couckuyt; Messem Arnout Van; Neve Wesley De

http://arxiv.org/abs/2101.10710
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method.
Satya M. Muddamsetty; Mohammad N. S. Jahromi; Andreea E. Ciontos; Laura M. Fenoy; Thomas B. Moeslund

http://arxiv.org/abs/2101.10747
Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models.
Mazen Abdelfattah; Kaiwen Yuan; Z. Jane Wang; Rabab Ward

http://arxiv.org/abs/2101.10001
Diverse Adversaries for Mitigating Bias in Training.
Xudong Han; Timothy Baldwin; Trevor Cohn

http://arxiv.org/abs/2101.10011
They See Me Rollin': Inherent Vulnerability of the Rolling Shutter in CMOS Image Sensors.
Sebastian Köhler; Giulio Lovisotto; Simon Birnbach; Richard Baker; Ivan Martinovic

http://arxiv.org/abs/2101.09930
Generalizing Adversarial Examples by AdaBelief Optimizer.
Yixiang Wang; Jiqiang Liu; Xiaolin Chang

http://arxiv.org/abs/2101.10102
Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning.
Renjue Li; Pengfei Yang; Cheng-Chao Huang; Youcheng Sun; Bai Xue; Lijun Zhang

http://arxiv.org/abs/2101.10063
Few-Shot Website Fingerprinting Attack.
Mantun Chen; Yongjun Wang; Zhiquan Qin; Xiatian Zhu

http://arxiv.org/abs/2101.10027
Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning.
Anh Bui; Trung Le; He Zhao; Paul Montague; Seyit Camtepe; Dinh Phung

http://arxiv.org/abs/2101.09568
A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network.
Xinwei Zhao; Chen Chen; Matthew C. Stamm

http://arxiv.org/abs/2101.09451
Error Diffusion Halftoning Against Adversarial Examples.
Shao-Yuan Lo; Vishal M. Patel

http://arxiv.org/abs/2101.09617
A Comprehensive Evaluation Framework for Deep Model Robustness.
Jun Guo; Wei Bao; Jiakai Wang; Yuqing Ma; Xinghai Gao; Gang Xiao; Aishan Liu; Jian Dong; Xianglong Liu; Wenjun Wu

http://arxiv.org/abs/2101.09387
Online Adversarial Purification based on Self-Supervision.
Changhao Shi; Chester Holtz; Gal Mishne

http://arxiv.org/abs/2101.09306
Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification.
Brendon G. Anderson; Ziye Ma; Jingqi Li; Somayeh Sojoudi

http://arxiv.org/abs/2101.09324
Generating Black-Box Adversarial Examples in Sparse Domain.
Hadi Zanddizari; Behnam Zeinali; J. Morris Chang

http://arxiv.org/abs/2101.09108
Adaptive Neighbourhoods for the Discovery of Adversarial Examples.
Jay Morgan; Adeline Paiement; Arno Pauly; Monika Seisenberger

http://arxiv.org/abs/2101.08452
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary.
Huan Zhang; Hongge Chen; Duane Boning; Cho-Jui Hsieh

http://arxiv.org/abs/2101.08523
Adv-OLM: Generating Textual Adversaries via OLM.
Vijit Malik; Ashwani Bhat; Ashutosh Modi

http://arxiv.org/abs/2101.08732
Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning.
Lang Huang; Chao Zhang; Hongyang Zhang

http://arxiv.org/abs/2101.08783
A Person Re-identification Data Augmentation Method with Adversarial Defense Effect.
Yunpeng Gong; Zhiyong Zeng; Liwen Chen; Yifan Luo; Bin Weng; Feng Ye

http://arxiv.org/abs/2101.08909
Adversarial Attacks and Defenses for Speaker Identification Systems.
Sonal Joshi; Jesús Villalba; Piotr Żelasko; Laureano Moro-Velázquez; Najim Dehak

http://arxiv.org/abs/2101.08533
A general multi-modal data learning method for Person Re-identification. (78%)
Yunpeng Gong

http://arxiv.org/abs/2101.08030
Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data.
Francesco Cartella; Orlando Anunciacao; Yuki Funabiki; Daisuke Yamaguchi; Toru Akishita; Olivier Elshocht

http://arxiv.org/abs/2101.08386
Invariance, encodings, and generalization: learning identity effects with neural networks.
S. Brugiapaglia; M. Liu; P. Tupper

http://arxiv.org/abs/2101.08154
Fooling thermal infrared pedestrian detectors in real world using small bulbs.
Xiaopei Zhu; Xiao Li; Jianmin Li; Zheyao Wang; Xiaolin Hu

http://arxiv.org/abs/2101.07922
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition.
Valeriia Cherepanova; Micah Goldblum; Harrison Foley; Shiyuan Duan; John Dickerson; Gavin Taylor; Tom Goldstein

http://arxiv.org/abs/2101.07910
A Search-Based Testing Framework for Deep Neural Networks of Source Code Embedding.
Maryam Vahdat Pour; Zhuo Li; Lei Ma; Hadi Hemmati

http://arxiv.org/abs/2101.07538
PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack.
Jie Wang; Zhaoxia Yin; Jin Tang; Jing Jiang; Bin Luo

http://arxiv.org/abs/2101.07512
Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization.
Jie Wang; Zhaoxia Yin; Jing Jiang; Yang Du

http://arxiv.org/abs/2101.06898
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space.
Shihao Zhao; Xingjun Ma; Yisen Wang; James Bailey; Bo Li; Yu-Gang Jiang

http://arxiv.org/abs/2101.06969
Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-Level Backdoor Attacks. (1%)
Zhengyan Zhang; Guangxuan Xiao; Yongwei Li; Tian Lv; Fanchao Qi; Zhiyuan Liu; Yasheng Wang; Xin Jiang; Maosong Sun

http://arxiv.org/abs/2101.06704
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions.
Nodens Koren; Qiuhong Ke; Yisen Wang; James Bailey; Xingjun Ma

http://arxiv.org/abs/2101.06855
GraphAttacker: A General Multi-Task GraphAttack Framework.
Jinyin Chen; Dunjie Zhang; Zhaoyan Ming; Kejie Huang; Wenrong Jiang; Chen Cui

http://arxiv.org/abs/2101.06784
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving.
James Tu; Huichen Li; Xinchen Yan; Mengye Ren; Yun Chen; Ming Liang; Eilyan Bitar; Ersin Yumer; Raquel Urtasun

http://arxiv.org/abs/2101.06507
Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks.
Jia Liu; Yaochu Jin

http://arxiv.org/abs/2101.06560
Adversarial Attacks On Multi-Agent Communication.
James Tu; Tsunhsuan Wang; Jingkang Wang; Sivabalan Manivasagam; Mengye Ren; Raquel Urtasun

http://arxiv.org/abs/2101.06309
Fundamental Tradeoffs in Distributionally Adversarial Training.
Mohammad Mehrabi; Adel Javanmard; Ryan A. Rossi; Anup Rao; Tung Mai

http://arxiv.org/abs/2101.06092
Black-box Adversarial Attacks in Autonomous Vehicle Technology.
K Naveen Kumar; C Vishnu; Reshmi Mitra; C Krishna Mohan

http://arxiv.org/abs/2101.06061
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds.
Bogdan Georgiev; Lukas Franken; Mayukh Mukherjee

http://arxiv.org/abs/2101.06069
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data.
Gaurav Kumar Nayak; Konda Reddy Mopuri; Saksham Jain; Anirban Chakraborty

http://arxiv.org/abs/2101.05833
Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbation.
Li-Yun Wang; Yeganeh Jalalpour; Wu-chi Feng

http://arxiv.org/abs/2101.05950
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning.
Xiaoyang Wang; Bo Li; Yibo Zhang; Bhavya Kailkhura; Klara Nahrstedt

http://arxiv.org/abs/2101.05930
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks.
Yige Li; Xixiang Lyu; Nodens Koren; Lingjuan Lyu; Bo Li; Xingjun Ma

http://arxiv.org/abs/2101.05639
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series.
Pradeep Rathore; Arghya Basak; Sri Harsha Nistala; Venkataramana Runkana

http://arxiv.org/abs/2101.05209
Image Steganography based on Iteratively Adversarial Samples of A Synchronized-directions Sub-image.
Xinghong Qin; Shunquan Tan; Bin Li; Weixuan Tang; Jiwu Huang

http://arxiv.org/abs/2101.04840
Robustness Gym: Unifying the NLP Evaluation Landscape.
Karan Goel; Nazneen Rajani; Jesse Vig; Samson Tan; Jason Wu; Stephan Zheng; Caiming Xiong; Mohit Bansal; Christopher Ré

http://arxiv.org/abs/2101.04401
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps.
Yujin Huang; Han Hu; Chunyang Chen

http://arxiv.org/abs/2101.04321
Random Transformation of Image Brightness for Adversarial Attack.
Bo Yang; Kaiyong Xu; Hengjun Wang; Hengwei Zhang

http://arxiv.org/abs/2101.04829
On the Effectiveness of Small Input Noise for Defending Against Query-based Black-Box Attacks.
Junyoung Byun; Hyojun Go; Changick Kim

http://arxiv.org/abs/2101.03924
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing.
Andreas Bär; Jonas Löhdefink; Nikhil Kapoor; Serin J. Varghese; Fabian Hüger; Peter Schlicht; Tim Fingscheidt

http://arxiv.org/abs/2101.05624
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification.
Yao Qiang; Supriya Tumkur Suresh Kumar; Marco Brocanelli; Dongxiao Zhu

http://arxiv.org/abs/2101.02899
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks.
Marissa Dotter; Sherry Xie; Keith Manville; Josh Harguess; Colin Busho; Mikel Rodriguez

http://arxiv.org/abs/2101.03218
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning.
Olakunle Ibitoye; M. Omair Shafiq; Ashraf Matrawy

http://arxiv.org/abs/2101.03272
Exploring Adversarial Fake Images on Face Manifold.
Dongze Li; Wei Wang; Hongxing Fan; Jing Dong

http://arxiv.org/abs/2101.02689
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks.
Arno Blaas; Stephen J. Roberts

http://arxiv.org/abs/2101.02483
Robust Text CAPTCHAs Using Adversarial Examples.
Rulin Shao; Zhouxing Shi; Jinfeng Yi; Pin-Yu Chen; Cho-Jui Hsieh

http://arxiv.org/abs/2101.02115
Adversarial Robustness by Design through Analog Computing and Synthetic Gradients.
Alessandro Cappelli; Ruben Ohana; Julien Launay; Laurent Meunier; Iacopo Poli; Florent Krzakala

http://arxiv.org/abs/2101.02325
Understanding the Error in Evaluating Adversarial Robustness.
Pengfei Xia; Ziqiang Li; Hongjing Niu; Bin Li

http://arxiv.org/abs/2101.01543
Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks.
Rachel Sterneck; Abhishek Moitra; Priyadarshini Panda

http://arxiv.org/abs/2101.00989
Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks.
Yanghao Zhang; Fu Wang; Wenjie Ruan

http://arxiv.org/abs/2101.01121
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning.
Konstantinos P. Panousis; Sotirios Chatzis; Antonios Alexos; Sergios Theodoridis

http://arxiv.org/abs/2101.01032
Local Black-box Adversarial Attacks: A Query Efficient Approach.
Tao Xiang; Hangcheng Liu; Shangwei Guo; Tianwei Zhang; Xiaofeng Liao

http://arxiv.org/abs/2101.02559
Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead.
Muhammad Shafique; Mahum Naseer; Theocharis Theocharides; Christos Kyrkou; Onur Mutlu; Lois Orosa; Jungwook Choi

http://arxiv.org/abs/2101.00521
Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning.
Ibrahim Yilmaz; Ambareen Siraj; Denis Ulybyshev

http://arxiv.org/abs/2012.15699
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning.
Chenglei Si; Zhengyan Zhang; Fanchao Qi; Zhiyuan Liu; Yasheng Wang; Qun Liu; Maosong Sun

http://arxiv.org/abs/2012.15503
Patch-wise++ Perturbation for Adversarial Targeted Attacks.
Lianli Gao; Qilong Zhang; Jingkuan Song; Heng Tao Shen

http://arxiv.org/abs/2012.15183
Temporally-Transferable Perturbations: Efficient, One-Shot Adversarial Attacks for Online Visual Object Trackers.
Krishna Kanth Nakka; Mathieu Salzmann

http://arxiv.org/abs/2012.15386
Beating Attackers At Their Own Games: Adversarial Example Detection Using Adversarial Gradient Directions.
Yuhang Wu; Sunpreet S. Arora; Yanhong Wu; Hao Yang

http://arxiv.org/abs/2101.10452
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization.
Renya Department of Information Science and Biomedical Engineering, Graduate School of Science and Engineering, Kagoshima University Daimo; Satoshi Department of Information Science and Biomedical Engineering, Graduate School of Science and Engineering, Kagoshima University Ono; Takahiro Department of Information Science and Biomedical Engineering, Graduate School of Science and Engineering, Kagoshima University Suzuki

http://arxiv.org/abs/2012.14769
Generating Adversarial Examples in Chinese Texts Using Sentence-Pieces.
Linyang Li; Yunfan Shao; Demin Song; Xipeng Qiu; Xuanjing Huang

http://arxiv.org/abs/2012.14965
Improving Adversarial Robustness in Weight-quantized Neural Networks.
Chang Song; Elias Fallon; Hai Li

http://arxiv.org/abs/2012.14738
With False Friends Like These, Who Can Have Self-Knowledge?
Lue Tao; Songcan Chen

http://arxiv.org/abs/2012.14956
Generating Natural Language Attacks in a Hard Label Black Box Setting.
Rishabh Maheshwary; Saket Maheshwary; Vikram Pudi

http://arxiv.org/abs/2012.14395
Enhanced Regularizers for Attributional Robustness.
Anindya Sarkar; Anirban Sarkar; Vineeth N Balasubramanian

http://arxiv.org/abs/2012.14352
Analysis of Dominant Classes in Universal Adversarial Perturbations.
Jon Vadillo; Roberto Santana; Jose A. Lozano

http://arxiv.org/abs/2012.14057
Person Re-identification with Adversarial Triplet Embedding.
Xinglu Wang

http://arxiv.org/abs/2012.13872
My Teacher Thinks The World Is Flat! Interpreting Automatic Essay Scoring Mechanism.
Swapnil Parekh; Yaman Kumar Singla; Changyou Chen; Junyi Jessy Li; Rajiv Ratn Shah

http://arxiv.org/abs/2012.13692
Sparse Adversarial Attack to Object Detection.
Jiayu Bao

http://arxiv.org/abs/2012.14427
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS.
Mohit Sewak; Sanjay K. Sahay; Hemant Rathore

http://arxiv.org/abs/2012.13573
Robustness, Privacy, and Generalization of Adversarial Training.
Fengxiang He; Shaopeng Fu; Bohan Wang; Dacheng Tao

http://arxiv.org/abs/2012.13628
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning.
Ahmadreza Jeddi; Mohammad Javad Shafiee; Alexander Wong

http://arxiv.org/abs/2012.13339
A Context Aware Approach for Generating Natural Language Attacks.
Rishabh Maheshwary; Saket Maheshwary; Vikram Pudi

http://arxiv.org/abs/2012.13111
Exploring Adversarial Examples via Invertible Neural Networks.
Ruqi Bai; Saurabh Bagchi; David I. Inouye

http://arxiv.org/abs/2012.13103
Improving the Certified Robustness of Neural Networks via Consistency Regularization.
Mengting Xu; Tao Zhang; Zhongnian Li; Daoqiang Zhang

http://arxiv.org/abs/2012.13154
Adversarial Momentum-Contrastive Pre-Training.
Cong Xu; Min Yang

http://arxiv.org/abs/2012.13489
Learning Robust Representation for Clustering through Locality Preserving Variational Discriminative Network.
Ruixuan Luo; Wei Li; Zhiyuan Zhang; Ruihan Bao; Keiko Harimoto; Xu Sun

http://arxiv.org/abs/2012.12528
The Translucent Patch: A Physical and Universal Attack on Object Detectors.
Alon Zolfi; Moshe Kravchik; Yuval Elovici; Asaf Shabtai

http://arxiv.org/abs/2012.12640
Gradient-Free Adversarial Attacks for Bayesian Neural Networks.
Matthew Yuan; Matthew Wicker; Luca Laurenti

http://arxiv.org/abs/2012.12529
SCOPE CPS: Secure Compiling of PLCs in Cyber-Physical Systems.
Eyasu Getahun Chekole; Martin Ochoa; Sudipta Chattopadhyay

http://arxiv.org/abs/2012.15740
Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems.
Moshe Kravchik; Battista Biggio; Asaf Shabtai

http://arxiv.org/abs/2012.12141
Learning to Initialize Gradient Descent Using Gradient Descent.
Kartik Ahuja; Amit Dhurandhar; Kush R. Varshney

http://arxiv.org/abs/2012.12235
Unadversarial Examples: Designing Objects for Robust Vision.
Hadi Salman; Andrew Ilyas; Logan Engstrom; Sai Vemprala; Aleksander Madry; Ashish Kapoor

http://arxiv.org/abs/2012.11835
Multi-shot NAS for Discovering Adversarially Robust Convolutional Neural Architectures at Targeted Capacities.
Xuefei Ning; Junbo Zhao; Wenshuo Li; Tianchen Zhao; Huazhong Yang; Yu Wang

http://arxiv.org/abs/2012.12368
On Frank-Wolfe Optimization for Adversarial Robustness and Interpretability.
Theodoros Tsiligkaridis; Jay Roberts

http://arxiv.org/abs/2012.11352
Genetic Adversarial Training of Decision Trees.
Francesco Ranzato; Marco Zanella

http://arxiv.org/abs/2012.11220
Incremental Verification of Fixed-Point Implementations of Neural Networks.
Luiz Sena; Erickson Alves; Iury Bessa; Eddie Filho; Lucas Cordeiro

http://arxiv.org/abs/2012.11442
Blurring Fools the Network -- Adversarial Attacks by Feature Peak Suppression and Gaussian Blurring.
Chenchen Zhao; Hao Li

http://arxiv.org/abs/2012.11413
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks.
Chenchen Zhao; Hao Li

http://arxiv.org/abs/2012.11212
Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification.
Siyuan Cheng; Yingqi Liu; Shiqing Ma; Xiangyu Zhang

http://arxiv.org/abs/2012.11769
Self-Progressing Robust Training.
Minhao Cheng; Pin-Yu Chen; Sijia Liu; Shiyu Chang; Cho-Jui Hsieh; Payel Das

http://arxiv.org/abs/2012.11138
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition.
Shoma Ishida; Satoshi Ono

http://arxiv.org/abs/2012.11619
Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines.
Aidan Kehoe; Peter Wittek; Yanbo Xue; Alejandro Pozas-Kerstjens

http://arxiv.org/abs/2012.11207
On Success and Simplicity: A Second Look at Transferable Targeted Attacks.
Zhengyu Zhao; Zhuoran Liu; Martha Larson

http://arxiv.org/abs/2012.11701
Learning from What We Know: How to Perform Vulnerability Prediction using Noisy Historical Data. (1%)
Aayush Garg; Renzo Degiovanni; Matthieu Jimenez; Maxime Cordy; Mike Papadakis; Yves Le Traon

http://arxiv.org/abs/2012.14456
Color Channel Perturbation Attacks for Fooling Convolutional Neural Networks and A Defense Against Such Attacks.
Jayendra Kantipudi; Shiv Ram Dubey; Soumendu Chakraborty

http://arxiv.org/abs/2012.10794
Sample Complexity of Adversarially Robust Linear Classification on Separated Data.
Robi Bhattacharjee; Somesh Jha; Kamalika Chaudhuri

http://arxiv.org/abs/2012.10076
Semantics and explanation: why counterfactual explanations produce adversarial examples in deep neural networks.
Kieran Browne; Ben Swift

http://arxiv.org/abs/2012.10282
ROBY: Evaluating the Robustness of a Deep Model by its Decision Boundaries.
Jinyin Chen; Zhen Wang; Haibin Zheng; Jun Xiao; Zhaoyan Ming

http://arxiv.org/abs/2012.10235
AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text.
Zhihong Shao; Zitao Liu; Jiyong Zhang; Zhongqin Wu; Minlie Huang

http://arxiv.org/abs/2012.10278
Adversarially Robust Estimate and Risk Analysis in Linear Regression.
Yue Xing; Ruizhi Zhang; Guang Cheng

http://arxiv.org/abs/2012.10485
RAILS: A Robust Adversarial Immune-inspired Learning System.
Ren Wang; Tianqi Chen; Stephen Lindsly; Alnawaz Rehemtulla; Alfred Hero; Indika Rajapakse

http://arxiv.org/abs/2012.10438
Efficient Training of Robust Decision Trees Against Adversarial Examples.
Daniël Vos; Sicco Verwer

http://arxiv.org/abs/2101.05219
On the human-recognizability phenomenon of adversarially trained deep image classifiers.
Jonathan Helland; Nathan VanHoudnos

http://arxiv.org/abs/2012.09427
Characterizing the Evasion Attackability of Multi-label Classifiers.
Zhuo Yang; Yufei Han; Xiangliang Zhang

http://arxiv.org/abs/2012.09501
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks.
Qingsong Yao; Zecheng He; Yi Lin; Kai Ma; Yefeng Zheng; S. Kevin Zhou

http://arxiv.org/abs/2012.09384
On the Limitations of Denoising Strategies as Adversarial Defenses.
Zhonghan Niu; Zhaoxi Chen; Linyi Li; Yubin Yang; Bo Li; Jinfeng Yi

http://arxiv.org/abs/2012.08588
FoggySight: A Scheme for Facial Lookup Privacy.
Ivan Evtimov; Pascal Sturmfels; Tadayoshi Kohno

http://arxiv.org/abs/2012.08096
FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) Systems.
Lu Chen; Jiao Sun; Wei Xu

http://arxiv.org/abs/2012.08112
Amata: An Annealing Mechanism for Adversarial Training Acceleration.
Nanyang Ye; Qianxiao Li; Xiao-Yun Zhou; Zhanxing Zhu

http://arxiv.org/abs/2012.07372
Disentangled Information Bottleneck.
Ziqi Pan; Li Niu; Jianfu Zhang; Liqing Zhang

http://arxiv.org/abs/2012.07887
Adaptive Verifiable Training Using Pairwise Class Similarity.
Shiqi Wang; Kevin Eykholt; Taesung Lee; Jiyong Jang; Ian Molloy

http://arxiv.org/abs/2012.07828
Robustness Threats of Differential Privacy.
Nurislam Tursynbek; Aleksandr Petiushko; Ivan Oseledets

http://arxiv.org/abs/2012.07474
HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios.
Hassan Ali; Surya Nepal; Salil S. Kanhere; Sanjay Jha

http://arxiv.org/abs/2012.07688
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints.
Xin Li; Xiangrui Li; Deng Pan; Dongxiao Zhu

http://arxiv.org/abs/2012.07994
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model.
Mohammadreza Ebrahimi; Ning Zhang; James Hu; Muhammad Taqi Raza; Hsinchun Chen

http://arxiv.org/abs/2012.07280
Contrastive Learning with Adversarial Perturbations for Conditional Text Generation.
Seanie Lee; Dong Bok Lee; Sung Ju Hwang

http://arxiv.org/abs/2012.07233
Achieving Adversarial Robustness Requires An Active Teacher.
Chao Ma; Lexing Ying

http://arxiv.org/abs/2012.06757
Query-free Black-box Adversarial Attacks on Graphs.
Jiarong Xu; Yizhou Sun; Xin Jiang; Yanhao Wang; Yang Yang; Chunping Wang; Jiangang Lu

http://arxiv.org/abs/2012.06390
Closeness and Uncertainty Aware Adversarial Examples Detection in Adversarial Machine Learning.
Omer Faruk Tuna; Ferhat Ozgur Catak; M. Taner Eskil

http://arxiv.org/abs/2012.06405
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis.
Nathan Drenkow; Neil Fendley; Philippe Burlina

http://arxiv.org/abs/2012.06568
Analyzing and Improving Adversarial Training for Generative Modeling. (86%)
Xuwang Yin; Shiying Li; Gustavo K. Rohde

http://arxiv.org/abs/2012.05948
GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking.
Lilas Alrahis; Satwik Patnaik; Faiq Khalid; Muhammad Abdullah Hanif; Hani Saleh; Muhammad Shafique; Ozgur Sinanoglu

http://arxiv.org/abs/2012.06058
Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable.
Odest Chadwicke Jenkins; Daniel Lopresti; Melanie Mitchell

http://arxiv.org/abs/2012.06122
DSRNA: Differentiable Search of Robust Neural Architectures.
Ramtin Hosseini; Xingyi Yang; Pengtao Xie

http://arxiv.org/abs/2012.06110
I-GCN: Robust Graph Convolutional Network via Influence Mechanism.
Haoxi Zhan; Xiaobing Pei

http://arxiv.org/abs/2012.06332
An Empirical Review of Adversarial Defenses.
Ayush Goel

http://arxiv.org/abs/2012.06024
Robustness and Transferability of Universal Attacks on Compressed Models.
Alberto G. Matachana; Kenneth T. Co; Luis Muñoz-González; David Martinez; Emil C. Lupu

http://arxiv.org/abs/2012.05657
Geometric Adversarial Attacks and Defenses on 3D Point Clouds.
Itai Lang; Uriel Kotlicki; Shai Avidan

http://arxiv.org/abs/2012.05858
SPAA: Stealthy Projector-based Adversarial Attacks on Deep Image Classifiers.
Bingyao Huang; Haibin Ling

http://arxiv.org/abs/2012.05027
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning.
Ujjwal Upadhyay; Prerana Mukherjee

http://arxiv.org/abs/2012.06330
Detection of Adversarial Supports in Few-shot Classifiers Using Self-Similarity and Filtering.
Yi Xiang Marcus Tan; Penny Chong; Jiamei Sun; Ngai-Man Cheung; Yuval Elovici; Alexander Binder

http://arxiv.org/abs/2012.05321
Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters.
Rida El-Allami; Alberto Marchisio; Muhammad Shafique; Ihsen Alouani

http://arxiv.org/abs/2012.05434
Composite Adversarial Attacks.
Xiaofeng Mao; Yuefeng Chen; Shuhui Wang; Hang Su; Yuan He; Hui Xue

http://arxiv.org/abs/2012.06043
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective.
Jingwei Sun; Ang Li; Binghui Wang; Huanrui Yang; Hai Li; Yiran Chen

http://arxiv.org/abs/2012.04729
On 1/n neural representation and robustness.
Josue Nassar; Piotr Aleksander Sokol; SueYeon Chung; Kenneth D. Harris; Il Memming Park

http://arxiv.org/abs/2012.04692
Locally optimal detection of stochastic targeted universal adversarial perturbations.
Amish Goel; Pierre Moulin

http://arxiv.org/abs/2012.04734
A Deep Marginal-Contrastive Defense against Adversarial Attacks on 1D Models.
Mohammed Hassanin; Nour Moustafa; Murat Tahtali

http://arxiv.org/abs/2012.04382
Using Feature Alignment can Improve Clean Average Precision and Adversarial Robustness in Object Detection.
Weipeng Xu; Hongcheng Huang

http://arxiv.org/abs/2012.04864
EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation.
Qi Zhou; Haipeng Chen; Yitao Zheng; Zhen Wang

http://arxiv.org/abs/2012.04262
Overcomplete Representations Against Adversarial Videos.
Shao-Yuan Lo; Jeya Maria Jose Valanarasu; Vishal M. Patel

http://arxiv.org/abs/2012.04750
Mitigating the Impact of Adversarial Attacks in Very Deep Networks.
Mohammed Hassanin; Ibrahim Radwan; Nour Moustafa; Murat Tahtali; Neeraj Kumar

http://arxiv.org/abs/2012.04353
Reinforcement Based Learning on Classification Task Could Yield Better Generalization and Adversarial Accuracy.
Shashi Kant Gupta

http://arxiv.org/abs/2012.04432
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses. (95%)
Yi Liu; Xingliang Yuan; Ruihui Zhao; Cong Wang; Dusit Niyato; Yefeng Zheng

http://arxiv.org/abs/2012.04351
Data Dependent Randomized Smoothing. (1%)
Motasem Alfarra; Adel Bibi; Philip H. S. Torr; Bernard Ghanem

http://arxiv.org/abs/2012.03516
A Singular Value Perspective on Model Robustness.
Malhar Jere; Maghav Kumar; Farinaz Koushanfar

http://arxiv.org/abs/2012.03528
Backpropagating Linearly Improves Transferability of Adversarial Examples.
Yiwen Guo; Qizhang Li; Hao Chen

http://arxiv.org/abs/2012.03483
Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection.
Byunggill Joe; Jihun Hamm; Sung Ju Hwang; Sooel Son; Insik Shin

http://arxiv.org/abs/2012.03843
Are DNNs fooled by extremely unrecognizable images?
Soichiro Kumano; Hiroshi Kera; Toshihiko Yamasaki

http://arxiv.org/abs/2012.03460
Reprogramming Language Models for Molecular Representation Learning.
Ria Vinod; Pin-Yu Chen; Payel Das

http://arxiv.org/abs/2012.03404
Black-box Model Inversion Attribute Inference Attacks on Classification Models.
Shagufta Mehnaz; Ninghui Li; Elisa Bertino

http://arxiv.org/abs/2012.03310
PAC-Learning for Strategic Classification.
Ravi Sundaram; Anil Vullikanti; Haifeng Xu; Fan Yao

http://arxiv.org/abs/2012.02976
Evaluating adversarial robustness in simulated cerebellum.
Liu Yuezhang; Bo Li; Qifeng Chen

http://arxiv.org/abs/2012.02632
Advocating for Multiple Defense Strategies against Adversarial Examples.
Alexandre Araujo; Laurent Meunier; Rafael Pinot; Benjamin Negrevergne

http://arxiv.org/abs/2012.02525
Practical No-box Adversarial Attacks against DNNs.
Qizhang Li; Yiwen Guo; Hao Chen

http://arxiv.org/abs/2012.02452
Towards Natural Robustness Against Adversarial Examples.
Haoyu Chu; Shikui Wei; Yao Zhao

http://arxiv.org/abs/2012.02486
Unsupervised Adversarially-Robust Representation Learning on Graphs.
Jiarong Xu; Yang Yang; Junru Chen; Chunping Wang; Xin Jiang; Jiangang Lu; Yizhou Sun

http://arxiv.org/abs/2012.02521
Kernel-convoluted Deep Neural Networks with Data Augmentation.
Minjin Kim; Young-geun Kim; Dongha Kim; Yongdai Kim; Myunghee Cho Paik

http://arxiv.org/abs/2012.02048
Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning.
Kendra Albert; Maggie Delano; Jonathon Penney; Afsaneh Rigot; Ram Shankar Siva Kumar

http://arxiv.org/abs/2012.01791
FAT: Federated Adversarial Training.
Giulio Zizzo; Ambrish Rawat; Mathieu Sinn; Beat Buesser

http://arxiv.org/abs/2012.01901
An Empirical Study of Derivative-Free-Optimization Algorithms for Targeted Black-Box Attacks in Deep Neural Networks.
Giuseppe Ughi; Vinayak Abrol; Jared Tanner

http://arxiv.org/abs/2012.02160
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers.
Brian Kim; Yalin E. Sagduyu; Tugba Erpek; Kemal Davaslioglu; Sennur Ulukus

http://arxiv.org/abs/2012.01806
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations.
Tejas Gokhale; Rushil Anirudh; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Chitta Baral; Yezhou Yang

http://arxiv.org/abs/2012.01558
From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation.
Nikhil Kapoor; Andreas Bär; Serin Varghese; Jan David Schneider; Fabian Hüger; Peter Schlicht; Tim Fingscheidt

http://arxiv.org/abs/2012.01701
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques.
Han Qiu; Yi Zeng; Tianwei Zhang; Yong Jiang; Meikang Qiu

http://arxiv.org/abs/2012.01654
Towards Defending Multiple $\ell_p$-norm Bounded Adversarial Perturbations via Gated Batch Normalization.
Aishan Liu; Shiyu Tang; Xinyun Chen; Lei Huang; Haotong Qin; Xianglong Liu; Dacheng Tao

http://arxiv.org/abs/2012.01699
Content-Adaptive Pixel Discretization to Improve Model Robustness.
Ryan Feng; Wu-chi Feng; Atul Prakash

http://arxiv.org/abs/2012.01274
How Robust are Randomized Smoothing based Defenses to Data Poisoning?
Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Jihun Hamm

http://arxiv.org/abs/2012.00802
Adversarial Robustness Across Representation Spaces.
Pranjal Awasthi; George Yu; Chun-Sung Ferng; Andrew Tomkins; Da-Cheng Juan

http://arxiv.org/abs/2012.00558
Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks.
Christian Cosgrove; Adam Kortylewski; Chenglin Yang; Alan Yuille

http://arxiv.org/abs/2012.00567
Boosting Adversarial Attacks on Neural Networks with Better Optimizer.
Heng Yin; Hengwei Zhang; Jindong Wang; Ruiyu Dou

http://arxiv.org/abs/2012.00517
One-Pixel Attack Deceives Computer-Assisted Diagnosis of Cancer.
Joni Korpihalkola; Tuomo Sipola; Samir Puuska; Tero Kokkonen

http://arxiv.org/abs/2012.00909
Towards Imperceptible Adversarial Image Patches Based on Network Explanations.
Yaguan Qian; Jiamin Wang; Bin Wang; Zhaoquan Gu; Xiang Ling; Chunming Wu

http://arxiv.org/abs/2011.14969
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses.
Gaurang Sriramanan; Sravanti Addepalli; Arya Baburaj; R. Venkatesh Babu

http://arxiv.org/abs/2011.14585
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack.
Jaehui Hwang; Jun-Hyuk Kim; Jun-Ho Choi; Jong-Seok Lee

http://arxiv.org/abs/2011.14427
Architectural Adversarial Robustness: The Case for Deep Pursuit.
George Cazenavette; Calvin Murdock; Simon Lucey

http://arxiv.org/abs/2011.14365
A Targeted Universal Attack on Graph Convolutional Network.
Jiazhu Dai; Weifeng Zhu; Xiangfeng Luo

http://arxiv.org/abs/2011.14498
SwitchX: Gmin-Gmax Switching for Energy-Efficient and Robust Implementation of Binary Neural Networks on ReRAM Xbars.
Abhiroop Bhattacharjee; Priyadarshini Panda

http://arxiv.org/abs/2011.14224
Cyberbiosecurity: DNA Injection Attack in Synthetic Biology.
Dor Farbiash; Rami Puzis

http://arxiv.org/abs/2011.14085
Deterministic Certification to Adversarial Attacks via Bernstein Polynomial Approximation.
Ching-Chia Kao; Jhe-Bang Ko; Chun-Shien Lu

http://arxiv.org/abs/2011.14218
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images.
Debayan Deb; Xiaoming Liu; Anil K. Jain

http://arxiv.org/abs/2011.13705
3D Invisible Cloak.
Mingfu Xue; Can He; Zhiyu Wu; Jian Wang; Zhe Liu; Weiqiang Liu

http://arxiv.org/abs/2011.13824
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers.
Kaidi Xu; Huan Zhang; Shiqi Wang; Yihan Wang; Suman Jana; Xue Lin; Cho-Jui Hsieh

http://arxiv.org/abs/2011.14031
Voting based ensemble improves robustness of defensive models.
Devvrit; Minhao Cheng; Cho-Jui Hsieh; Inderjit Dhillon

http://arxiv.org/abs/2011.14045
Generalized Adversarial Examples: Attacks and Defenses.
Haojing Shen; Sihong Chen; Ran Wang; Xizhao Wang

http://arxiv.org/abs/2011.13692
Robust and Natural Physical Adversarial Examples for Object Detectors.
Mingfu Xue; Chengxiang Yuan; Can He; Jian Wang; Weiqiang Liu

http://arxiv.org/abs/2011.13560
SocialGuard: An Adversarial Example Based Privacy-Preserving Technique for Social Images.
Mingfu Xue; Shichang Sun; Zhiyu Wu; Can He; Jian Wang; Weiqiang Liu

http://arxiv.org/abs/2011.13696
Use the Spear as a Shield: A Novel Adversarial Example based Privacy-Preserving Technique against Membership Inference Attacks.
Mingfu Xue; Chengxiang Yuan; Can He; Zhiyu Wu; Yushu Zhang; Zhe Liu; Weiqiang Liu

http://arxiv.org/abs/2011.13538
Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness.
Yilun Jin; Lixin Fan; Kam Woh Ng; Ce Ju; Qiang Yang

http://arxiv.org/abs/2011.13392
Exposing the Robustness and Vulnerability of Hybrid 8T-6T SRAM Memory Architectures to Adversarial Attacks in Deep Neural Networks.
Abhishek Moitra; Priyadarshini Panda

http://arxiv.org/abs/2011.13526
Robust Attacks on Deep Learning Face Recognition in the Physical World.
Meng Shen; Hao Yu; Liehuang Zhu; Ke Xu; Qi Li; Xiaojiang Du

http://arxiv.org/abs/2011.13181
Regularization with Latent Space Virtual Adversarial Training.
Genki Osada; Budrul Ahsan; Revoti Prasad Bora; Takashi Nishide

http://arxiv.org/abs/2011.13375
Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect.
Athena Sayles; Ashish Hooda; Mohit Gupta; Rahul Chatterjee; Earlence Fernandes

http://arxiv.org/abs/2011.12680
Adversarial Attack on Facial Recognition using Visible Light.
Morgan Frearson; Kien Nguyen

http://arxiv.org/abs/2011.12902
Adversarial Evaluation of Multimodal Models under Realistic Gray Box Assumption.
Ivan Evtimov; Russel Howes; Brian Dolhansky; Hamed Firooz; Cristian Canton Ferrer

http://arxiv.org/abs/2011.12807
SurFree: a fast surrogate-free black-box attack.
Thibault Maho; Teddy Furon; Erwan Le Merrer

http://arxiv.org/abs/2011.13011
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization.
Tianyu Han; Sven Nebelung; Federico Pedersoli; Markus Zimmermann; Maximilian Schulze-Hagen; Michael Ho; Christoph Haarburger; Fabian Kiessling; Christiane Kuhl; Volkmar Schulz; Daniel Truhn

http://arxiv.org/abs/2011.14934
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization. (80%)
Sahil Suneja; Yunhui Zheng; Yufan Zhuang; Jim Laredo; Alessandro Morari

http://arxiv.org/abs/2011.12344
Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning.
Luiz F. O. Chamon; Santiago Paternain; Alejandro Ribeiro

http://arxiv.org/abs/2011.12423
Stochastic sparse adversarial attacks.
Manon Césaire; Hatem Hajri; Sylvain Lamprier; Patrick Gallinari

http://arxiv.org/abs/2011.11922
On the Adversarial Robustness of 3D Point Cloud Classification.
Jiachen Sun; Karl Koenig; Yulong Cao; Qi Alfred Chen; Z. Morley Mao

http://arxiv.org/abs/2011.11957
Towards Imperceptible Universal Attacks on Texture Recognition.
Yingpeng Deng; Lina J. Karam

http://arxiv.org/abs/2011.12720
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack.
Rui Shu; Tianpei Xia; Laurie Williams; Tim Menzies

http://arxiv.org/abs/2011.11857
Augmented Lagrangian Adversarial Attacks.
Jérôme Rony; Eric Granger; Marco Pedersoli; Ismail Ben Ayed

http://arxiv.org/abs/2011.11164
Learnable Boundary Guided Adversarial Training.
Jiequan Cui; Shu Liu; Liwei Wang; Jiaya Jia

http://arxiv.org/abs/2011.11637
Nudge Attacks on Point-Cloud DNNs.
Yiren Zhao; Ilia Shumailov; Robert Mullins; Ross Anderson

http://arxiv.org/abs/2011.10794
Spatially Correlated Patterns in Adversarial Images.
Nandish Chattopadhyay; Lionell Yip En Zhi; Bryan Tan Bing Xing; Anupam Chattopadhyay

http://arxiv.org/abs/2011.10867
A Neuro-Inspired Autoencoding Defense Against Adversarial Perturbations.
Can Bakiskan; Metehan Cekic; Ahmet Dundar Sezer; Upamanyu Madhow

http://arxiv.org/abs/2011.10850
Robust Data Hiding Using Inverse Gradient Attention. (2%)
Honglei Zhang; Hu Wang; Yuanzhouhan Cao; Chunhua Shen; Yidong Li

http://arxiv.org/abs/2011.10280
Are Chess Discussions Racist? An Adversarial Hate Speech Data Set.
Rupak Sarkar; Ashiqur R. KhudaBukhsh

http://arxiv.org/abs/2011.10492
Detecting Universal Trigger's Adversarial Attack with Honeypot.
Thai Le; Noseong Park; Dongwon Lee

http://arxiv.org/abs/2011.09789
An Experimental Study of Semantic Continuity for Deep Learning Models.
Shangxi Wu; Jitao Sang; Xian Zhao; Lizhang Chen

http://arxiv.org/abs/2011.09719
Adversarial Examples for $k$-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams.
Chawin Sitawarin; Evgenios M. Kornaropoulos; Dawn Song; David Wagner

http://arxiv.org/abs/2011.09957
Adversarial Threats to DeepFake Detection: A Practical Perspective.
Paarth Neekhara; Brian Dolhansky; Joanna Bitton; Cristian Canton Ferrer

http://arxiv.org/abs/2011.09824
Multi-Task Adversarial Attack.
Pengxin Guo; Yuancheng Xu; Baijiong Lin; Yu Zhang

http://arxiv.org/abs/2011.11486
Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks.
Luke Darlow; Stanisław Jastrzębski; Amos Storkey

http://arxiv.org/abs/2011.09563
Robustified Domain Adaptation.
Jiajin Zhang; Hanqing Chao; Pingkun Yan

http://arxiv.org/abs/2011.09473
Adversarial collision attacks on image hashing functions.
Brian Dolhansky; Cristian Canton Ferrer

http://arxiv.org/abs/2011.09526
Contextual Fusion For Adversarial Robustness.
Aiswarya Akumalla; Seth Haney; Maksim Bazhenov

http://arxiv.org/abs/2011.09393
Adversarial Turing Patterns from Cellular Automata.
Nurislam Tursynbek; Ilya Vilkoviskiy; Maria Sindeeva; Ivan Oseledets

http://arxiv.org/abs/2011.09364
Self-Gradient Networks.
Hossein Aboutalebi; Mohammad Javad Shafiee Alexander Wong

http://arxiv.org/abs/2011.09123
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs.
Arezoo Rajabi; Rakesh B. Bobba

http://arxiv.org/abs/2011.08483
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances.
Ali Shahin Shamsabadi; Francisco Sepúlveda Teixeira; Alberto Abad; Bhiksha Raj; Andrea Cavallaro; Isabel Trancoso

http://arxiv.org/abs/2011.08908
SIENA: Stochastic Multi-Expert Neural Patcher.
Thai Le; Noseong Park; Dongwon Lee

http://arxiv.org/abs/2011.09066
Shaping Deep Feature Space towards Gaussian Mixture for Visual Classification.
Weitao Wan; Jiansheng Chen; Cheng Yu; Tong Wu; Yuanyi Zhong; Ming-Hsuan Yang

http://arxiv.org/abs/2011.08558
Generating universal language adversarial examples by understanding and enhancing the transferability across neural models.
Liping Yuan; Xiaoqing Zheng; Yi Zhou; Cho-Jui Hsieh; Kai-wei Chang; Xuanjing Huang

http://arxiv.org/abs/2011.08485
Probing Predictions on OOD Images via Nearest Categories. (75%)
Yao-Yuan Yang; Cyrus Rashtchian; Ruslan Salakhutdinov; Kamalika Chaudhuri

http://arxiv.org/abs/2011.07793
MAAC: Novel Alert Correlation Method To Detect Multi-step Attack.
Xiaoyu Wang; Lei Yu; Houhua He; Xiaorui Gong

http://arxiv.org/abs/2011.08105
Enforcing robust control guarantees within neural network policies.
Priya L. Donti; Melrose Roderick; Mahyar Fazlyab; J. Zico Kolter

http://arxiv.org/abs/2011.07835
Adversarially Robust Classification based on GLRT.
Bhagyashree Puranik; Upamanyu Madhow; Ramtin Pedarsani

http://arxiv.org/abs/2011.08102
Combining GANs and AutoEncoders for Efficient Anomaly Detection.
Fabio ISTI CNR, Pisa, Italy Carrara; Giuseppe ISTI CNR, Pisa, Italy Amato; Luca ISTI CNR, Pisa, Italy Brombin; Fabrizio ISTI CNR, Pisa, Italy Falchi; Claudio ISTI CNR, Pisa, Italy Gennaro

http://arxiv.org/abs/2011.08367
Extreme Value Preserving Networks.
Mingjie Sun; Jianguo Li; Changshui Zhang

http://arxiv.org/abs/2011.07478
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks.
Yuxin Wen; Shuai Li; Kui Jia

http://arxiv.org/abs/2011.07697
Ensemble of Models Trained by Key-based Transformed Images for Adversarially Robust Defense Against Black-box Attacks.
MaungMaung AprilPyone; Hitoshi Kiya

http://arxiv.org/abs/2011.07633
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations.
Jinyuan Jia; Binghui Wang; Xiaoyu Cao; Hongbin Liu; Neil Zhenqiang Gong

http://arxiv.org/abs/2011.07603
Power Side-Channel Attacks on BNN Accelerators in Remote FPGAs. (1%)
Shayan Moini; Shanquan Tian; Jakub Szefer; Daniel Holcomb; Russell Tessier

http://arxiv.org/abs/2011.07430
Audio-Visual Event Recognition through the lens of Adversary.
Juncheng B Li; Kaixin Ma; Shuhui Qu; Po-Yao Huang; Florian Metze

http://arxiv.org/abs/2011.06978
Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection.
Faisal Alamri; Sinan Kalkan; Nicolas Pugeault

http://arxiv.org/abs/2011.07114
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents.
Xian Yeow Lee; Yasaman Esfandiari; Kai Liang Tan; Soumik Sarkar

http://arxiv.org/abs/2011.06690
Adversarial Robustness Against Image Color Transformation within Parametric Filter Space.
Zhengyu Zhao; Zhuoran Liu; Martha Larson

http://arxiv.org/abs/2011.06585
Sparse PCA: Algorithms, Adversarial Perturbations and Certificates.
Tommaso d'Orsi; Pravesh K. Kothari; Gleb Novikov; David Steurer

http://arxiv.org/abs/2011.05623
Adversarial images for the primate brain.
Li Yuan; Will Xiao; Gabriel Kreiman; Francis E. H. Tay; Jiashi Feng; Margaret S. Livingstone

http://arxiv.org/abs/2011.05850
Detecting Adversarial Patches with Class Conditional Reconstruction Networks.
Perry Deng; Mohammad Saidur Rahman; Matthew Wright

http://arxiv.org/abs/2011.05074
Efficient and Transferable Adversarial Examples from Bayesian Neural Networks.
Martin Gubri; Maxime Cordy; Mike Papadakis; Yves Le Traon; Koushik Sen

http://arxiv.org/abs/2011.04268
Solving Inverse Problems With Deep Neural Networks -- Robustness Included?
Martin Genzel; Jan Macdonald; Maximilian März

http://arxiv.org/abs/2011.03901
Adversarial Black-Box Attacks On Text Classifiers Using Multi-Objective Genetic Optimization Guided By Deep Networks.
Alex Mathai; Shreya Khare; Srikanth Tamilselvam; Senthil Mani

http://arxiv.org/abs/2011.05157
Bridging the Performance Gap between FGSM and PGD Adversarial Training.
Tianjin Huang; Vlado Menkovski; Yulong Pei; Mykola Pechenizkiy

http://arxiv.org/abs/2011.03574
Single-Node Attacks for Fooling Graph Neural Networks.
Ben Finkelshtein; Chaim Baskin; Evgenii Zheltonozhskii; Uri Alon

http://arxiv.org/abs/2011.05973
A survey on practical adversarial examples for malware classifiers.
Daniel Park; Bülent Yener

http://arxiv.org/abs/2011.02701
A Black-Box Attack Model for Visually-Aware Recommender Systems.
Rami Cohen; Oren Sar Shalom; Dietmar Jannach; Amihood Amir

http://arxiv.org/abs/2011.03010
Data Augmentation via Structured Adversarial Perturbations.
Calvin Luo; Hossein Mobahi; Samy Bengio

http://arxiv.org/abs/2011.02675
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics forPerturbation Difficulty.
Camilo Pestana; Wei Liu; David Glance; Ajmal Mian

http://arxiv.org/abs/2011.02707
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks.
Leo Schwinn; An Nguyen; René Raab; Dario Zanca; Bjoern Eskofier; Daniel Tenbrinck; Martin Burger

http://arxiv.org/abs/2011.01514
You Do (Not) Belong Here: Detecting DPI Evasion Attacks with Context Learning.
Shitong Zhu; Shasha Li; Zhongjie Wang; Xun Chen; Zhiyun Qian; Srikanth V. Krishnamurthy; Kevin S. Chan; Ananthram Swami

http://arxiv.org/abs/2011.01846
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks.
Denis Emelin; Ivan Titov; Rico Sennrich

http://arxiv.org/abs/2011.01538
Penetrating RF Fingerprinting-based Authentication with a Generative Adversarial Attack.
Samurdhi Karunaratne; Enes Krijestorac; Danijela Cabric

http://arxiv.org/abs/2011.01539
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks.
Tao Bai; Jinqi Luo; Jun Zhao

http://arxiv.org/abs/2011.03083
A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs.
Souvik Kundu; Mahdi Nazemi; Peter A. Beerel; Massoud Pedram

http://arxiv.org/abs/2011.01509
MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv-GANs Against Black-Box Detectors.
Fangtian Zhong; Xiuzhen Cheng; Dongxiao Yu; Bei Gong; Shuaiwen Song; Jiguo Yu

http://arxiv.org/abs/2011.01183
Adversarial Examples in Constrained Domains.
Ryan Sheatsley; Nicolas Papernot; Michael Weisman; Gunjan Verma; Patrick McDaniel

http://arxiv.org/abs/2011.01132
Frequency-based Automated Modulation Classification in the Presence of Adversaries.
Rajeev Sahay; Christopher G. Brinton; David J. Love

http://arxiv.org/abs/2011.01435
Robust Algorithms for Online Convex Problems via Primal-Dual.
Marco Molinaro

http://arxiv.org/abs/2011.02272
Trustworthy AI.
Richa Singh; Mayank Vatsa; Nalini Ratha

http://arxiv.org/abs/2011.00566
LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks.
Hang Zhou; Dongdong Chen; Jing Liao; Weiming Zhang; Kejiang Chen; Xiaoyi Dong; Kunlin Liu; Gang Hua; Nenghai Yu

http://arxiv.org/abs/2011.05976
Vulnerability of the Neural Networks Against Adversarial Examples: A Survey.
Rui Zhao

http://arxiv.org/abs/2011.01755
MAD-VAE: Manifold Awareness Defense Variational Autoencoder.
Frederick Morlock; Dingsu Wang

http://arxiv.org/abs/2011.00144
Integer Programming-based Error-Correcting Output Code Design for Robust Classification.
Samarth Gupta; Saurabh Amin

http://arxiv.org/abs/2010.16336
Leveraging Extracted Model Adversaries for Improved Black Box Attacks.
Naveen Jafer Nizar; Ari Kobren

http://arxiv.org/abs/2011.00101
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks.
Lubin Meng; Jian Huang; Zhigang Zeng; Xue Jiang; Shan Yu; Tzyy-Ping Jung; Chin-Teng Lin; Ricardo Chavarriaga; Dongrui Wu

http://arxiv.org/abs/2011.00095
Adversarial Attacks on Optimization based Planners.
Sai Vemprala; Ashish Kapoor

http://arxiv.org/abs/2010.16204
Capture the Bot: Using Adversarial Examples to Improve CAPTCHA Robustness to Bot Attacks.
Dorjan Hitaj; Briland Hitaj; Sushil Jajodia; Luigi V. Mancini

http://arxiv.org/abs/2011.05254
Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classification.
Yongwei Wang; Mingquan Feng; Rabab Ward; Z. Jane Wang; Lanjun Wang

http://arxiv.org/abs/2011.00070
Adversarial Robust Training of Deep Learning MRI Reconstruction Models.
Francesco Calivá; Kaiyang Cheng; Rutwik Shah; Valentina Pedoia

http://arxiv.org/abs/2010.16074
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples.
Yingwei Li; Zhuotun Zhu; Yuyin Zhou; Yingda Xia; Wei Shen; Elliot K. Fishman; Alan L. Yuille

http://arxiv.org/abs/2010.15886
Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection.
Yongwei Wang; Xin Ding; Li Ding; Rabab Ward; Z. Jane Wang

http://arxiv.org/abs/2010.15974
Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks?
Roger Granda; Tinne Tuytelaars; Jose Oramas

http://arxiv.org/abs/2010.15651
Reliable Graph Neural Networks via Robust Aggregation.
Simon Geisler; Daniel Zügner; Stephan Günnemann

http://arxiv.org/abs/2010.15824
Passport-aware Normalization for Deep Model Protection.
Jie Zhang; Dongdong Chen; Jing Liao; Weiming Zhang; Gang Hua; Nenghai Yu

http://arxiv.org/abs/2010.15391
Robustifying Binary Classification to Adversarial Perturbation.
Fariborz Salehi; Babak Hassibi

http://arxiv.org/abs/2010.15487
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification.
Arslan Ali; Andrea Migliorati; Tiziano Bianchi; Enrico Magli

http://arxiv.org/abs/2010.15773
WaveTransform: Crafting Adversarial Examples via Input Decomposition.
Divyam Anshumaan; Akshay Agarwal; Mayank Vatsa; Richa Singh

http://arxiv.org/abs/2010.16045
Machine Learning (In) Security: A Stream of Problems. (8%)
Fabrício Ceschin; Marcus Botacin; Albert Bifet; Bernhard Pfahringer; Luiz S. Oliveira; Heitor Murilo Gomes; André Grégio

http://arxiv.org/abs/2010.14927
Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations.
Amit Daniely; Hadas Schacham

http://arxiv.org/abs/2010.14974
Object Hider: Adversarial Patch Attack Against Object Detectors.
Yusheng Zhao; Huanqian Yan; Xingxing Wei

http://arxiv.org/abs/2010.14986
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
Anna-Kathrin Kopetzki; Bertrand Charpentier; Daniel Zügner; Sandhya Giri; Stephan Günnemann

http://arxiv.org/abs/2010.14919
Transferable Universal Adversarial Perturbations Using Generative Models.
Atiye Sadat Hashemi; Andreas Bär; Saeed Mozaffari; Tim Fingscheidt

http://arxiv.org/abs/2010.14291
Fast Local Attack: Generating Local Adversarial Examples for Object Detectors.
Quanyu Liao; Xin Wang; Bin Kong; Siwei Lyu; Youbing Yin; Qi Song; Xi Wu

http://arxiv.org/abs/2010.14121
Anti-perturbation of Online Social Networks by Graph Label Transition.
Jun Zhuang; Mohammad Al Hasan

http://arxiv.org/abs/2010.13751
Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes.
Jinyuan Jia; Binghui Wang; Neil Zhenqiang Gong

http://arxiv.org/abs/2010.13773
GreedyFool: Distortion-Aware Sparse Adversarial Attack.
Xiaoyi Dong; Dongdong Chen; Jianmin Bao; Chuan Qin; Lu Yuan; Weiming Zhang; Nenghai Yu; Dong Chen

http://arxiv.org/abs/2010.13337
Robust Pre-Training by Adversarial Contrastive Learning.
Ziyu Jiang; Tianlong Chen; Ting Chen; Zhangyang Wang

http://arxiv.org/abs/2010.13880
Versatile Verification of Tree Ensembles.
Laurens Devos; Wannes Meert; Jesse Davis

http://arxiv.org/abs/2010.13365
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy.
Philipp Benz; Chaoning Zhang; Adil Karjauv; In So Kweon

http://arxiv.org/abs/2010.13356
Exploring the Security Boundary of Data Reconstruction via Neuron Exclusivity Analysis. (16%)
Xudong Pan; Mi Zhang; Yifan Yan; Jiaming Zhu; Min Yang

http://arxiv.org/abs/2010.13247
Attack Agnostic Adversarial Defense via Visual Imperceptible Bound.
Saheb Chhabra; Akshay Agarwal; Richa Singh; Mayank Vatsa

http://arxiv.org/abs/2010.13070
Dynamic Adversarial Patch for Evading Object Detection Models.
Shahar Hoory; Tzvika Shapira; Asaf Shabtai; Yuval Elovici

http://arxiv.org/abs/2010.13275
Asymptotic Behavior of Adversarial Training in Binary Classification.
Hossein Taheri; Ramtin Pedarsani; Christos Thrampoulidis

http://arxiv.org/abs/2010.12905
ATRO: Adversarial Training with a Rejection Option.
Masahiro Kato; Zhenghang Cui; Yoshihiro Fukuhara

http://arxiv.org/abs/2010.12989
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks.
Huimin Zeng; Chen Zhu; Tom Goldstein; Furong Huang

http://arxiv.org/abs/2010.12809
Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations.
Yael Mathov; Tal Ben Senior; Asaf Shabtai; Yuval Elovici

http://arxiv.org/abs/2010.12510
Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures.
Nafise Sadat Moosavi; Boer Marcel de; Prasetya Ajie Utama; Iryna Gurevych

http://arxiv.org/abs/2010.12190
Towards Robust Neural Networks via Orthogonal Diversity.
Kun Fang; Qinghua Tao; Yingwen Wu; Tao Li; Jia Cai; Feipeng Cai; Xiaolin Huang; Jie Yang

http://arxiv.org/abs/2010.12050
Contrastive Learning with Adversarial Examples.
Chih-Hui Ho; Nuno Vasconcelos

http://arxiv.org/abs/2010.11782
Adversarial Attacks on Binary Image Recognition Systems.
Eric Balkanski; Harrison Chase; Kojin Oshiba; Alexander Rilee; Yaron Singer; Richard Wang

http://arxiv.org/abs/2010.11869
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers.
Lei Xu; Ivan Ramirez; Kalyan Veeramachaneni

http://arxiv.org/abs/2010.11598
An Efficient Adversarial Attack for Tree Ensembles.
Chong Zhang; Huan Zhang; Cho-Jui Hsieh

http://arxiv.org/abs/2010.12088
Adversarial Robustness of Supervised Sparse Coding.
Jeremias Sulam; Ramchandran Muthukumar; Raman Arora

http://arxiv.org/abs/2010.11645
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming.
Sumanth Dathathri; Krishnamurthy Dvijotham; Alexey Kurakin; Aditi Raghunathan; Jonathan Uesato; Rudy Bunel; Shreya Shankar; Jacob Steinhardt; Ian Goodfellow; Percy Liang; Pushmeet Kohli

http://arxiv.org/abs/2010.11535
Defense-guided Transferable Adversarial Attacks.
Zifei Zhang; Kai Qiao; Jian Chen; Ningning Liang

http://arxiv.org/abs/2010.11828
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free.
Haotao Wang; Tianlong Chen; Shupeng Gui; Ting-Kuei Hu; Ji Liu; Zhangyang Wang

http://arxiv.org/abs/2010.11388
Adversarial Attacks on Deep Algorithmic Trading Policies.
Yaser Faghan; Nancirose Piazza; Vahid Behzadan; Ali Fathi

http://arxiv.org/abs/2010.11415
Maximum Mean Discrepancy is Aware of Adversarial Attacks.
Ruize Gao; Feng Liu; Jingfeng Zhang; Bo Han; Tongliang Liu; Gang Niu; Masashi Sugiyama

http://arxiv.org/abs/2010.11213
Precise Statistical Analysis of Classification Accuracies for Adversarial Training.
Adel Javanmard; Mahdi Soltanolkotabi

http://arxiv.org/abs/2010.11742
Learning Black-Box Attackers with Transferable Priors and Query Feedback.
Jiancheng Yang; Yangzhou Jiang; Xiaoyang Huang; Bingbing Ni; Chenglong Zhao

http://arxiv.org/abs/2010.11352
Class-Conditional Defense GAN Against End-to-End Speech Attacks.
Mohammad Esmaeilpour; Patrick Cardinal; Alessandro Lameiras Koerich

http://arxiv.org/abs/2010.10987
A Distributional Robustness Certificate by Randomized Smoothing.
Jungang Yang; Liyao Xiang; Ruidong Chen; Yukun Wang; Wei Wang; Xinbing Wang

http://arxiv.org/abs/2010.10242
Preventing Personal Data Theft in Images with Adversarial ML.
Thomas Cilloni; Wei Wang; Charles Walter; Charles Fleming

http://arxiv.org/abs/2010.10650
Towards Understanding the Dynamics of the First-Order Adversaries.
Zhun Deng; Hangfeng He; Jiaoyang Huang; Weijie J. Su

http://arxiv.org/abs/2010.10047
Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods.
Byungjoo Kim; Bryce Chudomelka; Jinyoung Park; Jaewoo Kang; Youngjoon Hong; Hyunwoo J. Kim

http://arxiv.org/abs/2010.10712
Boosting Gradient for White-Box Adversarial Attacks.
Hongying Liu; Zhenyu Zhou; Fanhua Shang; Xiaoyu Qi; Yuanyuan Liu; Licheng Jiao

http://arxiv.org/abs/2010.10549
Tight Second-Order Certificates for Randomized Smoothing.
Alexander Levine; Aounon Kumar; Thomas Goldstein; Soheil Feizi

http://arxiv.org/abs/2010.09680
A Survey of Machine Learning Techniques in Adversarial Image Forensics.
Ehsan Nowroozi; Ali Dehghantanha; Reza M. Parizi; Kim-Kwang Raymond Choo

http://arxiv.org/abs/2010.09569
Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification.
Erwin Quiring; Lukas Pirch; Michael Reimsbach; Daniel Arp; Konrad Rieck

http://arxiv.org/abs/2010.09670
RobustBench: a standardized adversarial robustness benchmark.
Francesco Croce; Maksym Andriushchenko; Vikash Sehwag; Nicolas Flammarion; Mung Chiang; Prateek Mittal; Matthias Hein

http://arxiv.org/abs/2010.09624
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness.
Guillermo Ortiz-Jimenez; Apostolos Modas; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard

http://arxiv.org/abs/2010.09633
Verifying the Causes of Adversarial Examples.
Honglin Li; Yifei Fan; Frieder Ganz; Anthony Yezzi; Payam Barnaghi

http://arxiv.org/abs/2010.09246
When Bots Take Over the Stock Market: Evasion Attacks Against Algorithmic Traders.
Elior Nehemya; Yael Mathov; Asaf Shabtai; Yuval Elovici

http://arxiv.org/abs/2010.09891
FLAG: Adversarial Data Augmentation for Graph Neural Networks.
Kezhi Kong; Guohao Li; Mucong Ding; Zuxuan Wu; Chen Zhu; Bernard Ghanem; Gavin Taylor; Tom Goldstein

http://arxiv.org/abs/2010.09119
FADER: Fast Adversarial Example Rejection.
Francesco Crecchi; Marco Melis; Angelo Sotgiu; Davide Bacciu; Battista Biggio

http://arxiv.org/abs/2010.09080
Poisoned classifiers are not only backdoored, they are fundamentally broken.
Mingjie Sun; Siddhant Agarwal; J. Zico Kolter

http://arxiv.org/abs/2010.08546
A Generative Model based Adversarial Security of Deep Learning and Linear Classifier Models.
erhat Ozgur Catak; Samed Sivaslioglu; Kevser Sahinbas

http://arxiv.org/abs/2010.08844
Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image Compositing.
Jinghan Yang; Adith Boloor; Ayan Chakrabarti; Xuan Zhang; Yevgeniy Vorobeychik

http://arxiv.org/abs/2010.08852
Weight-Covariance Alignment for Adversarially Robust Neural Networks.
Panagiotis Eustratiadis; Henry Gouk; Da Li; Timothy Hospedales

http://arxiv.org/abs/2010.11679
DPAttack: Diffused Patch Attacks against Universal Object Detection.
Shudeng Wu; Tao Dai; Shu-Tao Xia

http://arxiv.org/abs/2010.08542
Mischief: A Simple Black-Box Attack Against Transformer Architectures.
Wynter Adrian de

http://arxiv.org/abs/2010.08418
Learning Robust Algorithms for Online Allocation Problems Using Adversarial Training.
Goran Zuzic; Di Wang; Aranyak Mehta; D. Sivakumar

http://arxiv.org/abs/2010.07542
Adversarial Images through Stega Glasses.
Benoît CRIStAL Bonnet; Teddy CRIStAL Furon; Patrick CRIStAL Bas

http://arxiv.org/abs/2010.07849
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning.
Hongjun Wang; Guanbin Li; Xiaobai Liu; Liang Lin

http://arxiv.org/abs/2010.07788
Generalizing Universal Adversarial Attacks Beyond Additive Perturbations.
Yanghao Zhang; Wenjie Ruan; Fu Wang; Xiaowei Huang

http://arxiv.org/abs/2010.07532
Certifying Neural Network Robustness to Random Input Noise from Samples.
Brendon G. Anderson; Somayeh Sojoudi

http://arxiv.org/abs/2010.08034
Overfitting or Underfitting? Understand Robustness Drop in Adversarial Training.
Zichao Li; Liyuan Liu; Chengyu Dong; Jingbo Shang

http://arxiv.org/abs/2010.08001
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness.
Long Zhao; Ting Liu; Xi Peng; Dimitris Metaxas

http://arxiv.org/abs/2010.09212
Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks.
Jiangnan Li; Yingyuan Yang; Jinyuan Stella Sun

http://arxiv.org/abs/2010.11143
Progressive Defense Against Adversarial Attacks for Deep Learning as a Service in Internet of Things.
Ling Wang; Cheng Zhang; Zejian Luo; Chenguang Liu; Jie Liu; Xi Zheng; Athanasios Vasilakos

http://arxiv.org/abs/2010.06943
Pair the Dots: Jointly Examining Training History and Test Stimuli for Model Interpretability.
Yuxian Meng; Chun Fan; Zijun Sun; Eduard Hovy; Fei Wu; Jiwei Li

http://arxiv.org/abs/2010.07190
Towards Resistant Audio Adversarial Examples.
Tom Dörr; Karla Markert; Nicolas M. Müller; Konstantin Böttinger

http://arxiv.org/abs/2010.07230
An Adversarial Attack against Stacked Capsule Autoencoder.
Jiazhu Dai; Siwei Xiong

http://arxiv.org/abs/2010.06812
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability.
Mahmoud Hossam; Trung Le; He Zhao; Dinh Phung

http://arxiv.org/abs/2010.06855
GreedyFool: Multi-Factor Imperceptibility and Its Application to Designing Black-box Adversarial Example Attack.
Hui Liu; Bo Zhao; Jiabao Guo; Yang An; Peng Liu

http://arxiv.org/abs/2010.06545
Toward Few-step Adversarial Training from a Frequency Perspective.
Hans Shih-Han Wang; Cory Cornelius; Brandon Edwards; Jason Martin

http://arxiv.org/abs/2010.06651
Higher-Order Certification for Randomized Smoothing.
Jeet Mohapatra; Ching-Yun Ko; Tsui-Wei Weng; Pin-Yu Chen; Sijia Liu; Luca Daniel

http://arxiv.org/abs/2010.07693
Linking average- and worst-case perturbation robustness via class selectivity and dimensionality.
Matthew L. Leavitt; Ari Morcos

http://arxiv.org/abs/2010.06107
Universal Model for 3D Medical Image Analysis.
Xiaoman Zhang; Ya Zhang; Xiaoyun Zhang; Yanfeng Wang

http://arxiv.org/abs/2010.06121
To be Robust or to be Fair: Towards Fairness in Adversarial Training.
Han Xu; Xiaorui Liu; Yaxin Li; Jiliang Tang

http://arxiv.org/abs/2010.06131
Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks.
He Zhao; Thanh Nguyen; Trung Le; Paul Montague; Vel Olivier De; Tamas Abraham; Dinh Phung

http://arxiv.org/abs/2010.05981
Shape-Texture Debiased Neural Network Training.
Yingwei Li; Qihang Yu; Mingxing Tan; Jieru Mei; Peng Tang; Wei Shen; Alan Yuille; Cihang Xie

http://arxiv.org/abs/2010.06154
On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness.
Maria-Florina Balcan; Avrim Blum; Dravyansh Sharma; Hongyang Zhang

http://arxiv.org/abs/2010.05648
From Hero to Z\'eroe: A Benchmark of Low-Level Adversarial Attacks.
Steffen Eger; Yannik Benz

http://arxiv.org/abs/2010.05736
EFSG: Evolutionary Fooling Sentences Generator.
Giovanni Marco Di; Marco Brambilla

http://arxiv.org/abs/2010.06087
Contrast and Classify: Training Robust VQA Models. (2%)
Yash Kant; Abhinav Moudgil; Dhruv Batra; Devi Parikh; Harsh Agrawal

http://arxiv.org/abs/2010.05419
Gradient-based Analysis of NLP Models is Manipulable.
Junlin Wang; Jens Tuyls; Eric Wallace; Sameer Singh

http://arxiv.org/abs/2010.05272
IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration.
Ziyi Wu; Yueqi Duan; He Wang; Qingnan Fan; Leonidas J. Guibas

http://arxiv.org/abs/2010.05125
Is It Time to Redefine the Classification Task for Deep Neural Networks?
Keji Han; Yun Li

http://arxiv.org/abs/2010.04925
Regularizing Neural Networks via Adversarial Model Perturbation. (1%)
Yaowei Zheng; Richong Zhang; Yongyi Mao

http://arxiv.org/abs/2010.04821
Understanding Spatial Robustness of Deep Neural Networks.
Ziyuan Zhong; Yuchi Tian; Baishakhi Ray

http://arxiv.org/abs/2010.04819
How Does Mixup Help With Robustness and Generalization?
Linjun Zhang; Zhun Deng; Kenji Kawaguchi; Amirata Ghorbani; James Zou

http://arxiv.org/abs/2010.03856
Transcending Transcend: Revisiting Malware Classification with Conformal Evaluation.
Federico Barbero; Feargus Pendlebury; Fabio Pierazzi; Lorenzo Cavallaro

http://arxiv.org/abs/2010.03844
Improve Adversarial Robustness via Weight Penalization on Classification Layer.
Cong Xu; Dan Li; Min Yang

http://arxiv.org/abs/2010.04055
A Unified Approach to Interpreting and Boosting Adversarial Transferability.
Xin Wang; Jie Ren; Shuyun Lin; Xiangming Zhu; Yisen Wang; Quanshi Zhang

http://arxiv.org/abs/2010.04092
Improved Techniques for Model Inversion Attacks.
Si Chen; Ruoxi Jia; Guo-Jun Qi

http://arxiv.org/abs/2010.04216
Affine-Invariant Robust Training.
Oriol Barbany Mayor

http://arxiv.org/abs/2010.04331
Targeted Attention Attack on Deep Learning Models in Road Sign Recognition.
Xinghao Yang; Weifeng Liu; Shengli Zhang; Wei Liu; Dacheng Tao

http://arxiv.org/abs/2010.04205
Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks.
Anit Kumar Sahu; Satya Narayan Shukla; J. Zico Kolter

http://arxiv.org/abs/2010.03465
Hiding the Access Pattern is Not Enough: Exploiting Search Pattern Leakage in Searchable Encryption.
Simon Oya; Florian Kerschbaum

http://arxiv.org/abs/2010.03245
Learning Clusterable Visual Features for Zero-Shot Recognition.
Jingyi Xu; Zhixin Shu; Dimitris Samaras

http://arxiv.org/abs/2010.03282
Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks.
Ahmed Salem; Michael Backes; Yang Zhang

http://arxiv.org/abs/2010.03630
Revisiting Batch Normalization for Improving Corruption Robustness.
Philipp Benz; Chaoning Zhang; Adil Karjauv; In So Kweon

http://arxiv.org/abs/2010.03316
Batch Normalization Increases Adversarial Vulnerability: Disentangling Usefulness and Robustness of Model Features.
Philipp Benz; Chaoning Zhang; In So Kweon

http://arxiv.org/abs/2010.03735
Decamouflage: A Framework to Detect Image-Scaling Attacks on Convolutional Neural Networks.
Bedeuro Kim; Alsharif Abuadbba; Yansong Gao; Yifeng Zheng; Muhammad Ejaz Ahmed; Hyoungshick Kim; Surya Nepal

http://arxiv.org/abs/2010.03258
Global Optimization of Objective Functions Represented by ReLU Networks.
Christopher A. Strong; Haoze Wu; Aleksandar Zeljić; Kyle D. Julian; Guy Katz; Clark Barrett; Mykel J. Kochenderfer

http://arxiv.org/abs/2010.03300
CD-UAP: Class Discriminative Universal Adversarial Perturbation.
Chaoning Zhang; Philipp Benz; Tooba Imtiaz; In So Kweon

http://arxiv.org/abs/2010.03180
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples.
Eden Levy; Yael Mathov; Ziv Katzir; Asaf Shabtai; Yuval Elovici

http://arxiv.org/abs/2010.03288
Double Targeted Universal Adversarial Perturbations.
Philipp Benz; Chaoning Zhang; Tooba Imtiaz; In So Kweon

http://arxiv.org/abs/2010.03593
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples.
Sven Gowal; Chongli Qin; Jonathan Uesato; Timothy Mann; Pushmeet Kohli

http://arxiv.org/abs/2010.03671
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems.
AKM Iqtidar Newaz; Nur Imtiazul Haque; Amit Kumar Sikder; Mohammad Ashiqur Rahman; A. Selcuk Uluagac

http://arxiv.org/abs/2010.03164
Adversarial attacks on audio source separation.
Naoya Takahashi; Shota Inoue; Yuki Mitsufuji

http://arxiv.org/abs/2010.02468
Visualizing Color-wise Saliency of Black-Box Image Classification Models.
Yuhki SenseTime Japan Hatakeyama; Hiroki SenseTime Japan Sakuma; Yoshinori SenseTime Japan Konishi; Kohei Kyoto University Suenaga

http://arxiv.org/abs/2010.02558
Constraining Logits by Bounded Function for Adversarial Robustness.
Sekitoshi Kanai; Masanori Yamada; Shin'ya Yamaguchi; Hiroshi Takahashi; Yasutoshi Ida

http://arxiv.org/abs/2010.03072
Adversarial Patch Attacks on Monocular Depth Estimation Networks.
Koichiro Yamanaka; Ryutaroh Matsumoto; Keita Takahashi; Toshiaki Fujii

http://arxiv.org/abs/2010.03007
BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models.
Ahmed Salem; Yannick Sautter; Michael Backes; Mathias Humbert; Yang Zhang

http://arxiv.org/abs/2010.02065
Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model.
Xin Qiu; Risto Miikkulainen

http://arxiv.org/abs/2010.02508
Adversarial Boot Camp: label free certified robustness in one epoch.
Ryan Campbell; Chris Finlay; Adam M Oberman

http://arxiv.org/abs/2010.02364
Understanding Classifier Mistakes with Generative Models.
Laëtitia Shao; Yang Song; Stefano Ermon

http://arxiv.org/abs/2010.02338
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation.
Tianlu Wang; Xuezhi Wang; Yao Qin; Ben Packer; Kang Li; Jilin Chen; Alex Beutel; Ed Chi

http://arxiv.org/abs/2010.01770
Second-Order NLP Adversarial Examples.
John X. Morris

http://arxiv.org/abs/2010.02432
A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference.
Sanghyun Hong; Yiğitcan Kaya; Ionuţ-Vlad Modoranu; Tudor Dumitraş

http://arxiv.org/abs/2010.02329
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective.
Boxin Wang; Shuohang Wang; Yu Cheng; Zhe Gan; Ruoxi Jia; Bo Li; Jingjing Liu

http://arxiv.org/abs/2010.01799
Understanding Catastrophic Overfitting in Single-step Adversarial Training.
Hoki Kim; Woojin Lee; Jaewook Lee

http://arxiv.org/abs/2010.02456
Downscaling Attack and Defense: Turning What You See Back Into What You Get.
Andrew J. Lohn

http://arxiv.org/abs/2010.02387
Metadata-Based Detection of Child Sexual Abuse Material. (1%)
Mayana Pereira; Rahul Dodhia; Hyrum Anderson; Richard Brown

http://arxiv.org/abs/2010.01724
TextAttack: Lessons learned in designing Python frameworks for NLP.
John X. Morris; Jin Yong Yoo; Yanjun Qi

http://arxiv.org/abs/2010.01506
A Study for Universal Adversarial Attacks on Texture Recognition.
Yingpeng Deng; Lina J. Karam

http://arxiv.org/abs/2010.01610
Adversarial Attack and Defense of Structured Prediction Models.
Wenjuan Han; Liwen Zhang; Yong Jiang; Kewei Tu

http://arxiv.org/abs/2010.01736
Geometry-aware Instance-reweighted Adversarial Training.
Jingfeng Zhang; Jianing Zhu; Gang Niu; Bo Han; Masashi Sugiyama; Mohan Kankanhalli

http://arxiv.org/abs/2010.01592
Unknown Presentation Attack Detection against Rational Attackers.
Ali Khodabakhsh; Zahid Akhtar

http://arxiv.org/abs/2010.01401
Adversarial and Natural Perturbations for General Robustness.
Sadaf Gulshad; Jan Hendrik Metzen; Arnold Smeulders

http://arxiv.org/abs/2010.01329
Multi-Step Adversarial Perturbations on Recommender Systems Embeddings.
Vito Walter Anelli; Alejandro Bellogín; Yashar Deldjoo; Noia Tommaso Di; Felice Antonio Merra

http://arxiv.org/abs/2010.01345
A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples.
Zhao Meng; Roger Wattenhofer

http://arxiv.org/abs/2010.01278
Efficient Robust Training via Backward Smoothing.
Jinghui Chen; Yu Cheng; Zhe Gan; Quanquan Gu; Jingjing Liu

http://arxiv.org/abs/2010.01279
Do Wider Neural Networks Really Help Adversarial Robustness?
Boxi Wu; Jinghui Chen; Deng Cai; Xiaofei He; Quanquan Gu

http://arxiv.org/abs/2010.00990
Note: An alternative proof of the vulnerability of $k$-NN classifiers in high intrinsic dimensionality regions.
Teddy Furon

http://arxiv.org/abs/2010.00984
An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders.
Vito Walter Anelli; Noia Tommaso Di; Daniele Malitesta; Felice Antonio Merra

http://arxiv.org/abs/2010.00801
Block-wise Image Transformation with Secret Key for Adversarially Robust Defense.
MaungMaung AprilPyone; Hitoshi Kiya

http://arxiv.org/abs/2010.01039
Query complexity of adversarial attacks.
Grzegorz Głuch; Rüdiger Urbanke

http://arxiv.org/abs/2010.01250
CorrAttack: Black-box Adversarial Attack with Structured Search.
Zhichao Huang; Yaowei Huang; Tong Zhang

http://arxiv.org/abs/2010.01238
A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations.
Gustavo Olague; Gerardo Ibarra-Vazquez; Mariana Chan-Ley; Cesar Puente; Carlos Soubervielle-Montalvo; Axel Martinez

http://arxiv.org/abs/2010.01171
Data-Driven Certification of Neural Networks with Random Input Noise. (16%)
Brendon G. Anderson; Somayeh Sojoudi

http://arxiv.org/abs/2010.02004
Assessing Robustness of Text Classification through Maximal Safe Radius Computation.
Malfa Emanuele La; Min Wu; Luca Laurenti; Benjie Wang; Anthony Hartshorn; Marta Kwiatkowska

http://arxiv.org/abs/2010.00467
Bag of Tricks for Adversarial Training.
Tianyu Pang; Xiao Yang; Yinpeng Dong; Hang Su; Jun Zhu

http://arxiv.org/abs/2010.00071
Erratum Concerning the Obfuscated Gradients Attack on Stochastic Activation Pruning.
Guneet S. Dhillon; Nicholas Carlini

http://arxiv.org/abs/2009.14454
Accurate and Robust Feature Importance Estimation under Distribution Shifts.
Jayaraman J. Thiagarajan; Vivek Narayanaswamy; Rushil Anirudh; Peer-Timo Bremer; Andreas Spanias

http://arxiv.org/abs/2009.14455
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks.
Uday Shankar Shanthamallu; Jayaraman J. Thiagarajan; Andreas Spanias

http://arxiv.org/abs/2009.14720
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles.
Huanrui Yang; Jingyang Zhang; Hongliang Dong; Nathan Inkawhich; Andrew Gardner; Andrew Touchet; Wesley Wilkes; Heath Berry; Hai Li

http://arxiv.org/abs/2009.13971
Neural Topic Modeling with Cycle-Consistent Adversarial Training.
Xuemeng Hu; Rui Wang; Deyu Zhou; Yuxuan Xiong

http://arxiv.org/abs/2009.14075
Fast Fr\'echet Inception Distance.
Alexander Mathiasen; Frederik Hvilshøj

http://arxiv.org/abs/2009.13720
Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment.
Sharan Raja; Rudraksh Tuwani

http://arxiv.org/abs/2009.13562
STRATA: Building Robustness with a Simple Method for Generating Black-box Adversarial Attacks for Models of Code.
Jacob M. Springer; Bryn Marie Reinstadler; Una-May O'Reilly

http://arxiv.org/abs/2009.13504
Graph Adversarial Networks: Protecting Information against Adversarial Attacks.
Peiyuan Liao; Han Zhao; Keyulu Xu; Tommi Jaakkola; Geoffrey Gordon; Stefanie Jegelka; Ruslan Salakhutdinov

http://arxiv.org/abs/2009.13145
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients.
Yifei Huang; Yaodong Yu; Hongyang Zhang; Yi Ma; Yuan Yao

http://arxiv.org/abs/2009.13243
Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability.
Ishai Rosenberg; Shai Meir; Jonathan Berrebi; Ilay Gordon; Guillaume Sicard; Eli David

http://arxiv.org/abs/2009.13714
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations. (92%)
Pu Zhao; Parikshit Ram; Songtao Lu; Yuguang Yao; Djallel Bouneffouf; Xue Lin; Sijia Liu

http://arxiv.org/abs/2009.12927
Learning to Improve Image Compression without Changing the Standard Decoder.
Yannick Strümpler; Ren Yang; Radu Timofte

http://arxiv.org/abs/2009.13038
RoGAT: a robust GNN combined revised GAT with adjusted graphs.
Xianchen Zhou; Yaoyun Zeng; Hongxia Wang

http://arxiv.org/abs/2009.13033
Where Does the Robustness Come from? A Study of the Transformation-based Ensemble Defence.
Chang Liao; Yao Cheng; Chengfang Fang; Jie Shi

http://arxiv.org/abs/2009.12718
Differentially Private Adversarial Robustness Through Randomized Perturbations.
Nan Xu; Oluwaseyi Feyisetan; Abhinav Aggarwal; Zekun Xu; Nathanael Teissier

http://arxiv.org/abs/2009.12724
Beneficial Perturbations Network for Defending Adversarial Examples.
Shixian Wen; Amanda Rios; Laurent Itti

http://arxiv.org/abs/2009.12088
Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision.
Sara Mandelli; Nicolò Bonettini; Paolo Bestagini; Stefano Tubaro

http://arxiv.org/abs/2009.12064
Attention Meets Perturbations: Robust and Interpretable Attention with Adversarial Training.
Shunsuke Kitada; Hitoshi Iyatomi

http://arxiv.org/abs/2009.13250
Advancing the Research and Development of Assured Artificial Intelligence and Machine Learning Capabilities.
Tyler J. Shipp; Daniel J. Clouse; Lucia Michael J. De; Metin B. Ahiskali; Kai Steverson; Jonathan M. Mullin; Nathaniel D. Bastian

http://arxiv.org/abs/2009.11911
Adversarial Examples in Deep Learning for Multivariate Time Series Regression.
Gautam Raj Mode; Khaza Anuarul Hoque

http://arxiv.org/abs/2009.11508
Improving Query Efficiency of Black-box Adversarial Attack.
Yang Bai; Yuyuan Zeng; Yong Jiang; Yisen Wang; Shu-Tao Xia; Weiwei Guo

http://arxiv.org/abs/2009.11416
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization.
Prashnna Kumar Gyawali; Sandesh Ghimire; Linwei Wang

http://arxiv.org/abs/2009.11321
Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining.
Ananya B. Sai; Akash Kumar Mohankumar; Siddhartha Arora; Mitesh M. Khapra

http://arxiv.org/abs/2009.11349
Adversarial robustness via stochastic regularization of neural activation sensitivity.
Gil Fidel; Ron Bitton; Ziv Katzir; Asaf Shabtai

http://arxiv.org/abs/2009.10975
A Partial Break of the Honeypots Defense to Catch Adversarial Attacks.
Nicholas Carlini

http://arxiv.org/abs/2009.10978
Semantics-Preserving Adversarial Training.
Wonseok Lee; Hanbit Lee; Sang-goo Lee

http://arxiv.org/abs/2009.11090
Robustification of Segmentation Models Against Adversarial Perturbations In Medical Imaging.
Hanwool Park; Amirhossein Bayat; Mohammad Sabokrou; Jan S. Kirschke; Bjoern H. Menze

http://arxiv.org/abs/2009.11397
Detection of Iterative Adversarial Attacks via Counter Attack.
Matthias Rottmann; Kira Maag; Mathis Peyron; Natasa Krejic; Hanno Gottschalk

http://arxiv.org/abs/2010.01950
Torchattacks: A PyTorch Repository for Adversarial Attacks.
Hoki Kim

http://arxiv.org/abs/2009.10639
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors.
Yi-Shan Lin; Wen-Chuan Lee; Z. Berkay Celik

http://arxiv.org/abs/2009.10623
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time.
Ferran Alet; Kenji Kawaguchi; Tomas Lozano-Perez; Leslie Pack Kaelbling

http://arxiv.org/abs/2009.10568
Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks.
Ruizhe Gu; Ping Wang; Mengce Zheng; Honggang Hu; Nenghai Yu

http://arxiv.org/abs/2009.10235
Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks.
Boyuan Feng; Yuke Wang; Zheng Wang; Yufei Ding

http://arxiv.org/abs/2009.10233
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers.
Boyuan Feng; Yuke Wang; Xu Li; Yufei Ding

http://arxiv.org/abs/2009.09774
Generating Adversarial yet Inconspicuous Patches with a Single Image.
Jinqi Luo; Tao Bai; Jun Zhao; Bo Li

http://arxiv.org/abs/2009.10526
Adversarial Training with Stochastic Weight Average.
Joong-Won Hwang; Youngwan Lee; Sungchan Oh; Yuseok Bae

http://arxiv.org/abs/2009.09612
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness.
Anh Bui; Trung Le; He Zhao; Paul Montague; Olivier deVel; Tamas Abraham; Dinh Phung

http://arxiv.org/abs/2009.09663
DeepDyve: Dynamic Verification for Deep Neural Networks.
Yu Li; Min Li; Bo Luo; Ye Tian; Qiang Xu

http://arxiv.org/abs/2009.09922
Feature Distillation With Guided Adversarial Contrastive Learning.
Tao Bai; Jinnan Chen; Jun Zhao; Bihan Wen; Xudong Jiang; Alex Kot

http://arxiv.org/abs/2009.10149
Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version).
Gautam Raj Mode; Khaza Anuarul Hoque

http://arxiv.org/abs/2009.10142
Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations.
Alex Wong; Mukund Mundhra; Stefano Soatto

http://arxiv.org/abs/2009.10064
Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing.
Maurice Weber; Nana Liu; Bo Li; Ce Zhang; Zhikuan Zhao

http://arxiv.org/abs/2009.10060
Password Strength Signaling: A Counter-Intuitive Defense Against Password Cracking. (1%)
Wenjie Bai; Jeremiah Blocki; Ben Harsha

http://arxiv.org/abs/2009.09587
Improving Robustness and Generality of NLP Models Using Disentangled Representations.
Jiawei Wu; Xiaoya Li; Xiang Ao; Yuxian Meng; Fei Wu; Jiwei Li

http://arxiv.org/abs/2009.09318
Efficient Certification of Spatial Robustness.
Anian Ruoss; Maximilian Baader; Mislav Balunović; Martin Vechev

http://arxiv.org/abs/2009.09191
OpenAttack: An Open-source Textual Adversarial Attack Toolkit.
Guoyang Zeng; Fanchao Qi; Qianrui Zhou; Tingji Zhang; Bairu Hou; Yuan Zang; Zhiyuan Liu; Maosong Sun

http://arxiv.org/abs/2009.09258
Making Images Undiscoverable from Co-Saliency Detection.
Ruijun Gao; Qing Guo; Felix Juefei-Xu; Hongkai Yu; Xuhong Ren; Wei Feng; Song Wang

http://arxiv.org/abs/2009.09231
Adversarial Exposure Attack on Diabetic Retinopathy Imagery.
Yupeng Cheng; Felix Juefei-Xu; Qing Guo; Huazhu Fu; Xiaofei Xie; Shang-Wei Lin; Weisi Lin; Yang Liu

http://arxiv.org/abs/2009.09247
Bias Field Poses a Threat to DNN-based X-Ray Recognition.
Binyu Tian; Qing Guo; Felix Juefei-Xu; Wen Le Chan; Yupeng Cheng; Xiaohong Li; Xiaofei Xie; Shengchao Qin

http://arxiv.org/abs/2009.09192
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations.
Yuan Zang; Bairu Hou; Fanchao Qi; Zhiyuan Liu; Xiaojun Meng; Maosong Sun

http://arxiv.org/abs/2009.09205
Adversarial Rain Attack and Defensive Deraining for DNN Perception.
Liming Zhai; Felix Juefei-Xu; Qing Guo; Xiaofei Xie; Lei Ma; Wei Feng; Shengchao Qin; Yang Liu

http://arxiv.org/abs/2009.10537
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks.
Yaguan Qian; Qiqi Shao; Jiamin Wang; Xiang Lin; Yankai Guo; Zhaoquan Gu; Bin Wang; Chunming Wu

http://arxiv.org/abs/2009.09026
Robust Decentralized Learning for Neural Networks.
Yao Zhou; Jun Wu; Jingrui He

http://arxiv.org/abs/2009.09090
MIRAGE: Mitigating Conflict-Based Cache Attacks with a Practical Fully-Associative Design. (1%)
Gururaj Saileshwar; Moinuddin Qureshi

http://arxiv.org/abs/2009.08061
Certifying Confidence via Randomized Smoothing.
Aounon Kumar; Alexander Levine; Soheil Feizi; Tom Goldstein

http://arxiv.org/abs/2009.08205
Generating Label Cohesive and Well-Formed Adversarial Claims.
Pepa Atanasova; Dustin Wright; Isabelle Augenstein

http://arxiv.org/abs/2009.08194
Vax-a-Net: Training-time Defence Against Adversarial Patch Attacks.
T. Gittings; S. Schneider; J. Collomosse

http://arxiv.org/abs/2009.08233
Label Smoothing and Adversarial Robustness.
Chaohao Fu; Hongbin Chen; Na Ruan; Weijia Jia

http://arxiv.org/abs/2009.08110
Online Alternate Generator against Adversarial Attacks.
Haofeng Li; Yirui Zeng; Guanbin Li; Liang Lin; Yizhou Yu

http://arxiv.org/abs/2009.08058
MultAV: Multiplicative Adversarial Videos.
Shao-Yuan Lo; Vishal M. Patel

http://arxiv.org/abs/2009.08070
On the Transferability of Minimal Prediction Preserving Inputs in Question Answering.
Shayne Longpre; Yi Lu; Christopher DuBois

http://arxiv.org/abs/2009.08435
Large Norms of CNN Layers Do Not Hurt Adversarial Robustness.
Youwei Liang; Dong Huang

http://arxiv.org/abs/2009.08311
Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation.
Wenhao Ding; Baiming Chen; Bo Li; Kim Ji Eun; Ding Zhao

http://arxiv.org/abs/2009.07974
Analysis of Generalizability of Deep Neural Networks Based on the Complexity of Decision Boundary.
Shuyue Guan; Murray Loew

http://arxiv.org/abs/2009.07753
Malicious Network Traffic Detection via Deep Learning: An Information Theoretic View.
Erick Galinkin

http://arxiv.org/abs/2009.07502
Contextualized Perturbation for Textual Adversarial Attack.
Dianqi Li; Yizhe Zhang; Hao Peng; Liqun Chen; Chris Brockett; Ming-Ting Sun; Bill Dolan

http://arxiv.org/abs/2009.06962
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup.
Jang-Hyun Kim; Wonho Choo; Hyun Oh Song

http://arxiv.org/abs/2009.06996
Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems.
Haoliang Nanyang Technological University, Singapore Li; Yufei Nanyang Technological University, Singapore Wang; Xiaofei Nanyang Technological University, Singapore Xie; Yang Nanyang Technological University, Singapore Liu; Shiqi City University of Hong Kong Wang; Renjie Nanyang Technological University, Singapore Wan; Lap-Pui Nanyang Technological University, Singapore Chau; Alex C. Nanyang Technological University, Singapore Kot

http://arxiv.org/abs/2009.07191
Switching Gradient Directions for Query-Efficient Black-Box Adversarial Attacks.
Chen Ma; Shuyu Cheng; Li Chen; Junhai Yong

http://arxiv.org/abs/2009.07024
Decision-based Universal Adversarial Attack.
Jing Wu; Mingyi Zhou; Shuaicheng Liu; Yipeng Liu; Ce Zhu

http://arxiv.org/abs/2009.06530
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses.
Ambar Pal; René Vidal

http://arxiv.org/abs/2009.06571
Input Hessian Regularization of Neural Networks.
Waleed Mustafa; Robert A. Vandermeulen; Marius Kloft

http://arxiv.org/abs/2009.06589
Robust Deep Learning Ensemble against Deception.
Wenqi Wei; Ling Liu

http://arxiv.org/abs/2009.06701
Hold Tight and Never Let Go: Security of Deep Learning based Automated Lane Centering under Physical-World Attack.
Takami Sato; Junjie Shen; Ningfei Wang; Yunhan Jack Jia; Xue Lin; Qi Alfred Chen

http://arxiv.org/abs/2009.05965
Manifold attack.
Khanh-Hung Tran; Fred-Maurice Ngole-Mboula; Jean-Luc Starck

http://arxiv.org/abs/2009.06114
Towards the Quantification of Safety Risks in Deep Neural Networks.
Peipei Xu; Wenjie Ruan; Xiaowei Huang

http://arxiv.org/abs/2009.05872
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing.
Zhidong Gao; Rui Hu; Yanmin Gong

http://arxiv.org/abs/2009.05244
Defending Against Multiple and Unforeseen Adversarial Videos.
Shao-Yuan Lo; Vishal M. Patel

http://arxiv.org/abs/2009.05460
Robust Neural Machine Translation: Modeling Orthographic and Interpunctual Variation.
Toms Bergmanis; Artūrs Stafanovičs; Mārcis Pinnis

http://arxiv.org/abs/2009.05423
Achieving Adversarial Robustness via Sparsity.
Shufan Wang; Ningyi Liao; Liyao Xiang; Nanyang Ye; Quanshi Zhang

http://arxiv.org/abs/2009.05487
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples.
Timo Freiesleben

http://arxiv.org/abs/2009.05602
Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection.
Lan Zhang; Peng Liu; Yoon-Ho Choi

http://arxiv.org/abs/2009.04923
Second Order Optimization for Adversarial Robustness and Interpretability.
Theodoros Tsiligkaridis; Jay Roberts

http://arxiv.org/abs/2009.04709
Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent.
Ricardo Bigolin Lanfredi; Joyce D. Schroeder; Tolga Tasdizen

http://arxiv.org/abs/2009.04614
End-to-end Kernel Learning via Generative Random Fourier Features.
Kun Fang; Xiaolin Huang; Fanghui Liu; Jie Yang

http://arxiv.org/abs/2009.06368
Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples.
Jin Yong Yoo; John X. Morris; Eli Lifland; Yanjun Qi

http://arxiv.org/abs/2009.05474
A black-box adversarial attack for poisoning clustering.
Antonio Emanuele Cinà; Alessandro Torcinovich; Marcello Pelillo

http://arxiv.org/abs/2009.04131
SoK: Certified Robustness for Deep Neural Networks.
Linyi Li; Tao Xie; Bo Li

http://arxiv.org/abs/2009.04004
Fuzzy Unique Image Transformation: Defense Against Adversarial Attacks On Deep COVID-19 Models.
Achyut Mani Tripathi; Ashish Mishra

http://arxiv.org/abs/2009.03728
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective.
Gabriel Resende Machado; Eugênio Silva; Ronaldo Ribeiro Goldschmidt

http://arxiv.org/abs/2009.03364
Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method.
Michal Byra; Grzegorz Styczynski; Cezary Szmigielski; Piotr Kalinowski; Lukasz Michalowski; Rafal Paluszkiewicz; Bogna Ziarkiewicz-Wroblewska; Krzysztof Zieniewicz; Andrzej Nowicki

http://arxiv.org/abs/2009.03488
Adversarial Attack on Large Scale Graph.
Jintang Li; Tao Xie; Liang Chen; Fenfang Xie; Xiangnan He; Zibin Zheng

http://arxiv.org/abs/2009.03136
Black Box to White Box: Discover Model Characteristics Based on Strategic Probing.
Josh Kalin; Matthew Ciolino; David Noever; Gerry Dozier

http://arxiv.org/abs/2009.02877
A Game Theoretic Analysis of LQG Control under Adversarial Attack.
Zuxing Li; György Dán; Dong Liu

http://arxiv.org/abs/2009.02874
Dynamically Computing Adversarial Perturbations for Recurrent Neural Networks.
Shankar A. Deka; Dušan M. Stipanović; Claire J. Tomlin

http://arxiv.org/abs/2009.02738
Detection Defense Against Adversarial Attacks with Saliency Map.
Dengpan Ye; Chuanxi Chen; Changrui Liu; Hao Wang; Shunzhi Jiang

http://arxiv.org/abs/2009.02608
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks.
Nilaksh Polo Das; Haekyu Polo Park; Zijie J. Polo Wang; Fred Polo Hohman; Robert Polo Firstman; Emily Polo Rogers; Duen Polo Horng; Chau

http://arxiv.org/abs/2009.02470
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks.
Wei-An Lin; Chun Pong Lau; Alexander Levine; Rama Chellappa; Soheil Feizi

http://arxiv.org/abs/2009.01729
MIPGAN -- Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN. (10%)
Haoyu Zhang; Sushma Venkatesh; Raghavendra Ramachandra; Kiran Raja; Naser Damer; Christoph Busch

http://arxiv.org/abs/2009.01672
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily.
Han Xu; Yaxin Li; Xiaorui Liu; Hui Liu; Jiliang Tang

http://arxiv.org/abs/2009.01110
Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation.
Danilo Vasconcellos Vargas; Bingli Liao; Takahiro Kanzaki

http://arxiv.org/abs/2009.00814
Open-set Adversarial Defense.
Rui Shao; Pramuditha Perera; Pong C. Yuen; Vishal M. Patel

http://arxiv.org/abs/2009.00902
Adversarially Robust Neural Architectures.
Minjing Dong; Yanxi Li; Yunhe Wang; Chang Xu

http://arxiv.org/abs/2009.01122
Flow-based detection and proxy-based evasion of encrypted malware C2 traffic.
Carlos University of Porto and INESC TEC Novo; Ricardo University of Porto and INESC TEC Morla

http://arxiv.org/abs/2009.01109
Adversarial Attacks on Deep Learning Systems for User Identification based on Motion Sensors.
Cezara Benegui; Radu Tudor Ionescu

http://arxiv.org/abs/2009.00960
Simulating Unknown Target Models for Query-Efficient Black-box Attacks.
Chen Ma; Li Chen; Jun-Hai Yong

http://arxiv.org/abs/2009.09803
Defending against substitute model black box adversarial attacks with the 01 loss.
Yunzhe Xue; Meiyan Xie; Usman Roshan

http://arxiv.org/abs/2008.13671
Adversarial Patch Camouflage against Aerial Detection.
Ajaya Adhikari; Richard den Hollander; Ioannis Tolios; Bekkum Michael van; Anneloes Bal; Stijn Hendriks; Maarten Kruithof; Dennis Gross; Nils Jansen; Guillermo Pérez; Kit Buurman; Stephan Raaijmakers

http://arxiv.org/abs/2009.01048
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models.
Thai Le; Suhang Wang; Dongwon Lee

http://arxiv.org/abs/2009.00203
Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function.
Binghui Wang; Tianxiang Zhou; Minhua Lin; Pan Zhou; Ang Li; Meng Pang; Hai Li; Yiran Chen

http://arxiv.org/abs/2008.13261
Benchmarking adversarial attacks and defenses for time-series data.
Shoaib Ahmed Siddiqui; Andreas Dengel; Sheraz Ahmed

http://arxiv.org/abs/2008.13305
An Integrated Approach to Produce Robust Models with High Efficiency.
Zhijian Li; Bao Wang; Jack Xin

http://arxiv.org/abs/2008.13336
Shape Defense Against Adversarial Attacks.
Ali Borji

http://arxiv.org/abs/2008.12997
Improving Resistance to Adversarial Deformations by Regularizing Gradients.
Pengfei Xia; Bin Li

http://arxiv.org/abs/2008.12328
A Scene-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video.
Mariana-Iuliana Georgescu; Radu Tudor Ionescu; Fahad Shahbaz Khan; Marius Popescu; Mubarak Shah

http://arxiv.org/abs/2008.12008
GhostBuster: Looking Into Shadows to Detect Ghost Objects in Autonomous Vehicle 3D Sensing.
Zhongyuan Hau; Soteris Demetriou; Luis Muñoz-González; Emil C. Lupu

http://arxiv.org/abs/2008.12066
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds.
Jaeyeon Kim; Binh-Son Hua; Duc Thanh Nguyen; Sai-Kit Yeung

http://arxiv.org/abs/2008.12016
On the Intrinsic Robustness of NVM Crossbars Against Adversarial Attacks.
Deboleena Roy; Indranil Chakraborty; Timur Ibrayev; Kaushik Roy

http://arxiv.org/abs/2009.00097
Adversarial Eigen Attack on Black-Box Models.
Linjun Zhou; Peng Cui; Yinan Jiang; Shiqiang Yang

http://arxiv.org/abs/2008.12454
Color and Edge-Aware Adversarial Image Perturbations.
Robert Bassett; Mitchell Graves; Patrick Reilly

http://arxiv.org/abs/2008.12338
Adversarially Robust Learning via Entropic Regularization.
Gauri Jagatap; Ameya Joshi; Animesh Basak Chowdhury; Siddharth Garg; Chinmay Hegde

http://arxiv.org/abs/2008.11618
Adversarially Training for Audio Classifiers.
Raymel Alfonso Sallo; Mohammad Esmaeilpour; Patrick Cardinal

http://arxiv.org/abs/2008.11300
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses.
Fu Lin; Rohit Mittapalli; Prithvijit Chattopadhyay; Daniel Bolya; Judy Hoffman

http://arxiv.org/abs/2008.11089
Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning.
Yinghua Zhang; Yangqiu Song; Jian Liang; Kun Bai; Qiang Yang

http://arxiv.org/abs/2008.11298
Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks.
Abhiroop Bhattacharjee; Priyadarshini Panda

http://arxiv.org/abs/2008.11278
An Adversarial Attack Defending System for Securing In-Vehicle Networks.
Yi Li; Jing Lin; Kaiqi Xiong

http://arxiv.org/abs/2008.10715
Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation.
Binghui Wang; Jinyuan Jia; Xiaoyu Cao; Neil Zhenqiang Gong

http://arxiv.org/abs/2008.10106
Developing and Defeating Adversarial Examples.
Ian McDiarmid-Sterling; Allan Moser

http://arxiv.org/abs/2008.09954
Ptolemy: Architecture Support for Robust Deep Learning.
Yiming Gan; Yuxian Qiu; Jingwen Leng; Minyi Guo; Yuhao Zhu

http://arxiv.org/abs/2008.10138
PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards.
Masoud Hashemi; Ali Fathi

http://arxiv.org/abs/2008.09824
Self-Competitive Neural Networks.
Iman Saberi; Fathiyeh Faghih

http://arxiv.org/abs/2008.09381
A Survey on Assessing the Generalization Envelope of Deep Neural Networks: Predictive Uncertainty, Out-of-distribution and Adversarial Samples.
Julia Lust; Alexandru Paul Condurache

http://arxiv.org/abs/2008.09148
Towards adversarial robustness with 01 loss neural networks.
Yunzhe Xue; Meiyan Xie; Usman Roshan

http://arxiv.org/abs/2008.09194
On Attribution of Deepfakes.
Baiwu Zhang; Jin Peng Zhou; Ilia Shumailov; Nicolas Papernot

http://arxiv.org/abs/2008.09010
$\beta$-Variational Classifiers Under Attack.
Marco Maggipinto; Matteo Terzi; Gian Antonio Susto

http://arxiv.org/abs/2008.08847
Yet Another Intermediate-Level Attack.
Qizhang Li; Yiwen Guo; Hao Chen

http://arxiv.org/abs/2008.08750
Prototype-based interpretation of the functionality of neurons in winner-take-all neural networks.
Ramin Zarei Sabzevar; Kamaledin Ghiasi-Shirazi; Ahad Harati

http://arxiv.org/abs/2008.08384
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training.
Alfred Laugros; Alice Caplier; Matthieu Ospici

http://arxiv.org/abs/2008.08755
On $\ell_p$-norm Robustness of Ensemble Stumps and Trees.
Yihan Wang; Huan Zhang; Hongge Chen; Duane Boning; Cho-Jui Hsieh

http://arxiv.org/abs/2008.07838
Improving adversarial robustness of deep neural networks by using semantic information.
Lina Wang; Rui Tang; Yawei Yue; Xingshu Chen; Wei Wang; Yi Zhu; Xuemei Zeng

http://arxiv.org/abs/2008.09041
Direct Adversarial Training for GANs.
Ziqiang Li

http://arxiv.org/abs/2008.08170
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization.
Feihu Huang; Shangqian Gao; Jian Pei; Heng Huang

http://arxiv.org/abs/2008.07651
A Deep Dive into Adversarial Robustness in Zero-Shot Learning.
Mehmet Kerim Yucel; Ramazan Gokberk Cinbis; Pinar Duygulu

http://arxiv.org/abs/2008.07685
Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems.
Arindam Jati; Chin-Cheng Hsu; Monisankha Pal; Raghuveer Peri; Wael AbdAlmageed; Shrikanth Narayanan

http://arxiv.org/abs/2008.07125
Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection.
Luca Demetrio; Scott E. Coull; Battista Biggio; Giovanni Lagorio; Alessandro Armando; Fabio Roli

http://arxiv.org/abs/2008.07230
Robustness Verification of Quantum Classifiers. (81%)
Ji Guan; Wang Fang; Mingsheng Ying

http://arxiv.org/abs/2008.06860
TextDecepter: Hard Label Black Box Attack on Text Classifiers.
Sachin Saxena

http://arxiv.org/abs/2008.07015
Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural Networks.
Elahe Arani; Fahad Sarfraz; Bahram Zonooz

http://arxiv.org/abs/2008.06822
Relevance Attack on Detectors.
Sizhe Chen; Fan He; Xiaolin Huang; Kun Zhang

http://arxiv.org/abs/2008.06199
Defending Adversarial Attacks without Adversarial Attacks in Deep Reinforcement Learning.
Xinghua Qu; Yew-Soon Ong; Abhishek Gupta; Zhu Sun

http://arxiv.org/abs/2008.06631
On the Generalization Properties of Adversarial Training.
Yue Xing; Qifan Song; Guang Cheng

http://arxiv.org/abs/2009.05107
Generating Image Adversarial Examples by Embedding Digital Watermarks.
Yuexin Xiang; Tiantian Li; Wei Ren; Tianqing Zhu; Kim-Kwang Raymond Choo

http://arxiv.org/abs/2008.06081
Adversarial Training and Provable Robustness: A Tale of Two Objectives.
Jiameng Fan; Wenchao Li

http://arxiv.org/abs/2008.06069
Semantically Adversarial Learnable Filters.
Ali Shahin Shamsabadi; Changjae Oh; Andrea Cavallaro

http://arxiv.org/abs/2008.07369
Continuous Patrolling Games. (45%)
Steve Alpern; Thuy Bui; Thomas Lidbetter; Katerina Papadaki

http://arxiv.org/abs/2008.05247
Learning to Learn from Mistakes: Robust Optimization for Adversarial Noise.
Alex Serban; Erik Poll; Joost Visser

http://arxiv.org/abs/2008.05230
Defending Adversarial Examples via DNN Bottleneck Reinforcement.
Wenqing Liu; Miaojing Shi; Teddy Furon; Li Li

http://arxiv.org/abs/2008.05667
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness.
Md Amirul Islam; Matthew Kowal; Konstantinos G. Derpanis; Neil D. B. Bruce

http://arxiv.org/abs/2008.05536
Semantics-preserving adversarial attacks in NLP.
Rahul Singh; Tarun Joshi; Vijayan N. Nair; Agus Sudjianto

http://arxiv.org/abs/2008.04876
Revisiting Adversarially Learned Injection Attacks Against Recommender Systems.
Jiaxi Tang; Hongyi Wen; Ke Wang

http://arxiv.org/abs/2008.04254
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective.
Baifeng Shi; Dinghuai Zhang; Qi Dai; Zhanxing Zhu; Yadong Mu; Jingdong Wang

http://arxiv.org/abs/2008.04203
FireBERT: Hardening BERT-based classifiers against adversarial attack.
Gunnar Mein; Kevin Hartman; Andrew Morris

http://arxiv.org/abs/2008.03677
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems.
Mohammad J. Hashemi; Eric Keller

http://arxiv.org/abs/2008.03709
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks.
Xiaosen Wang; Yichen Yang; Yihe Deng; Kun He

http://arxiv.org/abs/2008.03609
Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length.
Linhai Ma; Liang Liang

http://arxiv.org/abs/2008.10356
Visual Attack and Defense on Text.
Shengjun Liu; Ningkang Jiang; Yuanbin Wu

http://arxiv.org/abs/2008.03072
Optimizing Information Loss Towards Robust Neural Networks.
Philip Sperl; Konstantin Böttinger

http://arxiv.org/abs/2008.04094
Adversarial Examples on Object Recognition: A Comprehensive Survey.
Alex Serban; Erik Poll; Joost Visser

http://arxiv.org/abs/2008.02883
Stronger and Faster Wasserstein Adversarial Attacks.
Kaiwen Wu; Allen Houze Wang; Yaoliang Yu

http://arxiv.org/abs/2008.02965
Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations.
Ziquan Liu; Yufei Cui; Antoni B. Chan

http://arxiv.org/abs/2008.02197
One word at a time: adversarial attacks on retrieval models.
Nisarg Raval; Manisha Verma

http://arxiv.org/abs/2008.01976
Robust Deep Reinforcement Learning through Adversarial Loss.
Tuomas Oikarinen; Wang Zhang; Alexandre Megretski; Luca Daniel; Tsui-Wei Weng

http://arxiv.org/abs/2008.01919
Adv-watermark: A Novel Watermark Perturbation for Adversarial Examples.
Xiaojun Jia; Xingxing Wei; Xiaochun Cao; Xiaoguang Han

http://arxiv.org/abs/2008.01524
TREND: Transferability based Robust ENsemble Design.
Deepak Ravikumar; Sangamesh Kodge; Isha Garg; Kaushik Roy

http://arxiv.org/abs/2008.01761
Can Adversarial Weight Perturbations Inject Neural Backdoors?
Siddhant Garg; Adarsh Kumar; Vibhor Goel; Yingyu Liang

http://arxiv.org/abs/2008.01786
Entropy Guided Adversarial Model for Weakly Supervised Object Localization.
Sabrina Narimene Benassou; Wuzhen Shi; Feng Jiang

http://arxiv.org/abs/2008.01219
Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks.
Haoqiang Guo; Lu Peng; Jian Zhang; Fang Qi; Lide Duan

http://arxiv.org/abs/2008.00698
Anti-Bandit Neural Architecture Search for Model Defense.
Hanlin Chen; Baochang Zhang; Song Xue; Xuan Gong; Hong Liu; Rongrong Ji; David Doermann

http://arxiv.org/abs/2008.00217
Efficient Adversarial Attacks for Visual Object Tracking.
Siyuan Liang; Xingxing Wei; Siyuan Yao; Xiaochun Cao

http://arxiv.org/abs/2008.00312
Trojaning Language Models for Fun and Profit.
Xinyang Zhang; Zheng Zhang; Shouling Ji; Ting Wang

http://arxiv.org/abs/2008.00138
Vulnerability Under Adversarial Machine Learning: Bias or Variance?
Hossein Aboutalebi; Mohammad Javad Shafiee; Michelle Karg; Christian Scharfenberger; Alexander Wong

http://arxiv.org/abs/2007.16118
Physical Adversarial Attack on Vehicle Detector in the Carla Simulator.
Tong Wu; Xuefei Ning; Wenshuo Li; Ranran Huang; Huazhong Yang; Yu Wang

http://arxiv.org/abs/2007.16204
Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers.
Brian Kim; Yalin E. Sagduyu; Tugba Erpek; Kemal Davaslioglu; Sennur Ulukus

http://arxiv.org/abs/2007.15836
TEAM: We Need More Powerful Adversarial Examples for DNNs.
Yaguan Qian; Ximin Zhang; Bin Wang; Wei Li; Zhaoquan Gu; Haijiang Wang; Wassim Swaileh

http://arxiv.org/abs/2007.15310
Black-box Adversarial Sample Generation Based on Differential Evolution.
Junyu Lin; Lei Xu; Yingqi Liu; Xiangyu Zhang

http://arxiv.org/abs/2007.15290
A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks.
Yi Zeng; Han Qiu; Gerard Memmi; Meikang Qiu

http://arxiv.org/abs/2007.15805
vWitness: Certifying Web Page Interactions with Computer Vision. (83%)
He Shuang; Lianying Zhao; David Lie

http://arxiv.org/abs/2007.14714
End-to-End Adversarial White Box Attacks on Music Instrument Classification.
Katharina Johannes Kepler University Linz Prinz; Arthur Johannes Kepler University Linz Flexer

http://arxiv.org/abs/2007.14983
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data.
Kai Steverson; Jonathan Mullin; Metin Ahiskali

http://arxiv.org/abs/2007.15036
Generative Classifiers as a Basis for Trustworthy Computer Vision.
Radek Mackowiak; Lynton Ardizzone; Ullrich Köthe; Carsten Rother

http://arxiv.org/abs/2007.14672
Stylized Adversarial Defense.
Muzammal Naseer; Salman Khan; Munawar Hayat; Fahad Shahbaz Khan; Fatih Porikli

http://arxiv.org/abs/2007.15147
Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers.
Jayaram Raghuram; Varun Chandrasekaran; Somesh Jha; Suman Banerjee

http://arxiv.org/abs/2007.14433
Cassandra: Detecting Trojaned Networks from Adversarial Perturbations.
Xiaoyu Zhang; Ajmal Mian; Rohit Gupta; Nazanin Rahnavard; Mubarak Shah

http://arxiv.org/abs/2007.14042
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning.
Jirong Yi; Raghu Mudumbai; Weiyu Xu

http://arxiv.org/abs/2007.14120
Reachable Sets of Classifiers and Regression Models: (Non-)Robustness Analysis and Robust Training.
Anna-Kathrin Kopetzki; Stephan Günnemann

http://arxiv.org/abs/2007.14321
Label-Only Membership Inference Attacks.
Christopher A. Choquette-Choo; Florian Tramer; Nicholas Carlini; Nicolas Papernot

http://arxiv.org/abs/2008.02076
Attacking and Defending Machine Learning Applications of Public Cloud.
Dou Goodman; Hao Xin

http://arxiv.org/abs/2007.13960
KOVIS: Keypoint-based Visual Servoing with Zero-Shot Sim-to-Real Transfer for Robotics Manipulation.
En Yen Puang; Keng Peng Tee; Wei Jing

http://arxiv.org/abs/2007.13703
From Sound Representation to Model Robustness.
Mohamad Esmaeilpour; Patrick Cardinal; Alessandro Lameiras Koerich

http://arxiv.org/abs/2007.13632
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing.
Yi Zhang; Jitao Sang

http://arxiv.org/abs/2007.14249
RANDOM MASK: Towards Robust Convolutional Neural Networks.
Tiange Luo; Tianle Cai; Mengxiao Zhang; Siyu Chen; Liwei Wang

http://arxiv.org/abs/2007.13073
Robust Collective Classification against Structural Attacks.
Kai Zhou; Yevgeniy Vorobeychik

http://arxiv.org/abs/2007.13171
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection. (1%)
Elizabeth Newman; Lars Ruthotto; Joseph Hart; Bart van Bloemen Waanders

http://arxiv.org/abs/2007.12881
MirrorNet: Bio-Inspired Adversarial Attack for Camouflaged Object Segmentation.
Jinnan Yan; Trung-Nghia Le; Khanh-Duy Nguyen; Minh-Triet Tran; Thanh-Toan Do; Tam V. Nguyen

http://arxiv.org/abs/2007.12861
Adversarial Privacy-preserving Filter.
Jiaming Zhang; Jitao Sang; Xian Zhao; Xiaowen Huang; Yanfeng Sun; Yongli Hu

http://arxiv.org/abs/2007.12892
MP3 Compression To Diminish Adversarial Noise in End-to-End Speech Recognition.
Iustina Andronic; Ludwig Kürzinger; Edgar Ricardo Chavez Rosas; Gerhard Rigoll; Bernhard U. Seeber

http://arxiv.org/abs/2007.12684
Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation. (1%)
Luyu Yang; Yan Wang; Mingfei Gao; Abhinav Shrivastava; Kilian Q. Weinberger; Wei-Lun Chao; Ser-Nam Lim

http://arxiv.org/abs/2007.12133
Provably Robust Adversarial Examples.
Dimitar I. Dimitrov; Gagandeep Singh; Timon Gehr; Martin Vechev

http://arxiv.org/abs/2007.11206
SOCRATES: Towards a Unified Platform for Neural Network Verification.
Long H. Pham; Jiaying Li; Jun Sun

http://arxiv.org/abs/2007.11259
Adversarial Training Reduces Information and Improves Transferability.
Matteo Terzi; Alessandro Achille; Marco Maggipinto; Gian Antonio Susto

http://arxiv.org/abs/2007.11693
Robust Machine Learning via Privacy/Rate-Distortion Theory.
Ye Wang; Shuchin Aeron; Adnan Siraj Rakin; Toshiaki Koike-Akino; Pierre Moulin

http://arxiv.org/abs/2007.11709
Threat of Adversarial Attacks on Face Recognition: A Comprehensive Survey.
Fatemeh Vakhshiteh; Raghavendra Ramachandra; Ahmad Nickabadi

http://arxiv.org/abs/2007.10723
Audio Adversarial Examples for Robust Hybrid CTC/Attention Speech Recognition.
Ludwig Kürzinger; Edgar Ricardo Chavez Rosas; Lujun Li; Tobias Watzel; Gerhard Rigoll

http://arxiv.org/abs/2007.10593
Towards Visual Distortion in Black-Box Attacks.
Nannan Li; Zhenzhong Chen

http://arxiv.org/abs/2007.10505
DeepNNK: Explaining deep models and their generalization using polytope interpolation.
Sarath Shekkizhar; Antonio Ortega

http://arxiv.org/abs/2007.09916
Evaluating a Simple Retraining Strategy as a Defense Against Adversarial Attacks.
Nupur Thakur; Yuzhen Ding; Baoxin Li

http://arxiv.org/abs/2007.09919
Robust Tracking against Adversarial Attacks.
Shuai Jia; Chao Ma; Yibing Song; Xiaokang Yang

http://arxiv.org/abs/2007.10868
Scaling Polyhedral Neural Network Verification on GPUs.
Christoph Müller; François Serre; Gagandeep Singh; Markus Püschel; Martin Vechev

http://arxiv.org/abs/2007.10485
AdvFoolGen: Creating Persistent Troubles for Deep Classifiers.
Yuzhen Ding; Nupur Thakur; Baoxin Li

http://arxiv.org/abs/2007.09592
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering.
Ruixue Tang; Chao Ma; Wei Emma Zhang; Qi Wu; Xiaokang Yang

http://arxiv.org/abs/2007.09766
Exploiting vulnerabilities of deep neural networks for privacy protection.
Ricardo Sanchez-Matilla; Chau Yi Li; Ali Shahin Shamsabadi; Riccardo Mazzon; Andrea Cavallaro

http://arxiv.org/abs/2007.09763
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency.
Shasha Li; Shitong Zhu; Sudipta Paul; Amit Roy-Chowdhury; Chengyu Song; Srikanth Krishnamurthy; Ananthram Swami; Kevin S Chan

http://arxiv.org/abs/2007.09647
Adversarial Immunization for Improving Certifiable Robustness on Graphs.
Shuchang Tao; Huawei Shen; Qi Cao; Liang Hou; Xueqi Cheng

http://arxiv.org/abs/2007.09431
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection.
Dongyun Lin; Yiqun Li; Shudong Xie; Tin Lay Nwe; Sheng Dong

http://arxiv.org/abs/2007.09327
Towards Quantum-Secure Authentication and Key Agreement via Abstract Multi-Agent Interaction. (1%)
Ibrahim H. Ahmed; Josiah P. Hanna; Elliot Fosong; Stefano V. Albrecht

http://arxiv.org/abs/2007.10812
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense.
Sayeed Shafayet Chowdhury; Kaji Mejbaul Islam; Rouhan Noor

http://arxiv.org/abs/2007.09200
Neural Networks with Recurrent Generative Feedback.
Yujia Huang; James Gornet; Sihui Dai; Zhiding Yu; Tan Nguyen; Doris Y. Tsao; Anima Anandkumar

http://arxiv.org/abs/2007.08716
Understanding and Diagnosing Vulnerability under Adversarial Attacks.
Haizhong Zheng; Ziqi Zhang; Honglak Lee; Atul Prakash

http://arxiv.org/abs/2007.08714
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources.
Yun-Yun Tsai; Pin-Yu Chen; Tsung-Yi Ho

http://arxiv.org/abs/2007.12625
Accelerated Stochastic Gradient-free and Projection-free Methods.
Feihu Huang; Lue Tao; Songcan Chen

http://arxiv.org/abs/2007.08473
Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data.
Julian Bitterwolf; Alexander Meinke; Matthias Hein

http://arxiv.org/abs/2007.08428
An Empirical Study on the Robustness of NAS based Architectures.
Chaitanya Devaguptapu; Devansh Agarwal; Gaurav Mittal; Vineeth N Balasubramanian

http://arxiv.org/abs/2007.08489
Do Adversarially Robust ImageNet Models Transfer Better?
Hadi Salman; Andrew Ilyas; Logan Engstrom; Ashish Kapoor; Aleksander Madry

http://arxiv.org/abs/2007.08450
Learning perturbation sets for robust machine learning.
Eric Wong; J. Zico Kolter

http://arxiv.org/abs/2007.08558
On Robustness and Transferability of Convolutional Neural Networks. (1%)
Josip Djolonga; Jessica Yung; Michael Tschannen; Rob Romijnders; Lucas Beyer; Alexander Kolesnikov; Joan Puigcerver; Matthias Minderer; Alexander D'Amour; Dan Moldovan; Sylvain Gelly; Neil Houlsby; Xiaohua Zhai; Mario Lucic

http://arxiv.org/abs/2007.08319
Less is More: A privacy-respecting Android malware classifier using Federated Learning. (1%)
Rafa Gálvez; Veelasha Moonsamy; Claudia Diaz

http://arxiv.org/abs/2007.07646
A Survey of Privacy Attacks in Machine Learning.
Maria Rigaki; Sebastian Garcia

http://arxiv.org/abs/2007.08520
Accelerating Robustness Verification of Deep Neural Networks Guided by Target Labels.
Wenjie Wan; Zhaodi Zhang; Yiwei Zhu; Min Zhang; Fu Song

http://arxiv.org/abs/2007.08041
A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles.
Minh Pham; Kaiqi Xiong

http://arxiv.org/abs/2007.10115
Towards robust sensing for Autonomous Vehicles: An adversarial perspective.
Apostolos Modas; Ricardo Sanchez-Matilla; Pascal Frossard; Andrea Cavallaro

http://arxiv.org/abs/2007.07176
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training.
Kai Liang Tan; Yasaman Esfandiari; Xian Yeow Lee; Aakanksha; Soumik Sarkar

http://arxiv.org/abs/2007.06803
Bounding The Number of Linear Regions in Local Area for Neural Networks with ReLU Activations.
Rui Zhu; Bo Lin; Haixu Tang

http://arxiv.org/abs/2007.07236
Multitask Learning Strengthens Adversarial Robustness.
Chengzhi Mao; Amogh Gupta; Vikram Nitin; Baishakhi Ray; Shuran Song; Junfeng Yang; Carl Vondrick

http://arxiv.org/abs/2007.06993
Adversarial Examples and Metrics.
Nico Döttling; Kathrin Grosse; Michael Backes; Ian Molloy

http://arxiv.org/abs/2007.07435
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows.
Hadi M. Dolatabadi; Sarah Erfani; Christopher Leckie

http://arxiv.org/abs/2007.07097
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack.
Yupeng Cheng; Qing Guo; Felix Juefei-Xu; Wei Feng; Shang-Wei Lin; Weisi Lin; Yang Liu

http://arxiv.org/abs/2007.07001
Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components.
Ken Alparslan; Yigit Alparslan; Matthew Burlick

http://arxiv.org/abs/2007.07365
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders.
Alexander Camuto; Matthew Willetts; Stephen Roberts; Chris Holmes; Tom Rainforth

http://arxiv.org/abs/2007.06381
A simple defense against adversarial attacks on heatmap explanations.
Laura Rieger; Lars Kai Hansen

http://arxiv.org/abs/2007.06189
Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations.
Chaoning Zhang; Philipp Benz; Tooba Imtiaz; In-So Kweon

http://arxiv.org/abs/2007.06555
Adversarial robustness via robust low rank representations.
Pranjal Awasthi; Himanshu Jain; Ankit Singh Rawat; Aravindan Vijayaraghavan

http://arxiv.org/abs/2007.07205
Security and Machine Learning in the Real World.
Ivan Evtimov; Weidong Cui; Ece Kamar; Emre Kiciman; Tadayoshi Kohno; Jerry Li

http://arxiv.org/abs/2007.07210
Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes.
Satya Narayan Shukla; Anit Kumar Sahu; Devin Willmott; J. Zico Kolter

http://arxiv.org/abs/2007.06796
Calling Out Bluff: Attacking the Robustness of Automatic Scoring Systems with Simple Adversarial Testing.
Yaman Kumar; Mehar Bhatia; Anubha Kabra; Jessy Junyi Li; Di Jin; Rajiv Ratn Shah

http://arxiv.org/abs/2007.06622
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems.
Hadi Abdullah; Kevin Warren; Vincent Bindschaedler; Nicolas Papernot; Patrick Traynor

http://arxiv.org/abs/2007.06765
Patch-wise Attack for Fooling Deep Neural Network.
Lianli Gao; Qilong Zhang; Jingkuan Song; Xianglong Liu; Heng Tao Shen

http://arxiv.org/abs/2007.06174
Generating Fluent Adversarial Examples for Natural Languages.
Huangzhao Zhang; Hao Zhou; Ning Miao; Lei Li

http://arxiv.org/abs/2007.06055
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning.
Feng Wang; Chen Zhong; M. Cenk Gursoy; Senem Velipasalar

http://arxiv.org/abs/2007.06032
Probabilistic Jacobian-based Saliency Maps Attacks.
Théo Combey; António Loison; Maxime Faucher; Hatem Hajri

http://arxiv.org/abs/2007.05828
Understanding Object Detection Through An Adversarial Lens.
Ka-Ho Chow; Ling Liu; Mehmet Emre Gursoy; Stacey Truex; Wenqi Wei; Yanzhao Wu

http://arxiv.org/abs/2007.05817
ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples.
Guanxiong Liu; Issa Khalil; Abdallah Khreishah; Abdulelah Algosaibi; Adel Aldalbahi; Mohammed Alaneem; Abdulaziz Alhumam; Mohammed Anan

http://arxiv.org/abs/2007.05869
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. (15%)
Francisco Utrera; Evan Kravitz; N. Benjamin Erichson; Rajiv Khanna; Michael W. Mahoney

http://arxiv.org/abs/2007.05573
Improved Detection of Adversarial Images Using Deep Neural Networks.
Yutong Gao; Yi Pan

http://arxiv.org/abs/2007.05225
Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection.
Qingsong Yao; Zecheng He; Hu Han; S. Kevin Zhou

http://arxiv.org/abs/2007.05315
Generating Adversarial Inputs Using A Black-box Differential Technique.
João Batista Pereira Matos Juúnior; Lucas Carvalho Cordeiro; Marcelo d'Amorim; Xiaowei Huang

http://arxiv.org/abs/2007.05123
Improving Adversarial Robustness by Enforcing Local and Global Compactness.
Anh Bui; Trung Le; He Zhao; Paul Montague; Olivier deVel; Tamas Abraham; Dinh Phung

http://arxiv.org/abs/2007.05086
Boundary thickness and robustness in learning models.
Yaoqing Yang; Rajiv Khanna; Yaodong Yu; Amir Gholami; Kurt Keutzer; Joseph E. Gonzalez; Kannan Ramchandran; Michael W. Mahoney

http://arxiv.org/abs/2007.06704
Node Copying for Protection Against Graph Neural Network Topology Attacks.
Florence Regol; Soumyasundar Pal; Mark Coates

http://arxiv.org/abs/2007.04564
Efficient detection of adversarial images.
Darpan Kumar Yadav; Kartik Mundra; Rahul Modpur; Arpan Chattopadhyay; Indra Narayan Kar

http://arxiv.org/abs/2007.04028
How benign is benign overfitting?
Amartya Sanyal; Puneet K Dokania; Varun Kanade; Philip H. S. Torr

http://arxiv.org/abs/2007.04137
SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations.
Giulio Lovisotto; Henry Turner; Ivo Sluganovic; Martin Strohmeier; Ivan Martinovic

http://arxiv.org/abs/2007.04118
RobFR: Benchmarking Adversarial Robustness on Face Recognition.
Xiao Yang; Dingcheng Yang; Yinpeng Dong; Hang Su; Wenjian Yu; Jun Zhu

http://arxiv.org/abs/2007.04391
A Critical Evaluation of Open-World Machine Learning.
Liwei Song; Vikash Sehwag; Arjun Nitin Bhagoji; Prateek Mittal

http://arxiv.org/abs/2007.04440
On the relationship between class selectivity, dimensionality, and robustness.
Matthew L. Leavitt; Ari S. Morcos

http://arxiv.org/abs/2007.04472
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs.
Rana Abou Khamis; Ashraf Matrawy

http://arxiv.org/abs/2007.03244
Robust Learning with Frequency Domain Regularization.
Weiyu Guo; Yidong Ouyang

http://arxiv.org/abs/2007.03198
Regional Image Perturbation Reduces $L_p$ Norms of Adversarial Examples While Maintaining Model-to-model Transferability.
Utku Ozbulak; Jonathan Peck; Neve Wesley De; Bart Goossens; Yvan Saeys; Messem Arnout Van

http://arxiv.org/abs/2007.03832
Fast Training of Deep Neural Networks Robust to Adversarial Perturbations.
Justin Goodwin; Olivia Brown; Victoria Helus

http://arxiv.org/abs/2007.03838
Making Adversarial Examples More Transferable and Indistinguishable.
Junhua Zou; Yexin Duan; Boyu Li; Wu Zhang; Yu Pan; Zhisong Pan

http://arxiv.org/abs/2007.03730
Detection as Regression: Certified Object Detection by Median Smoothing.
Ping-yeh Chiang; Michael J. Curry; Ahmed Abdelkader; Aounon Kumar; John Dickerson; Tom Goldstein

http://arxiv.org/abs/2007.02771
Certifying Decision Trees Against Evasion Attacks by Program Analysis.
Stefano Calzavara; Pietro Ferrara; Claudio Lucchese

http://arxiv.org/abs/2007.02650
On Data Augmentation and Adversarial Risk: An Empirical Analysis.
Hamid Eghbal-zadeh; Khaled Koutini; Paul Primus; Verena Haunschmid; Michal Lewandowski; Werner Zellinger; Bernhard A. Moser; Gerhard Widmer

http://arxiv.org/abs/2007.02617
Understanding and Improving Fast Adversarial Training.
Maksym Andriushchenko; Nicolas Flammarion

http://arxiv.org/abs/2007.02734
Black-box Adversarial Example Generation with Normalizing Flows.
Hadi M. Dolatabadi; Sarah Erfani; Christopher Leckie

http://arxiv.org/abs/2007.02407
Adversarial Learning in the Cyber Security Domain.
Ihai Rosenberg; Asaf Shabtai; Yuval Elovici; Lior Rokach

http://arxiv.org/abs/2007.02209
On Connections between Regularizations for Improving DNN Robustness.
Yiwen Guo; Long Chen; Yurong Chen; Changshui Zhang

http://arxiv.org/abs/2007.02047
Relationship between manifold smoothness and adversarial vulnerability in deep learning with local errors.
Zijian Jiang; Jianwen Zhou; Haiping Huang

http://arxiv.org/abs/2007.02196
Deep Active Learning via Open Set Recognition. (1%)
Jaya Krishna Mandivarapu; Blake Camp; Rolando Estrada

http://arxiv.org/abs/2007.01507
Towards Robust Deep Learning with Ensemble Networks and Noisy Layers.
Yuting Liang; Reza Samavi

http://arxiv.org/abs/2007.01003
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks.
Fabian Latorre; Paul Rolland; Nadav Hallak; Volkan Cevher

http://arxiv.org/abs/2007.01017
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment.
Xabier Echeberria-Barrio; Amaia Gil-Lerchundi; Ines Goicoechea-Telleria; Raul Orduna-Urrutia

http://arxiv.org/abs/2007.01356
Decoder-free Robustness Disentanglement without (Additional) Supervision.
Yifei Wang; Dan Peng; Furui Liu; Zhenguo Li; Zhitang Chen; Jiansheng Yang

http://arxiv.org/abs/2007.01472
Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring.
Zhihui Shao; Jianyi Yang; Shaolei Ren

http://arxiv.org/abs/2007.01855
Trace-Norm Adversarial Examples.
Ehsan Kazemi; Thomas Kerdreux; Liqiang Wang

http://arxiv.org/abs/2007.01299
Generating Adversarial Examples withControllable Non-transferability.
Renzhi Wang; Tianwei Zhang; Xiaofei Xie; Lei Ma; Cong Tian; Felix Juefei-Xu; Yang Liu

http://arxiv.org/abs/2007.00251
Unifying Model Explainability and Robustness via Machine-Checkable Concepts.
Vedant Nanda; Till Speicher; John P. Dickerson; Krishna P. Gummadi; Muhammad Bilal Zafar

http://arxiv.org/abs/2007.00644
Measuring Robustness to Natural Distribution Shifts in Image Classification.
Rohan Taori; Achal Dave; Vaishaal Shankar; Nicholas Carlini; Benjamin Recht; Ludwig Schmidt

http://arxiv.org/abs/2007.00337
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks.
Kishor Datta Gupta; Dipankar Dasgupta; Zahid Akhtar

http://arxiv.org/abs/2007.00806
Query-Free Adversarial Transfer via Undertrained Surrogates.
Chris Miller; Soroush Vosoughi

http://arxiv.org/abs/2007.00720
Adversarial Example Games.
Avishek Joey Bose; Gauthier Gidel; Hugo Berard; Andre Cianflone; Pascal Vincent; Simon Lacoste-Julien; William L. Hamilton

http://arxiv.org/abs/2007.00772
Robustness against Relational Adversary.
Yizhen Wang; Xiaozhu Meng; Ke Wang; Mihai Christodorescu; Somesh Jha

http://arxiv.org/abs/2007.00289
A Le Cam Type Bound for Adversarial Learning and Applications.
Qiuling Xu; Kevin Bello; Jean Honorio

http://arxiv.org/abs/2007.00753
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey.
Samuel Henrique Silva; Peyman Najafirad

http://arxiv.org/abs/2006.16974
Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures.
Jiachen Sun; Yulong Cao; Qi Alfred Chen; Z. Morley Mao

http://arxiv.org/abs/2006.16545
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection.
Deqiang Li; Qianmu Li

http://arxiv.org/abs/2006.16520
Black-box Certification and Learning under Adversarial Perturbations.
Hassan Ashtiani; Vinayak Pathak; Ruth Urner

http://arxiv.org/abs/2007.00147
Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications.
Eric Wong; Tim Schneider; Joerg Schmitt; Frank R. Schmidt; J. Zico Kolter

http://arxiv.org/abs/2007.00146
Generating Adversarial Examples with an Optimized Quality.
Aminollah Khormali; DaeHun Nyang; David Mohaisen

http://arxiv.org/abs/2006.16055
Harnessing Adversarial Distances to Discover High-Confidence Errors.
Walter Bennette; Karsten Maurer; Sean Sisti

http://arxiv.org/abs/2006.16384
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification.
Chen Dan; Yuting Wei; Pradeep Ravikumar

http://arxiv.org/abs/2006.16179
Legal Risks of Adversarial Machine Learning Research.
Ram Shankar Siva Kumar; Jonathon Penney; Bruce Schneier; Kendra Albert

http://arxiv.org/abs/2006.16427
Biologically Inspired Mechanisms for Adversarial Robustness.
Manish V. Reddy; Andrzej Banburski; Nishka Pant; Tomaso Poggio

http://arxiv.org/abs/2006.16375
Improving Uncertainty Estimates through the Relationship with Adversarial Robustness.
Yao Qin; Xuezhi Wang; Alex Beutel; Ed H. Chi

http://arxiv.org/abs/2006.15632
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications.
Yunfei Song; Tian Liu; Tongquan Wei; Xiangfeng Wang; Zhe Tao; Mingsong Chen

http://arxiv.org/abs/2006.15669
Geometry-Inspired Top-k Adversarial Perturbations.
Nurislam Tursynbek; Aleksandr Petiushko; Ivan Oseledets

http://arxiv.org/abs/2006.14856
Orthogonal Deep Models As Defense Against Black-Box Attacks.
Mohammad A. A. K. Jalwana; Naveed Akhtar; Mohammed Bennamoun; Ajmal Mian

http://arxiv.org/abs/2006.15207
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining.
Jiefeng Chen; Yixuan Li; Xi Wu; Yingyu Liang; Somesh Jha

http://arxiv.org/abs/2006.15127
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks.
Ali Mirzaeian; Jana Kosecka; Houman Homayoun; Tinoosh Mohsenin; Avesta Sasan

http://arxiv.org/abs/2006.14871
Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?
Kaidi Jin; Tianwei Zhang; Chao Shen; Yufei Chen; Ming Fan; Chenhao Lin; Ting Liu

http://arxiv.org/abs/2006.14536
Smooth Adversarial Training.
Cihang Xie; Mingxing Tan; Boqing Gong; Alan Yuille; Quoc V. Le

http://arxiv.org/abs/2006.14748
Proper Network Interpretability Helps Adversarial Robustness in Classification.
Akhilan Boopathy; Sijia Liu; Gaoyuan Zhang; Cynthia Liu; Pin-Yu Chen; Shiyu Chang; Luca Daniel

http://arxiv.org/abs/2006.14512
Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability.
Kaizhao Liang; Jacky Y. Zhang; Boxin Wang; Zhuolin Yang; Oluwasanmi Koyejo; Bo Li

http://arxiv.org/abs/2006.14655
Can 3D Adversarial Logos Cloak Humans?
Yi Wang; Jingyang Zhou; Tianlong Chen; Sijia Liu; Shiyu Chang; Chandrajit Bajaj; Zhangyang Wang

http://arxiv.org/abs/2006.13555
Defending against adversarial attacks on medical imaging AI system, classification or detection?
Xin Li; Deng Pan; Dongxiao Zhu

http://arxiv.org/abs/2006.14032
Compositional Explanations of Neurons.
Jesse Mu; Jacob Andreas

http://arxiv.org/abs/2006.14042
Blacklight: Defending Black-Box Adversarial Attacks on Deep Neural Networks.
Huiying Li; Shawn Shan; Emily Wenger; Jiayun Zhang; Haitao Zheng; Ben Y. Zhao

http://arxiv.org/abs/2006.13726
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness.
Xingjun Ma; Linxi Jiang; Hanxun Huang; Zejia Weng; James Bailey; Yu-Gang Jiang

http://arxiv.org/abs/2006.12792
RayS: A Ray Searching Method for Hard-label Adversarial Attack.
Jinghui Chen; Quanquan Gu

http://arxiv.org/abs/2006.12834
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks.
Francesco Croce; Maksym Andriushchenko; Naman D. Singh; Nicolas Flammarion; Matthias Hein

http://arxiv.org/abs/2006.13192
Adversarial Robustness of Deep Sensor Fusion Models.
Shaojie Wang; Tong Wu; Ayan Chakrabarti; Yevgeniy Vorobeychik

http://arxiv.org/abs/2006.12135
Learning to Generate Noise for Multi-Attack Robustness.
Divyam Madaan; Jinwoo Shin; Sung Ju Hwang

http://arxiv.org/abs/2006.12655
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models.
Cassidy Laidlaw; Sahil Singla; Soheil Feizi

http://arxiv.org/abs/2006.11776
Network Moments: Extensions and Sparse-Smooth Attacks.
Modar Alfadly; Adel Bibi; Emilio Botero; Salman Alsubaihi; Bernard Ghanem

http://arxiv.org/abs/2006.11604
How do SGD hyperparameters in natural training affect adversarial robustness?
Sandesh Kamath; Amit Deshpande; K V Subrahmanyam

http://arxiv.org/abs/2006.11627
Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble.
Yi Zhou; Xiaoqing Zheng; Cho-Jui Hsieh; Kai-wei Chang; Xuanjing Huang

http://arxiv.org/abs/2006.11561
Stochastic Shortest Path with Adversarially Changing Costs. (1%)
Aviv Rosenberg; Yishay Mansour

http://arxiv.org/abs/2006.11440
Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples.
Josue Ortega Caro; Yilong Ju; Ryan Pyle; Sourav Dey; Wieland Brendel; Fabio Anselmi; Ankit Patel

http://arxiv.org/abs/2006.11122
A general framework for defining and optimizing robustness.
Alessandro Tibo; Manfred Jaeger; Kim G. Larsen

http://arxiv.org/abs/2006.11103
Analyzing the Real-World Applicability of DGA Classifiers.
Arthur Drichel; Ulrike Meyer; Samuel Schüppen; Dominik Teubert

http://arxiv.org/abs/2006.11007
Towards an Adversarially Robust Normalization Approach.
Muhammad Awais; Fahad Shamshad; Sung-Ho Bae

http://arxiv.org/abs/2006.11078
Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers.
I. Fursov; A. Zaytsev; N. Kluchnikov; A. Kravchenko; E. Burnaev

http://arxiv.org/abs/2006.11004
Adversarial Attacks for Multi-view Deep Models.
Xuli Sun; Shiliang Sun

http://arxiv.org/abs/2006.10620
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning.
Antonios Alexos; Konstantinos P. Panousis; Sotirios Chatzis

http://arxiv.org/abs/2006.10679
Dissecting Deep Networks into an Ensemble of Generative Classifiers for Robust Predictions.
Lokender Tiwari; Anish Madan; Saket Anand; Subhashis Banerjee

http://arxiv.org/abs/2006.10885
The Dilemma Between Dimensionality Reduction and Adversarial Robustness.
Sheila Alemany; Niki Pissinou

http://arxiv.org/abs/2006.10876
Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples.
Kaleel Mahmood; Deniz Gurevin; Dijk Marten van; Phuong Ha Nguyen

http://arxiv.org/abs/2006.09994
Noise or Signal: The Role of Image Backgrounds in Object Recognition.
Kai Xiao; Logan Engstrom; Andrew Ilyas; Aleksander Madry

http://arxiv.org/abs/2006.10013
Adversarial Examples Detection and Analysis with Layer-wise Autoencoders.
Bartosz Wójcik; Paweł Morawiecki; Marek Śmieja; Tomasz Krzyżek; Przemysław Spurek; Jacek Tabor

http://arxiv.org/abs/2006.09701
Adversarial Defense by Latent Style Transformations.
Shuo Wang; Surya Nepal; Alsharif Abuadbba; Carsten Rudolph; Marthie Grobler

http://arxiv.org/abs/2006.12247
Disrupting Deepfakes with an Adversarial Attack that Survives Training.
Eran Segalis

http://arxiv.org/abs/2006.09989
Universal Lower-Bounds on Classification Error under Adversarial Attacks and Random Corruption.
Elvis Dohmatob

http://arxiv.org/abs/2006.12621
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning.
Vedant Nanda; Samuel Dooley; Sahil Singla; Soheil Feizi; John P. Dickerson

http://arxiv.org/abs/2006.08914
Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets.
Zhihui Shao; Jianyi Yang; Shaolei Ren

http://arxiv.org/abs/2006.08947
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness.
Mohammadamin Tavakoli; Forest Agostinelli; Pierre Baldi

http://arxiv.org/abs/2006.09040
Debona: Decoupled Boundary Network Analysis for Tighter Bounds and Faster Adversarial Robustness Proofs.
Christopher Brix; Thomas Noll

http://arxiv.org/abs/2006.09510
On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron.
Sergey Bochkanov

http://arxiv.org/abs/2006.09539
AdvMind: Inferring Adversary Intent of Black-Box Attacks.
Ren Pang; Xinyang Zhang; Shouling Ji; Xiapu Luo; Ting Wang

http://arxiv.org/abs/2006.09373
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs.
Peijie Chen; Chirag Agarwal; Anh Nguyen

http://arxiv.org/abs/2006.08789
Total Deep Variation: A Stable Regularizer for Inverse Problems.
Erich Kobler; Alexander Effland; Karl Kunisch; Thomas Pock

http://arxiv.org/abs/2006.08900
DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder.
Ao Zhang; Jinwen Ma

http://arxiv.org/abs/2006.08476
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data.
Zhun Deng; Linjun Zhang; Amirata Ghorbani; James Zou

http://arxiv.org/abs/2006.08391
Fast & Accurate Method for Bounding the Singular Values of Convolutional Layers with Application to Lipschitz Regularization.
Alexandre Araujo; Benjamin Negrevergne; Yann Chevaleyre; Jamal Atif

http://arxiv.org/abs/2006.08149
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks.
Xiang Zhang; Marinka Zitnik

http://arxiv.org/abs/2006.08538
CG-ATTACK: Modeling the Conditional Distribution of Adversarial Perturbations to Boost Black-Box Attack.
Yan Feng; Baoyuan Wu; Yanbo Fan; Li Liu; Zhifeng Li; Shutao Xia

http://arxiv.org/abs/2006.08656
Multiscale Deep Equilibrium Models.
Shaojie Bai; Vladlen Koltun; J. Zico Kolter

http://arxiv.org/abs/2006.07989
GradAug: A New Regularization Method for Deep Neural Networks.
Taojiannan Yang; Sijie Zhu; Chen Chen

http://arxiv.org/abs/2006.07794
PatchUp: A Regularization Technique for Convolutional Neural Networks.
Mojtaba Faramarzi; Mohammad Amini; Akilesh Badrinaaraayanan; Vikas Verma; Sarath Chandar

http://arxiv.org/abs/2006.07828
On Saliency Maps and Adversarial Robustness.
Puneet Mangla; Vedant Singh; Vineeth N Balasubramanian

http://arxiv.org/abs/2006.07800
On the transferability of adversarial examples between convex and 01 loss models.
Yunzhe Xue; Meiyan Xie; Usman Roshan

http://arxiv.org/abs/2006.07934
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems.
Yuanjiang Cao; Xiaocong Chen; Lina Yao; Xianzhi Wang; Wei Emma Zhang

http://arxiv.org/abs/2006.08020
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks.
Sarada Krithivasan; Sanchari Sen; Anand Raghunathan

http://arxiv.org/abs/2006.07942
Duplicity Games for Deception Design with an Application to Insider Threat Mitigation. (11%)
Linan Huang; Quanyan Zhu

http://arxiv.org/abs/2006.07710
The Pitfalls of Simplicity Bias in Neural Networks.
Harshay Shah; Kaustav Tamuly; Aditi Raghunathan; Prateek Jain; Praneeth Netrapalli

http://arxiv.org/abs/2006.07589
Adversarial Self-Supervised Contrastive Learning.
Minseon Kim; Jihoon Tack; Sung Ju Hwang

http://arxiv.org/abs/2006.07682
Rethinking Clustering for Robustness.
Motasem Alfarra; Juan C. Pérez; Adel Bibi; Ali Thabet; Pablo Arbeláez; Bernard Ghanem

http://arxiv.org/abs/2006.07700
Defensive Approximation: Securing CNNs using Approximate Computing.
Amira Guesmi; Ihsen Alouani; Khaled Khasawneh; Mouna Baklouti; Tarek Frikha; Mohamed Abid; Nael Abu-Ghazaleh

http://arxiv.org/abs/2006.07024
Provably Robust Metric Learning.
Lu Wang; Xuanqing Liu; Jinfeng Yi; Yuan Jiang; Cho-Jui Hsieh

http://arxiv.org/abs/2006.07421
Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces.
Chaofei Yang; Lei Ding; Yiran Chen; Hai Li

http://arxiv.org/abs/2006.07258
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack.
Qiuling Xu; Guanhong Tao; Xiangyu Zhang

http://arxiv.org/abs/2006.08602
Targeted Adversarial Perturbations for Monocular Depth Prediction.
Alex Wong; Safa Cicek; Stefano Soatto

http://arxiv.org/abs/2006.06195
Large-Scale Adversarial Training for Vision-and-Language Representation Learning.
Zhe Gan; Yen-Chun Chen; Linjie Li; Chen Zhu; Yu Cheng; Jingjing Liu

http://arxiv.org/abs/2006.06643
Smoothed Geometry for Robust Attribution.
Zifan Wang; Haofan Wang; Shakul Ramkumar; Matt Fredrikson; Piotr Mardziel; Anupam Datta

http://arxiv.org/abs/2006.06493
Protecting Against Image Translation Deepfakes by Leaking Universal Perturbations from Black-Box Neural Networks.
Nataniel Ruiz; Sarah Adel Bargal; Stan Sclaroff

http://arxiv.org/abs/2006.06186
Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification.
Xu Li; Na Li; Jinghua Zhong; Xixin Wu; Xunying Liu; Dan Su; Dong Yu; Helen Meng

http://arxiv.org/abs/2006.06861
Robustness to Adversarial Attacks in Learning-Enabled Controllers.
Zikang Xiong; Joe Eappen; He Zhu; Suresh Jagannathan

http://arxiv.org/abs/2006.06759
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples.
Richard Y. Zhang

http://arxiv.org/abs/2006.06356
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors.
Suzanne C. Wetstein; Cristina González-Gonzalo; Gerda Bortsova; Bart Liefers; Florian Dubost; Ioannis Katramados; Laurens Hogeweg; Ginneken Bram van; Josien P. W. Pluim; Bruijne Marleen de; Clara I. Sánchez; Mitko Veta

http://arxiv.org/abs/2006.06520
Achieving robustness in classification using optimal transport with hinge regularization.
Mathieu Serrurier; Franck Mamalet; Alberto González-Sanz; Thibaut Boissin; Jean-Michel Loubes; Barrio Eustasio del

http://arxiv.org/abs/2006.06721
Backdoor Smoothing: Demystifying Backdoor Attacks on Deep Neural Networks. (96%)
Kathrin Grosse; Taesung Lee; Battista Biggio; Youngja Park; Michael Backes; Ian Molloy

http://arxiv.org/abs/2006.05648
Evaluating Graph Vulnerability and Robustness using TIGER.
Scott Freitas; Duen Horng Chau

http://arxiv.org/abs/2006.06028
Towards Robust Fine-grained Recognition by Maximal Separation of Discriminative Features.
Krishna Kanth Nakka; Mathieu Salzmann

http://arxiv.org/abs/2006.06061
Deterministic Gaussian Averaged Neural Networks.
Ryan Campbell; Chris Finlay; Adam M Oberman

http://arxiv.org/abs/2006.05749
Interpolation between Residual and Non-Residual Networks.
Zonghan Yang; Yang Liu; Chenglong Bao; Zuoqiang Shi

http://arxiv.org/abs/2006.05945
Towards Certified Robustness of Metric Learning.
Xiaochen Yang; Yiwen Guo; Mingzhi Dong; Jing-Hao Xue

http://arxiv.org/abs/2006.05095
Towards an Intrinsic Definition of Robustness for a Classifier.
Théo Giraudon; Vincent Gripon; Matthias Löwe; Franck Vermet

http://arxiv.org/abs/2006.05057
Black-Box Adversarial Attacks on Graph Neural Networks with Limited Node Access.
Jiaqi Ma; Shuangrui Ding; Qiaozhu Mei

http://arxiv.org/abs/2006.05097
GAP++: Learning to generate target-conditioned adversarial examples.
Xiaofeng Mao; Yuefeng Chen; Yuhong Li; Yuan He; Hui Xue

http://arxiv.org/abs/2006.05594
Adversarial Attacks on Brain-Inspired Hyperdimensional Computing-Based Classifiers.
Fangfang Yang; Shaolei Ren

http://arxiv.org/abs/2006.05161
Provable tradeoffs in adversarially robust classification.
Edgar Dobriban; Hamed Hassani; David Hong; Alexander Robey

http://arxiv.org/abs/2006.05630
Distributional Robust Batch Contextual Bandits. (1%)
Nian Si; Fan Zhang; Zhengyuan Zhou; Jose Blanchet

http://arxiv.org/abs/2006.04935
Calibrated neighborhood aware confidence measure for deep metric learning.
Maryna Karpusha; Sunghee Yun; Istvan Fehervari

http://arxiv.org/abs/2006.04924
A Self-supervised Approach for Adversarial Robustness.
Muzammal Naseer; Salman Khan; Munawar Hayat; Fahad Shahbaz Khan; Fatih Porikli

http://arxiv.org/abs/2006.04349
Distributional Robustness with IPMs and links to Regularization and GANs.
Hisham Husain

http://arxiv.org/abs/2006.04449
On Universalized Adversarial and Invariant Perturbations.
Sandesh Kamath; Amit Deshpande; K V Subrahmanyam

http://arxiv.org/abs/2006.04504
Tricking Adversarial Attacks To Fail.
Blerta Lindqvist

http://arxiv.org/abs/2006.04403
Global Robustness Verification Networks.
Weidi Sun; Yuteng Lu; Xiyue Zhang; Zhanxing Zhu; Meng Sun

http://arxiv.org/abs/2006.04622
Trade-offs between membership privacy & adversarially robust learning.
Jamie Hayes

http://arxiv.org/abs/2006.04621
Adversarial Feature Desensitization.
Pouya Bashivan; Reza Bayat; Adam Ibrahim; Kartik Ahuja; Mojtaba Faramarzi; Touraj Laleh; Blake Aaron Richards; Irina Rish

http://arxiv.org/abs/2006.04208
Extensions and limitations of randomized smoothing for robustness guarantees.
Jamie Hayes

http://arxiv.org/abs/2006.04183
Uncertainty-Aware Deep Classifiers using Generative Models.
Murat Sensoy; Lance Kaplan; Federico Cerutti; Maryam Saleki

http://arxiv.org/abs/2006.03873
Unique properties of adversarially trained linear classifiers on Gaussian data.
Jamie Hayes

http://arxiv.org/abs/2006.03833
Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers?
Stefano Melacci; Gabriele Ciravegna; Angelo Sotgiu; Ambra Demontis; Battista Biggio; Marco Gori; Fabio Roli

http://arxiv.org/abs/2006.03243
Adversarial Image Generation and Training for Deep Convolutional Neural Networks.
Ronghua Shi; Hai Shu; Hongtu Zhu; Ziqi Chen

http://arxiv.org/abs/2006.03712
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing.
Vishaal Krishnan; Abed AlRahman Al Makdah; Fabio Pasqualetti

http://arxiv.org/abs/2006.03463
Sponge Examples: Energy-Latency Attacks on Neural Networks.
Ilia Shumailov; Yiren Zhao; Daniel Bates; Nicolas Papernot; Robert Mullins; Ross Anderson

http://arxiv.org/abs/2006.02724
Characterizing the Weight Space for Different Learning Models.
Saurav Musunuru; Jay N. Paranjape; Rahul Kumar Dubey; Vijendran G. Venkoparao

http://arxiv.org/abs/2006.03089
Towards Understanding Fast Adversarial Training.
Bai Li; Shiqi Wang; Suman Jana; Lawrence Carin

http://arxiv.org/abs/2006.03214
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning.
Haibin Wu; Andy T. Liu; Hung-yi Lee

http://arxiv.org/abs/2006.03184
Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection.
Omid Mohamad Nezami; Akshay Chaturvedi; Mark Dras; Utpal Garain

http://arxiv.org/abs/2006.01791
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization.
A. F. M. Shahab Uddin; Mst. Sirazam Monira; Wheemyung Shin; TaeChoong Chung; Sung-Ho Bae

http://arxiv.org/abs/2006.01408
Exploring the role of Input and Output Layers of a Deep Neural Network in Adversarial Defense.
Jay N. Paranjape; Rahul Kumar Dubey; Vijendran V Gopalan

http://arxiv.org/abs/2006.01456
Perturbation Analysis of Gradient-based Adversarial Attacks.
Utku Ozbulak; Manvel Gasparyan; Neve Wesley De; Messem Arnout Van

http://arxiv.org/abs/2006.01888
Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start.
Zhuoran Liu; Martha Larson

http://arxiv.org/abs/2006.01906
Detecting Audio Attacks on ASR Systems with Dropout Uncertainty.
Tejas Jayashankar; Jonathan Le Roux; Pierre Moulin

http://arxiv.org/abs/2006.00731
Second-Order Provable Defenses against Adversarial Attacks.
Sahil Singla; Soheil Feizi

http://arxiv.org/abs/2006.00817
Adversarial Attacks on Reinforcement Learning based Energy Management Systems of Extended Range Electric Delivery Vehicles.
Pengyue Wang; Yan Li; Shashi Shekhar; William F. Northrop

http://arxiv.org/abs/2006.00860
Adversarial Attacks on Classifiers for Eye-based User Modelling.
Inken CISPA Helmholtz Center for Information Security Hagestedt; Michael CISPA Helmholtz Center for Information Security Backes; Andreas University of Stuttgart Bulling

http://arxiv.org/abs/2006.01304
Rethinking Empirical Evaluation of Adversarial Robustness Using First-Order Attack Methods.
Kyungmi Lee; Anantha P. Chandrakasan

http://arxiv.org/abs/2006.00442
Evaluations and Methods for Explanation through Robustness Analysis.
Cheng-Yu Hsieh; Chih-Kuan Yeh; Xuanqing Liu; Pradeep Ravikumar; Seungyeon Kim; Sanjiv Kumar; Cho-Jui Hsieh

http://arxiv.org/abs/2006.00602
Estimating Principal Components under Adversarial Perturbations.
Pranjal Awasthi; Xue Chen; Aravindan Vijayaraghavan

http://arxiv.org/abs/2006.00387
Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training.
Zheng Xu; Ali Shafahi; Tom Goldstein

http://arxiv.org/abs/2005.14424
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions.
Mao Ye; Chengyue Gong; Qiang Liu

http://arxiv.org/abs/2005.14302
Monocular Depth Estimators: Vulnerabilities and Attacks.
Alwyn Mathew; Aditya Prakash Patra; Jimson Mathew

http://arxiv.org/abs/2005.14137
QEBA: Query-Efficient Boundary-Based Blackbox Attack.
Huichen Li; Xiaojun Xu; Xiaolu Zhang; Shuang Yang; Bo Li

http://arxiv.org/abs/2005.14108
Adversarial Attacks and Defense on Texts: A Survey.
Aminul Huq; Mst. Tasnim Pervin

http://arxiv.org/abs/2006.03686
Adversarial Robustness of Deep Convolutional Candlestick Learner.
Jun-Hao Chen; Samuel Yen-Chi Chen; Yun-Cheng Tsai; Chih-Shiang Shur

http://arxiv.org/abs/2005.13293
Enhancing Resilience of Deep Learning Networks by Means of Transferable Adversaries.
Moritz Seiler; Heike Trautmann; Pascal Kerschke

http://arxiv.org/abs/2005.13712
Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques.
Han Qiu; Yi Zeng; Qinkai Zheng; Tianwei Zhang; Meikang Qiu; Gerard Memmi

http://arxiv.org/abs/2005.13525
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models.
Mitch Hill; Jonathan Mitchell; Song-Chun Zhu

http://arxiv.org/abs/2005.13748
Calibrated Surrogate Losses for Adversarially Robust Classification.
Han Bao; Clayton Scott; Masashi Sugiyama

http://arxiv.org/abs/2005.13123
Effects of Forward Error Correction on Communications Aware Evasion Attacks.
Matthew DelVecchio; Bryse Flowers; William C. Headley

http://arxiv.org/abs/2005.13124
Investigating a Spectral Deception Loss Metric for Training Machine Learning-based Evasion Attacks.
Matthew DelVecchio; Vanessa Arndorfer; William C. Headley

http://arxiv.org/abs/2005.12696
Generating Semantically Valid Adversarial Questions for TableQA.
Yi Zhu; Menglin Xia; Yiwei Zhou

http://arxiv.org/abs/2005.12154
Adversarial Feature Selection against Evasion Attacks.
Fei Zhang; Patrick P. K. Chan; Battista Biggio; Daniel S. Yeung; Fabio Roli

http://arxiv.org/abs/2005.14611
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification.
Sina Däubener; Lea Schönherr; Asja Fischer; Dorothea Kolossa

http://arxiv.org/abs/2005.11671
SoK: Arms Race in Adversarial Malware Detection.
Deqiang Li; Qianmu Li; Yanfang Ye; Shouhuai Xu

http://arxiv.org/abs/2005.11904
Adaptive Adversarial Logits Pairing.
Shangxi Wu; Jitao Sang; Kaiyuan Xu; Guanhua Zheng; Changsheng Xu

http://arxiv.org/abs/2005.11626
ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds.
Kibok Lee; Zhuoyuan Chen; Xinchen Yan; Raquel Urtasun; Ersin Yumer

http://arxiv.org/abs/2005.11560
Adversarial Attack on Hierarchical Graph Pooling Neural Networks.
Haoteng Tang; Guixiang Ma; Yurong Chen; Lei Guo; Wei Wang; Bo Zeng; Liang Zhan

http://arxiv.org/abs/2005.11516
Frontal Attack: Leaking Control-Flow in SGX via the CPU Frontend. (1%)
Ivan Puddu; Moritz Schneider; Miro Haller; Srdjan Čapkun

http://arxiv.org/abs/2005.11061
Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks.
Hokuto Hirano; Kazuki Koga; Kazuhiro Takemoto

http://arxiv.org/abs/2005.10750
Revisiting Role of Autoencoders in Adversarial Settings.
Byeong Cheon Kim; Jung Uk Kim; Hakmin Lee; Yong Man Ro

http://arxiv.org/abs/2005.10757
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack.
Hakmin Lee; Hong Joo Lee; Seong Tae Kim; Yong Man Ro

http://arxiv.org/abs/2005.10637
Inaudible Adversarial Perturbations for Targeted Attack in Speaker Recognition.
Qing Wang; Pengcheng Guo; Lei Xie

http://arxiv.org/abs/2005.10987
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning.
Youngjoon Yu; Hong Joo Lee; Byeong Cheon Kim; Jung Uk Kim; Yong Man Ro

http://arxiv.org/abs/2005.10203
Graph Structure Learning for Robust Graph Neural Networks.
Wei Jin; Yao Ma; Xiaorui Liu; Xianfeng Tang; Suhang Wang; Jiliang Tang

http://arxiv.org/abs/2005.10247
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data.
Alexander Robey; Hamed Hassani; George J. Pappas

http://arxiv.org/abs/2005.10284
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks.
Arash Rahnama; Andrew Tseng

http://arxiv.org/abs/2005.10322
A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks.
Yashar Deldjoo; Noia Tommaso Di; Felice Antonio Merra

http://arxiv.org/abs/2005.10190
Feature Purification: How Adversarial Training Performs Robust Deep Learning.
Zeyuan Allen-Zhu; Yuanzhi Li

http://arxiv.org/abs/2005.09294
Synthesizing Unrestricted False Positive Adversarial Objects Using Generative Models.
Martin Kotuliak; Sandro E. Schoenborn; Andrei Dan

http://arxiv.org/abs/2005.09257
Bias-based Universal Adversarial Patch Attack for Automatic Check-out.
Aishan Liu; Jiakai Wang; Xianglong Liu; Bowen Cao; Chongzhi Zhang; Hang Yu

http://arxiv.org/abs/2005.08632
Universalization of any adversarial attack using very few test examples.
Sandesh Kamath; Amit Deshpande; K V Subrahmanyam

http://arxiv.org/abs/2005.09170
On Intrinsic Dataset Properties for Adversarial Machine Learning.
Jeffrey Z. Pan; Nicholas Zufelt

http://arxiv.org/abs/2005.08781
Defending Your Voice: Adversarial Attack on Voice Conversion.
Chien-yu Huang; Yist Y. Lin; Hung-yi Lee; Lin-shan Lee

http://arxiv.org/abs/2005.08454
Reliability and Robustness analysis of Machine Learning based Phishing URL Detectors.
Bushra University of Adelaide, CREST - The Centre for Research on Engineering Software Technologies, CSIROs Data61 Sabir; M. Ali University of Adelaide, CREST - The Centre for Research on Engineering Software Technologies Babar; Raj CSIROs Data61 Gaire; Alsharif CSIROs DATA61 Abuadbba

http://arxiv.org/abs/2005.09134
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective.
Linhai Ma; Liang Liang

http://arxiv.org/abs/2005.09147
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks.
Linhai Ma; Liang Liang

http://arxiv.org/abs/2005.09161
Spatiotemporal Attacks for Embodied Agents.
Aishan Liu; Tairan Huang; Xianglong Liu; Yitao Xu; Yuqing Ma; Xinyun Chen; Stephen J. Maybank; Dacheng Tao

http://arxiv.org/abs/2005.08321
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks.
Mahdieh Abbasi; Arezoo Rajabi; Christian Gagne; Rakesh B. Bobba

http://arxiv.org/abs/2005.08087
Universal Adversarial Perturbations: A Survey.
Ashutosh Chaubey; Nikhil Agrawal; Kavya Barnwal; Keerat K. Guliani; Pramod Mehta

http://arxiv.org/abs/2005.07998
Encryption Inspired Adversarial Defense for Visual Classification.
MaungMaung AprilPyone; Hitoshi Kiya

http://arxiv.org/abs/2005.10884
PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields.
Chong Xiang; Arjun Nitin Bhagoji; Vikash Sehwag; Prateek Mittal

http://arxiv.org/abs/2005.07675
How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy.
Brian Kim; Yalin E. Sagduyu; Kemal Davaslioglu; Tugba Erpek; Sennur Ulukus

http://arxiv.org/abs/2005.07519
Practical Traffic-space Adversarial Attacks on Learning-based NIDSs.
Dongqi Han; Zhiliang Wang; Ying Zhong; Wenqi Chen; Jiahai Yang; Shuqiang Lu; Xingang Shi; Xia Yin

http://arxiv.org/abs/2005.07606
Initializing Perturbations in Multiple Directions for Fast Adversarial Training.
Xunguang Wang; Ship Peng Xu; Eric Ke Wang

http://arxiv.org/abs/2005.07099
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning.
Jianwen Sun; Tianwei Zhang; Xiaofei Xie; Lei Ma; Yan Zheng; Kangjie Chen; Yang Liu

http://arxiv.org/abs/2005.07347
Towards Assessment of Randomized Mechanisms for Certifying Adversarial Robustness.
Tianhang Zheng; Di Wang; Baochun Li; Jinhui Xu

http://arxiv.org/abs/2005.07145
A Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification.
Ahmed Abusnaina; Mohammed Abuhamad; Hisham Alasmary; Afsah Anwar; Rhongho Jang; Saeed Salem; DaeHun Nyang; David Mohaisen

http://arxiv.org/abs/2005.06149
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses.
Yaxin Li; Wei Jin; Han Xu; Jiliang Tang

http://arxiv.org/abs/2005.05750
Evaluating Ensemble Robustness Against Adversarial Attacks.
George Adam; Romain Speciel

http://arxiv.org/abs/2005.06023
Increased-confidence adversarial examples for improved transferability of Counter-Forensic attacks.
Wenjie Li; Benedetta Tondi; Rongrong Ni; Mauro Barni

http://arxiv.org/abs/2005.06107
Adversarial examples are useful too!
Ali Borji

http://arxiv.org/abs/2005.05552
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients.
Chengcheng Ma; Baoyuan Wu; Shibiao Xu; Yanbo Fan; Yong Zhang; Xiaopeng Zhang; Zhifeng Li

http://arxiv.org/abs/2005.05321
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers.
Brian Kim; Yalin E. Sagduyu; Kemal Davaslioglu; Tugba Erpek; Sennur Ulukus

http://arxiv.org/abs/2005.04871
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data.
Lu Wang; Huan Zhang; Jinfeng Yi; Cho-Jui Hsieh; Yuan Jiang

http://arxiv.org/abs/2005.04364
It's Morphin' Time! Combating Linguistic Discrimination with Inflectional Perturbations.
Samson Tan; Shafiq Joty; Min-Yen Kan; Richard Socher

http://arxiv.org/abs/2005.04564
Class-Aware Domain Adaptation for Improving Adversarial Robustness.
Xianxu Hou; Jingxin Liu; Bolei Xu; Xiaolong Wang; Bozhi Liu; Guoping Qiu

http://arxiv.org/abs/2005.04272
Towards Robustness against Unsuspicious Adversarial Examples.
Liang Tong; Minzhe Guo; Atul Prakash; Yevgeniy Vorobeychik

http://arxiv.org/abs/2005.03597
Efficient Exact Verification of Binarized Neural Networks.
Kai Jia; Martin Rinard

http://arxiv.org/abs/2005.03837
Projection & Probability-Driven Black-Box Attack.
Jie Li; Rongrong Ji; Hong Liu; Jianzhuang Liu; Bineng Zhong; Cheng Deng; Qi Tian

http://arxiv.org/abs/2005.03644
Defending Hardware-based Malware Detectors against Adversarial Attacks.
Abraham Peedikayil Kuruvila; Shamik Kundu; Kanad Basu

http://arxiv.org/abs/2005.02936
GraCIAS: Grassmannian of Corrupted Images for Adversarial Security.
Ankita Shukla; Pavan Turaga; Saket Anand

http://arxiv.org/abs/2005.02929
Training robust neural networks using Lipschitz bounds.
Patricia Pauli; Anne Koch; Julian Berberich; Paul Kohler; Frank Allgöwer

http://arxiv.org/abs/2005.02552
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder.
Guanlin Li; Shuya Ding; Jun Luo; Chang Liu

http://arxiv.org/abs/2005.02270
Hacking the Waveform: Generalized Wireless Adversarial Deep Learning.
Francesco Restuccia; Salvatore D'Oro; Amani Al-Shawabka; Bruno Costa Rendon; Kaushik Chowdhury; Stratis Ioannidis; Tommaso Melodia

http://arxiv.org/abs/2005.02313
Adversarial Training against Location-Optimized Adversarial Patches.
Sukrut Rao; David Stutz; Bernt Schiele

http://arxiv.org/abs/2005.02540
Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary.
Hyeongji Kim; Pekka Parviainen; Ketil Malde

http://arxiv.org/abs/2005.01499
On the Benefits of Models with Perceptually-Aligned Gradients.
Gunjan Aggarwal; Abhishek Sinha; Nupur Kumari; Mayank Singh

http://arxiv.org/abs/2005.01452
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?
Marco Melis; Michele Scalas; Ambra Demontis; Davide Maiorca; Battista Biggio; Giorgio Giacinto; Fabio Roli

http://arxiv.org/abs/2005.01229
Robust Encodings: A Framework for Combating Adversarial Typos.
Erik Jones; Robin Jia; Aditi Raghunathan; Percy Liang

http://arxiv.org/abs/2005.00695
On the Generalization Effects of Linear Transformations in Data Augmentation. (1%)
Sen Wu; Hongyang R. Zhang; Gregory Valiant; Christopher Ré

http://arxiv.org/abs/2005.00656
Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks.
Neil Fendley; Max Lennon; I-Jeng Wang; Philippe Burlina; Nathan Drenkow

http://arxiv.org/abs/2005.00683
Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models.
Bill Yuchen Lin; Seyeon Lee; Rahul Khanna; Xiang Ren

http://arxiv.org/abs/2005.00616
Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees.
Jacob H. Seidman; Mahyar Fazlyab; Victor M. Preciado; George J. Pappas

http://arxiv.org/abs/2005.00446
Defense of Word-level Adversarial Attacks via Random Substitution Encoding.
Zhaoyang Wang; Hongtao Wang

http://arxiv.org/abs/2005.00190
Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks.
Winston Wu; Dustin Arendt; Svitlana Volkova

http://arxiv.org/abs/2004.15015
Imitation Attacks and Defenses for Black-box Machine Translation Systems.
Eric Wallace; Mitchell Stern; Dawn Song

http://arxiv.org/abs/2005.00174
Universal Adversarial Attacks with Natural Triggers for Text Classification.
Liwei Song; Xinwei Yu; Hsuan-Tung Peng; Karthik Narasimhan

http://arxiv.org/abs/2005.00060
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness.
Pu Zhao; Pin-Yu Chen; Payel Das; Karthikeyan Natesan Ramamurthy; Xue Lin

http://arxiv.org/abs/2004.14861
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability.
Nathan Inkawhich; Kevin J Liang; Binghui Wang; Matthew Inkawhich; Lawrence Carin; Yiran Chen

http://arxiv.org/abs/2004.14543
TAVAT: Token-Aware Virtual Adversarial Training for Language Understanding.
Linyang Li; Xipeng Qiu

http://arxiv.org/abs/2005.05909
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP.
John X. Morris; Eli Lifland; Jin Yong Yoo; Jake Grigsby; Di Jin; Yanjun Qi

http://arxiv.org/abs/2004.13617
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks.
Pranjal Awasthi; Natalie Frank; Mehryar Mohri

http://arxiv.org/abs/2004.13799
Minority Reports Defense: Defending Against Adversarial Patches.
Michael McCoyd; Won Park; Steven Chen; Neil Shah; Ryan Roggenkemper; Minjune Hwang; Jason Xinyu Liu; David Wagner

http://arxiv.org/abs/2004.12864
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking.
Christopher Hidey; Tuhin Chakrabarty; Tariq Alhindi; Siddharth Varia; Kriste Krstovski; Mona Diab; Smaranda Muresan

http://arxiv.org/abs/2004.12771
Adversarial Fooling Beyond "Flipping the Label".
Konda Reddy Mopuri; Vaisakh Shaj; R. Venkatesh Babu

http://arxiv.org/abs/2004.12764
"Call me sexist, but...": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples. (81%)
Mattia Samory; Indira Sen; Julian Kohne; Fabian Floeck; Claudia Wagner

http://arxiv.org/abs/2004.12519
Transferable Perturbations of Deep Feature Distributions.
Nathan Inkawhich; Kevin J Liang; Lawrence Carin; Yiran Chen

http://arxiv.org/abs/2004.12385
Towards Feature Space Adversarial Attack.
Qiuling Xu; Guanhong Tao; Siyuan Cheng; Xiangyu Zhang

http://arxiv.org/abs/2005.02160
Printing and Scanning Attack for Image Counter Forensics.
Hailey James; Otkrist Gupta; Dan Raviv

http://arxiv.org/abs/2004.12478
Improved Image Wasserstein Attacks and Defenses.
Edward J. Hu; Adith Swaminathan; Hadi Salman; Greg Yang

http://arxiv.org/abs/2004.12227
Improved Adversarial Training via Learned Optimizer.
Yuanhao Xiong; Cho-Jui Hsieh

http://arxiv.org/abs/2004.12261
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model.
Yi Xie; Zhuohang Li; Cong Shi; Jian Liu; Yingying Chen; Bo Yuan

http://arxiv.org/abs/2004.13013
Harnessing adversarial examples with a surprisingly simple defense.
Ali Borji

http://arxiv.org/abs/2004.11573
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty.
Xiyue Zhang; Xiaofei Xie; Lei Ma; Xiaoning Du; Qiang Hu; Yang Liu; Jianjun Zhao; Meng Sun

http://arxiv.org/abs/2004.13002
A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers.
Arka Ghosh; Sankha Subhra Mullick; Shounak Datta; Swagatam Das; Rammohan Mallipeddi; Asit Kr. Das

http://arxiv.org/abs/2004.14174
Reevaluating Adversarial Examples in Natural Language.
John X. Morris; Eli Lifland; Jack Lanchantin; Yangfeng Ji; Yanjun Qi

http://arxiv.org/abs/2004.11898
Adversarial Machine Learning in Network Intrusion Detection Systems.
Elie Alhajjar; Paul Maxwell; Nathaniel D. Bastian

http://arxiv.org/abs/2004.11488
Adversarial Attacks and Defenses: An Interpretation Perspective.
Ninghao Liu; Mengnan Du; Ruocheng Guo; Huan Liu; Xia Hu

http://arxiv.org/abs/2004.11114
Evaluating Adversarial Robustness for Deep Neural Network Interpretability using fMRI Decoding.
Patrick McClure; Dustin Moraczewski; Ka Chun Lam; Adam Thomas; Francisco Pereira

http://arxiv.org/abs/2004.11157
On Adversarial Examples for Biomedical NLP Tasks.
Vladimir Araujo; Andres Carvallo; Carlos Aspillaga; Denis Parra

http://arxiv.org/abs/2004.11273
Ensemble Generative Cleaning with Feedback Loops for Defending Adversarial Attacks.
Jianhe Yuan; Zhihai He

http://arxiv.org/abs/2004.11072
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation.
Marvin Klingner; Andreas Bär; Tim Fingscheidt

http://arxiv.org/abs/2004.14798
RAIN: A Simple Approach for Robust and Accurate Image Classification Networks.
Jiawei Du; Hanshu Yan; Vincent Y. F. Tan; Joey Tianyi Zhou; Rick Siow Mong Goh; Jiashi Feng

http://arxiv.org/abs/2004.10700
CodNN -- Robust Neural Networks From Coded Classification.
Netanel Andrew Raviv; Siddharth Andrew Jain; Pulakesh Andrew Upadhyaya; Jehoshua Andrew Bruck; Andrew Anxiao; Jiang

http://arxiv.org/abs/2004.10608
Provably robust deep generative models.
Filipe Condessa; Zico Kolter

http://arxiv.org/abs/2004.11233
QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks.
Priyadarshini Panda

http://arxiv.org/abs/2004.10882
Adversarial examples and where to find them.
Niklas Risse; Christina Göpfert; Jan Philip Göpfert

http://arxiv.org/abs/2004.13825
Scalable Attack on Graph Data by Injecting Vicious Nodes.
Jihong Wang; Minnan Luo; Fnu Suya; Jundong Li; Zijiang Yang; Qinghua Zheng

http://arxiv.org/abs/2004.10250
Certifying Joint Adversarial Robustness for Model Ensembles.
Mainuddin Ahmad Jonas; David Evans

http://arxiv.org/abs/2004.10281
Probabilistic Safety for Bayesian Neural Networks.
Matthew Wicker; Luca Laurenti; Andrea Patane; Marta Kwiatkowska

http://arxiv.org/abs/2004.09984
BERT-ATTACK: Adversarial Attack Against BERT Using BERT.
Linyang Li; Ruotian Ma; Qipeng Guo; Xiangyang Xue; Xipeng Qiu

http://arxiv.org/abs/2004.10162
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks.
Sanchari Sen; Balaraman Ravindran; Anand Raghunathan

http://arxiv.org/abs/2004.09179
GraN: An Efficient Gradient-Norm Based Detector for Adversarial and Misclassified Examples.
Julia Lust; Alexandru Paul Condurache

http://arxiv.org/abs/2004.09677
Approximate exploitability: Learning a best response in large games. (74%)
Finbarr Timbers; Nolan Bard; Edward Lockhart; Marc Lanctot; Martin Schmid; Neil Burch; Julian Schrittwieser; Thomas Hubert; Michael Bowling

http://arxiv.org/abs/2004.08833
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning.
Hongcai Xu; Junpeng Bao; Gaojie Zhang

http://arxiv.org/abs/2004.08994
Adversarial Training for Large Neural Language Models.
Xiaodong Liu; Hao Cheng; Pengcheng He; Weizhu Chen; Yu Wang; Hoifung Poon; Jianfeng Gao

http://arxiv.org/abs/2004.09007
Headless Horseman: Adversarial Attacks on Transfer Learning Models.
Ahmed Abdelkader; Michael J. Curry; Liam Fowl; Tom Goldstein; Avi Schwarzschild; Manli Shu; Christoph Studer; Chen Zhu

http://arxiv.org/abs/2004.08705
Protecting Classifiers From Attacks. A Bayesian Approach.
Victor Gallego; Roi Naveiro; Alberto Redondo; David Rios Insua; Fabrizio Ruggeri

http://arxiv.org/abs/2004.08628
Single-step Adversarial training with Dropout Scheduling.
Vivek B. S.; R. Venkatesh Babu

http://arxiv.org/abs/2004.08443
Adversarial Attack on Deep Learning-Based Splice Localization.
Andras Rozsa; Zheng Zhong; Terrance E. Boult

http://arxiv.org/abs/2004.07780
Shortcut Learning in Deep Neural Networks.
Robert Geirhos; Jörn-Henrik Jacobsen; Claudio Michaelis; Richard Zemel; Wieland Brendel; Matthias Bethge; Felix A. Wichmann

http://arxiv.org/abs/2004.07955
Targeted Attack for Deep Hashing based Retrieval.
Jiawang Bai; Bin Chen; Yiming Li; Dongxian Wu; Weiwei Guo; Shu-tao Xia; En-hui Yang

http://arxiv.org/abs/2004.07919
A Framework for Enhancing Deep Neural Networks Against Adversarial Malware.
Deqiang Li; Qianmu Li; Yanfang Ye; Shouhuai Xu

http://arxiv.org/abs/2004.06954
Advanced Evasion Attacks and Mitigations on Practical ML-Based Phishing Website Classifiers.
Yusi Lei; Sen Chen; Lingling Fan; Fu Song; Yang Liu

http://arxiv.org/abs/2004.06562
On the Optimal Interaction Range for Multi-Agent Systems Under Adversarial Attack.
Saad J Saleh

http://arxiv.org/abs/2004.06383
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions.
Jon Vadillo; Roberto Santana; Jose A. Lozano

http://arxiv.org/abs/2004.05923
Adversarial Robustness Guarantees for Random Deep Neural Networks.
Palma Giacomo De; Bobak T. Kiani; Seth Lloyd

http://arxiv.org/abs/2004.05887
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples.
Maximilian Mozes; Pontus Stenetorp; Bennett Kleinberg; Lewis D. Griffin

http://arxiv.org/abs/2004.05884
Adversarial Weight Perturbation Helps Robust Generalization.
Dongxian Wu; Shu-tao Xia; Yisen Wang

http://arxiv.org/abs/2004.06076
Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension.
Adyasha Maharana; Mohit Bansal

http://arxiv.org/abs/2004.06288
Towards Robust Classification with Image Quality Assessment.
Yeli Feng; Yiyu Cai

http://arxiv.org/abs/2004.05790
Towards Transferable Adversarial Attack against Deep Face Recognition.
Yaoyao Zhong; Weihong Deng

http://arxiv.org/abs/2004.05682
PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning.
Chenglin Yang; Adam Kortylewski; Cihang Xie; Yinzhi Cao; Alan Yuille

http://arxiv.org/abs/2004.11819
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images.
Younghwan Na; Jun Hee Kim; Kyungsu Lee; Juhum Park; Jae Youn Hwang; Jihwan P. Choi

http://arxiv.org/abs/2004.06496
Certified Adversarial Robustness for Deep Reinforcement Learning.
Michael Everett; Bjorn Lutjens; Jonathan P. How

http://arxiv.org/abs/2004.05465
Robust Large-Margin Learning in Hyperbolic Space.
Melanie Weber; Manzil Zaheer; Ankit Singh Rawat; Aditya Menon; Sanjiv Kumar

http://arxiv.org/abs/2004.05511
Verification of Deep Convolutional Neural Networks Using ImageStars.
Hoang-Dung Tran; Stanley Bak; Weiming Xiang; Taylor T. Johnson

http://arxiv.org/abs/2004.05005
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems.
Eirini Anthi; Lowri Williams; Matilda Rhode; Pete Burnap; Adam Wedgbury

http://arxiv.org/abs/2004.04919
Luring of transferable adversarial perturbations in the black-box paradigm.
Rémi Bernhard; Pierre-Alain Moellic; Jean-Max Dutertre

http://arxiv.org/abs/2004.05914
Blind Adversarial Training: Balance Accuracy and Robustness.
Haidong Xie; Xueshuang Xiang; Naijin Liu; Bin Dong

http://arxiv.org/abs/2004.05913
Blind Adversarial Pruning: Balance Accuracy, Efficiency and Robustness.
Haidong Xie; Lixin Qian; Xueshuang Xiang; Naijin Liu

http://arxiv.org/abs/2004.04479
On Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems.
Ivan Y. Tyukin; Desmond J. Higham; Alexander N. Gorban

http://arxiv.org/abs/2004.04199
Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking.
Hongjun Wang; Guangrun Wang; Ya Li; Dongyu Zhang; Liang Lin

http://arxiv.org/abs/2004.03742
Towards Evaluating the Robustness of Chinese BERT Classifiers.
Boxin Wang; Boyuan Pan; Xin Li; Bo Li

http://arxiv.org/abs/2004.03295
Feature Partitioning for Robust Tree Ensembles and their Certification in Adversarial Scenarios.
Stefano Calzavara; Claudio Lucchese; Federico Marcuzzi; Salvatore Orlando

http://arxiv.org/abs/2004.03434
Learning to fool the speaker recognition.
Jiguo Li; Xinfeng Zhang; Jizheng Xu; Li Zhang; Yue Wang; Siwei Ma; Wen Gao

http://arxiv.org/abs/2004.03428
Universal Adversarial Perturbations Generative Network for Speaker Recognition.
Jiguo Li; Xinfeng Zhang; Chuanmin Jia; Jizheng Xu; Li Zhang; Yue Wang; Siwei Ma; Wen Gao

http://arxiv.org/abs/2004.02183
Approximate Manifold Defense Against Multiple Adversarial Perturbations.
Jay Nandy; Wynne Hsu; Mong Li Lee

http://arxiv.org/abs/2004.01903
Understanding (Non-)Robust Feature Disentanglement and the Relationship Between Low- and High-Dimensional Adversarial Attacks.
Zuowen Wang; Leo Horne

http://arxiv.org/abs/2004.01970
BAE: BERT-based Adversarial Examples for Text Classification.
Siddhant Garg; Goutham Ramakrishnan

http://arxiv.org/abs/2004.01832
Adversarial Robustness through Regularization: A Second-Order Approach.
Avery Ma; Fartash Faghri; Amir-massoud Farahmand

http://arxiv.org/abs/2004.00622
Evading Deepfake-Image Detectors with White- and Black-Box Attacks.
Nicholas Carlini; Hany Farid

http://arxiv.org/abs/2004.00306
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes.
Sravanti Addepalli; Vivek B. S.; Arya Baburaj; Gaurang Sriramanan; R. Venkatesh Babu

http://arxiv.org/abs/2004.00543
Physically Realizable Adversarial Examples for LiDAR Object Detection.
James Tu; Mengye Ren; Siva Manivasagam; Ming Liang; Bin Yang; Richard Du; Frank Cheng; Raquel Urtasun

http://arxiv.org/abs/2003.13969
A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays.
Chendi Rao; Jiezhang Cao; Runhao Zeng; Qi Chen; Huazhu Fu; Yanwu Xu; Mingkui Tan

http://arxiv.org/abs/2003.13917
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement.
Chao-Han Huck Yang; Jun Qi; Pin-Yu Chen; Xiaoli Ma; Chin-Hui Lee

http://arxiv.org/abs/2004.00410
Adversarial Attacks on Multivariate Time Series.
Samuel Harford; Fazle Karim; Houshang Darabi

http://arxiv.org/abs/2003.13511
Improved Gradient based Adversarial Attacks for Quantized Networks.
Kartik Gupta; Thalaiyasingam Ajanthan

http://arxiv.org/abs/2003.13370
Towards Deep Learning Models Resistant to Large Perturbations.
Amirreza Shaeiri; Rozhin Nobahari; Mohammad Hossein Rohban

http://arxiv.org/abs/2003.13526
Efficient Black-box Optimization of Adversarial Windows Malware with Constrained Manipulations.
Luca Demetrio; Battista Biggio; Giovanni Lagorio; Fabio Roli; Alessandro Armando

http://arxiv.org/abs/2003.12862
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning.
Tianlong Chen; Sijia Liu; Shiyu Chang; Yu Cheng; Lisa Amini; Zhangyang Wang

http://arxiv.org/abs/2003.12703
DaST: Data-free Substitute Training for Adversarial Attacks.
Mingyi Zhou; Jing Wu; Yipeng Liu; Shuaicheng Liu; Ce Zhu

http://arxiv.org/abs/2003.12760
Adversarial Imitation Attack.
Mingyi Zhou; Jing Wu; Yipeng Liu; Shuaicheng Liu; Xiang Zhang; Ce Zhu

http://arxiv.org/abs/2003.11816
Do Deep Minds Think Alike? Selective Adversarial Attacks for Fine-Grained Manipulation of Multiple Deep Neural Networks.
Zain Khan; Jirong Yi; Raghu Mudumbai; Xiaodong Wu; Weiyu Xu

http://arxiv.org/abs/2003.11855
Challenging the adversarial robustness of DNNs based on error-correcting output codes.
Bowen Zhang; Benedetta Tondi; Xixiang Lv; Mauro Barni

http://arxiv.org/abs/2003.11323
Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples.
Alejandro Barredo-Arrieta; Ser Javier Del

http://arxiv.org/abs/2003.11145
Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study.
Luan Nguyen; Sunpreet S. Arora; Yuhang Wu; Hao Yang

http://arxiv.org/abs/2003.10602
Defense Through Diverse Directions.
Christopher M. Bender; Yang Li; Yifeng Shi; Michael K. Reiter; Junier B. Oliva

http://arxiv.org/abs/2003.10315
Adversarial Attacks on Monocular Depth Estimation.
Ziqi Zhang; Xinge Zhu; Yingwei Li; Xiangqun Chen; Yao Guo

http://arxiv.org/abs/2003.10399
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations.
Saima Sharmin; Nitin Rathi; Priyadarshini Panda; Kaushik Roy

http://arxiv.org/abs/2003.10596
Adversarial Perturbations Fool Deepfake Detectors.
Apurva Gandhi; Shomik Jain

http://arxiv.org/abs/2003.10041
Understanding the robustness of deep neural network classifiers for breast cancer screening.
Witold Oleszkiewicz; Taro Makino; Stanisław Jastrzębski; Tomasz Trzciński; Linda Moy; Kyunghyun Cho; Laura Heacock; Krzysztof J. Geras

http://arxiv.org/abs/2003.10045
Architectural Resilience to Foreground-and-Background Adversarial Noise.
Carl Cheng; Evan Hu

http://arxiv.org/abs/2003.10804
Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression.
Feiyang Cai; Jiani Li; Xenofon Koutsoukos

http://arxiv.org/abs/2003.09711
Robust Out-of-distribution Detection in Neural Networks.
Jiefeng Chen; Yixuan Li; Xi Wu; Yingyu Liang; Somesh Jha

http://arxiv.org/abs/2003.09595
Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises.
Bin Yan; Dong Wang; Huchuan Lu; Xiaoyun Yang

http://arxiv.org/abs/2003.11917
Adversarial Examples and the Deeper Riddle of Induction: The Need for a Theory of Artifacts in Deep Learning.
Cameron Buckner

http://arxiv.org/abs/2004.02756
Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques.
Qinkai Zheng; Han Qiu; Gerard Memmi; Isabelle Bloch

http://arxiv.org/abs/2003.09416
Quantum noise protects quantum classifiers against adversaries.
Yuxuan Du; Min-Hsiu Hsieh; Tongliang Liu; Dacheng Tao; Nana Liu

http://arxiv.org/abs/2003.09372
One Neuron to Fool Them All.
Anshuman Suri; David Evans

http://arxiv.org/abs/2003.09461
Adversarial Robustness on In- and Out-Distribution Improves Explainability.
Maximilian Augustin; Alexander Meinke; Matthias Hein

http://arxiv.org/abs/2003.08937
Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates.
Amin Ghiasi; Ali Shafahi; Tom Goldstein

http://arxiv.org/abs/2003.08861
Face-Off: Adversarial Face Obfuscation.
Varun Chandrasekaran; Chuhan Gao; Brian Tang; Kassem Fawaz; Somesh Jha; Suman Banerjee

http://arxiv.org/abs/2003.08938
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations.
Huan Zhang; Hongge Chen; Chaowei Xiao; Bo Li; Mingyan Liu; Duane Boning; Cho-Jui Hsieh

http://arxiv.org/abs/2003.08907
Overinterpretation reveals image classification model pathologies. (81%)
Brandon Carter; Siddhartha Jain; Jonas Mueller; David Gifford

http://arxiv.org/abs/2003.08837
Vulnerabilities of Connectionist AI Applications: Evaluation and Defence.
Christian Berghoff; Matthias Neu; Twickel Arndt von

http://arxiv.org/abs/2003.08034
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles.
Songan Zhang; Huei Peng; Subramanya Nageshrao; H. Eric Tseng

http://arxiv.org/abs/2003.08093
Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method.
Babak Barazandeh; Meisam Razaviyayn

http://arxiv.org/abs/2003.09347
SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing.
Chawin Sitawarin; Supriyo Chakraborty; David Wagner

http://arxiv.org/abs/2003.07637
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior.
Hu Zhang; Linchao Zhu; Yi Zhu; Yi Yang

http://arxiv.org/abs/2003.07573
Heat and Blur: An Effective and Fast Defense Against Adversarial Examples.
Haya Brama; Tal Grinshpoun

http://arxiv.org/abs/2003.07982
Adversarial Transferability in Wearable Sensor Systems.
Ramesh Kumar Sah; Hassan Ghasemzadeh

http://arxiv.org/abs/2003.06878
Output Diversified Initialization for Adversarial Attacks.
Yusuke Tashiro; Yang Song; Stefano Ermon

http://arxiv.org/abs/2003.06979
Anomalous Example Detection in Deep Learning: A Survey.
Saikiran Bulusu; Bhavya Kailkhura; Bo Li; Pramod K. Varshney; Dawn Song

http://arxiv.org/abs/2003.06814
Towards Face Encryption by Generating Adversarial Identity Masks.
Xiao Yang; Yinpeng Dong; Tianyu Pang; Hang Su; Jun Zhu; Yuefeng Chen; Hui Xue

http://arxiv.org/abs/2003.06974
Toward Adversarial Robustness via Semi-supervised Robust Training.
Yiming Li; Baoyuan Wu; Yan Feng; Yanbo Fan; Yong Jiang; Zhifeng Li; Shutao Xia

http://arxiv.org/abs/2003.06559
Minimum-Norm Adversarial Examples on KNN and KNN-Based Models.
Chawin Sitawarin; David Wagner

http://arxiv.org/abs/2003.06693
Certified Defenses for Adversarial Patches.
Ping-Yeh Chiang; Renkun Ni; Ahmed Abdelkader; Chen Zhu; Christoph Studer; Tom Goldstein

http://arxiv.org/abs/2003.06555
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation.
Xiaogang Xu; Hengshuang Zhao; Jiaya Jia

http://arxiv.org/abs/2003.06566
On the benefits of defining vicinal distributions in latent space.
Puneet Mangla; Vedant Singh; Shreyas Jayant Havaldar; Vineeth N Balasubramanian

http://arxiv.org/abs/2003.06428
Towards a Resilient Machine Learning Classifier -- a Case Study of Ransomware Detection.
Chih-Yuan Yang; Ravi Sahita

http://arxiv.org/abs/2003.06468
GeoDA: a geometric framework for black-box adversarial attacks.
Ali Rahmati; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard; Huaiyu Dai

http://arxiv.org/abs/2003.06121
When are Non-Parametric Methods Robust?
Robi Bhattacharjee; Kamalika Chaudhuri

http://arxiv.org/abs/2003.05822
Topological Effects on Attacks Against Vertex Classification.
Benjamin A. Miller; Mustafa Çamurcu; Alexander J. Gomez; Kevin Chan; Tina Eliassi-Rad

http://arxiv.org/abs/2003.05703
Inline Detection of DGA Domains Using Side Information.
Raaghavi Sivaguru; Jonathan Peck; Femi Olumofin; Anderson Nascimento; Cock Martine De

http://arxiv.org/abs/2003.05669
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection.
Mohammadreza Salehi; Atrin Arya; Barbod Pajoum; Mohammad Otoofi; Amirreza Shaeiri; Mohammad Hossein Rohban; Hamid R. Rabiee

http://arxiv.org/abs/2003.05631
ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems.
Jiangnan Li; Yingyuan Yang; Jinyuan Stella Sun; Kevin Tomsovic; Hairong Qi

http://arxiv.org/abs/2003.05549
Frequency-Tuned Universal Adversarial Attacks.
Yingpeng Deng; Lina J. Karam

http://arxiv.org/abs/2003.04820
SAD: Saliency-based Defenses Against Adversarial Examples.
Richard Tran; David Patrick; Michael Geyer; Amanda Fernandez

http://arxiv.org/abs/2003.05005
Using an ensemble color space model to tackle adversarial examples.
Shreyank N Gowda; Chun Yuan

http://arxiv.org/abs/2003.04884
Cryptanalytic Extraction of Neural Network Models.
Nicholas Carlini; Matthew Jagielski; Ilya Mironov

http://arxiv.org/abs/2003.05730
A Survey of Adversarial Learning on Graphs.
Liang Chen; Jintang Li; Jiaying Peng; Tao Xie; Zengxu Cao; Kun Xu; Xiangnan He; Zibin Zheng

http://arxiv.org/abs/2003.04475
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift.
Remi Tachet des Combes; Han Zhao; Yu-Xiang Wang; Geoff Gordon

http://arxiv.org/abs/2003.04247
Towards Probabilistic Verification of Machine Unlearning.
David Marco Sommer; Liwei Song; Sameer Wagh; Prateek Mittal

http://arxiv.org/abs/2003.04286
Manifold Regularization for Locally Stable Deep Neural Networks.
Charles Jin; Martin Rinard

http://arxiv.org/abs/2003.10388
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models.
Yankun Ren; Jianbin Lin; Siliang Tang; Jun Zhou; Shuang Yang; Yuan Qi; Xiang Ren

http://arxiv.org/abs/2003.04173
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world.
Ivan Fursov; Alexey Zaytsev; Nikita Kluchnikov; Andrey Kravchenko; Evgeny Burnaev

http://arxiv.org/abs/2003.04735
Security of Distributed Machine Learning: A Game-Theoretic Approach to Design Secure DSVM.
Rui Zhang; Quanyan Zhu

http://arxiv.org/abs/2003.03879
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods.
Sanghyuk Chun; Seong Joon Oh; Sangdoo Yun; Dongyoon Han; Junsuk Choe; Youngjoon Yoo

http://arxiv.org/abs/2003.03722
On the Robustness of Cooperative Multi-Agent Reinforcement Learning.
Jieyu Lin; Kristina Dzeparoska; Sai Qian Zhang; Alberto Leon-Garcia; Nicolas Papernot

http://arxiv.org/abs/2003.03778
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.
Raphaël Dang-Nhu; Gagandeep Singh; Pavol Bielik; Martin Vechev

http://arxiv.org/abs/2003.08757
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles.
Ranjie Duan; Xingjun Ma; Yisen Wang; James Bailey; A. K. Qin; Yun Yang

http://arxiv.org/abs/2003.03824
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks.
Siqi Liu; Arnaud Arindra Adiyoso Setio; Florin C. Ghesu; Eli Gibson; Sasa Grbic; Bogdan Georgescu; Dorin Comaniciu

http://arxiv.org/abs/2003.03675
Dynamic Backdoor Attacks Against Machine Learning Models.
Ahmed Salem; Rui Wen; Michael Backes; Shiqing Ma; Yang Zhang

http://arxiv.org/abs/2003.03546
Adversarial Machine Learning: Bayesian Perspectives. (26%)
David Rios Insua; Roi Naveiro; Victor Gallego; Jason Poulos

http://arxiv.org/abs/2003.03065
Defense against adversarial attacks on spoofing countermeasures of ASV.
Haibin Wu; Songxiang Liu; Helen Meng; Hung-yi Lee

http://arxiv.org/abs/2003.03143
Triple Memory Networks: a Brain-Inspired Method for Continual Learning.
Liyuan Wang; Bo Lei; Qian Li; Hang Su; Jun Zhu; Yi Zhong

http://arxiv.org/abs/2003.03100
MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers.
Wei Song; Xuezixiang Li; Sadia Afroz; Deepali Garg; Dmitry Kuznetsov; Heng Yin

http://arxiv.org/abs/2003.05733
Towards Practical Lottery Ticket Hypothesis for Adversarial Training.
Bai Li; Shiqi Wang; Yunhan Jia; Yantao Lu; Zhenyu Zhong; Lawrence Carin; Suman Jana

http://arxiv.org/abs/2003.03021
Exploiting Verified Neural Networks via Floating Point Numerical Error.
Kai Jia; Martin Rinard

http://arxiv.org/abs/2003.02732
Detection and Recovery of Adversarial Attacks with Injected Attractors.
Jiyi Zhang; Ee-Chien Chang; Hwee Kuan Lee

http://arxiv.org/abs/2003.02460
Adversarial Robustness Through Local Lipschitzness.
Yao-Yuan Yang; Cyrus Rashtchian; Hongyang Zhang; Ruslan Salakhutdinov; Kamalika Chaudhuri

http://arxiv.org/abs/2003.02484
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization.
Saehyung Lee; Hyungyu Lee; Sungroh Yoon

http://arxiv.org/abs/2003.02750
Search Space of Adversarial Perturbations against Image Filters.
Dang Duy Thang; Toshihiro Matsui

http://arxiv.org/abs/2003.02301
Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems.
Yi Xie; Cong Shi; Zhuohang Li; Jian Liu; Yingying Chen; Bo Yuan

http://arxiv.org/abs/2003.02188
Colored Noise Injection for Training Adversarially Robust Neural Networks.
Evgenii Zheltonozhskii; Chaim Baskin; Yaniv Nemcovsky; Brian Chmiel; Avi Mendelson; Alex M. Bronstein

http://arxiv.org/abs/2003.01895
Double Backpropagation for Training Autoencoders against Adversarial Attack.
Chengjin Sun; Sizhe Chen; Xiaolin Huang

http://arxiv.org/abs/2003.01908
Black-box Smoothing: A Provable Defense for Pretrained Classifiers.
Hadi Salman; Mingjie Sun; Greg Yang; Ashish Kapoor; J. Zico Kolter

http://arxiv.org/abs/2003.01993
Metrics and methods for robustness evaluation of neural networks with generative models.
Igor Buzhinsky; Arseny Nerinovsky; Stavros Tripakis

http://arxiv.org/abs/2003.01690
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.
Francesco Croce; Matthias Hein

http://arxiv.org/abs/2003.01595
Analyzing Accuracy Loss in Randomized Smoothing Defenses.
Yue Gao; Harrison Rosenberg; Kassem Fawaz; Somesh Jha; Justin Hsu

http://arxiv.org/abs/2003.01665
Discriminative Multi-level Reconstruction under Compact Latent Space for One-Class Novelty Detection.
Jaewoo Park; Yoon Gyo Jung; Andrew Beng Jin Teoh

http://arxiv.org/abs/2003.01782
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack.
Takami Sato; Junjie Shen; Ningfei Wang; Yunhan Jack Jia; Xue Lin; Qi Alfred Chen

http://arxiv.org/abs/2003.01872
Type I Attack for Generative Models.
Chengjin Sun; Sizhe Chen; Jia Cai; Xiaolin Huang

http://arxiv.org/abs/2003.01295
Data-Free Adversarial Perturbations for Practical Black-Box Attack.
ZhaoXin Huan; Yulong Wang; Xiaolu Zhang; Lin Shang; Chilin Fu; Jun Zhou

http://arxiv.org/abs/2003.01090
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness.
Ahmadreza Jeddi; Mohammad Javad Shafiee; Michelle Karg; Christian Scharfenberger; Alexander Wong

http://arxiv.org/abs/2003.01279
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems.
Nataniel Ruiz; Sarah Adel Bargal; Stan Sclaroff

http://arxiv.org/abs/2003.01249
Hidden Cost of Randomized Smoothing.
Jeet Lily Mohapatra; Ching-Yun Lily Ko; Lily Tsui-Wei; Weng; Sijia Liu; Pin-Yu Chen; Luca Daniel

http://arxiv.org/abs/2003.01261
Adversarial Network Traffic: Towards Evaluating the Robustness of Deep Learning-Based Network Traffic Classification.
Amir Mahdi Sadeghzadeh; Saeed Shiravi; Rasool Jalili

http://arxiv.org/abs/2003.00653
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies.
Wei Jin; Yaxin Li; Han Xu; Yiqi Wang; Shuiwang Ji; Charu Aggarwal; Jiliang Tang

http://arxiv.org/abs/2003.00378
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models.
Xiao Zhang; Jinghui Chen; Quanquan Gu; David Evans

http://arxiv.org/abs/2003.00402
Why is the Mahalanobis Distance Effective for Anomaly Detection?
Ryo Kamoi; Kei Kobayashi

http://arxiv.org/abs/2003.00120
Improving Certified Robustness via Statistical Learning with Logical Reasoning.
Zhuolin Yang; Zhikuan Zhao; Boxin Wang; Jiawei Zhang; Linyi Li; Hengzhi Pei; Bojan Karlas; Ji Liu; Heng Guo; Ce Zhang; Bo Li

http://arxiv.org/abs/2002.12913
Applying Tensor Decomposition to image for Robustness against Adversarial Attack.
Seungju Cho; Tae Joon Jun; Mingu Kang; Daeyoung Kim

http://arxiv.org/abs/2003.04985
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT.
Lichao Sun; Kazuma Hashimoto; Wenpeng Yin; Akari Asai; Jia Li; Philip Yu; Caiming Xiong

http://arxiv.org/abs/2002.12504
Detecting Patch Adversarial Attacks with Image Residuals.
Marius Arvinte; Ahmed Tewfik; Sriram Vishwanath

http://arxiv.org/abs/2002.12463
Certified Defense to Image Transformations via Randomized Smoothing.
Marc Fischer; Maximilian Baader; Martin Vechev

http://arxiv.org/abs/2002.12527
Are L2 adversarial examples intrinsically different?
Mingxuan Li; Jingyuan Wang; Yufan Wu

http://arxiv.org/abs/2002.12398
TSS: Transformation-Specific Smoothing for Robustness Certification.
Linyi Li; Maurice Weber; Xiaojun Xu; Luka Rimanic; Bhavya Kailkhura; Tao Xie; Ce Zhang; Bo Li

http://arxiv.org/abs/2002.12222
On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks.
Yue Zhao; Yuwei Wu; Caihua Chen; Andrew Lim

http://arxiv.org/abs/2002.12520
Utilizing Network Properties to Detect Erroneous Inputs.
Matt Gorbett; Nathaniel Blanchard

http://arxiv.org/abs/2002.12047
FMix: Enhancing Mixed Sample Data Augmentation. (22%)
Ethan Harris; Antonia Marcu; Matthew Painter; Mahesan Niranjan; Adam Prügel-Bennett; Jonathon Hare

http://arxiv.org/abs/2002.11572
Revisiting Ensembles in an Adversarial Context: Improving Natural Accuracy.
Aditya Saligrama; Guillaume Leclerc

http://arxiv.org/abs/2002.11318
Invariance vs. Robustness of Neural Networks.
Sandesh Kamath; Amit Deshpande; K V Subrahmanyam

http://arxiv.org/abs/2002.11569
Overfitting in adversarially robust deep learning.
Leslie Rice; Eric Wong; J. Zico Kolter

http://arxiv.org/abs/2002.11320
MGA: Momentum Gradient Attack on Network.
Jinyin Chen; Yixian Chen; Haibin Zheng; Shijing Shen; Shanqing Yu; Dan Zhang; Qi Xuan

http://arxiv.org/abs/2002.11821
Improving Robustness of Deep-Learning-Based Image Reconstruction.
Ankit Raj; Yoram Bresler; Bo Li

http://arxiv.org/abs/2002.11881
Defense-PointNet: Protecting PointNet Against Adversarial Attacks.
Yu Zhang; Gongbo Liang; Tawfiq Salem; Nathan Jacobs

http://arxiv.org/abs/2002.11374
Adversarial Attack on Deep Product Quantization Network for Image Retrieval.
Yan Feng; Bin Chen; Tao Dai; Shutao Xia

http://arxiv.org/abs/2002.11565
Randomization matters. How to defend against strong adversarial attacks.
Rafael Pinot; Raphael Ettedgui; Geovani Rizk; Yann Chevaleyre; Jamal Atif

http://arxiv.org/abs/2002.11798
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization.
Sicheng Zhu; Xiao Zhang; David Evans

http://arxiv.org/abs/2002.10716
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy.
Aditi Raghunathan; Sang Michael Xie; Fanny Yang; John Duchi; Percy Liang

http://arxiv.org/abs/2002.11080
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization.
Yifei Min; Lin Chen; Amin Karbasi

http://arxiv.org/abs/2002.10703
G\"odel's Sentence Is An Adversarial Example But Unsolvable.
Xiaodong Qi; Lansheng Han

http://arxiv.org/abs/2002.10947
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks.
Kaidi Xu; Sijia Liu; Pin-Yu Chen; Mengshu Sun; Caiwen Ding; Bhavya Kailkhura; Xue Lin

http://arxiv.org/abs/2002.10733
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks.
Alexander Levine; Soheil Feizi

http://arxiv.org/abs/2002.11242
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger.
Jingfeng Zhang; Xilie Xu; Bo Han; Gang Niu; Lizhen Cui; Masashi Sugiyama; Mohan Kankanhalli

http://arxiv.org/abs/2002.11293
Adversarial Ranking Attack and Defense.
Mo Zhou; Zhenxing Niu; Le Wang; Qilin Zhang; Gang Hua

http://arxiv.org/abs/2002.10349
A Model-Based Derivative-Free Approach to Black-Box Adversarial Examples: BOBYQA.
Giuseppe Ughi; Vinayak Abrol; Jared Tanner

http://arxiv.org/abs/2002.10084
Utilizing a null class to restrict decision spaces and defend against neural network adversarial attacks.
Matthew J. Roos

http://arxiv.org/abs/2003.00883
Adversarial Perturbations Prevail in the Y-Channel of the YCbCr Color Space.
Camilo Pestana; Naveed Akhtar; Wei Liu; David Glance; Ajmal Mian

http://arxiv.org/abs/2002.10097
Towards Rapid and Robust Adversarial Training with One-Step Attacks.
Leo Schwinn; René Raab; Björn Eskofier

http://arxiv.org/abs/2002.10477
Precise Tradeoffs in Adversarial Training for Linear Regression.
Adel Javanmard; Mahdi Soltanolkotabi; Hamed Hassani

http://arxiv.org/abs/2002.10509
HYDRA: Pruning Adversarially Robust Neural Networks.
Vikash Sehwag; Shiqi Wang; Prateek Mittal; Suman Jana

http://arxiv.org/abs/2002.09896
Adversarial Attack on DL-based Massive MIMO CSI Feedback.
Qing Liu; Jiajia Guo; Chao-Kai Wen; Shi Jin

http://arxiv.org/abs/2002.10025
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference.
Ting-Kuei Hu; Tianlong Chen; Haotao Wang; Zhangyang Wang

http://arxiv.org/abs/2002.09772
Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks.
Kirthi Shankar Sivamani; Rajeev Sahay; Aly El Gamal

http://arxiv.org/abs/2002.09674
Temporal Sparse Adversarial Attack on Sequence-based Gait Recognition.
Ziwen He; Wei Wang; Jing Dong; Tieniu Tan

http://arxiv.org/abs/2002.09792
Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems.
Yiannis Kantaros; Taylor Carpenter; Kaustubh Sridhar; Yahan Yang; Insup Lee; James Weimer

http://arxiv.org/abs/2002.09632
Using Single-Step Adversarial Training to Defend Iterative Adversarial Examples.
Guanxiong Liu; Issa Khalil; Abdallah Khreishah

http://arxiv.org/abs/2002.09580
Polarizing Front Ends for Robust CNNs.
Can Bakiskan; Soorya Gopalakrishnan; Metehan Cekic; Upamanyu Madhow; Ramtin Pedarsani

http://arxiv.org/abs/2002.09422
Robustness from Simple Classifiers.
Sharon Qian; Dimitris Kalimeris; Gal Kaplun; Yaron Singer

http://arxiv.org/abs/2002.09364
Adversarial Detection and Correction by Matching Prediction Distributions.
Giovanni Vacanti; Looveren Arnaud Van

http://arxiv.org/abs/2002.09576
UnMask: Adversarial Detection and Defense Through Robust Feature Alignment.
Scott Freitas; Shang-Tse Chen; Zijie J. Wang; Duen Horng Chau

http://arxiv.org/abs/2002.09579
Robustness to Programmable String Transformations via Augmented Abstract Training.
Yuhao Zhang; Aws Albarghouthi; Loris D'Antoni

http://arxiv.org/abs/2002.09169
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework.
Dinghuai Zhang; Mao Ye; Chengyue Gong; Zhanxing Zhu; Qiang Liu

http://arxiv.org/abs/2002.09565
Adversarial Attacks on Machine Learning Systems for High-Frequency Trading.
Micah Goldblum; Avi Schwarzschild; Ankit B. Patel; Tom Goldstein

http://arxiv.org/abs/2002.09027
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning.
Chao-Han Huck Yang; Jun Qi; Pin-Yu Chen; Yi Ouyang; I-Te Danny Hung; Chin-Hui Lee; Xiaoli Ma

http://arxiv.org/abs/2002.08838
On the Decision Boundaries of Deep Neural Networks: A Tropical Geometry Perspective.
Motasem Alfarra; Adel Bibi; Hasan Hammoud; Mohamed Gaafar; Bernard Ghanem

http://arxiv.org/abs/2002.08859
A Bayes-Optimal View on Adversarial Examples.
Eitan Richardson; Yair Weiss

http://arxiv.org/abs/2002.08740
Towards Certifiable Adversarial Sample Detection.
Ilia Shumailov; Yiren Zhao; Robert Mullins; Ross Anderson

http://arxiv.org/abs/2002.08619
Boosting Adversarial Training with Hypersphere Embedding.
Tianyu Pang; Xiao Yang; Yinpeng Dong; Kun Xu; Hang Su; Jun Zhu

http://arxiv.org/abs/2002.08569
Byzantine-resilient Decentralized Stochastic Gradient Descent. (5%)
Shangwei Guo; Tianwei Zhang; Han Yu; Xiaofei Xie; Lei Ma; Tao Xiang; Yang Liu

http://arxiv.org/abs/2002.10248
Bayes-TrEx: Model Transparency by Example.
Serena Booth; Yilun Zhou; Ankit Shah; Julie Shah

http://arxiv.org/abs/2002.08439
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks.
Xiao Wang; Siyue Wang; Pin-Yu Chen; Xue Lin; Peter Chin

http://arxiv.org/abs/2002.08527
NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion.
Aritran Piplai; Sai Sree Laya Chukkapalli; Anupam Joshi

http://arxiv.org/abs/2002.08347
On Adaptive Attacks to Adversarial Example Defenses.
Florian Tramer; Nicholas Carlini; Wieland Brendel; Aleksander Madry

http://arxiv.org/abs/2002.08012
Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks.
Tsubasa Takahashi

http://arxiv.org/abs/2002.08118
Randomized Smoothing of All Shapes and Sizes.
Greg Yang; Tony Duan; J. Edward Hu; Hadi Salman; Ilya Razenshteyn; Jerry Li

http://arxiv.org/abs/2002.08000
Action-Manipulation Attacks Against Stochastic Bandits: Attacks and Defense.
Guanlin Liu; Lifeng lai

http://arxiv.org/abs/2002.07405
Deflecting Adversarial Attacks.
Yao Qin; Nicholas Frosst; Colin Raffel; Garrison Cottrell; Geoffrey Hinton

http://arxiv.org/abs/2002.07891
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent.
Pu Zhao; Pin-Yu Chen; Siyue Wang; Xue Lin

http://arxiv.org/abs/2002.07920
Block Switching: A Stochastic Approach for Deep Learning Security.
Xiao Wang; Siyue Wang; Pin-Yu Chen; Xue Lin; Peter Chin

http://arxiv.org/abs/2002.10252
TensorShield: Tensor-based Defense Against Adversarial Attacks on Images.
Negin Entezari; Evangelos E. Papalexakis

http://arxiv.org/abs/2002.06816
On the Similarity of Deep Learning Representations Across Didactic and Adversarial Examples.
Pamela K. Douglas; Farzad Vasheghani Farahani

http://arxiv.org/abs/2002.06864
Scalable Quantitative Verification For Deep Neural Networks.
Teodora Baluta; Zheng Leong Chua; Kuldeep S. Meel; Prateek Saxena

http://arxiv.org/abs/2002.06789
CAT: Customized Adversarial Training for Improved Robustness.
Minhao Cheng; Qi Lei; Pin-Yu Chen; Inderjit Dhillon; Cho-Jui Hsieh

http://arxiv.org/abs/2002.07317
On the Matrix-Free Generation of Adversarial Perturbations for Black-Box Attacks.
Hisaichi Shibata; Shouhei Hanaoka; Yukihiro Nomura; Naoto Hayashi; Osamu Abe

http://arxiv.org/abs/2002.07214
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack.
Ziwei Guan; Kaiyi Ji; Donald J Jr Bucci; Timothy Y Hu; Joseph Palombo; Michael Liston; Yingbin Liang

http://arxiv.org/abs/2002.07246
Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness.
Huijie Feng; Chunpeng Wu; Guoyang Chen; Weifeng Zhang; Yang Ning

http://arxiv.org/abs/2002.07088
GRAPHITE: A Practical Framework for Generating Automatic Physical Adversarial Machine Learning Attacks.
Ryan Feng; Neal Mangaokar; Jiefeng Chen; Earlence Fernandes; Somesh Jha; Atul Prakash

http://arxiv.org/abs/2002.06668
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality.
Yi Zhang; Orestis Plevrakis; Simon S. Du; Xingguo Li; Zhao Song; Sanjeev Arora

http://arxiv.org/abs/2003.04808
Undersensitivity in Neural Reading Comprehension.
Johannes Welbl; Pasquale Minervini; Max Bartolo; Pontus Stenetorp; Sebastian Riedel

http://arxiv.org/abs/2002.06349
Hold me tight! Influence of discriminative features on deep network boundaries.
Guillermo Ortiz-Jimenez; Apostolos Modas; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard

http://arxiv.org/abs/2002.06495
Blind Adversarial Network Perturbations.
Milad Nasr; Alireza Bahramali; Amir Houmansadr

http://arxiv.org/abs/2002.05990
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets.
Dongxian Wu; Yisen Wang; Shu-Tao Xia; James Bailey; Xingjun Ma

http://arxiv.org/abs/2002.05999
Adversarial Distributional Training for Robust Deep Learning.
Yinpeng Dong; Zhijie Deng; Tianyu Pang; Hang Su; Jun Zhu

http://arxiv.org/abs/2002.05388
Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks.
Taro Kiritani; Koji Ono

http://arxiv.org/abs/2002.05379
The Conditional Entropy Bottleneck.
Ian Fischer

http://arxiv.org/abs/2002.05463
Identifying Audio Adversarial Examples via Anomalous Pattern Detection.
Victor Akinwande; Celia Cintas; Skyler Speakman; Srihari Sridharan

http://arxiv.org/abs/2002.05283
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization.
Xiangning Chen; Cho-Jui Hsieh

http://arxiv.org/abs/2002.05123
Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks.
Roi Pony; Itay Naeh; Shie Mannor

http://arxiv.org/abs/2002.04694
Adversarial Robustness for Code.
Pavol Bielik; Martin Vechev

http://arxiv.org/abs/2002.04599
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations.
Florian Tramèr; Jens Behrmann; Nicholas Carlini; Nicolas Papernot; Jörn-Henrik Jacobsen

http://arxiv.org/abs/2002.04359
Robustness of Bayesian Neural Networks to Gradient-Based Attacks.
Ginevra Carbone; Matthew Wicker; Luca Laurenti; Andrea Patane; Luca Bortolussi; Guido Sanguinetti

http://arxiv.org/abs/2002.04237
Improving the affordability of robustness training for DNNs.
Sidharth Gupta; Parijat Dube; Ashish Verma

http://arxiv.org/abs/2002.04742
Fast Geometric Projections for Local Robustness Certification.
Aymeric Fromherz; Klas Leino; Matt Fredrikson; Bryan Parno; Corina Păsăreanu

http://arxiv.org/abs/2002.04784
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models.
Xiao Zang; Yi Xie; Jie Chen; Bo Yuan

http://arxiv.org/abs/2002.04725
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models.
Lin Chen; Yifei Min; Mingrui Zhang; Amin Karbasi

http://arxiv.org/abs/2002.03924
Playing to Learn Better: Repeated Games for Adversarial Learning with Multiple Classifiers.
Prithviraj Dasgupta; Joseph B. Collins; Michael McCarrick

http://arxiv.org/abs/2002.03793
Adversarial Data Encryption.
Yingdong Hu; Liang Zhang; Wei Shan; Xiaoxiao Qin; Jing Qi; Zhenzhou Wu; Yang Yuan

http://arxiv.org/abs/2002.04197
Generalised Lipschitz Regularisation Equals Distributional Robustness.
Zac Cranko; Zhan Shi; Xinhua Zhang; Richard Nock; Simon Kornblith

http://arxiv.org/abs/2002.03331
MDEA: Malware Detection with Evolutionary Adversarial Learning.
Xiruo Wang; Risto Miikkulainen

http://arxiv.org/abs/2002.03444
Robust binary classification with the 01 loss.
Yunzhe Xue; Meiyan Xie; Usman Roshan

http://arxiv.org/abs/2002.03500
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks.
Qing Guo; Felix Juefei-Xu; Xiaofei Xie; Lei Ma; Jian Wang; Bing Yu; Wei Feng; Yang Liu

http://arxiv.org/abs/2002.05517
Feature-level Malware Obfuscation in Deep Learning.
Keith Dillon

http://arxiv.org/abs/2002.12749
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples.
Paarth Neekhara; Shehzeen Hussain; Malhar Jere; Farinaz Koushanfar; Julian McAuley

http://arxiv.org/abs/2003.04367
Category-wise Attack: Transferable Adversarial Examples for Anchor Free Object Detection.
Quanyu Liao; Xin Wang; Bin Kong; Siwei Lyu; Youbing Yin; Qi Song; Xi Wu

http://arxiv.org/abs/2002.03421
Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing.
Jinyuan Jia; Binghui Wang; Xiaoyu Cao; Neil Zhenqiang Gong

http://arxiv.org/abs/2002.03517
Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images.
Avrim Blum; Travis Dick; Naren Manoj; Hongyang Zhang

http://arxiv.org/abs/2002.03339
Input Validation for Neural Networks via Runtime Local Robustness Verification.
Jiangchao Liu; Liqian Chen; Antoine Mine; Ji Wang

http://arxiv.org/abs/2002.03095
Attacking Optical Character Recognition (OCR) Systems with Adversarial Watermarks.
Lu Chen; Wei Xu

http://arxiv.org/abs/2002.03239
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness.
Aounon Kumar; Alexander Levine; Tom Goldstein; Soheil Feizi

http://arxiv.org/abs/2002.02998
Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness.
Ting-Wu Chin; Cha Zhang; Diana Marculescu

http://arxiv.org/abs/2002.03080
Analysis of Random Perturbations for Robust Convolutional Neural Networks.
Adam Dziedzic; Sanjay Krishnan

http://arxiv.org/abs/2002.02776
RAID: Randomized Adversarial-Input Detection for Neural Networks.
Hasan Ferit Eniser; Maria Christakis; Valentin Wüstholz

http://arxiv.org/abs/2002.02842
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification.
Meet P. Vadera; Satya Narayan Shukla; Brian Jalaian; Benjamin M. Marlin

http://arxiv.org/abs/2002.03043
Semantic Robustness of Models of Source Code.
Goutham Ramakrishnan; Jordan Henkel; Zi Wang; Aws Albarghouthi; Somesh Jha; Thomas Reps

http://arxiv.org/abs/2002.02424
Reliability Validation of Learning Enabled Vehicle Tracking.
Youcheng Sun; Yifan Zhou; Simon Maskell; James Sharp; Xiaowei Huang

http://arxiv.org/abs/2002.02175
An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models.
Yao Deng; Xi Zheng; Tianyi Zhang; Chen Chen; Guannan Lou; Miryung Kim

http://arxiv.org/abs/2002.02196
AI-GAN: Attack-Inspired Generation of Adversarial Examples.
Tao Bai; Jun Zhao; Jinlin Zhu; Shoudong Han; Jiefeng Chen; Bo Li; Alex Kot

http://arxiv.org/abs/2002.02400
Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels.
Brian Kim; Yalin E. Sagduyu; Kemal Davaslioglu; Tugba Erpek; Sennur Ulukus

http://arxiv.org/abs/2002.01810
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study.
David Mickisch; Felix Assion; Florens Greßner; Wiebke Günther; Mariele Motta

http://arxiv.org/abs/2002.01147
Adversarially Robust Frame Sampling with Bounded Irregularities.
Hanhan Li; Pin Wang

http://arxiv.org/abs/2002.01249
Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria.
Qi Xuan; Yalu Shan; Jinhuan Wang; Zhongyuan Ruan; Guanrong Chen

http://arxiv.org/abs/2002.01256
Minimax Defense against Gradient-based Adversarial Attacks.
Blerta Lindqvist; Rauf Izmailov

http://arxiv.org/abs/2002.01008
A Differentiable Color Filter for Generating Unrestricted Adversarial Images.
Zhengyu Zhao; Zhuoran Liu; Martha Larson

http://arxiv.org/abs/2002.00614
Regularizers for Single-step Adversarial Training.
B. S. Vivek; R. Venkatesh Babu

http://arxiv.org/abs/2002.02007
Defending Adversarial Attacks via Semantic Feature Manipulation.
Shuo Wang; Tianle Chen; Surya Nepal; Carsten Rudolph; Marthie Grobler; Shangyu Chen

http://arxiv.org/abs/2002.00526
Robust saliency maps with decoy-enhanced saliency score.
Yang Lu; Wenbo Guo; Xinyu Xing; William Stafford Noble

http://arxiv.org/abs/2002.02372
Towards Sharper First-Order Adversary with Quantized Gradients.
Zhuanghua Liu; Ivor W. Tsang

http://arxiv.org/abs/2002.00179
AdvJND: Generating Adversarial Examples with Just Noticeable Difference.
Zifei Zhang; Kai Qiao; Lingyun Jiang; Linyuan Wang; Bin Yan

http://arxiv.org/abs/2001.11905
Additive Tree Ensembles: Reasoning About Potential Instances.
Laurens Devos; Wannes Meert; Jesse Davis

http://arxiv.org/abs/2002.05648
Politics of Adversarial Machine Learning.
Kendra Albert; Jonathon Penney; Bruce Schneier; Ram Shankar Siva Kumar

http://arxiv.org/abs/2002.00760
FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications.
Dou Goodman; Lv Zhonghou; Wang minghua

http://arxiv.org/abs/2001.11569
Tiny Noise Can Make an EEG-Based Brain-Computer Interface Speller Output Anything.
Xiao Zhang; Dongrui Wu; Lieyun Ding; Hanbin Luo; Chin-Teng Lin; Tzyy-Ping Jung; Ricardo Chavarriaga

http://arxiv.org/abs/2001.10999
A4 : Evading Learning-based Adblockers.
Shitong Zhu; Zhongjie Wang; Xun Chen; Shasha Li; Umar Iqbal; Zhiyun Qian; Kevin S. Chan; Srikanth V. Krishnamurthy; Zubair Shafiq

http://arxiv.org/abs/2001.11108
D2M: Dynamic Defense and Modeling of Adversarial Movement in Networks.
Scott Freitas; Andrew Wicker; Duen Horng Chau; Joshua Neil

http://arxiv.org/abs/2001.11064
Just Noticeable Difference for Machines to Generate Adversarial Images.
Adil Kaan Akan; Mehmet Ali Genc; Fatos T. Yarman Vural

http://arxiv.org/abs/2001.11055
Semantic Adversarial Perturbations using Learnt Representations.
Isaac Dunn; Tom Melham; Daniel Kroening

http://arxiv.org/abs/2001.11137
Adversarial Attacks on Convolutional Neural Networks in Facial Recognition Domain.
Yigit Alparslan; Ken Alparslan; Jeremy Keim-Shenk; Shweta Khade; Rachel Greenstadt

http://arxiv.org/abs/2001.10648
Modelling and Quantifying Membership Information Leakage in Machine Learning.
Farhad Farokhi; Mohamed Ali Kaafar

http://arxiv.org/abs/2001.10916
Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysis.
William Briguglio; Sherif Saad

http://arxiv.org/abs/2001.09993
Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN.
Jean-Christophe OBELIX Burnel; Kilian OBELIX Fatras; Nicolas OBELIX Courty

http://arxiv.org/abs/2001.09598
FakeLocator: Robust Localization of GAN-Based Face Manipulations via Semantic Segmentation Networks with Bells and Whistles.
Yihao Huang; Felix Juefei-Xu; Run Wang; Xiaofei Xie; Lei Ma; Jianwen Li; Weikai Miao; Yang Liu; Geguang Pu

http://arxiv.org/abs/2001.09684
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning.
Inaam Ilahi; Muhammad Usama; Junaid Qadir; Muhammad Umar Janjua; Ala Al-Fuqaha; Dinh Thai Hoang; Dusit Niyato

http://arxiv.org/abs/2001.09610
Practical Fast Gradient Sign Attack against Mammographic Image Classifier.
Ibrahim Yilmaz

http://arxiv.org/abs/2001.09486
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks.
Rehana Mahfuz; Rajeev Sahay; Aly El Gamal

http://arxiv.org/abs/2002.03751
Weighted Average Precision: Adversarial Example Detection in the Visual Perception of Autonomous Vehicles.
Yilan Li; Senem Velipasalar

http://arxiv.org/abs/2001.09388
AI-Powered GUI Attack and Its Defensive Methods.
Ning Yu; Zachary Tuttle; Carl Jake Thurnau; Emmanuel Mireku

http://arxiv.org/abs/2001.09395
Analyzing the Noise Robustness of Deep Neural Networks.
Kelei Cao; Mengchen Liu; Hang Su; Jing Wu; Jun Zhu; Shixia Liu

http://arxiv.org/abs/2001.08883
When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions.
Yalin E. Sagduyu; Yi Shi; Tugba Erpek; William Headley; Bryse Flowers; George Stantchev; Zhuo Lu

http://arxiv.org/abs/2001.08855
Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack.
Bo Zhang; Ruotong Yu; Haipei Sun; Yanying Li; Jun Xu; Hui Wang

http://arxiv.org/abs/2001.08389
Towards Robust DNNs: An Taylor Expansion-Based Method for Generating Powerful Adversarial Examples.
Ya-guan Qian; Xi-Ming Zhang; Bin Wang; Wei Li; Jian-Hai Chen; Wu-Jie Zhou; Jing-Sheng Lei

http://arxiv.org/abs/2001.08444
On the human evaluation of audio adversarial examples.
Jon Vadillo; Roberto Santana

http://arxiv.org/abs/2001.07933
Adversarial Attack on Community Detection by Hiding Individuals.
Jia Li; Honglei Zhang; Zhichao Han; Yu Rong; Hong Cheng; Junzhou Huang

http://arxiv.org/abs/2001.07645
SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation.
Jesse Sun; Fatemeh Darbeha; Mark Zaidi; Bo Wang

http://arxiv.org/abs/2001.08103
Secure and Robust Machine Learning for Healthcare: A Survey.
Adnan Qayyum; Junaid Qadir; Muhammad Bilal; Ala Al-Fuqaha

http://arxiv.org/abs/2001.07685
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence.
Kihyuk Sohn; David Berthelot; Chun-Liang Li; Zizhao Zhang; Nicholas Carlini; Ekin D. Cubuk; Alex Kurakin; Han Zhang; Colin Raffel

http://arxiv.org/abs/2001.07792
GhostImage: Perception Domain Attacks against Vision-based Object Classification Systems.
Yanmao Man; Ming Li; Ryan Gerdes

http://arxiv.org/abs/2001.07631
Generate High-Resolution Adversarial Samples by Identifying Effective Features.
Sizhe Chen; Peidong Zhang; Chengjin Sun; Jia Cai; Xiaolin Huang

http://arxiv.org/abs/2001.07769
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning.
Nilaksh Polo Das; Haekyu Polo Park; Zijie J. Polo Wang; Fred Polo Hohman; Robert Polo Firstman; Emily Polo Rogers; Duen Polo Horng; Chau

http://arxiv.org/abs/2001.07820
Elephant in the Room: An Evaluation Framework for Assessing Adversarial Examples in NLP.
Ying Xu; Xu Zhong; Antonio Jose Jimeno Yepes; Jey Han Lau

http://arxiv.org/abs/2001.06309
Cyber Attack Detection thanks to Machine Learning Algorithms.
Antoine Delplace; Sheryl Hermoso; Kristofer Anandita

http://arxiv.org/abs/2001.06099
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks.
Farnaz Behnia; Ali Mirzaeian; Mohammad Sabokrou; Sai Manoj; Tinoosh Mohsenin; Khaled N. Khasawneh; Liang Zhao; Houman Homayoun; Avesta Sasan

http://arxiv.org/abs/2001.05873
A Little Fog for a Large Turn.
Harshitha Machiraju; Vineeth N Balasubramanian

http://arxiv.org/abs/2001.07523
The gap between theory and practice in function approximation with deep neural networks.
Ben Adcock; Nick Dexter

http://arxiv.org/abs/2001.06325
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet.
Sizhe Chen; Zhengbao He; Chengjin Sun; Jie Yang; Xiaolin Huang

http://arxiv.org/abs/2001.06057
Increasing the robustness of DNNs against image corruptions by playing the Game of Noise.
Evgenia Rusak; Lukas Schott; Roland S. Zimmermann; Julian Bitterwolf; Oliver Bringmann; Matthias Bethge; Wieland Brendel

http://arxiv.org/abs/2001.04974
Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation.
Chuteng Zhou; Prad Kadambi; Matthew Mattina; Paul N. Whatmough

http://arxiv.org/abs/2001.05574
Advbox: a toolbox to generate adversarial examples that fool neural networks.
Dou Goodman; Hao Xin; Wang Yang; Wu Yuesheng; Xiong Junfeng; Zhang Huan

http://arxiv.org/abs/2001.04011
Membership Inference Attacks Against Object Detection Models.
Yeachan Park; Myungjoo Kang

http://arxiv.org/abs/2001.04051
An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs.
Joseph D. Janizek; Gabriel Erion; Alex J. DeGrave; Su-In Lee

http://arxiv.org/abs/2001.03994
Fast is better than free: Revisiting adversarial training.
Eric Wong; Leslie Rice; J. Zico Kolter

http://arxiv.org/abs/2001.05286
Exploring and Improving Robustness of Multi Task Deep Neural Networks via Domain Agnostic Defenses.
Kashyap Coimbatore Murali

http://arxiv.org/abs/2001.03754
Sparse Black-box Video Attack with Reinforcement Learning.
Huanqian Yan; Xingxing Wei; Bo Li

http://arxiv.org/abs/2001.03662
ReluDiff: Differential Verification of Deep Neural Networks.
Brandon Paulsen; Jingbo Wang; Chao Wang

http://arxiv.org/abs/2001.03311
Guess First to Enable Better Compression and Adversarial Robustness.
Sicheng Zhu; Bang An; Shiyu Niu

http://arxiv.org/abs/2001.02438
To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers.
Bijeeta Pal; Shruti Tople

http://arxiv.org/abs/2001.02378
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius.
Runtian Zhai; Chen Dan; Di He; Huan Zhang; Boqing Gong; Pradeep Ravikumar; Cho-Jui Hsieh; Liwei Wang

http://arxiv.org/abs/2001.03460
Transferability of Adversarial Examples to Attack Cloud-based Image Classifier Service.
Dou Goodman

http://arxiv.org/abs/2001.01987
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring.
Sibylle Hess; Wouter Duivesteijn; Decebal Mocanu

http://arxiv.org/abs/2001.01506
Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations.
Lin Wang; Wonjune Cho; Kuk-Jin Yoon

http://arxiv.org/abs/2001.02297
Generating Semantic Adversarial Examples via Feature Manipulation.
Shuo Wang; Surya Nepal; Carsten Rudolph; Marthie Grobler; Shangyu Chen; Tianle Chen

http://arxiv.org/abs/2001.01172
The Human Visual System and Adversarial AI.
Yaoshiang Ho; Samuel Wookey

http://arxiv.org/abs/2001.00483
Reject Illegal Inputs with Generative Classifier Derived from Any Discriminative Classifier.
Xin Wang

http://arxiv.org/abs/2001.01587
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient.
Ling Liang; Xing Hu; Lei Deng; Yujie Wu; Guoqi Li; Yufei Ding; Peng Li; Yuan Xie

http://arxiv.org/abs/2001.00308
Ensembles of Many Diverse Weak Defenses can be Strong: Defending Deep Neural Networks Against Adversarial Attacks.
Ying Meng; Jianhai Su; Jason O'Kane; Pooyan Jamshidi

http://arxiv.org/abs/1912.13258
Automated Testing for Deep Learning Systems with Differential Behavior Criteria.
Yuan Gao; Yiqiang Han

http://arxiv.org/abs/2001.00071
Protecting GANs against privacy attacks by preventing overfitting.
Sumit Mukherjee; Yixi Xu; Anusua Trivedi; Juan Lavista Ferres

http://arxiv.org/abs/2001.00116
Erase and Restore: Simple, Accurate and Resilient Detection of $L_2$ Adversarial Examples.
Fei Zuo; Qiang Zeng

http://arxiv.org/abs/2001.00030
Quantum Adversarial Machine Learning.
Sirui Lu; Lu-Ming Duan; Dong-Ling Deng

http://arxiv.org/abs/2001.05844
Adversarial Example Generation using Evolutionary Multi-objective Optimization.
Takahiro Suzuki; Shingo Takeshita; Satoshi Ono

http://arxiv.org/abs/1912.12859
Defending from adversarial examples with a two-stream architecture.
Hao Ge; Xiaoguang Tu; Mei Xie; Zheng Ma

http://arxiv.org/abs/1912.12510
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices.
Chandramouli Shama Sastry; Sageev Oore

http://arxiv.org/abs/1912.12463
Search Based Repair of Deep Neural Networks.
Jeongju Sohn; Sungmin Kang; Shin Yoo

http://arxiv.org/abs/1912.11852
Benchmarking Adversarial Robustness.
Yinpeng Dong; Qi-An Fu; Xiao Yang; Tianyu Pang; Hang Su; Zihao Xiao; Jun Zhu

http://arxiv.org/abs/1912.11969
Efficient Adversarial Training with Transferable Adversarial Examples.
Haizhong Zheng; Ziqi Zhang; Juncheng Gu; Honglak Lee; Atul Prakash

http://arxiv.org/abs/1912.11464
Attack-Resistant Federated Learning with Residual-based Reweighting.
Shuhao Fu; Chulin Xie; Bo Li; Qifeng Chen

http://arxiv.org/abs/1912.11372
Analysis of Moving Target Defense Against False Data Injection Attacks on Power Grid.
Zhenyong Zhang; Ruilong Deng; Member; IEEE; David K. Y. Yau; Senior Member; IEEE; Peng Cheng; Member; IEEE; Jiming Chen; Fellow; IEEE

http://arxiv.org/abs/1912.11279
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer.
Hongyan Chang; Virat Shejwalkar; Reza Shokri; Amir Houmansadr

http://arxiv.org/abs/1912.11460
Characterizing the Decision Boundary of Deep Neural Networks.
Hamid Karimi; Tyler Derr; Jiliang Tang

http://arxiv.org/abs/1912.12106
White Noise Analysis of Neural Networks.
Ali Borji; Sikun Lin

http://arxiv.org/abs/1912.11188
Adversarial AutoAugment.
Xinyu Zhang; Qiang Wang; Jian Zhang; Zhao Zhong

http://arxiv.org/abs/1912.11171
Geometry-aware Generation of Adversarial and Cooperative Point Clouds.
Yuxin Wen; Jiehong Lin; Ke Chen; Kui Jia

http://arxiv.org/abs/1912.10375
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack.
Boxin Wang; Hengzhi Pei; Boyuan Pan; Qian Chen; Shuohang Wang; Bo Li

http://arxiv.org/abs/1912.10154
Measuring Dataset Granularity.
Yin Cui; Zeqi Gu; Dhruv Mahajan; der Maaten Laurens van; Serge Belongie; Ser-Nam Lim

http://arxiv.org/abs/1912.09899
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing.
Jinyuan Jia; Xiaoyu Cao; Binghui Wang; Neil Zhenqiang Gong

http://arxiv.org/abs/1912.10013
secml: A Python Library for Secure and Explainable Machine Learning.
Maura Pintor; Luca Demetrio; Angelo Sotgiu; Marco Melis; Ambra Demontis; Battista Biggio

http://arxiv.org/abs/1912.10185
Jacobian Adversarially Regularized Networks for Robustness.
Alvin Chan; Yi Tay; Yew Soon Ong; Jie Fu

http://arxiv.org/abs/1912.09855
Explainability and Adversarial Robustness for RNNs.
Alexander Hartl; Maximilian Bachl; Joachim Fabini; Tanja Zseby

http://arxiv.org/abs/1912.09670
Adversarial symmetric GANs: bridging adversarial samples and adversarial networks.
Faqiang Liu; Mingkun Xu; Guoqi Li; Jing Pei; Luping Shi; Rong Zhao

http://arxiv.org/abs/1912.10834
Does Symbolic Knowledge Prevent Adversarial Fooling?
Stefano Teso

http://arxiv.org/abs/1912.10833
A New Ensemble Method for Concessively Targeted Multi-model Attack.
Ziwen He; Wei Wang; Xinsheng Xuan; Jing Dong; Tieniu Tan

http://arxiv.org/abs/1912.12170
Mitigating large adversarial perturbations on X-MAS (X minus Moving Averaged Samples).
Woohyung Chun; Sung-Min Hong; Junho Huh; Inyup Kang

http://arxiv.org/abs/1912.09064
Optimization-Guided Binary Diversification to Mislead Neural Networks for Malware Detection.
Mahmood Sharif; Keane Lucas; Lujo Bauer; Michael K. Reiter; Saurabh Shintre

http://arxiv.org/abs/1912.09059
$n$-ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers.
Mahmood Sharif; Lujo Bauer; Michael K. Reiter

http://arxiv.org/abs/1912.09533
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations.
Jeet Lily Mohapatra; Lily Tsui-Wei; Weng; Pin-Yu Chen; Sijia Liu; Luca Daniel

http://arxiv.org/abs/1912.09405
Perturbations on the Perceptual Ball.
Andrew Elliott; Stephen Law; Chris Russell

http://arxiv.org/abs/1912.08981
Identifying Adversarial Sentences by Analyzing Text Complexity.
Hoang-Quoc Nguyen-Son; Tran Phuong Thao; Seira Hidano; Shinsaku Kiyomoto

http://arxiv.org/abs/1912.08954
An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation.
Jihan Yang; Ruijia Xu; Ruiyu Li; Xiaojuan Qi; Xiaoyong Shen; Guanbin Li; Liang Lin

http://arxiv.org/abs/1912.08865
Adversarial VC-dimension and Sample Complexity of Neural Networks.
Zetong Qi; T. J. Wilder

http://arxiv.org/abs/1912.09303
SIGMA : Strengthening IDS with GAN and Metaheuristics Attacks.
Simon Msika; Alejandro Quintero; Foutse Khomh

http://arxiv.org/abs/1912.08639
Detecting Adversarial Attacks On Audio-Visual Speech Recognition.
Pingchuan Ma; Stavros Petridis; Maja Pantic

http://arxiv.org/abs/1912.08166
APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection.
A. Braunegg; Amartya Chakraborty; Michael Krumdick; Nicole Lape; Sara Leary; Keith Manville; Elizabeth Merkhofer; Laura Strickhart; Matthew Walmer

http://arxiv.org/abs/1912.07742
CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator.
Huy Phan; Yi Xie; Siyu Liao; Jie Chen; Bo Yuan

http://arxiv.org/abs/1912.07748
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking.
Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Timo Bremer

http://arxiv.org/abs/1912.07458
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration.
Kanil Patel; William Beluch; Dan Zhang; Michael Pfeiffer; Bin Yang

http://arxiv.org/abs/1912.07561
Constructing a provably adversarially-robust classifier from a high accuracy one.
Grzegorz Głuch; Rüdiger Urbanke

http://arxiv.org/abs/1912.07160
DAmageNet: A Universal Adversarial Dataset.
Sizhe Chen; Xiaolin Huang; Zhengbao He; Chengjin Sun

http://arxiv.org/abs/1912.06960
What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance.
Mahmoud Afifi; Michael S Brown

http://arxiv.org/abs/1912.06872
Towards Robust Toxic Content Classification.
Keita Kurita; Anna Belova; Antonios Anastasopoulos

http://arxiv.org/abs/1912.06409
Potential adversarial samples for white-box attacks.
Amir Nazemi; Paul Fieguth

http://arxiv.org/abs/1912.05683
Learning to Model Aspects of Hearing Perception Using Neural Loss Functions.
Prateek Verma; Jonathan Berger

http://arxiv.org/abs/1912.05661
Gabor Layers Enhance Network Robustness.
Juan C. Pérez; Motasem Alfarra; Guillaume Jeanneret; Adel Bibi; Ali Thabet; Bernard Ghanem; Pablo Arbeláez

http://arxiv.org/abs/1912.05333
An Efficient Approach for Using Expectation Maximization Algorithm in Capsule Networks.
Moein Hasani; Amin Nasim Saravi; Hassan Khotanlou

http://arxiv.org/abs/1912.05391
Detecting and Correcting Adversarial Images Using Image Processing Operations and Convolutional Neural Networks.
Huy H. Nguyen; Minoru Kuribayashi; Junichi Yamagishi; Isao Echizen

http://arxiv.org/abs/1912.05699
What it Thinks is Important is Important: Robustness Transfers through Input Gradients.
Alvin Chan; Yi Tay; Yew-Soon Ong

http://arxiv.org/abs/1912.05945
Towards a Robust Classifier: An MDL-Based Method for Generating Adversarial Examples.
Behzad Asadi; Vijay Varadharajan

http://arxiv.org/abs/1912.04538
Appending Adversarial Frames for Universal Video Attack.
Zhikai Chen; Lingxi Xie; Shanmin Pang; Yong He; Qi Tian

http://arxiv.org/abs/1912.04792
Training Provably Robust Models by Polyhedral Envelope Regularization.
Chen Liu; Mathieu Salzmann; Sabine Süsstrunk

http://arxiv.org/abs/1912.04884
Statistically Robust Neural Network Classification. (22%)
Benjie Wang; Stefan Webb; Tom Rainforth

http://arxiv.org/abs/1912.04497
Feature Losses for Adversarial Robustness.
Kirthi Shankar Sivamani

http://arxiv.org/abs/1912.03790
Hardening Random Forest Cyber Detectors Against Adversarial Attacks.
Giovanni Apruzzese; Mauro Andreolini; Michele Colajanni; Mirco Marchetti

http://arxiv.org/abs/1912.03829
Amora: Black-box Adversarial Morphing Attack.
Run Wang; Felix Juefei-Xu; Xiaofei Xie; Lei Ma; Yihao Huang; Yang Liu

http://arxiv.org/abs/1912.03609
Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks.
Yi Xiang Marcus Tan; Yuval Elovici; Alexander Binder

http://arxiv.org/abs/1912.03192
Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations.
Sven Gowal; Chongli Qin; Po-Sen Huang; Taylan Cemgil; Krishnamurthy Dvijotham; Timothy Mann; Pushmeet Kohli

http://arxiv.org/abs/1912.03406
Principal Component Properties of Adversarial Samples.
Malhar Jere; Sandro Herbig; Christine Lind; Farinaz Koushanfar

http://arxiv.org/abs/1912.03430
Training Deep Neural Networks for Interpretability and Adversarial Robustness.
Adam Noack; Isaac Ahern; Dejing Dou; Boyang Li

http://arxiv.org/abs/1912.02918
Detection of Face Recognition Adversarial Attacks.
Fabio Valerio Massoli; Fabio Carrara; Giuseppe Amato; Fabrizio Falchi

http://arxiv.org/abs/1912.02386
The Search for Sparse, Robust Neural Networks.
Justin Cosentino; Federico Zaiter; Dan Pei; Jun Zhu

http://arxiv.org/abs/1912.02598
Region-Wise Attack: On Efficient Generation of Robust Physical Adversarial Examples.
Bo Luo; Qiang Xu

http://arxiv.org/abs/1912.01810
Learning with Multiplicative Perturbations.
Xiulong Yang; Shihao Ji

http://arxiv.org/abs/1912.02258
A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks.
Prithviraj Dasgupta; Joseph B. Collins

http://arxiv.org/abs/1912.02153
Walking on the Edge: Fast, Low-Distortion Adversarial Examples.
Hanwei Zhang; Yannis Avrithis; Teddy Furon; Laurent Amsaleg

http://arxiv.org/abs/1912.02184
Towards Robust Image Classification Using Sequential Attention Models.
Daniel Zoran; Mike Chrzanowski; Po-Sen Huang; Sven Gowal; Alex Mott; Pushmeet Kohl

http://arxiv.org/abs/1912.02316
Scratch that! An Evolution-based Adversarial Attack against Neural Networks.
Malhar Jere; Briland Hitaj; Gabriela Ciocarlie; Farinaz Koushanfar

http://arxiv.org/abs/1912.01667
A Survey of Black-Box Adversarial Attacks on Computer Vision Models.
Siddhant Bhambri; Sumanyu Muku; Avinash Tulasi; Arun Balaji Buduru

http://arxiv.org/abs/1912.01978
FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks.
Mahum Naseer; Mishal Fatima Minhas; Faiq Khalid; Muhammad Abdullah Hanif; Osman Hasan; Muhammad Shafique

http://arxiv.org/abs/1912.01149
Cost-Aware Robust Tree Ensembles for Security Applications.
Yizheng Chen; Shiqi Wang; Weifan Jiang; Asaf Cidon; Suman Jana

http://arxiv.org/abs/1912.00888
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples.
Nils Lukas; Yuxuan Zhang; Florian Kerschbaum

http://arxiv.org/abs/1912.01171
Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs.
Zihan Liu; Xiao Zhang; Lubin Meng; Dongrui Wu

http://arxiv.org/abs/1912.00330
Adversary A3C for Robust Reinforcement Learning.
Zhaoyuan Gu; Zhenzhong Jia; Howie Choset

http://arxiv.org/abs/1912.00466
A Method for Computing Class-wise Universal Adversarial Perturbations.
Tejus Gupta; Abhishek Sinha; Nupur Kumari; Mayank Singh; Balaji Krishnamurthy

http://arxiv.org/abs/1912.00461
AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds.
Abdullah Hamdi; Sara Rojas; Ali Thabet; Bernard Ghanem

http://arxiv.org/abs/1912.05021
Design and Interpretation of Universal Adversarial Patches in Face Detection.
Xiao Yang; Fangyun Wei; Hongyang Zhang; Jun Zhu

http://arxiv.org/abs/1912.00181
Error-Correcting Neural Network.
Yang Song; Qiyu Kang; Wee Peng Tay

http://arxiv.org/abs/1912.00049
Square Attack: a query-efficient black-box adversarial attack via random search.
Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion; Matthias Hein

http://arxiv.org/abs/1911.12562
Towards Privacy and Security of Deep Learning Systems: A Survey.
Yingzhe He; Guozhu Meng; Kai Chen; Xingbo Hu; Jinwen He

http://arxiv.org/abs/1911.11932
Survey of Attacks and Defenses on Edge-Deployed Neural Networks.
Mihailo Isakov; Vijay Gadepally; Karen M. Gettings; Michel A. Kinsy

http://arxiv.org/abs/1911.11881
An Adaptive View of Adversarial Robustness from Test-time Smoothing Defense.
Chao Tang; Yifei Fan; Anthony Yezzi

http://arxiv.org/abs/1911.11946
Can Attention Masks Improve Adversarial Robustness?
Pratik Vaishnavi; Tianji Cong; Kevin Eykholt; Atul Prakash; Amir Rahmati

http://arxiv.org/abs/1911.11746
Defending Against Adversarial Machine Learning.
Alison Jenkins

http://arxiv.org/abs/1911.11484
Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting.
Weizhe Liu; Mathieu Salzmann; Pascal Fua

http://arxiv.org/abs/1911.11253
Playing it Safe: Adversarial Robustness with an Abstain Option.
Cassidy Laidlaw; Soheil Feizi

http://arxiv.org/abs/1911.10891
ColorFool: Semantic Adversarial Colorization.
Ali Shahin Shamsabadi; Ricardo Sanchez-Matilla; Andrea Cavallaro

http://arxiv.org/abs/1911.10875
Adversarial Attack with Pattern Replacement.
Ziang Dong; Liang Mao; Shiliang Sun

http://arxiv.org/abs/1911.11219
One Man's Trash is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples.
Chang Xiao; Changxi Zheng

http://arxiv.org/abs/1911.10695
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks.
Minghao Guo; Yuzhe Yang; Rui Xu; Ziwei Liu; Dahua Lin

http://arxiv.org/abs/1911.10561
Time-aware Gradient Attack on Dynamic Network Link Prediction.
Jinyin Chen; Jian Zhang; Zhi Chen; Min Du; Feifei Li; Qi Xuan

http://arxiv.org/abs/1911.10435
Robust Assessment of Real-World Adversarial Examples.
Brett Jefferson; Carlos Ortiz Marrero

http://arxiv.org/abs/1911.10364
Universal Adversarial Robustness of Texture and Shape-Biased Models.
Kenneth T. Co; Luis Muñoz-González; Leslie Kanthan; Ben Glocker; Emil C. Lupu

http://arxiv.org/abs/1911.10258
Bounding Singular Values of Convolution Layers.
Sahil Singla; Soheil Feizi

http://arxiv.org/abs/1911.11616
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction.
Yantao Lu; Yunhan Jia; Jianyu Wang; Bai Li; Weiheng Chai; Lawrence Carin; Senem Velipasalar

http://arxiv.org/abs/1911.10008
Attack Agnostic Statistical Method for Adversarial Detection.
Sambuddha Saha; Aashish Kumar; Pratyush Sahay; George Jose; Srinivas Kruthiventi; Harikrishna Muralidhara

http://arxiv.org/abs/1911.10182
Universal adversarial examples in speech command classification.
Jon Vadillo; Roberto Santana

http://arxiv.org/abs/1911.10291
Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference.
Wei-An Lin; Yogesh Balaji; Pouya Samangouei; Rama Chellappa

http://arxiv.org/abs/1911.09449
Heuristic Black-box Adversarial Attacks on Video Recognition Models.
Zhipeng Wei; Jingjing Chen; Xingxing Wei; Linxi Jiang; Tat-Seng Chua; Fengfeng Zhou; Yu-Gang Jiang

http://arxiv.org/abs/1911.09665
Adversarial Examples Improve Image Recognition.
Cihang Xie; Mingxing Tan; Boqing Gong; Jiang Wang; Alan Yuille; Quoc V. Le

http://arxiv.org/abs/1911.09307
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy. (1%)
Ke Sun; Bing Yu; Zhouchen Lin; Zhanxing Zhu

http://arxiv.org/abs/1911.09272
Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation.
Alexander Levine; Soheil Feizi

http://arxiv.org/abs/1911.08790
Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method.
Junjie Hu; Takayuki Okatani

http://arxiv.org/abs/1911.09058
Fine-grained Synthesis of Unrestricted Adversarial Examples.
Omid Poursaeed; Tianxing Jiang; Harry Yang; Serge Belongie; Ser-Nam Lim

http://arxiv.org/abs/1911.08723
Deep Minimax Probability Machine.
Lirong He; Ziyi Guo; Kaizhu Huang; Zenglin Xu

http://arxiv.org/abs/1911.08635
Logic-inspired Deep Neural Networks.
Minh Le

http://arxiv.org/abs/1911.08696
Where is the Bottleneck of Adversarial Learning with Unlabeled Data?
Jingfeng Zhang; Bo Han; Gang Niu; Tongliang Liu; Masashi Sugiyama

http://arxiv.org/abs/1911.08654
Adversarial Robustness of Flow-Based Generative Models.
Phillip Pope; Yogesh Balaji; Soheil Feizi

http://arxiv.org/abs/1911.08432
Defective Convolutional Layers Learn Robust CNNs.
Tiange Luo; Tianle Cai; Mengxiao Zhang; Siyu Chen; Di He; Liwei Wang

http://arxiv.org/abs/1911.08644
Generate (non-software) Bugs to Fool Classifiers.
Hiromu Yakura; Youhei Akimoto; Jun Sakuma

http://arxiv.org/abs/1911.07682
A New Ensemble Adversarial Attack Powered by Long-term Gradient Memories.
Zhaohui Che; Ali Borji; Guangtao Zhai; Suiyi Ling; Jing Li; Patrick Le Callet

http://arxiv.org/abs/1911.08053
A novel method for identifying the deep neural network model with the Serial Number.
XiangRui Xu; YaQin Li; Cao Yuan

http://arxiv.org/abs/1911.08011
Adversarial Attacks on Grid Events Classification: An Adversarial Machine Learning Approach.
Iman Niazazari; Hanif Livani

http://arxiv.org/abs/1911.07989
WITCHcraft: Efficient PGD attacks with random step size.
Ping-Yeh Chiang; Jonas Geiping; Micah Goldblum; Tom Goldstein; Renkun Ni; Steven Reich; Ali Shafahi

http://arxiv.org/abs/1911.08090
Deep Detector Health Management under Adversarial Campaigns.
Javier Echauz; Keith Kenemer; Sarfaraz Hussein; Jay Dhaliwal; Saurabh Shintre; Slawomir Grzonkowski; Andrew Gardner

http://arxiv.org/abs/1911.07201
Countering Inconsistent Labelling by Google's Vision API for Rotated Images.
Aman Apte; Aritra Bandyopadhyay; K Akhilesh Shenoy; Jason Peter Andrews; Aditya Rathod; Manish Agnihotri; Aditya Jajodia

http://arxiv.org/abs/1911.07421
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models.
Tong Che; Xiaofeng Liu; Site Li; Yubin Ge; Ruixiang Zhang; Caiming Xiong; Yoshua Bengio

http://arxiv.org/abs/1911.07198
Smoothed Inference for Adversarially-Trained Models.
Yaniv Nemcovsky; Evgenii Zheltonozhskii; Chaim Baskin; Brian Chmiel; Maxim Fishman; Alex M. Bronstein; Avi Mendelson

http://arxiv.org/abs/1911.07107
SMART: Skeletal Motion Action Recognition aTtack.
He Wang; Feixiang He; Zexi Peng; Yongliang Yang; Tianjia Shao; Kun Zhou; David Hogg

http://arxiv.org/abs/1911.07015
Suspicion-Free Adversarial Attacks on Clustering Algorithms.
Anshuman Chhabra; Abhishek Roy; Prasant Mohapatra

http://arxiv.org/abs/1911.07140
Black-Box Adversarial Attack with Transferable Model-based Embedding.
Zhichao Huang; Tong Zhang

http://arxiv.org/abs/1911.06968
Defensive Few-shot Learning.
Wenbin Li; Lei Wang; Xingxing Zhang; Lei Qi; Jing Huo; Yang Gao; Jiebo Luo

http://arxiv.org/abs/1911.06587
Learning To Characterize Adversarial Subspaces.
Xiaofeng Mao; Yuefeng Chen; Yuhong Li; Yuan He; Hui Xue

http://arxiv.org/abs/1911.06479
On Model Robustness Against Adversarial Examples.
Shufei Zhang; Kaizhu Huang; Zenglin Xu

http://arxiv.org/abs/1911.06502
Simple iterative method for generating targeted universal adversarial perturbations.
Hokuto Hirano; Kazuhiro Takemoto

http://arxiv.org/abs/1911.06591
AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients.
Xiaodan Li; Yuefeng Chen; Yuan He; Hui Xue

http://arxiv.org/abs/1912.01487
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique.
Salah Ghamizi; Maxime Cordy; Mike Papadakis; Yves Le Traon

http://arxiv.org/abs/1911.06470
Self-supervised Adversarial Training.
Kejiang Chen; Hang Zhou; Yuefeng Chen; Xiaofeng Mao; Yuhong Li; Yuan He; Hui Xue; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/1911.06285
DomainGAN: Generating Adversarial Examples to Attack Domain Generation Algorithm Classifiers.
Isaac Corley; Jonathan Lwowski; Justin Hoffman

http://arxiv.org/abs/1911.07931
CAGFuzz: Coverage-Guided Adversarial Generative Fuzzing Testing of Deep Learning Systems.
Pengcheng Zhang; Qiyin Dai; Patrizio Pelliccione

http://arxiv.org/abs/1911.05904
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks.
Yizhen Dong; Peixin Zhang; Jingyi Wang; Shuang Liu; Jun Sun; Jianye Hao; Xinyu Wang; Li Wang; Jin Song Dong; Dai Ting

http://arxiv.org/abs/1911.05916
Adversarial Margin Maximization Networks.
Ziang Yan; Yiwen Guo; Changshui Zhang

http://arxiv.org/abs/1911.05153
Improving Robustness of Task Oriented Dialog Systems.
Arash Einolghozati; Sonal Gupta; Mrinal Mohit; Rushin Shah

http://arxiv.org/abs/1911.04681
On Robustness to Adversarial Examples and Polynomial Optimization.
Pranjal Awasthi; Abhratanu Dutta; Aravindan Vijayaraghavan

http://arxiv.org/abs/1911.05268
Adversarial Examples in Modern Machine Learning: A Review.
Rey Reza Wiyatno; Anqi Xu; Ousmane Dia; Berker Archy de

http://arxiv.org/abs/1911.06269
Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN.
Feng Chen; Yunkai Shang; Bo Xu; Jincheng Hu

http://arxiv.org/abs/1911.06155
RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems.
Jianmin Guo; Yue Zhao; Quan Zhang; Yu Jiang

http://arxiv.org/abs/1911.05072
Learning From Brains How to Regularize Machines.
Zhe Li; Wieland Brendel; Edgar Y. Walker; Erick Cobos; Taliah Muhammad; Jacob Reimer; Matthias Bethge; Fabian H. Sinz; Xaq Pitkow; Andreas S. Tolias

http://arxiv.org/abs/1911.04636
Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory.
Arash Rahnama; Andre T. Nguyen; Edward Raff

http://arxiv.org/abs/1911.04657
CALPA-NET: Channel-pruning-assisted Deep Residual Network for Steganalysis of Digital Images.
Shunquan Tan; Weilong Wu; Zilong Shao; Qiushi Li; Bin Li; Jiwu Huang

http://arxiv.org/abs/1911.04429
GraphDefense: Towards Robust Graph Convolutional Networks.
Xiaoyun Wang; Xuanqing Liu; Cho-Jui Hsieh

http://arxiv.org/abs/1911.03677
A Reinforced Generation of Adversarial Samples for Neural Machine Translation.
Wei Zou; Shujian Huang; Jun Xie; Xinyu Dai; Jiajun Chen

http://arxiv.org/abs/1911.03614
Improving Machine Reading Comprehension via Adversarial Training.
Ziqing Yang; Yiming Cui; Wanxiang Che; Ting Liu; Shijin Wang; Guoping Hu

http://arxiv.org/abs/1911.03784
Adaptive versus Standard Descent Methods and Robustness Against Adversarial Examples.
Marc Khoury

http://arxiv.org/abs/1911.03849
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy.
Xinghua Qu; Zhu Sun; Yew-Soon Ong; Abhishek Gupta; Pengfei Wei

http://arxiv.org/abs/1911.04278
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems.
Giulio Zizzo; Chris Hankin; Sergio Maffeis; Kevin Jones

http://arxiv.org/abs/1911.07922
Patch augmentation: Towards efficient decision boundaries for neural networks.
Marcus D. Bloice; Andreas Holzinger

http://arxiv.org/abs/1911.03109
Domain Robustness in Neural Machine Translation.
Mathias Müller; Annette Rios; Rico Sennrich

http://arxiv.org/abs/1911.03078
Adversarial Attacks on GMM i-vector based Speaker Verification Systems.
Xu Li; Jinghua Zhong; Xixin Wu; Jianwei Yu; Xunying Liu; Helen Meng

http://arxiv.org/abs/1911.03274
Imperceptible Adversarial Attacks on Tabular Data.
Vincent Ballet; Xavier Renard; Jonathan Aigrain; Thibault Laugel; Pascal Frossard; Marcin Detyniecki

http://arxiv.org/abs/1911.04606
White-Box Target Attack for EEG-Based BCI Regression Problems.
Lubin Meng; Chin-Teng Lin; Tzyy-Ring Jung; Dongrui Wu

http://arxiv.org/abs/1911.04338
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces.
Xue Jiang; Xiao Zhang; Dongrui Wu

http://arxiv.org/abs/1911.02466
Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance.
Zhengyu Zhao; Zhuoran Liu; Martha Larson

http://arxiv.org/abs/1911.02508
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods.
Dylan Slack; Sophie Hilgard; Emily Jia; Sameer Singh; Himabindu Lakkaraju

http://arxiv.org/abs/1911.02621
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey.
Olakunle Ibitoye; Rana Abou-Khamis; Ashraf Matrawy; M. Omair Shafiq

http://arxiv.org/abs/1911.02360
Reversible Adversarial Example based on Reversible Image Transformation.
Zhaoxia Yin; Hua Wang; Weiming Zhang

http://arxiv.org/abs/1911.01670
Adversarial Enhancement for Community Detection in Complex Networks.
Jiajun Zhou; Zhi Chen; Min Du; Lihong Chen; Shanqing Yu; Feifei Li; Guanrong Chen; Qi Xuan

http://arxiv.org/abs/1911.01921
DLA: Dense-Layer-Analysis for Adversarial Example Detection.
Philip Sperl; Ching-Yu Kao; Peng Chen; Konstantin Böttinger

http://arxiv.org/abs/1911.02142
Intriguing Properties of Adversarial ML Attacks in the Problem Space.
Fabio Pierazzi; Feargus Pendlebury; Jacopo Cortellazzi; Lorenzo Cavallaro

http://arxiv.org/abs/1911.01952
Coverage Guided Testing for Recurrent Neural Networks.
Wei Huang; Youcheng Sun; Xingyu Zhao; James Sharp; Wenjie Ruan; Jie Meng; Xiaowei Huang

http://arxiv.org/abs/1911.01043
Persistency of Excitation for Robustness of Neural Networks.
Kamil Nar; S. Shankar Sastry

http://arxiv.org/abs/1911.01172
Fast-UAP: An Algorithm for Speeding up Universal Adversarial Perturbation Generation with Orientation of Perturbation Vectors.
Jiazhu Dai; Le Shu

http://arxiv.org/abs/1911.01559
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models.
Ren Pang; Hua Shen; Xinyang Zhang; Shouling Ji; Yevgeniy Vorobeychik; Xiapu Luo; Alex Liu; Ting Wang

http://arxiv.org/abs/1911.01840
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems.
Guangke Chen; Sen Chen; Lingling Fan; Xiaoning Du; Zhe Zhao; Fu Song; Yang Liu

http://arxiv.org/abs/1911.00870
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks.
Shai Rozenberg; Gal Elidan; Ran El-Yaniv

http://arxiv.org/abs/1911.00650
Automatic Detection of Generated Text is Easiest when Humans are Fooled.
Daphne Ippolito; Daniel Duckworth; Chris Callison-Burch; Douglas Eck

http://arxiv.org/abs/1911.00660
Security of Facial Forensics Models Against Adversarial Attacks.
Rong Huang; Fuming Fang; Huy H. Nguyen; Junichi Yamagishi; Isao Echizen

http://arxiv.org/abs/1910.14655
Enhancing Certifiable Robustness via a Deep Model Ensemble.
Huan Zhang; Minhao Cheng; Cho-Jui Hsieh

http://arxiv.org/abs/1910.14356
Certifiable Robustness to Graph Perturbations.
Aleksandar Bojchevski; Stephan Günnemann

http://arxiv.org/abs/1911.00126
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System.
Juncheng B. Li; Shuhui Qu; Xinjian Li; Joseph Szurley; J. Zico Kolter; Florian Metze

http://arxiv.org/abs/1910.14107
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization.
Rana Abou Khamis; Omair Shafiq; Ashraf Matrawy

http://arxiv.org/abs/1910.14184
Beyond Universal Person Re-ID Attack.
Wenjie Ding; Xing Wei; Rongrong Ji; Xiaopeng Hong; Qi Tian; Yihong Gong

http://arxiv.org/abs/1910.13222
Adversarial Example in Remote Sensing Image Recognition.
Li Chen; Guowei Zhu; Qi Li; Haifeng Li

http://arxiv.org/abs/1910.13025
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks.
Chunfeng Cui; Kaiqi Zhang; Talgat Daulbaev; Julia Gusak; Ivan Oseledets; Zheng Zhang

http://arxiv.org/abs/1910.12908
Certified Adversarial Robustness for Deep Reinforcement Learning.
Björn Lütjens; Michael Everett; Jonathan P. How

http://arxiv.org/abs/1910.12196
Word-level Textual Adversarial Attacking as Combinatorial Optimization.
Yuan Zang; Fanchao Qi; Chenghao Yang; Zhiyuan Liu; Meng Zhang; Qun Liu; Maosong Sun

http://arxiv.org/abs/1910.12227
EdgeFool: An Adversarial Image Enhancement Filter.
Ali Shahin Shamsabadi; Changjae Oh; Andrea Cavallaro

http://arxiv.org/abs/1911.00927
Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks.
Ya-guan Qian; Dan-feng Ma; Bin Wang; Jun Pan; Jia-min Wang; Jian-hai Chen; Wu-jie Zhou; Jing-sheng Lei

http://arxiv.org/abs/1910.12084
Detection of Adversarial Attacks and Characterization of Adversarial Subspace.
Mohammad Esmaeilpour; Patrick Cardinal; Alessandro Lameiras Koerich

http://arxiv.org/abs/1910.12163
Understanding and Quantifying Adversarial Examples Existence in Linear Classification.
Xupeng Shi; A. Adam Ding

http://arxiv.org/abs/1910.12165
Adversarial Defense Via Local Flatness Regularization.
Jia Xu; Yiming Li; Yong Jiang; Shu-Tao Xia

http://arxiv.org/abs/1910.12392
Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples.
Mauro Barni; Ehsan Nowroozi; Benedetta Tondi; Bowen Zhang

http://arxiv.org/abs/1910.11603
MediaEval 2019: Concealed FGSM Perturbations for Privacy Preservation.
Panagiotis Linardos; Suzanne Little; Kevin McGuinness

http://arxiv.org/abs/1910.11585
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?
Ali Shafahi; Amin Ghiasi; Furong Huang; Tom Goldstein

http://arxiv.org/abs/1910.10994
ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries.
Xingxing Zhang; Shupeng Gui; Zhenfeng Zhu; Yao Zhao; Ji Liu

http://arxiv.org/abs/1910.10679
A Useful Taxonomy for Adversarial Robustness of Neural Networks.
Leslie N. Smith

http://arxiv.org/abs/1910.10783
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks.
Alexander Levine; Soheil Feizi

http://arxiv.org/abs/1910.10053
Attacking Optical Flow.
Anurag Ranjan; Joel Janai; Andreas Geiger; Michael J. Black

http://arxiv.org/abs/1910.10013
Adversarial Example Detection by Classification for Deep Speech Recognition.
Saeid Samizade; Zheng-Hua Tan; Chao Shen; Xiaohong Guan

http://arxiv.org/abs/1910.10106
Cross-Representation Transferability of Adversarial Attacks: From Spectrograms to Audio Waveforms.
Karl M. Koerich; Mohammad Esmailpour; Sajjad Abdoli; Alceu S. Jr. Britto; Alessandro L. Koerich

http://arxiv.org/abs/1910.09821
Structure Matters: Towards Generating Transferable Adversarial Images.
Dan Peng; Zizhan Zheng; Linhao Luo; Xiaofeng Zhang

http://arxiv.org/abs/1910.09239
Recovering Localized Adversarial Attacks.
Jan Philip Göpfert; Heiko Wersing; Barbara Hammer

http://arxiv.org/abs/1910.09464
Learning to Learn by Zeroth-Order Oracle.
Yangjun Ruan; Yuanhao Xiong; Sashank Reddi; Sanjiv Kumar; Cho-Jui Hsieh

http://arxiv.org/abs/1910.09338
An Alternative Surrogate Loss for PGD-based Adversarial Testing.
Sven Gowal; Jonathan Uesato; Chongli Qin; Po-Sen Huang; Timothy Mann; Pushmeet Kohli

http://arxiv.org/abs/1910.08910
Enhancing Recurrent Neural Networks with Sememes.
Yujia Qin; Fanchao Qi; Sicong Ouyang; Zhiyuan Liu; Cheng Yang; Yasheng Wang; Qun Liu; Maosong Sun

http://arxiv.org/abs/1910.08716
Adversarial Attacks on Spoofing Countermeasures of automatic speaker verification.
Songxiang Liu; Haibin Wu; Hung-yi Lee; Helen Meng

http://arxiv.org/abs/1910.08650
Toward Metrics for Differentiating Out-of-Distribution Sets.
Mahdieh Abbasi; Changjian Shui; Arezoo Rajabi; Christian Gagne; Rakesh Bobba

http://arxiv.org/abs/1910.08640
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?
Simran Kaur; Jeremy Cohen; Zachary C. Lipton

http://arxiv.org/abs/1910.08681
Spatial-aware Online Adversarial Perturbations Against Visual Object Tracking.
Qing Guo; Xiaofei Xie; Lei Ma; Zhongguo Li; Wei Feng; Yang Liu

http://arxiv.org/abs/1910.08623
A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning.
Yasaman Esfandiari; Aditya Balu; Keivan Ebrahimi; Umesh Vaidya; Nicola Elia; Soumik Sarkar

http://arxiv.org/abs/1910.08051
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets.
Yogesh Balaji; Tom Goldstein; Judy Hoffman

http://arxiv.org/abs/1910.08108
Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation.
Anindya Sarkar; Nikhil Kumar Gupta; Raghu Iyengar

http://arxiv.org/abs/1910.08536
LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications.
Zirui Xu; Fuxun Yu; Xiang Chen

http://arxiv.org/abs/1910.11099
Adversarial T-shirt! Evading Person Detectors in A Physical World.
Kaidi Xu; Gaoyuan Zhang; Sijia Liu; Quanfu Fan; Mengshu Sun; Hongge Chen; Pin-Yu Chen; Yanzhi Wang; Xue Lin

http://arxiv.org/abs/1910.07629
A New Defense Against Adversarial Images: Turning a Weakness into a Strength.
Tao Yu; Shengyuan Hu; Chuan Guo; Wei-Lun Chao; Kilian Q. Weinberger

http://arxiv.org/abs/1910.06813
Improving Robustness of time series classifier with Neural ODE guided gradient based data augmentation.
Anindya Sarkar; Anirudh Sunder Raj; Raghu Sesha Iyengar

http://arxiv.org/abs/1910.07416
Understanding Misclassifications by Attributes.
Sadaf Gulshad; Zeynep Akata; Jan Hendrik Metzen; Arnold Smeulders

http://arxiv.org/abs/1910.07517
Adversarial Examples for Models of Code.
Noam Yefet; Uri Alon; Eran Yahav

http://arxiv.org/abs/1910.07067
On adversarial patches: real-world attack on ArcFace-100 face recognition system.
Mikhail Pautov; Grigorii Melnikov; Edgar Kaziakhmedov; Klim Kireev; Aleksandr Petiushko

http://arxiv.org/abs/1910.06296
DeepSearch: Simple and Effective Blackbox Fuzzing of Deep Neural Networks.
Fuyuan Zhang; Sankalan Pal Chowdhury; Maria Christakis

http://arxiv.org/abs/1910.06259
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks.
David Stutz; Matthias Hein; Bernt Schiele

http://arxiv.org/abs/1910.06513
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization.
Xiangyi Chen; Sijia Liu; Kaidi Xu; Xingguo Li; Xue Lin; Mingyi Hong; David Cox

http://arxiv.org/abs/1910.06838
Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models.
Derek Derui; Wang; Chaoran Li; Sheng Wen; Surya Nepal; Yang Xiang

http://arxiv.org/abs/1910.06261
Real-world adversarial attack on MTCNN face detection system.
Edgar Kaziakhmedov; Klim Kireev; Grigorii Melnikov; Mikhail Pautov; Aleksandr Petiushko

http://arxiv.org/abs/1910.05513
On Robustness of Neural Ordinary Differential Equations.
Hanshu Yan; Jiawei Du; Vincent Y. F. Tan; Jiashi Feng

http://arxiv.org/abs/1910.05262
Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems.
Hadi Abdullah; Muhammad Sajidur Rahman; Washington Garcia; Logan Blue; Kevin Warren; Anurag Swarnim Yadav; Tom Shrimpton; Patrick Traynor

http://arxiv.org/abs/1910.05018
Verification of Neural Networks: Specifying Global Robustness using Generative Models.
Nathanaël Fijalkow; Mohit Kumar Gupta

http://arxiv.org/abs/1910.04618
Universal Adversarial Perturbation for Text Classification.
Hang Gao; Tim Oates

http://arxiv.org/abs/1910.04819
Information Aware Max-Norm Dirichlet Networks for Predictive Uncertainty Estimation.
Theodoros Tsiligkaridis

http://arxiv.org/abs/1910.03850
Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing.
Rizhao Cai; Changsheng Chen

http://arxiv.org/abs/1910.03810
Adversarial Learning of Deepfakes in Accounting.
Marco Schreyer; Timur Sattarov; Bernd Reimer; Damian Borth

http://arxiv.org/abs/1910.03916
Deep Latent Defence.
Giulio Zizzo; Chris Hankin; Sergio Maffeis; Kevin Jones

http://arxiv.org/abs/1910.04279
Adversarial Training: embedding adversarial perturbations into the parameter space of a neural network to build a robust system.
Shixian Wen; Laurent Itti

http://arxiv.org/abs/1910.03468
Directional Adversarial Training for Cost Sensitive Deep Learning Classification Applications.
Matteo Terzi; Gian Antonio Susto; Pratik Chaudhari

http://arxiv.org/abs/1910.03624
SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations.
Ali Dabouei; Sobhan Soleymani; Fariborz Taherkhani; Jeremy Dawson; Nasser M. Nasrabadi

http://arxiv.org/abs/1910.02673
Interpretable Disentanglement of Neural Networks by Extracting Class-Specific Subnetwork.
Yulong Wang; Xiaolin Hu; Hang Su

http://arxiv.org/abs/1910.02354
Unrestricted Adversarial Attacks for Semantic Segmentation.
Guangyu Shen; Chengzhi Mao; Junfeng Yang; Baishakhi Ray

http://arxiv.org/abs/1910.02244
Yet another but more efficient black-box adversarial attack: tiling and evolution strategies.
Laurent Meunier; Jamal Atif; Olivier Teytaud

http://arxiv.org/abs/1910.02125
Requirements for Developing Robust Neural Networks.
John S. Hyatt; Michael S. Lee

http://arxiv.org/abs/1910.02095
Adversarial Examples for Cost-Sensitive Classifiers.
Gavin S. Hartnett; Andrew J. Lohn; Alexander P. Sedlack

http://arxiv.org/abs/1910.01329
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions.
He Zhao; Trung Le; Paul Montague; Vel Olivier De; Tamas Abraham; Dinh Phung

http://arxiv.org/abs/1910.02785
BUZz: BUffer Zones for defending adversarial examples in image classification.
Kaleel Mahmood; Phuong Ha Nguyen; Lam M. Nguyen; Thanh Nguyen; Dijk Marten van

http://arxiv.org/abs/1910.01624
Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications.
Andreas Venzke; Spyros Chatzivasileiadis

http://arxiv.org/abs/1910.01907
Attacking Vision-based Perception in End-to-End Autonomous Driving Models.
Adith Boloor; Karthik Garimella; Xin He; Christopher Gill; Yevgeniy Vorobeychik; Xuan Zhang

http://arxiv.org/abs/1910.00982
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach.
Micah Goldblum; Liam Fowl; Tom Goldstein

http://arxiv.org/abs/1910.00736
Boosting Image Recognition with Non-differentiable Constraints.
Xuan Li; Yuchen Lu; Peng Xu; Jizong Peng; Christian Desrosiers; Xue Liu

http://arxiv.org/abs/1910.00727
Generating Semantic Adversarial Examples with Differentiable Rendering.
Lakshya Jain; Wilson Wu; Steven Chen; Uyeong Jang; Varun Chandrasekaran; Sanjit Seshia; Somesh Jha

http://arxiv.org/abs/1910.00327
Attacking CNN-based anti-spoofing face authentication in the physical domain.
Bowen Zhang; Benedetta Tondi; Mauro Barni

http://arxiv.org/abs/1910.00511
An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack.
Yang Zhang; Shiyu Chang; Mo Yu; Kaizhi Qian

http://arxiv.org/abs/1910.01742
Cross-Layer Strategic Ensemble Defense Against Adversarial Examples.
Wenqi Wei; Ling Liu; Margaret Loper; Ka-Ho Chow; Emre Gursoy; Stacey Truex; Yanzhao Wu

http://arxiv.org/abs/1910.00470
Deep Neural Rejection against Adversarial Examples.
Angelo Sotgiu; Ambra Demontis; Marco Melis; Battista Biggio; Giorgio Fumera; Xiaoyi Feng; Fabio Roli

http://arxiv.org/abs/1909.13857
Black-box Adversarial Attacks with Bayesian Optimization.
Satya Narayan Shukla; Anit Kumar Sahu; Devin Willmott; J. Zico Kolter

http://arxiv.org/abs/1909.13806
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML.
Sijia Liu; Songtao Lu; Xiangyi Chen; Yao Feng; Kaidi Xu; Abdullah Al-Dujaili; Minyi Hong; Una-May O'Reilly

http://arxiv.org/abs/1910.00068
Role of Spatial Context in Adversarial Robustness for Object Detection.
Aniruddha Saha; Akshayvarun Subramanya; Koninika Patil; Hamed Pirsiavash

http://arxiv.org/abs/1910.06907
Techniques for Adversarial Examples Threatening the Safety of Artificial Intelligence Based Systems.
Utku Kose

http://arxiv.org/abs/1909.12734
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest.
Indu Ilanchezian; Praneeth Vepakomma; Abhishek Singh; Otkrist Gupta; G. N. Srinivasa Prasanna; Ramesh Raskar

http://arxiv.org/abs/1909.12741
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks.
Rémi Bernhard; Pierre-Alain Moellic; Jean-Max Dutertre

http://arxiv.org/abs/1910.04858
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate. (1%)
Lu Mi; Hao Wang; Yonglong Tian; Hao He; Nir Shavit

http://arxiv.org/abs/1909.12031
Towards Understanding the Transferability of Deep Representations.
Hong Liu; Mingsheng Long; Jianmin Wang; Michael I. Jordan

http://arxiv.org/abs/1909.12167
Adversarial Machine Learning Attack on Modulation Classification.
Muhammad Usama; Muhammad Asim; Junaid Qadir; Ala Al-Fuqaha; Muhammad Ali Imran

http://arxiv.org/abs/1909.12161
Adversarial ML Attack on Self Organizing Cellular Networks.
Salah-ud-din Farooq; Muhammad Usama; Junaid Qadir; Muhammad Ali Imran

http://arxiv.org/abs/1909.12180
Towards neural networks that provably know when they don't know.
Alexander Meinke; Matthias Hein

http://arxiv.org/abs/1909.12272
Lower Bounds on Adversarial Robustness from Optimal Transport.
Arjun Nitin Bhagoji; Daniel Cullina; Prateek Mittal

http://arxiv.org/abs/1909.11786
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection.
Nilesh A. Ahuja; Ibrahima Ndiour; Trushant Kalyanpur; Omesh Tickoo

http://arxiv.org/abs/1909.11515
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks.
Tianyu Pang; Kun Xu; Jun Zhu

http://arxiv.org/abs/1909.11764
FreeLB: Enhanced Adversarial Training for Natural Language Understanding.
Chen Zhu; Yu Cheng; Zhe Gan; Siqi Sun; Tom Goldstein; Jingjing Liu

http://arxiv.org/abs/1909.11202
A Visual Analytics Framework for Adversarial Text Generation.
Brandon Laughlin; Christopher Collins; Karthik Sankaranarayanan; Khalil El-Khatib

http://arxiv.org/abs/1909.11167
Intelligent image synthesis to attack a segmentation CNN using adversarial learning.
Liang Chen; Paul Bentley; Kensaku Mori; Kazunari Misawa; Michitaka Fujiwara; Daniel Rueckert

http://arxiv.org/abs/1909.10773
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack.
Minhao Cheng; Simranjit Singh; Patrick Chen; Pin-Yu Chen; Sijia Liu; Cho-Jui Hsieh

http://arxiv.org/abs/1909.11201
Matrix Sketching for Secure Collaborative Machine Learning. (1%)
Mengjiao Zhang; Shusen Wang

http://arxiv.org/abs/1909.10594
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples.
Jinyuan Jia; Ahmed Salem; Michael Backes; Yang Zhang; Neil Zhenqiang Gong

http://arxiv.org/abs/1909.10147
Robust Local Features for Improving the Generalization of Adversarial Training.
Chuanbiao Song; Kun He; Jiadong Lin; Liwei Wang; John E. Hopcroft

http://arxiv.org/abs/1909.10480
FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments.
Alesia Chernikova; Alina Oprea

http://arxiv.org/abs/1909.09938
HAWKEYE: Adversarial Example Detector for Deep Neural Networks.
Jinkyu Koo; Michael Roth; Saurabh Bagchi

http://arxiv.org/abs/1909.10023
Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction.
Guoliang Dong; Jingyi Wang; Jun Sun; Yang Zhang; Xinyu Wang; Ting Dai; Jin Song Dong; Xingen Wang

http://arxiv.org/abs/1909.09481
Adversarial Learning with Margin-based Triplet Embedding Regularization.
Yaoyao Zhong; Weihong Deng

http://arxiv.org/abs/1909.09735
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection.
Aminollah Khormali; Ahmed Abusnaina; Songqing Chen; DaeHun Nyang; Aziz Mohaisen

http://arxiv.org/abs/1909.09552
Defending Against Physically Realizable Attacks on Image Classification.
Tong Wu; Liang Tong; Yevgeniy Vorobeychik

http://arxiv.org/abs/1909.09263
Propagated Perturbation of Adversarial Attack for well-known CNNs: Empirical Study and its Explanation.
Jihyeun Yoon; Kyungyul Kim; Jongseong Jang

http://arxiv.org/abs/1909.08864
Adversarial Vulnerability Bounds for Gaussian Process Classification.
Michael Thomas Smith; Kathrin Grosse; Michael Backes; Mauricio A Alvarez

http://arxiv.org/abs/1909.08830
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks.
Sekitoshi Kanai; Yasutoshi Ida; Yasuhiro Fujiwara; Masanori Yamada; Shuichi Adachi

http://arxiv.org/abs/1909.12927
Toward Robust Image Classification.
Basemah Alshemali; Alta Graham; Jugal Kalita

http://arxiv.org/abs/1909.09034
Training Robust Deep Neural Networks via Adversarial Noise Propagation.
Aishan Liu; Xianglong Liu; Chongzhi Zhang; Hang Yu; Qiang Liu; Dacheng Tao

http://arxiv.org/abs/1909.08072
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review.
Han Xu; Yao Ma; Haochen Liu; Debayan Deb; Hui Liu; Jiliang Tang; Anil Jain

http://arxiv.org/abs/1909.07873
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model.
Prashanth Vijayaraghavan; Deb Roy

http://arxiv.org/abs/1909.08526
Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges.
Jinyuan Jia; Neil Zhenqiang Gong

http://arxiv.org/abs/1909.07490
They Might NOT Be Giants: Crafting Black-Box Adversarial Examples with Fewer Queries Using Particle Swarm Optimization.
Rayan Mosli; Matthew Wright; Bo Yuan; Yin Pan

http://arxiv.org/abs/1909.07558
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples.
Wanting Yu; Hongyi Yu; Lingyun Jiang; Mengli Zhang; Kai Qiao; Linyuan Wang; Bin Yan

http://arxiv.org/abs/1909.07283
Towards Quality Assurance of Software Product Lines with Adversarial Configurations.
Paul Temple; Mathieu Acher; Gilles Perrouin; Battista Biggio; Jean-marc Jezequel; Fabio Roli

http://arxiv.org/abs/1909.06978
Interpreting and Improving Adversarial Robustness with Neuron Sensitivity.
Chongzhi Zhang; Aishan Liu; Xianglong Liu; Yitao Xu; Hang Yu; Yuqing Ma; Tianlin Li

http://arxiv.org/abs/1909.06727
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms.
Qianyu Guo; Sen Chen; Xiaofei Xie; Lei Ma; Qiang Hu; Hongtao Liu; Yang Liu; Jianjun Zhao; Xiaohong Li

http://arxiv.org/abs/1909.06872
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors.
Gilad Cohen; Guillermo Sapiro; Raja Giryes

http://arxiv.org/abs/1909.06723
Natural Language Adversarial Attacks and Defenses in Word Level.
Xiaosen Wang; Hao Jin; Kun He

http://arxiv.org/abs/1909.06500
Adversarial Attack on Skeleton-based Human Action Recognition.
Jian Liu; Naveed Akhtar; Ajmal Mian

http://arxiv.org/abs/1909.06044
Say What I Want: Towards the Dark Side of Neural Dialogue Models.
Haochen Liu; Tyler Derr; Zitao Liu; Jiliang Tang

http://arxiv.org/abs/1909.06271
White-Box Adversarial Defense via Self-Supervised Data Estimation.
Zudi Lin; Hanspeter Pfister; Ziming Zhang

http://arxiv.org/abs/1909.06137
Defending Against Adversarial Attacks by Suppressing the Largest Eigenvalue of Fisher Information Matrix.
Chaomin Shen; Yaxin Peng; Guixu Zhang; Jinsong Fan

http://arxiv.org/abs/1909.05527
Inspecting adversarial examples using the Fisher information.
Jörg Martin; Clemens Elster

http://arxiv.org/abs/1909.05580
An Empirical Investigation of Randomized Defenses against Adversarial Attacks.
Yannik Potdevin; Dirk Nowotka; Vijay Ganesh

http://arxiv.org/abs/1909.05921
Transferable Adversarial Robustness using Adversarially Trained Autoencoders.
Pratik Vaishnavi; Kevin Eykholt; Atul Prakash; Amir Rahmati

http://arxiv.org/abs/1909.05443
Feedback Learning for Improving the Robustness of Neural Networks.
Chang Song; Zuoguan Wang; Hai Li

http://arxiv.org/abs/1909.05040
Sparse and Imperceivable Adversarial Attacks.
Francesco Croce; Matthias Hein

http://arxiv.org/abs/1909.04779
Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification.
Eitan Rothberg; Tingting Chen; Luo Jie; Hao Ji

http://arxiv.org/abs/1909.04837
Identifying and Resisting Adversarial Videos Using Temporal Consistency.
Xiaojun Jia; Xingxing Wei; Xiaochun Cao

http://arxiv.org/abs/1909.04778
Effectiveness of Adversarial Examples and Defenses for Malware Classification.
Robert Podschwadt; Hassan Takabi

http://arxiv.org/abs/1909.04839
Towards Noise-Robust Neural Networks via Progressive Adversarial Training.
Hang Yu; Aishan Liu; Xianglong Liu; Jichen Yang; Chongzhi Zhang

http://arxiv.org/abs/1909.04326
UPC: Learning Universal Physical Camouflage Attacks on Object Detectors.
Lifeng Huang; Chengying Gao; Yuyin Zhou; Changqing Zou; Cihang Xie; Alan Yuille; Ning Liu

http://arxiv.org/abs/1909.04385
FDA: Feature Disruptive Attack.
Aditya Ganeshan; B. S. Vivek; R. Venkatesh Babu

http://arxiv.org/abs/1909.04311
Learning to Disentangle Robust and Vulnerable Features for Adversarial Detection.
Byunggill Joe; Sung Ju Hwang; Insik Shin

http://arxiv.org/abs/1909.04288
Toward Finding The Global Optimal of Adversarial Examples.
Zhenxin Xiao; Kai-Wei Chang; Cho-Jui Hsieh

http://arxiv.org/abs/1909.04068
Adversarial Robustness Against the Union of Multiple Perturbation Models.
Pratyush Maini; Eric Wong; J. Zico Kolter

http://arxiv.org/abs/1909.04126
DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones. (1%)
Ang Li; Jiayi Guo; Huanrui Yang; Flora D. Salim; Yiran Chen

http://arxiv.org/abs/1909.03413
STA: Adversarial Attacks on Siamese Trackers.
Xugang Wu; Xiaoping Wang; Xu Zhou; Songlei Jian

http://arxiv.org/abs/1909.03418
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures.
Gil Fidel; Ron Bitton; Asaf Shabtai

http://arxiv.org/abs/1909.03084
Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification.
Yichao Zhou; Jyun-Yu Jiang; Kai-Wei Chang; Wei Wang

http://arxiv.org/abs/1909.04495
Natural Adversarial Sentence Generation with Gradient-based Perturbation.
Yu-Lun Hsieh; Minhao Cheng; Da-Cheng Juan; Wei Wei; Wen-Lian Hsu; Cho-Jui Hsieh

http://arxiv.org/abs/1909.02918
Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information.
Yiren Zhao; Ilia Shumailov; Han Cui; Xitong Gao; Robert Mullins; Ross Anderson

http://arxiv.org/abs/1909.02583
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents.
Xian Yeow Lee; Sambit Ghadai; Kai Liang Tan; Chinmay Hegde; Soumik Sarkar

http://arxiv.org/abs/1909.02560
Adversarial Examples with Difficult Common Words for Paraphrase Identification.
Zhouxing Shi; Minlie Huang; Ting Yao; Jingfang Xu

http://arxiv.org/abs/1909.02436
Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?
Alfred Laugros; Alice Caplier; Matthieu Ospici

http://arxiv.org/abs/1909.00986
Certified Robustness to Adversarial Word Substitutions.
Robin Jia; Aditi Raghunathan; Kerem Göksel; Percy Liang

http://arxiv.org/abs/1909.01492
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation.
Po-Sen Huang; Robert Stanforth; Johannes Welbl; Chris Dyer; Dani Yogatama; Sven Gowal; Krishnamurthy Dvijotham; Pushmeet Kohli

http://arxiv.org/abs/1909.00900
Metric Learning for Adversarial Robustness.
Chengzhi Mao; Ziyuan Zhong; Junfeng Yang; Carl Vondrick; Baishakhi Ray

http://arxiv.org/abs/1908.11514
Adversarial Training Methods for Network Embedding.
Quanyu Dai; Xiao Shen; Liang Zhang; Qiang Li; Dan Wang

http://arxiv.org/abs/1908.11091
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness.
Ling Liu; Wenqi Wei; Ka-Ho Chow; Margaret Loper; Emre Gursoy; Stacey Truex; Yanzhao Wu

http://arxiv.org/abs/1908.11230
Defeating Misclassification Attacks Against Transfer Learning.
Bang Wu; Shuo Wang; Xingliang Yuan; Cong Wang; Carsten Rudolph; Xiangwen Yang

http://arxiv.org/abs/1908.11332
Universal, transferable and targeted adversarial attacks.
Junde Wu; Rao Fu

http://arxiv.org/abs/1908.09705
A Statistical Defense Approach for Detecting Adversarial Examples.
Alessandro Cennamo; Ido Freeman; Anton Kummert

http://arxiv.org/abs/1908.09699
Gated Convolutional Networks with Hybrid Connectivity for Image Classification.
Chuanguang Yang; Zhulin An; Hui Zhu; Xiaolong Hu; Kun Zhang; Kaiqiang Xu; Chao Li; Yongjun Xu

http://arxiv.org/abs/1908.09364
Adversarial Edit Attacks for Tree Data.
Benjamin Paaßen

http://arxiv.org/abs/1908.09327
advPattern: Physical-World Attacks on Deep Person Re-Identification via Adversarially Transformable Patterns.
Zhibo Wang; Siyan Zheng; Mengkai Song; Qian Wang; Alireza Rahimpour; Hairong Qi

http://arxiv.org/abs/1908.09163
Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower.
Giorgos Tolias; Filip Radenovic; Ond{ř}ej Chum

http://arxiv.org/abs/1908.11435
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing.
Dou Goodman; Xingjian Li; Jun Huan; Tao Wei

http://arxiv.org/abs/1908.08705
AdvHat: Real-world adversarial attack on ArcFace Face ID system.
Stepan Komkov; Aleksandr Petiushko

http://arxiv.org/abs/1908.08413
Saliency Methods for Explaining Adversarial Attacks.
Jindong Gu; Volker Tresp

http://arxiv.org/abs/1908.08016
Testing Robustness Against Unforeseen Adversaries.
Daniel Kang; Yi Sun; Dan Hendrycks; Tom Brown; Jacob Steinhardt

http://arxiv.org/abs/1908.07899
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples.
Marcus Soll; Tobias Hinz; Sven Magg; Stefan Wermter

http://arxiv.org/abs/1908.07667
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks.
Ka-Ho Chow; Wenqi Wei; Yanzhao Wu; Ling Liu

http://arxiv.org/abs/1908.07558
Transferring Robustness for Graph Neural Network Against Poisoning Attacks.
Xianfeng Tang; Yandong Li; Yiwei Sun; Huaxiu Yao; Prasenjit Mitra; Suhang Wang

http://arxiv.org/abs/1908.07125
Universal Adversarial Triggers for NLP.
Eric Wallace; Shi Feng; Nikhil Kandpal; Matt Gardner; Sameer Singh

http://arxiv.org/abs/1908.07116
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses.
Xiao Wang; Siyue Wang; Pin-Yu Chen; Yanzhi Wang; Brian Kulis; Xue Lin; Peter Chin

http://arxiv.org/abs/1908.07000
Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries.
Fnu Suya; Jianfeng Chi; David Evans; Yuan Tian

http://arxiv.org/abs/1908.06401
On the Robustness of Human Pose Estimation.
Sahil Shah; Naman Jain; Abhishek Sharma; Arjun Jain

http://arxiv.org/abs/1908.06566
Adversarial Defense by Suppressing High-frequency Components.
Zhendong Zhang; Cheolkon Jung; Xiaolong Liang

http://arxiv.org/abs/1908.06353
Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation.
Yuh-Shyang Wang; Tsui-Wei Weng; Luca Daniel

http://arxiv.org/abs/1908.06281
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks.
Jiadong Lin; Chuanbiao Song; Kun He; Liwei Wang; John E. Hopcroft

http://arxiv.org/abs/1908.06062
Adversarial point perturbations on 3D objects.
Daniel Liu; Ronald Yu; Hao Su

http://arxiv.org/abs/1908.05185
Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once.
Jiangfan Han; Xiaoyi Dong; Ruimao Zhang; Dongdong Chen; Weiming Zhang; Nenghai Yu; Ping Luo; Xiaogang Wang

http://arxiv.org/abs/1908.05008
AdvFaces: Adversarial Face Synthesis.
Debayan Deb; Jianbang Zhang; Anil K. Jain

http://arxiv.org/abs/1908.05195
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation.
Seungju Cho; Tae Joon Jun; Byungsoo Oh; Daeyoung Kim

http://arxiv.org/abs/1908.04473
On Defending Against Label Flipping Attacks on Malware Detection Systems.
Rahim Taheri; Reza Javidan; Mohammad Shojafar; Zahra Pooranian; Ali Miri; Mauro Conti

http://arxiv.org/abs/1908.04355
Adversarial Neural Pruning with Latent Vulnerability Suppression.
Divyam Madaan; Jinwoo Shin; Sung Ju Hwang

http://arxiv.org/abs/1908.03560
On the Adversarial Robustness of Neural Networks without Weight Transport.
Mohamed Akrout

http://arxiv.org/abs/1908.03176
Defending Against Adversarial Iris Examples Using Wavelet Decomposition.
Sobhan Soleymani; Ali Dabouei; Jeremy Dawson; Nasser M. Nasrabadi

http://arxiv.org/abs/1908.03173
Universal Adversarial Audio Perturbations.
Sajjad Abdoli; Luiz G. Hafemann; Jerome Rony; Ismail Ben Ayed; Patrick Cardinal; Alessandro L. Koerich

http://arxiv.org/abs/1908.02435
Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations.
Andras Rozsa; Terrance E. Boult

http://arxiv.org/abs/1908.02802
Investigating Decision Boundaries of Trained Neural Networks.
Roozbeh Yousefzadeh; Dianne P O'Leary

http://arxiv.org/abs/1908.02374
Explaining Deep Neural Networks Using Spectrum-Based Fault Localization.
Youcheng Sun; Hana Chockler; Xiaowei Huang; Daniel Kroening

http://arxiv.org/abs/1908.02199
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks.
Chen Ma; Chenxu Zhao; Hailin Shi; Li Chen; Junhai Yong; Dan Zeng

http://arxiv.org/abs/1908.02256
BlurNet: Defense by Filtering the Feature Maps.
Ravi Raju; Mikko Lipasti

http://arxiv.org/abs/1908.02658
Random Directional Attack for Fooling Deep Neural Networks.
Wenjian Luo; Chenwang Wu; Nan Zhou; Li Ni

http://arxiv.org/abs/1908.01517
Adversarial Self-Defense for Cycle-Consistent GANs.
Dina Bashkirova; Ben Usman; Kate Saenko

http://arxiv.org/abs/1908.01469
Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve.
Dang Duy Thang; Toshihiro Matsui

http://arxiv.org/abs/1908.01667
A principled approach for generating adversarial images under non-smooth dissimilarity metrics.
Aram-Alexandre Pooladian; Chris Finlay; Tim Hoheisel; Adam Oberman

http://arxiv.org/abs/1908.01551
Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems.
Lea Schönherr; Thorsten Eisenhofer; Steffen Zeiler; Thorsten Holz; Dorothea Kolossa

http://arxiv.org/abs/1908.01297
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models.
Heng Chang; Yu Rong; Tingyang Xu; Wenbing Huang; Honglei Zhang; Peng Cui; Wenwu Zhu; Junzhou Huang

http://arxiv.org/abs/1908.01165
Exploring the Robustness of NMT Systems to Nonsensical Inputs.
Akshay Chaturvedi; Abijith KP; Utpal Garain

http://arxiv.org/abs/1908.00706
AdvGAN++ : Harnessing latent layers for adversary generation.
Puneet Mangla; Surgan Jandial; Sakshi Varshney; Vineeth N Balasubramanian

http://arxiv.org/abs/1908.00635
Black-box Adversarial ML Attack on Modulation Classification.
Muhammad Usama; Junaid Qadir; Ala Al-Fuqaha

http://arxiv.org/abs/1908.00656
Robustifying deep networks for image segmentation.
Zheng Liu; Jinnian Zhang; Varun Jog; Po-Ling Loh; Alan B McMillan

http://arxiv.org/abs/1908.00096
Adversarial Robustness Curves.
Christina Göpfert; Jan Philip Göpfert; Barbara Hammer

http://arxiv.org/abs/1907.13548
Optimal Attacks on Reinforcement Learning Policies.
Alessio Russo; Alexandre Proutiere

http://arxiv.org/abs/1907.13124
Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation.
Utku Ozbulak; Messem Arnout Van; Neve Wesley De

http://arxiv.org/abs/1907.12744
Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding.
Utku Ozbulak; Messem Arnout Van; Neve Wesley De

http://arxiv.org/abs/1907.12138
Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples.
Hossein Hosseini; Sreeram Kannan; Radha Poovendran

http://arxiv.org/abs/1907.11932
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment.
Di Jin; Zhijing Jin; Joey Tianyi Zhou; Peter Szolovits

http://arxiv.org/abs/1907.11780
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin.
Kaiwen Wu; Yaoliang Yu

http://arxiv.org/abs/1907.11684
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method.
Pu Zhao; Sijia Liu; Pin-Yu Chen; Nghia Hoang; Kaidi Xu; Bhavya Kailkhura; Xue Lin

http://arxiv.org/abs/1907.10310
Towards Adversarially Robust Object Detection.
Haichao Zhang; Jianyu Wang

http://arxiv.org/abs/1907.10737
Joint Adversarial Training: Incorporating both Spatial and Pixel Attacks.
Haichao Zhang; Jianyu Wang

http://arxiv.org/abs/1907.10764
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training.
Haichao Zhang; Jianyu Wang

http://arxiv.org/abs/1907.12934
Weakly Supervised Localization using Min-Max Entropy: an Interpretable Framework.
Soufiane Belharbi; Jérôme Rony; Jose Dolz; Ismail Ben Ayed; Luke McCaffrey; Eric Granger

http://arxiv.org/abs/1907.10456
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems.
Xingjun Ma; Yuhao Niu; Lin Gu; Yisen Wang; Yitian Zhao; James Bailey; Feng Lu

http://arxiv.org/abs/1907.10823
Enhancing Adversarial Example Transferability with an Intermediate Level Attack.
Qian Huang; Isay Katsman; Horace He; Zeqi Gu; Serge Belongie; Ser-Nam Lim

http://arxiv.org/abs/1907.09470
Characterizing Attacks on Deep Reinforcement Learning.
Xinlei Pan; Chaowei Xiao; Warren He; Shuang Yang; Jian Peng; Mingjie Sun; Jinfeng Yi; Zijiang Yang; Mingyan Liu; Bo Li; Dawn Song

http://arxiv.org/abs/1907.07732
Connecting Lyapunov Control Theory to Adversarial Attacks.
Arash Rahnama; Andre T. Nguyen; Edward Raff

http://arxiv.org/abs/1907.07640
Robustness properties of Facebook's ResNeXt WSL models.
A. Emin Orhan

http://arxiv.org/abs/1907.07487
Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems.
Alessandro Erba; Riccardo Taormina; Stefano Galelli; Marcello Pogliani; Michele Carminati; Stefano Zanero; Nils Ole Tippenhauer

http://arxiv.org/abs/1907.07291
Adversarial Security Attacks and Perturbations on Machine Learning and Deep Learning Methods.
Arif Siddiqi

http://arxiv.org/abs/1907.07001
Latent Adversarial Defence with Boundary-guided Generation.
Xiaowei Zhou; Ivor W. Tsang; Jie Yin

http://arxiv.org/abs/1907.07174
Natural Adversarial Examples.
Dan Hendrycks; Kevin Zhao; Steven Basart; Jacob Steinhardt; Dawn Song

http://arxiv.org/abs/1907.06826
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving.
Yulong Cao; Chaowei Xiao; Benjamin Cyr; Yimeng Zhou; Won Park; Sara Rampazzi; Qi Alfred Chen; Kevin Fu; Z. Morley Mao

http://arxiv.org/abs/1907.07296
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics.
Yuxin Ma; Tiankai Xie; Jundong Li; Ross Maciejewski

http://arxiv.org/abs/1907.06800
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning.
Bao Wang; Stanley J. Osher

http://arxiv.org/abs/1907.06565
Recovery Guarantees for Compressible Signals with Adversarial Noise.
Jasjeet Dhaliwal; Kyle Hambrook

http://arxiv.org/abs/1907.06291
Measuring the Transferability of Adversarial Examples.
Deyan Petrov; Timothy M. Hospedales

http://arxiv.org/abs/1907.05793
Unsupervised Adversarial Attacks on Deep Feature-based Retrieval with GAN.
Guoping Zhao; Mingyu Zhang; Jiajun Liu; Ji-Rong Wen

http://arxiv.org/abs/1907.05587
Stateful Detection of Black-Box Adversarial Attacks.
Steven Chen; Nicholas Carlini; David Wagner

http://arxiv.org/abs/1907.05600
Generative Modeling by Estimating Gradients of the Data Distribution.
Yang Song; Stefano Ermon

http://arxiv.org/abs/1907.05718
Why Blocking Targeted Adversarial Perturbations Impairs the Ability to Learn.
Ziv Katzir; Yuval Elovici

http://arxiv.org/abs/1907.05418
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems.
Yulong Cao; Chaowei Xiao; Dawei Yang; Jing Fang; Ruigang Yang; Mingyan Liu; Bo Li

http://arxiv.org/abs/1907.04774
Metamorphic Detection of Adversarial Examples in Deep Learning Models With Affine Transformations.
Rohan Reddy Mekala; Gudjon Einar Magnusson; Adam Porter; Mikael Lindvall; Madeline Diep

http://arxiv.org/abs/1907.04449
PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving.
Zelun Kong; Junfeng Guo; Ang Li; Cong Liu

http://arxiv.org/abs/1907.05274
Affine Disentangled GAN for Interpretable and Robust AV Perception.
Letao Liu; Martin Saerbeck; Justin Dauwels

http://arxiv.org/abs/1907.02957
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions.
Yao Qin; Nicholas Frosst; Sara Sabour; Colin Raffel; Garrison Cottrell; Geoffrey Hinton

http://arxiv.org/abs/1907.02610
Adversarial Robustness through Local Linearization.
Chongli Qin; James Martens; Sven Gowal; Dilip Krishnan; Krishnamurthy Dvijotham; Alhussein Fawzi; Soham De; Robert Stanforth; Pushmeet Kohli

http://arxiv.org/abs/1907.02477
Adversarial Attacks in Sound Event Classification.
Vinod Subramanian; Emmanouil Benetos; Ning Xu; SKoT McDonald; Mark Sandler

http://arxiv.org/abs/1907.01996
Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior.
Thomas Gittings; Steve Schneider; John Collomosse

http://arxiv.org/abs/1907.02044
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack.
Francesco Croce; Matthias Hein

http://arxiv.org/abs/1907.01216
Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA.
Moshe Kravchik; Asaf Shabtai

http://arxiv.org/abs/1907.01197
Treant: Training Evasion-Aware Decision Trees.
Stefano Calzavara; Claudio Lucchese; Gabriele Tolomei; Seyum Assefa Abebe; Salvatore Orlando

http://arxiv.org/abs/1907.00895
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network".
Roland S. Zimmermann

http://arxiv.org/abs/1907.01023
Diminishing the Effect of Adversarial Perturbations via Refining Feature Representation.
Nader Asadi; AmirMohammad Sarfi; Sahba Tahsini; Mahdi Eftekhari

http://arxiv.org/abs/1907.01003
Accurate, reliable and fast robustness evaluation.
Wieland Brendel; Jonas Rauber; Matthias Kümmerer; Ivan Ustyuzhaninov; Matthias Bethge

http://arxiv.org/abs/1907.00374
Fooling a Real Car with Adversarial Traffic Signs.
Nir Morgulis; Alexander Kreines; Shachar Mendelowitz; Yuval Weisglass

http://arxiv.org/abs/1906.12340
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty.
Dan Hendrycks; Mantas Mazeika; Saurav Kadavath; Dawn Song

http://arxiv.org/abs/1906.12269
Certifiable Robustness and Robust Training for Graph Convolutional Networks.
Daniel Zügner; Stephan Günnemann

http://arxiv.org/abs/1906.12061
Learning to Cope with Adversarial Attacks.
Xian Yeow Lee; Aaron Havens; Girish Chowdhary; Soumik Sarkar

http://arxiv.org/abs/1907.00098
Robustness Guarantees for Deep Neural Networks on Videos.
Min Wu; Marta Kwiatkowska

http://arxiv.org/abs/1906.11729
Using Intuition from Empirical Properties to Simplify Adversarial Training Defense.
Guanxiong Liu; Issa Khalil; Abdallah Khreishah

http://arxiv.org/abs/1906.11567
Adversarial Robustness via Label-Smoothing.
Morgane Goibert; Elvis Dohmatob

http://arxiv.org/abs/1906.11667
Evolving Robust Neural Architectures to Defend from Adversarial Attacks.
Shashank Kotyan; Danilo Vasconcellos Vargas

http://arxiv.org/abs/1906.11327
The Adversarial Robustness of Sampling.
Omri Ben-Eliezer; Eylon Yogev

http://arxiv.org/abs/1906.10973
Defending Adversarial Attacks by Correcting logits.
Yifeng Li; Lingxi Xie; Ya Zhang; Rui Zhang; Yanfeng Wang; Qi Tian

http://arxiv.org/abs/1906.10395
Quantitative Verification of Neural Networks And its Security Applications.
Teodora Baluta; Shiqi Shen; Shweta Shinde; Kuldeep S. Meel; Prateek Saxena

http://arxiv.org/abs/1906.10773
Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection.
Kang Liu; Haoyu Yang; Yuzhe Ma; Benjamin Tan; Bei Yu; Evangeline F. Y. Young; Ramesh Karri; Siddharth Garg

http://arxiv.org/abs/1906.10571
Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals.
Yunhan Huang; Quanyan Zhu

http://arxiv.org/abs/1906.09525
Defending Against Adversarial Examples with K-Nearest Neighbor.
Chawin Sitawarin; David Wagner

http://arxiv.org/abs/1906.09288
Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations.
Yuezun Li; Xin Yang; Baoyuan Wu; Siwei Lyu

http://arxiv.org/abs/1906.08988
A Fourier Perspective on Model Robustness in Computer Vision.
Dong Yin; Raphael Gontijo Lopes; Jonathon Shlens; Ekin D. Cubuk; Justin Gilmer

http://arxiv.org/abs/1906.09072
Evolution Attack On Neural Networks.
YiGui Luo; RuiJia Yang; Wei Sha; WeiYi Ding; YouTeng Sun; YiSi Wang

http://arxiv.org/abs/1906.09300
Adversarial Examples to Fool Iris Recognition Systems.
Sobhan Soleymani; Ali Dabouei; Jeremy Dawson; Nasser M. Nasrabadi

http://arxiv.org/abs/1906.09313
A Cyclically-Trained Adversarial Network for Invariant Representation Learning.
Jiawei Chen; Janusz Konrad; Prakash Ishwar

http://arxiv.org/abs/1906.11897
On Physical Adversarial Patches for Object Detection.
Mark Lee; Zico Kolter

http://arxiv.org/abs/1907.03720
Catfish Effect Between Internal and External Attackers:Being Semi-honest is Helpful.
Hanqing Liu; Na Ruan; Joseph K. Liu

http://arxiv.org/abs/1906.08416
Improving the robustness of ImageNet classifiers using elements of human visual cognition.
A. Emin Orhan; Brenden M. Lake

http://arxiv.org/abs/1906.07982
A unified view on differential privacy and robustness to adversarial examples.
Rafael Pinot; Florian Yger; Cédric Gouy-Pailler; Jamal Atif

http://arxiv.org/abs/1906.07916
Convergence of Adversarial Training in Overparametrized Networks.
Ruiqi Gao; Tianle Cai; Haochuan Li; Liwei Wang; Cho-Jui Hsieh; Jason D. Lee

http://arxiv.org/abs/1906.07920
Global Adversarial Attacks for Assessing Deep Learning Robustness.
Hanbin Hu; Mit Shah; Jianhua Z. Huang; Peng Li

http://arxiv.org/abs/1906.07997
Cloud-based Image Classification Service Is Not Robust To Simple Transformations: A Forgotten Battlefield.
Dou Goodman; Tao Wei

http://arxiv.org/abs/1906.07927
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing.
Haonan Qiu; Chaowei Xiao; Lei Yang; Xinchen Yan; Honglak Lee; Bo Li

http://arxiv.org/abs/1906.07153
Adversarial attacks on Copyright Detection Systems.
Parsa Saadatpanah; Ali Shafahi; Tom Goldstein

http://arxiv.org/abs/1906.06919
Improving Black-box Adversarial Attacks with a Transfer-based Prior.
Shuyu Cheng; Yinpeng Dong; Tianyu Pang; Hang Su; Jun Zhu

http://arxiv.org/abs/1906.07077
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks.
Felix Assion; Peter Schlicht; Florens Greßner; Wiebke Günther; Fabian Hüger; Nico Schmidt; Umair Rasheed

http://arxiv.org/abs/1906.06784
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy.
Alex Lamb; Vikas Verma; Juho Kannala; Yoshua Bengio

http://arxiv.org/abs/1906.06765
Defending Against Adversarial Attacks Using Random Forests.
Yifan Ding; Liqiang Wang; Huan Zhang; Jinfeng Yi; Deliang Fan; Boqing Gong

http://arxiv.org/abs/1906.06627
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences.
Shashank Kotyan; Danilo Vasconcellos Vargas; Moe Matsuki

http://arxiv.org/abs/1906.06032
Adversarial Training Can Hurt Generalization.
Aditi Raghunathan; Sang Michael Xie; Fanny Yang; John C. Duchi; Percy Liang

http://arxiv.org/abs/1906.06110
Towards Compact and Robust Deep Neural Networks.
Vikash Sehwag; Shiqi Wang; Prateek Mittal; Suman Jana

http://arxiv.org/abs/1906.06355
Perceptual Based Adversarial Audio Attacks.
Joseph Szurley; J. Zico Kolter

http://arxiv.org/abs/1906.06086
Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks.
Thomas Brunner; Frederik Diehl; Alois Knoll

http://arxiv.org/abs/1906.06449
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks.
Felipe A. Mejia; Paul Gamble; Zigfried Hampel-Arias; Michael Lomnitz; Nina Lopatina; Lucas Tindall; Maria Alejandra Barrios

http://arxiv.org/abs/1906.06316
Towards Stable and Efficient Training of Verifiably Robust Neural Networks.
Huan Zhang; Hongge Chen; Chaowei Xiao; Bo Li; Duane Boning; Cho-Jui Hsieh

http://arxiv.org/abs/1906.06026
Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary.
Shashank Kotyan; Danilo Vasconcellos Vargas

http://arxiv.org/abs/1906.05599
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks.
Rajeev Sahay; Rehana Mahfuz; Aly El Gamal

http://arxiv.org/abs/1906.05815
Lower Bounds for Adversarially Robust PAC Learning.
Dimitrios I. Diochnos; Saeed Mahloujifar; Mohammad Mahmoody

http://arxiv.org/abs/1906.04948
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers.
Guang-He Lee; Yang Yuan; Shiyu Chang; Tommi S. Jaakkola

http://arxiv.org/abs/1906.04392
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks.
Ziang Yan; Yiwen Guo; Changshui Zhang

http://arxiv.org/abs/1906.04606
Mimic and Fool: A Task Agnostic Adversarial Attack.
Akshay Chaturvedi; Utpal Garain

http://arxiv.org/abs/1906.04893
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks.
Mahyar Fazlyab; Alexander Robey; Hamed Hassani; Manfred Morari; George J. Pappas

http://arxiv.org/abs/1906.03973
E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles.
Markus Kettunen; Erik Härkönen; Jaakko Lehtinen

http://arxiv.org/abs/1906.03972
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective.
Lu Wang; Xuanqing Liu; Jinfeng Yi; Zhi-Hua Zhou; Cho-Jui Hsieh

http://arxiv.org/abs/1906.03849
Robustness Verification of Tree-based Models.
Hongge Chen; Huan Zhang; Si Si; Yang Li; Duane Boning; Cho-Jui Hsieh

http://arxiv.org/abs/1906.04214
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective.
Kaidi Xu; Hongge Chen; Sijia Liu; Pin-Yu Chen; Tsui-Wei Weng; Mingyi Hong; Xue Lin

http://arxiv.org/abs/1906.03612
On the Vulnerability of Capsule Networks to Adversarial Attacks.
Felix Michels; Tobias Uelwer; Eric Upschulte; Stefan Harmeling

http://arxiv.org/abs/1906.03787
Intriguing properties of adversarial training.
Cihang Xie; Alan Yuille

http://arxiv.org/abs/1906.03749
Improved Adversarial Robustness via Logit Regularization Methods.
Cecilia Summers; Michael J. Dinneen

http://arxiv.org/abs/1906.03750
Attacking Graph Convolutional Networks via Rewiring.
Yao Ma; Suhang Wang; Tyler Derr; Lingfei Wu; Jiliang Tang

http://arxiv.org/abs/1906.03563
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness.
Jingkang Wang; Tianyun Zhang; Sijia Liu; Pin-Yu Chen; Jiacen Xu; Makan Fardad; Bo Li

http://arxiv.org/abs/1906.04584
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers.
Hadi Salman; Greg Yang; Jerry Li; Pengchuan Zhang; Huan Zhang; Ilya Razenshteyn; Sebastien Bubeck

http://arxiv.org/abs/1906.03466
Strategies to architect AI Safety: Defense to guard AI from Adversaries.
Rajagopal. A; Nirmala. V

http://arxiv.org/abs/1906.03455
Sensitivity of Deep Convolutional Networks to Gabor Noise.
Kenneth T. Co; Luis Muñoz-González; Emil C. Lupu

http://arxiv.org/abs/1906.03499
ML-LOO: Detecting Adversarial Examples with Feature Attribution.
Puyudi Yang; Jianbo Chen; Cho-Jui Hsieh; Jane-Ling Wang; Michael I. Jordan

http://arxiv.org/abs/1906.03526
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks.
Maksym Andriushchenko; Matthias Hein

http://arxiv.org/abs/1906.03397
Making targeted black-box evasion attacks effective and efficient.
Mika Juuti; Buse Gul Atli; N. Asokan

http://arxiv.org/abs/1906.03444
Defending Against Universal Attacks Through Selective Feature Regeneration.
Tejas Borkar; Felix Heide; Lina Karam

http://arxiv.org/abs/1906.03231
A cryptographic approach to black box adversarial machine learning.
Kevin Shi; Daniel Hsu; Allison Bishop

http://arxiv.org/abs/1906.03367
Using learned optimizers to make models robust to input noise.
Luke Metz; Niru Maheswaranathan; Jonathon Shlens; Jascha Sohl-Dickstein; Ekin D. Cubuk

http://arxiv.org/abs/1906.03333
Efficient Project Gradient Descent for Ensemble Adversarial Attack.
Fanyou Wu; Rado Gazo; Eva Haviarova; Bedrich Benes

http://arxiv.org/abs/1906.02931
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data.
Yan Li; Ethan X. Fang; Huan Xu; Tuo Zhao

http://arxiv.org/abs/1906.02896
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness.
Walt Woods; Jack Chen; Christof Teuscher

http://arxiv.org/abs/1906.03310
Robustness for Non-Parametric Classification: A Generic Attack and Defense.
Yao-Yuan Yang; Cyrus Rashtchian; Yizhen Wang; Kamalika Chaudhuri

http://arxiv.org/abs/1906.02816
Robust Attacks against Multiple Classifiers.
Juan C. Perdomo; Yaron Singer

http://arxiv.org/abs/1906.02611
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation.
Raphael Gontijo Lopes; Dong Yin; Ben Poole; Justin Gilmer; Ekin D. Cubuk

http://arxiv.org/abs/1906.02494
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric.
Yujun Shi; Benben Liao; Guangyong Chen; Yun Liu; Ming-Ming Cheng; Jiashi Feng

http://arxiv.org/abs/1906.02439
Should Adversarial Attacks Use Pixel p-Norm?
Ayon Sen; Xiaojin Zhu; Liam Marshall; Robert Nowak

http://arxiv.org/abs/1906.09453
Image Synthesis with a Single (Robust) Classifier.
Shibani Santurkar; Dimitris Tsipras; Brandon Tran; Andrew Ilyas; Logan Engstrom; Aleksander Madry

http://arxiv.org/abs/1906.02337
MNIST-C: A Robustness Benchmark for Computer Vision.
Norman Mu; Justin Gilmer

http://arxiv.org/abs/1906.02282
Enhancing Gradient-based Attacks with Symbolic Intervals.
Shiqi Wang; Yizheng Chen; Ahmed Abdou; Suman Jana

http://arxiv.org/abs/1906.02398
Query-efficient Meta Attack to Deep Neural Networks.
Jiawei Du; Hu Zhang; Joey Tianyi Zhou; Yi Yang; Jiashi Feng

http://arxiv.org/abs/1906.02032
c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation.
Minh N. Vu; Truc D. Nguyen; NhatHai Phan; Ralucca Gera; My T. Thai

http://arxiv.org/abs/1906.02033
Multi-way Encoding for Robustness.
Donghyun Kim; Sarah Adel Bargal; Jianming Zhang; Stan Sclaroff

http://arxiv.org/abs/1906.01527
Adversarial Training is a Form of Data-dependent Operator Norm Regularization.
Kevin Roth; Yannic Kilcher; Thomas Hofmann

http://arxiv.org/abs/1906.01121
Adversarial Exploitation of Policy Imitation.
Vahid Behzadan; William Hsu

http://arxiv.org/abs/1906.01110
RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies.
Vahid Behzadan; William Hsu

http://arxiv.org/abs/1906.00698
Adversarial Risk Bounds for Neural Networks through Sparsity based Compression.
Emilio Rafael Balda; Arash Behboodi; Niklas Koep; Rudolf Mathar

http://arxiv.org/abs/1906.00679
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?
Muhammad Usama; Junaid Qadir; Ala Al-Fuqaha; Mounir Hamdi

http://arxiv.org/abs/1906.00945
Adversarial Robustness as a Prior for Learned Representations.
Logan Engstrom; Andrew Ilyas; Shibani Santurkar; Dimitris Tsipras; Brandon Tran; Aleksander Madry

http://arxiv.org/abs/1906.00735
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training.
Jan Laermann; Wojciech Samek; Nils Strodthoff

http://arxiv.org/abs/1906.01040
A Surprising Density of Illusionable Natural Speech.
Melody Y. Guan; Gregory Valiant

http://arxiv.org/abs/1906.00628
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models.
Paweł Morawiecki; Przemysław Spurek; Marek Śmieja; Jacek Tabor

http://arxiv.org/abs/1906.01171
Understanding the Limitations of Conditional Generative Models.
Ethan Fetaya; Jörn-Henrik Jacobsen; Will Grathwohl; Richard Zemel

http://arxiv.org/abs/1906.00555
Adversarially Robust Generalization Just Requires More Unlabeled Data.
Runtian Zhai; Tianle Cai; Di He; Chen Dan; Kun He; John Hopcroft; Liwei Wang

http://arxiv.org/abs/1906.00335
Adversarial Examples for Edge Detection: They Exist, and They Transfer.
Christian Cosgrove; Alan L. Yuille

http://arxiv.org/abs/1906.00204
Perceptual Evaluation of Adversarial Attacks for CNN-based Image Classification.
Sid Ahmed Fezza; Yassine Bakhti; Wassim Hamidouche; Olivier Déforges

http://arxiv.org/abs/1906.00258
Enhancing Transformation-based Defenses using a Distribution Classifier.
Connie Kou; Hwee Kuan Lee; Ee-Chien Chang; Teck Khim Ng

http://arxiv.org/abs/1905.13736
Unlabeled Data Improves Adversarial Robustness.
Yair Carmon; Aditi Raghunathan; Ludwig Schmidt; Percy Liang; John C. Duchi

http://arxiv.org/abs/1905.13472
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness.
Andrey Malinin; Mark Gales

http://arxiv.org/abs/1905.13725
Are Labels Required for Improving Adversarial Robustness?
Jonathan Uesato; Jean-Baptiste Alayrac; Po-Sen Huang; Robert Stanforth; Alhussein Fawzi; Pushmeet Kohli

http://arxiv.org/abs/1905.13399
Real-Time Adversarial Attacks.
Yuan Gong; Boyang Li; Christian Poellabauer; Yiyu Shi

http://arxiv.org/abs/1905.13386
Residual Networks as Nonlinear Systems: Stability Analysis using Linearization.
Kai Rothauge; Zhewei Yao; Zixi Hu; Michael W. Mahoney

http://arxiv.org/abs/1905.13284
Identifying Classes Susceptible to Adversarial Attacks.
Rangeet Pan; Md Johirul Islam; Shibbir Ahmed; Hridesh Rajan

http://arxiv.org/abs/1905.13074
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness.
Adnan Siraj Rakin; Zhezhi He; Li Yang; Yanzhi Wang; Liqiang Wang; Deliang Fan

http://arxiv.org/abs/1905.12864
Interpretable Adversarial Training for Text.
Samuel Barham; Soheil Feizi

http://arxiv.org/abs/1905.12797
Bandlimiting Neural Networks Against Adversarial Attacks.
Yuping Lin; Kasra Ahmadi K. A.; Hui Jiang

http://arxiv.org/abs/1905.12386
Misleading Authorship Attribution of Source Code using Adversarial Learning.
Erwin Quiring; Alwin Maier; Konrad Rieck

http://arxiv.org/abs/1905.12762
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward.
Adnan Qayyum; Muhammad Usama; Junaid Qadir; Ala Al-Fuqaha

http://arxiv.org/abs/1906.00001
Functional Adversarial Attacks.
Cassidy Laidlaw; Soheil Feizi

http://arxiv.org/abs/1905.12282
CopyCAT: Taking Control of Neural Policies with Constant Attacks.
Léonard Hussenot; Matthieu Geist; Olivier Pietquin

http://arxiv.org/abs/1905.11971
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation.
Yuzhe Yang; Guo Zhang; Dina Katabi; Zhi Xu

http://arxiv.org/abs/1905.11831
Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics.
Yi Xiang Marcus Tan; Alfonso Iacovazzi; Ivan Homoliak; Yuval Elovici; Alexander Binder

http://arxiv.org/abs/1905.11713
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss.
Pengcheng Li; Jinfeng Yi; Bowen Zhou; Lijun Zhang

http://arxiv.org/abs/1905.11832
Snooping Attacks on Deep Reinforcement Learning.
Matthew Inkawhich; Yiran Chen; Hai Li

http://arxiv.org/abs/1905.13545
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks.
Haohan Wang; Xindi Wu; Zeyi Huang; Eric P. Xing

http://arxiv.org/abs/1905.12418
Expected Tight Bounds for Robust Training.
Salman Alsubaihi; Adel Bibi; Modar Alfadly; Abdullah Hamdi; Bernard Ghanem

http://arxiv.org/abs/1905.12202
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness.
Saeed Mahloujifar; Xiao Zhang; Mohammad Mahmoody; David Evans

http://arxiv.org/abs/1905.11736
Cross-Domain Transferability of Adversarial Perturbations.
Muzammal Naseer; Salman H. Khan; Harris Khan; Fahad Shahbaz Khan; Fatih Porikli

http://arxiv.org/abs/1905.12105
Certifiably Robust Interpretation in Deep Learning.
Alexander Levine; Sahil Singla; Soheil Feizi

http://arxiv.org/abs/1905.12171
Brain-inspired reverse adversarial examples.
Shaokai Ye; Sia Huat Tan; Kaidi Xu; Yanzhi Wang; Chenglong Bao; Kaisheng Ma

http://arxiv.org/abs/1905.11544
Label Universal Targeted Attack.
Naveed Akhtar; Mohammad A. A. K. Jalwana; Mohammed Bennamoun; Ajmal Mian

http://arxiv.org/abs/1905.11026
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking.
Yunhan Jia; Yantao Lu; Junjie Shen; Qi Alfred Chen; Zhenyu Zhong; Tao Wei

http://arxiv.org/abs/1905.11213
Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$.
Francesco Croce; Matthias Hein

http://arxiv.org/abs/1905.11468
Scaleable input gradient regularization for adversarial robustness.
Chris Finlay; Adam M Oberman

http://arxiv.org/abs/1905.11268
Combating Adversarial Misspellings with Robust Word Recognition.
Danish Pruthi; Bhuwan Dhingra; Zachary C. Lipton

http://arxiv.org/abs/1905.12429
Analyzing the Interpretability Robustness of Self-Explaining Models.
Haizhong Zheng; Earlence Fernandes; Atul Prakash

http://arxiv.org/abs/1905.11564
Adversarially Robust Learning Could Leverage Computational Hardness.
Sanjam Garg; Somesh Jha; Saeed Mahloujifar; Mohammad Mahmoody

http://arxiv.org/abs/1905.11015
Unsupervised Euclidean Distance Attack on Network Embedding.
Shanqing Yu; Jun Zheng; Jinhuan Wang; Jian Zhang; Lihong Chen; Qi Xuan; Jinyin Chen; Dan Zhang; Qingpeng Zhang

http://arxiv.org/abs/1905.11475
GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification.
Xuwang Yin; Soheil Kolouri; Gustavo K. Rohde

http://arxiv.org/abs/1905.11382
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations.
Alex Lamb; Jonathan Binas; Anirudh Goyal; Sandeep Subramanian; Ioannis Mitliagkas; Denis Kazakov; Yoshua Bengio; Michael C. Mozer

http://arxiv.org/abs/1905.10906
Non-Determinism in Neural Networks for Adversarial Robustness.
Daanish Ali Khan; Linhong Li; Ninghao Sha; Zhuoran Liu; Abelino Jimenez; Bhiksha Raj; Rita Singh

http://arxiv.org/abs/1905.10729
Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders.
Hebi Li; Qi Xiao; Shixin Tian; Jin Tian

http://arxiv.org/abs/1905.10900
Rearchitecting Classification Frameworks For Increased Robustness.
Varun Chandrasekaran; Brian Tang; Nicolas Papernot; Kassem Fawaz; Somesh Jha; Xi Wu

http://arxiv.org/abs/1905.10904
Robust Classification using Robust Feature Augmentation.
Kevin Eykholt; Swati Gupta; Atul Prakash; Amir Rahmati; Pratik Vaishnavi; Haizhong Zheng

http://arxiv.org/abs/1905.10864
Generalizable Adversarial Attacks Using Generative Models.
Avishek Joey Bose; Andre Cianflone; William L. Hamilton

http://arxiv.org/abs/1905.11381
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks.
Jirong Yi; Hui Xie; Leixin Zhou; Xiaodong Wu; Weiyu Xu; Raghuraman Mudumbai

http://arxiv.org/abs/1905.10695
Adversarial Distillation for Ordered Top-k Attacks.
Zekun Zhang; Tianfu Wu

http://arxiv.org/abs/1905.10615
Adversarial Policies: Attacking Deep Reinforcement Learning.
Adam Gleave; Michael Dennis; Cody Wild; Neel Kant; Sergey Levine; Stuart Russell

http://arxiv.org/abs/1905.10626
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness.
Tianyu Pang; Kun Xu; Yinpeng Dong; Chao Du; Ning Chen; Jun Zhu

http://arxiv.org/abs/1905.13021
Robustness to Adversarial Perturbations in Learning from Incomplete Data.
Amir Najafi; Shin-ichi Maeda; Masanori Koyama; Takeru Miyato

http://arxiv.org/abs/1905.10510
Enhancing Adversarial Defense by k-Winners-Take-All.
Chang Xiao; Peilin Zhong; Changxi Zheng

http://arxiv.org/abs/1905.10029
Power up! Robust Graph Convolutional Network via Graph Powering.
Ming Jin; Heng Chang; Wenwu Zhu; Somayeh Sojoudi

http://arxiv.org/abs/1905.09591
A Direct Approach to Robust Deep Learning Using Adversarial Networks.
Huaxia Wang; Chun-Nam Yu

http://arxiv.org/abs/1905.09894
PHom-GeM: Persistent Homology for Generative Models.
Jeremy Charlier; Radu State; Jean Hilger

http://arxiv.org/abs/1905.09871
Thwarting finite difference adversarial attacks with output randomization.
Haidar Khan; Daniel Park; Azer Khan; Bülent Yener

http://arxiv.org/abs/1905.09797
Interpreting Adversarially Trained Convolutional Neural Networks.
Tianyuan Zhang; Zhanxing Zhu

http://arxiv.org/abs/1905.09747
Adversarially Robust Distillation.
Micah Goldblum; Liam Fowl; Soheil Feizi; Tom Goldstein

http://arxiv.org/abs/1905.09209
Convergence and Margin of Adversarial Training on Separable Data.
Zachary Charles; Shashank Rajput; Stephen Wright; Dimitris Papailiopoulos

http://arxiv.org/abs/1905.09186
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection.
Jonathan Aigrain; Marcin Detyniecki

http://arxiv.org/abs/1905.08790
DoPa: A Fast and Comprehensive CNN Defense Methodology against Physical Adversarial Attacks.
Zirui Xu; Fuxun Yu; Xiang Chen

http://arxiv.org/abs/1905.08232
Adversarially robust transfer learning.
Ali Shafahi; Parsa Saadatpanah; Chen Zhu; Amin Ghiasi; Christoph Studer; David Jacobs; Tom Goldstein

http://arxiv.org/abs/1905.07831
Testing DNN Image Classifiers for Confusion & Bias Errors.
Yuchi Tian; Ziyuan Zhong; Vicente Ordonez; Gail Kaiser; Baishakhi Ray

http://arxiv.org/abs/1905.07666
What Do Adversarially Robust Models Look At?
Takahiro Itazuri; Yoshihiro Fukuhara; Hirokatsu Kataoka; Shigeo Morishima

http://arxiv.org/abs/1905.07672
Taking Care of The Discretization Problem:A Black-Box Adversarial Image Attack in Discrete Integer Domain.
Yuchao Duan; Zhe Zhao; Lei Bu; Fu Song

http://arxiv.org/abs/1905.07387
POPQORN: Quantifying Robustness of Recurrent Neural Networks.
Ching-Yun Ko; Zhaoyang Lyu; Tsui-Wei Weng; Luca Daniel; Ngai Wong; Dahua Lin

http://arxiv.org/abs/1905.07112
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models.
Nicholas Carlini

http://arxiv.org/abs/1905.07121
Simple Black-box Adversarial Attacks.
Chuan Guo; Jacob R. Gardner; Yurong You; Andrew Gordon Wilson; Kilian Q. Weinberger

http://arxiv.org/abs/1905.06635
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization.
Seungyong Moon; Gaon An; Hyun Oh Song

http://arxiv.org/abs/1905.06455
On Norm-Agnostic Robustness of Adversarial Training.
Bai Li; Changyou Chen; Wenlin Wang; Lawrence Carin

http://arxiv.org/abs/1905.08614
An Efficient Pre-processing Method to Eliminate Adversarial Effects.
Hua Wang; Jie Wang; Zhaoxia Yin

http://arxiv.org/abs/1905.05454
Robustification of deep net classifiers by key based diversified aggregation with pre-filtering.
Olga Taran; Shideh Rezaeifar; Taras Holotyak; Slava Voloshynovskiy

http://arxiv.org/abs/1905.05163
Adversarial Examples for Electrocardiograms.
Xintian Han; Yuxuan Hu; Luca Foschini; Larry Chinitz; Lior Jankelson; Rajesh Ranganath

http://arxiv.org/abs/1905.05137
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks.
Olakunle Ibitoye; Omair Shafiq; Ashraf Matrawy

http://arxiv.org/abs/1905.05186
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models.
Mayank Singh; Abhishek Sinha; Nupur Kumari; Harshitha Machiraju; Balaji Krishnamurthy; Vineeth N Balasubramanian

http://arxiv.org/abs/1905.13148
Moving Target Defense for Deep Visual Sensing against Adversarial Examples.
Qun Song; Zhenyu Yan; Rui Tan

http://arxiv.org/abs/1905.04270
Interpreting and Evaluating Neural Network Robustness.
Fuxun Yu; Zhuwei Qin; Chenchen Liu; Liang Zhao; Yanzhi Wang; Xiang Chen

http://arxiv.org/abs/1905.04172
On the Connection Between Adversarial Robustness and Saliency Map Interpretability.
Christian Etmann; Sebastian Lunz; Peter Maass; Carola-Bibiane Schönlieb

http://arxiv.org/abs/1905.04016
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables.
Yan Xu; Baoyuan Wu; Fumin Shen; Yanbo Fan; Yong Zhang; Heng Tao Shen; Wei Liu

http://arxiv.org/abs/1905.03679
Adversarial Defense Framework for Graph Neural Network.
Shen Wang; Zhengzhang Chen; Jingchao Ni; Xiao Yu; Zhichun Li; Haifeng Chen; Philip S. Yu

http://arxiv.org/abs/1905.03517
Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain.
Chris Einar San Agustin

http://arxiv.org/abs/1905.03837
Exploring the Hyperparameter Landscape of Adversarial Robustness.
Evelyn Duesterwald; Anupama Murthi; Ganesh Venkataraman; Mathieu Sinn; Deepak Vijaykeerthy

http://arxiv.org/abs/1905.03767
Learning Interpretable Features via Adversarially Robust Optimization.
Ashkan Khakzar; Shadi Albarqouni; Nassir Navab

http://arxiv.org/abs/1905.03828
Universal Adversarial Perturbations for Speech Recognition Systems.
Paarth Neekhara; Shehzeen Hussain; Prakhar Pandey; Shlomo Dubnov; Julian McAuley; Farinaz Koushanfar

http://arxiv.org/abs/1905.03434
ROSA: Robust Salient Object Detection against Adversarial Attacks.
Haofeng Li; Guanbin Li; Yizhou Yu

http://arxiv.org/abs/1905.03333
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction.
Yunhan Jia; Yantao Lu; Senem Velipasalar; Zhenyu Zhong; Tao Wei

http://arxiv.org/abs/1905.03421
Adversarial Image Translation: Unrestricted Adversarial Examples in Face Recognition Systems.
Kazuya Kakizaki; Kosuke Yoshida

http://arxiv.org/abs/1905.02704
A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks.
Saima Sharmin; Priyadarshini Panda; Syed Shakib Sarwar; Chankyu Lee; Wachirawit Ponghiran; Kaushik Roy

http://arxiv.org/abs/1905.02422
Representation of White- and Black-Box Adversarial Examples in Deep Neural Networks and Humans: A Functional Magnetic Resonance Imaging Study.
Chihye Han; Wonjun Yoon; Gihyun Kwon; Seungkyu Nam; Daeshik Kim

http://arxiv.org/abs/1905.02675
An Empirical Evaluation of Adversarial Robustness under Transfer Learning.
Todor Davchev; Timos Korres; Stathi Fotiadis; Nick Antonopoulos; Subramanian Ramamoorthy

http://arxiv.org/abs/1905.02463
Adaptive Generation of Unrestricted Adversarial Inputs.
Isaac Dunn; Hadrien Pouget; Tom Melham; Daniel Kroening

http://arxiv.org/abs/1905.02161
Batch Normalization is a Cause of Adversarial Vulnerability.
Angus Galloway; Anna Golubeva; Thomas Tanay; Medhat Moussa; Graham W. Taylor

http://arxiv.org/abs/1905.02175
Adversarial Examples Are Not Bugs, They Are Features.
Andrew Ilyas; Shibani Santurkar; Dimitris Tsipras; Logan Engstrom; Brandon Tran; Aleksander Madry

http://arxiv.org/abs/1905.01726
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples.
Vikash Sehwag; Arjun Nitin Bhagoji; Liwei Song; Chawin Sitawarin; Daniel Cullina; Mung Chiang; Prateek Mittal

http://arxiv.org/abs/1905.01034
Transfer of Adversarial Robustness Between Perturbation Types.
Daniel Kang; Yi Sun; Tom Brown; Dan Hendrycks; Jacob Steinhardt

http://arxiv.org/abs/1905.01019
Adversarial Training with Voronoi Constraints.
Marc Khoury; Dylan Hadfield-Menell

http://arxiv.org/abs/1905.00568
Weight Map Layer for Noise and Adversarial Attack Robustness.
Mohammed Amer; Tomás Maul

http://arxiv.org/abs/1905.00877
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle.
Dinghuai Zhang; Tianyuan Zhang; Yiping Lu; Zhanxing Zhu; Bin Dong

http://arxiv.org/abs/1906.03181
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm.
Jinyin Chen; Mengmeng Su; Shijing Shen; Hui Xiong; Haibin Zheng

http://arxiv.org/abs/1905.00180
Dropping Pixels for Adversarial Robustness.
Hossein Hosseini; Sreeram Kannan; Radha Poovendran

http://arxiv.org/abs/1905.00441
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks.
Yandong Li; Lijun Li; Liqiang Wang; Tong Zhang; Boqing Gong

http://arxiv.org/abs/1904.13195
Test Selection for Deep Learning Systems.
Wei Ma; Mike Papadakis; Anestis Tsakmalis; Maxime Cordy; Yves Le Traon

http://arxiv.org/abs/1904.13094
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction.
Francesco Crecchi; Davide Bacciu; Battista Biggio

http://arxiv.org/abs/1904.12843
Adversarial Training for Free!
Ali Shafahi; Mahyar Najibi; Amin Ghiasi; Zheng Xu; John Dickerson; Christoph Studer; Larry S. Davis; Gavin Taylor; Tom Goldstein

http://arxiv.org/abs/1904.13000
Adversarial Training and Robustness for Multiple Perturbations.
Florian Tramèr; Dan Boneh

http://arxiv.org/abs/1904.12181
Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks.
Xiang He; Sibei Yang; Guanbin Li?; Haofeng Li; Huiyou Chang; Yizhou Yu

http://arxiv.org/abs/1904.11803
Robustness Verification of Support Vector Machines.
Francesco Ranzato; Marco Zanella

http://arxiv.org/abs/1904.10990
A Robust Approach for Securing Audio Classification Against Adversarial Attacks.
Mohammad Esmaeilpour; Patrick Cardinal; Alessandro Lameiras Koerich

http://arxiv.org/abs/1904.11042
Physical Adversarial Textures that Fool Visual Object Tracking.
Rey Reza Wiyatno; Anqi Xu

http://arxiv.org/abs/1904.10390
Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping.
Kostiantyn Khabarlak; Larysa Koriashkina

http://arxiv.org/abs/1904.09804
blessing in disguise: Designing Robust Turing Test by Employing Algorithm Unrobustness.
Jiaming Zhang; Jitao Sang; Kaiyuan Xu; Shangxi Wu; Yongli Hu; Yanfeng Sun; Jian Yu

http://arxiv.org/abs/1904.10076
Using Videos to Evaluate Image Model Robustness.
Keren Gu; Brandon Yang; Jiquan Ngiam; Quoc Le; Jonathon Shlens

http://arxiv.org/abs/1904.09633
Beyond Explainability: Leveraging Interpretability for Improved Adversarial Learning.
Devinder Kumar; Ibrahim Ben-Daya; Kanav Vats; Jeffery Feng; Graham Taylor and; Alexander Wong

http://arxiv.org/abs/1904.09433
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach.
Rahim Taheri; Reza Javidan; Mohammad Shojafar; Vinod P; Mauro Conti

http://arxiv.org/abs/1904.09146
Salient Object Detection in the Deep Learning Era: An In-Depth Survey.
Wenguan Wang; Qiuxia Lai; Huazhu Fu; Jianbing Shen; Haibin Ling; Ruigang Yang

http://arxiv.org/abs/1904.08653
Fooling automated surveillance cameras: adversarial patches to attack person detection.
Simen Thys; Ranst Wiebe Van; Toon Goedemé

http://arxiv.org/abs/1904.08516
ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural Networks.
Guanxiong Liu; Issa Khalil; Abdallah Khreishah

http://arxiv.org/abs/1904.08444
Defensive Quantization: When Efficiency Meets Robustness.
Ji Lin; Chuang Gan; Song Han

http://arxiv.org/abs/1904.08279
Interpreting Adversarial Examples with Attributes.
Sadaf Gulshad; Jan Hendrik Metzen; Arnold Smeulders; Zeynep Akata

http://arxiv.org/abs/1904.08089
Adversarial Defense Through Network Profiling Based Path Extraction.
Yuxian Qiu; Jingwen Leng; Cong Guo; Quan Chen; Chao Li; Minyi Guo; Yuhao Zhu

http://arxiv.org/abs/1904.08554
Gotta Catch 'Em All: Using Concealed Trapdoors to Detect Adversarial Attacks on Neural Networks.
Shawn Shan; Emily Willson; Bolun Wang; Bo Li; Haitao Zheng; Ben Y. Zhao

http://arxiv.org/abs/1904.08489
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers.
Ameya Joshi; Amitangshu Mukherjee; Soumik Sarkar; Chinmay Hegde

http://arxiv.org/abs/1904.07980
Reducing Adversarial Example Transferability Using Gradient Regularization.
George Adam; Petr Smirnov; Benjamin Haibe-Kains; Anna Goldenberg

http://arxiv.org/abs/1904.07793
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples.
Xiaosen Wang; Kun He; Chuanbiao Song; Liwei Wang; John E. Hopcroft

http://arxiv.org/abs/1904.07370
Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Steering Angle Prediction.
Alesia Chernikova; Alina Oprea; Cristina Nita-Rotaru; BaekGyu Kim

http://arxiv.org/abs/1904.06964
Influence of Control Parameters and the Size of Biomedical Image Datasets on the Success of Adversarial Attacks.
Vassili Kovalev; Dmitry Voynov

http://arxiv.org/abs/1904.06606
Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks.
Yize Chen; Yushi Tan; Baosen Zhang

http://arxiv.org/abs/1904.06026
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense.
Lingyun Jiang; Kai Qiao; Ruoxi Qin; Linyuan Wang; Jian Chen; Haibing Bu; Bin Yan

http://arxiv.org/abs/1904.06186
Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients.
Yatie Xiao; Chi-Man Pun

http://arxiv.org/abs/1904.06097
Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks.
Jun-Ho Choi; Huan Zhang; Jun-Hyuk Kim; Cho-Jui Hsieh; Jong-Seok Lee

http://arxiv.org/abs/1904.06292
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks.
David J. Miller; Zhen Xiang; George Kesidis

http://arxiv.org/abs/1904.06347
Unrestricted Adversarial Examples via Semantic Manipulation.
Anand Bhattad; Min Jin Chong; Kaizhao Liang; Bo Li; D. A. Forsyth

http://arxiv.org/abs/1904.05586
Black-Box Decision based Adversarial Attack with Symmetric $\alpha$-stable Distribution.
Vignesh Srinivasan; Ercan E. Kuruoglu; Klaus-Robert Müller; Wojciech Samek; Shinichi Nakajima

http://arxiv.org/abs/1904.05475
Learning to Generate Synthetic Data via Compositing.
Shashank Tripathi; Siddhartha Chandra; Amit Agrawal; Ambrish Tyagi; James M. Rehg; Visesh Chari

http://arxiv.org/abs/1904.05181
Black-box Adversarial Attacks on Video Recognition Models.
Linxi Jiang; Xingjun Ma; Shaoxiang Chen; James Bailey; Yu-Gang Jiang

http://arxiv.org/abs/1904.04802
Generation & Evaluation of Adversarial Examples for Malware Obfuscation.
Daniel Park; Haidar Khan; Bülent Yener

http://arxiv.org/abs/1904.04433
Efficient Decision-based Black-box Adversarial Attacks on Face Recognition.
Yinpeng Dong; Hang Su; Baoyuan Wu; Zhifeng Li; Wei Liu; Tong Zhang; Jun Zhu

http://arxiv.org/abs/1904.04334
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning.
Shahbaz Rezaei; Xin Liu

http://arxiv.org/abs/1904.03750
JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks.
N. Benjamin Erichson; Zhewei Yao; Michael W. Mahoney

http://arxiv.org/abs/1904.05747
Malware Evasion Attack and Defense.
Yonghong Huang; Utkarsh Verma; Celeste Fralick; Gabriel Infante-Lopez; Brajesh Kumarz; Carl Woodward

http://arxiv.org/abs/1904.03542
On Training Robust PDF Malware Classifiers.
Yizheng Chen; Shiqi Wang; Dongdong She; Suman Jana

http://arxiv.org/abs/1904.02884
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks.
Yinpeng Dong; Tianyu Pang; Hang Su; Jun Zhu

http://arxiv.org/abs/1904.02405
White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks.
Yotam Gil; Yoav Chai; Or Gorodissky; Jonathan Berant

http://arxiv.org/abs/1904.02841
Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks.
Fatemeh Sheikholeslami; Swayambhoo Jain; Georgios B. Giannakis

http://arxiv.org/abs/1904.10504
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions.
Li Chen

http://arxiv.org/abs/1904.02057
Interpreting Adversarial Examples by Activation Promotion and Suppression.
Kaidi Xu; Sijia Liu; Gaoyuan Zhang; Mengshu Sun; Pu Zhao; Quanfu Fan; Chuang Gan; Xue Lin

http://arxiv.org/abs/1904.02144
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack.
Jianbo Chen; Michael I. Jordan; Martin J. Wainwright

http://arxiv.org/abs/1904.02323
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations.
Fred Hohman; Haekyu Park; Caleb Robinson; Duen Horng Chau

http://arxiv.org/abs/1904.01231
Adversarial Attacks against Deep Saliency Models.
Zhaohui Che; Ali Borji; Guangtao Zhai; Suiyi Ling; Guodong Guo; Patrick Le Callet

http://arxiv.org/abs/1904.01160
Curls & Whey: Boosting Black-Box Adversarial Attacks.
Yucheng Shi; Siyu Wang; Yahong Han

http://arxiv.org/abs/1904.00923
Robustness of 3D Deep Learning in an Adversarial Setting.
Matthew Wicker; Marta Kwiatkowska

http://arxiv.org/abs/1904.00689
Defending against adversarial attacks by randomized diversification.
Olga Taran; Shideh Rezaeifar; Taras Holotyak; Slava Voloshynovskiy

http://arxiv.org/abs/1904.00887
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks.
Aamir Mustafa; Salman Khan; Munawar Hayat; Roland Goecke; Jianbing Shen; Ling Shao

http://arxiv.org/abs/1904.00979
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses.
Yingwei Li; Song Bai; Cihang Xie; Zhenyu Liao; Xiaohui Shen; Alan L. Yuille

http://arxiv.org/abs/1904.01002
On the Vulnerability of CNN Classifiers in EEG-Based BCIs.
Xiao Zhang; Dongrui Wu

http://arxiv.org/abs/1903.12561
Adversarial Robustness vs Model Compression, or Both?
Shaokai Ye; Kaidi Xu; Sijia Liu; Hao Cheng; Jan-Henrik Lambrechts; Huan Zhang; Aojun Zhou; Kaisheng Ma; Yanzhi Wang; Xue Lin

http://arxiv.org/abs/1903.12261
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.
Dan Hendrycks; Thomas Dietterich

http://arxiv.org/abs/1903.11862
Smooth Adversarial Examples.
Hanwei Zhang; Yannis Avrithis; Teddy Furon; Laurent Amsaleg

http://arxiv.org/abs/1903.11626
Bridging Adversarial Robustness and Gradient Interpretability.
Beomsu Kim; Junghoon Seo; Taegyun Jeon

http://arxiv.org/abs/1903.11359
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks.
Francesco Croce; Jonas Rauber; Matthias Hein

http://arxiv.org/abs/1903.11688
Rallying Adversarial Techniques against Deep Learning for Network Security.
Joseph Clements; Yuzhe Yang; Ankur Sharma; Hongxin Hu; Yingjie Lao

http://arxiv.org/abs/1903.11508
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems.
Steffen Eger; Gözde Gül Şahin; Andreas Rücklé; Ji-Ung Lee; Claudia Schulz; Mohsen Mesgar; Krishnkant Swarnkar; Edwin Simpson; Iryna Gurevych

http://arxiv.org/abs/1903.11220
On the Adversarial Robustness of Multivariate Robust Estimation.
Erhan Bayraktar; Lifeng Lai

http://arxiv.org/abs/1903.10826
A geometry-inspired decision-based attack.
Yujia Liu; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard

http://arxiv.org/abs/1903.10586
Defending against Whitebox Adversarial Attacks via Randomized Discretization.
Yuchen Zhang; Percy Liang

http://arxiv.org/abs/1903.10484
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness.
Jörn-Henrik Jacobsen; Jens Behrmannn; Nicholas Carlini; Florian Tramèr; Nicolas Papernot

http://arxiv.org/abs/1903.10396
The LogBarrier adversarial attack: making effective use of decision boundary information.
Chris Finlay; Aram-Alexandre Pooladian; Adam M. Oberman

http://arxiv.org/abs/1903.10219
Robust Neural Networks using Randomized Adversarial Training.
Alexandre Araujo; Laurent Meunier; Rafael Pinot; Benjamin Negrevergne

http://arxiv.org/abs/1903.10033
A Formalization of Robustness for Deep Neural Networks.
Tommaso Dreossi; Shromona Ghosh; Alberto Sangiovanni-Vincentelli; Sanjit A. Seshia

http://arxiv.org/abs/1903.09940
Variational Inference with Latent Space Quantization for Adversarial Resilience.
Vinay Kyatham; Mayank Mishra; Tarun Kumar Yadav; Deepak Mishra; Prathosh AP

http://arxiv.org/abs/1903.09799
Improving Adversarial Robustness via Guided Complement Entropy.
Hao-Yun Chen; Jhao-Hong Liang; Shih-Chieh Chang; Jia-Yu Pan; Yu-Ting Chen; Wei Wei; Da-Cheng Juan

http://arxiv.org/abs/1903.10346
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition.
Yao Qin; Nicholas Carlini; Ian Goodfellow; Garrison Cottrell; Colin Raffel

http://arxiv.org/abs/1903.09410
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network. (45%)
Aupendu Kar; Prabir Kumar Biswas

http://arxiv.org/abs/1904.00759
Adversarial camera stickers: A physical camera-based attack on deep learning systems.
Juncheng Li; Frank R. Schmidt; J. Zico Kolter

http://arxiv.org/abs/1903.08778
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes.
Matt Jordan; Justin Lewis; Alexandros G. Dimakis

http://arxiv.org/abs/1903.08333
On the Robustness of Deep K-Nearest Neighbors.
Chawin Sitawarin; David Wagner

http://arxiv.org/abs/1903.07282
Generating Adversarial Examples With Conditional Generative Adversarial Net.
Ping Yu; Kaitao Song; Jianfeng Lu

http://arxiv.org/abs/1904.05734
Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems.
Hadi Abdullah; Washington Garcia; Christian Peeters; Patrick Traynor; Kevin R. B. Butler; Joseph Wilson

http://arxiv.org/abs/1903.07054
Adversarial Attacks on Deep Neural Networks for Time Series Classification.
Hassan Ismail Fawaz; Germain Forestier; Jonathan Weber; Lhassane Idoumghar; Pierre-Alain Muller

http://arxiv.org/abs/1903.06620
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models.
Paul Michel; Xian Li; Graham Neubig; Juan Miguel Pino

http://arxiv.org/abs/1903.06603
On Certifying Non-uniform Bound against Adversarial Attacks.
Chen Liu; Ryota Tomioka; Volkan Cevher

http://arxiv.org/abs/1903.06293
A Research Agenda: Dynamic Models to Defend Against Correlated Attacks.
Ian Goodfellow

http://arxiv.org/abs/1903.05821
Attribution-driven Causal Analysis for Detection of Adversarial Examples.
Susmit Jha; Sunny Raj; Steven Lawrence Fernandes; Sumit Kumar Jha; Somesh Jha; Gunjan Verma; Brian Jalaian; Ananthram Swami

http://arxiv.org/abs/1903.05543
Adversarial attacks against Fact Extraction and VERification.
James Thorne; Andreas Vlachos

http://arxiv.org/abs/1903.05157
Simple Physical Adversarial Examples against End-to-End Autonomous Driving Models.
Adith Boloor; Xin He; Christopher Gill; Yevgeniy Vorobeychik; Xuan Zhang

http://arxiv.org/abs/1903.05994
Can Adversarial Network Attack be Defended?
Jinyin Chen; Yangyang Wu; Xiang Lin; Qi Xuan

http://arxiv.org/abs/1903.03905
Manifold Preserving Adversarial Learning.
Ousmane Amadou Dia; Elnaz Barshan; Reza Babanezhad

http://arxiv.org/abs/1903.03029
Attack Type Agnostic Perceptual Enhancement of Adversarial Images.
Bilgin Aksoy; Alptekin Temizel

http://arxiv.org/abs/1903.02926
Out-domain examples for generative models.
Dario Pasquini; Marco Mingione; Massimo Bernaschi

http://arxiv.org/abs/1903.02585
GanDef: A GAN based Adversarial Training Defense for Neural Network Classifier.
Guanxiong Liu; Issa Khalil; Abdallah Khreishah

http://arxiv.org/abs/1903.01980
Statistical Guarantees for the Robustness of Bayesian Neural Networks.
Luca Cardelli; Marta Kwiatkowska; Luca Laurenti; Nicola Paoletti; Andrea Patane; Matthew Wicker

http://arxiv.org/abs/1903.01715
L 1-norm double backpropagation adversarial defense.
Ismaïla LIMOS, LITIS Seck; Gaëlle LIMOS Loosli; Stephane LITIS Canu

http://arxiv.org/abs/1903.01612
Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search.
Abhimanyu Dubey; der Maaten Laurens van; Zeki Yalniz; Yixuan Li; Dhruv Mahajan

http://arxiv.org/abs/1903.01610
The Vulnerabilities of Graph Convolutional Networks: Stronger Attacks and Defensive Techniques.
Huijun Wu; Chen Wang; Yuriy Tyshetskiy; Andrew Dotcherty; Kai Lu; Liming Zhu

http://arxiv.org/abs/1903.01182
Complement Objective Training.
Hao-Yun Chen; Pei-Hsin Wang; Chun-Hao Liu; Shih-Chieh Chang; Jia-Yu Pan; Yu-Ting Chen; Wei Wei; Da-Cheng Juan

http://arxiv.org/abs/1903.01287
Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming.
Mahyar Fazlyab; Manfred Morari; George J. Pappas

http://arxiv.org/abs/1903.01015
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations.
Saeid Asgari Taghanaki; Kumar Abhishek; Shekoofeh Azizi; Ghassan Hamarneh

http://arxiv.org/abs/1903.01563
Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications.
Bryse Flowers; R. Michael Buehrer; William C. Headley

http://arxiv.org/abs/1903.00585
PuVAE: A Variational Autoencoder to Purify Adversarial Examples.
Uiwon Hwang; Jaewoo Park; Hyemi Jang; Sungroh Yoon; Nam Ik Cho

http://arxiv.org/abs/1903.00553
Attacking Graph-based Classification via Manipulating the Graph Structure.
Binghui Wang; Neil Zhenqiang Gong

http://arxiv.org/abs/1903.00073
On the Effectiveness of Low Frequency Perturbations.
Yash Sharma; Gavin Weiguang Ding; Marcus Brubaker

http://arxiv.org/abs/1902.11029
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN.
Ke Sun; Zhanxing Zhu; Zhouchen Lin

http://arxiv.org/abs/1902.11019
Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors.
Ke Sun; Zhanxing Zhu; Zhouchen Lin

http://arxiv.org/abs/1902.10899
Adversarial Attack and Defense on Point Sets.
Qiang Zhang; Jiancheng Yang; Rongyao Fang; Bingbing Ni; Jinxian Liu; Qi Tian

http://arxiv.org/abs/1902.10755
Adversarial Attacks on Time Series.
Fazle Karim; Somshubra Majumdar; Houshang Darabi

http://arxiv.org/abs/1902.10660
Robust Decision Trees Against Adversarial Examples.
Hongge Chen; Huan Zhang; Duane Boning; Cho-Jui Hsieh

http://arxiv.org/abs/1902.10758
Tensor Dropout for Robust Learning.
Arinbjörn Kolbeinsson; Jean Kossaifi; Yannis Panagakis; Adrian Bulat; Anima Anandkumar; Ioanna Tzoulaki; Paul Matthews

http://arxiv.org/abs/1902.10674
The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection.
Muhammad Zaid Hameed; Andras Gyorgy; Deniz Gunduz

http://arxiv.org/abs/1902.10365
A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning. (76%)
Masoud Badiei Khuzani; Hongyi Ren; Md Tauhidul Islam; Lei Xing

http://arxiv.org/abs/1902.10799
AutoGAN-based Dimension Reduction for Privacy Preservation. (1%)
Hung Nguyen; Di Zhuang; Pei-Yuan Wu; Morris Chang

http://arxiv.org/abs/1902.11134
Disentangled Deep Autoencoding Regularization for Robust Image Classification.
Zhenyu Duan; Martin Renqiang Min; Li Erran Li; Mingbo Cai; Yi Xu; Bingbing Ni

http://arxiv.org/abs/1902.09866
Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification.
Jianlin Li; Pengfei Yang; Jiangchao Liu; Liqian Chen; Xiaowei Huang; Lijun Zhang

http://arxiv.org/abs/1902.09592
Verification of Non-Linear Specifications for Neural Networks.
Chongli Dj Qin; Dj Krishnamurthy; Dvijotham; Brendan O'Donoghue; Rudy Bunel; Robert Stanforth; Sven Gowal; Jonathan Uesato; Grzegorz Swirszcz; Pushmeet Kohli

http://arxiv.org/abs/1902.09286
Adversarial attacks hidden in plain sight.
Jan Philip Göpfert; André Artelt; Heiko Wersing; Barbara Hammer

http://arxiv.org/abs/1902.08909
MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses.
Lior Sidi; Asaf Nadler; Asaf Shabtai

http://arxiv.org/abs/1902.09062
Adversarial Reinforcement Learning under Partial Observability in Software-Defined Networking.
Yi Han; David Hubczenko; Paul Montague; Vel Olivier De; Tamas Abraham; Benjamin I. P. Rubinstein; Christopher Leckie; Tansu Alpcan; Sarah Erfani

http://arxiv.org/abs/1902.08832
Re-evaluating ADEM: A Deeper Look at Scoring Dialogue Responses.
Ananya B. Sai; Mithun Das Gupta; Mitesh M. Khapra; Mukundhan Srinivasan

http://arxiv.org/abs/1902.08785
A Deep, Information-theoretic Framework for Robust Biometric Recognition.
Renjie Xie; Yanzhi Chen; Yan Wo; Qiao Wang

http://arxiv.org/abs/1902.08391
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems.
Meysam Sadeghi; Erik G. Larsson

http://arxiv.org/abs/1902.08722
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks.
Hadi Salman; Greg Yang; Huan Zhang; Cho-Jui Hsieh; Pengchuan Zhang

http://arxiv.org/abs/1902.08412
Adversarial Attacks on Graph Neural Networks via Meta Learning.
Daniel Zügner; Stephan Günnemann

http://arxiv.org/abs/1902.08336
On the Sensitivity of Adversarial Robustness to Input Data Distributions.
Gavin Weiguang Ding; Kry Yik Chau Lui; Xiaomeng Jin; Luyu Wang; Ruitong Huang

http://arxiv.org/abs/1902.08265
Quantifying Perceptual Distortion of Adversarial Examples.
Matt Jordan; Naren Manoj; Surbhi Goel; Alexandros G. Dimakis

http://arxiv.org/abs/1902.07906
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations.
Eric Wong; Frank R. Schmidt; J. Zico Kolter

http://arxiv.org/abs/1902.07623
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch.
Gavin Weiguang Ding; Luyu Wang; Xiaomeng Jin

http://arxiv.org/abs/1902.07776
Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers.
Diego Gragnaniello; Francesco Marra; Giovanni Poggi; Luisa Verdoliva

http://arxiv.org/abs/1902.08226
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure.
Fuli Feng; Xiangnan He; Jie Tang; Tat-Seng Chua

http://arxiv.org/abs/1902.06894
There are No Bit Parts for Sign Bits in Black-Box Attacks.
Abdullah Al-Dujaili; Una-May O'Reilly

http://arxiv.org/abs/1902.06705
On Evaluating Adversarial Robustness.
Nicholas Carlini; Anish Athalye; Nicolas Papernot; Wieland Brendel; Jonas Rauber; Dimitris Tsipras; Ian Goodfellow; Aleksander Madry; Alexey Kurakin

http://arxiv.org/abs/1902.06415
AuxBlocks: Defense Adversarial Example via Auxiliary Blocks.
Yueyao Yu; Pengfei Yu; Wenye Li

http://arxiv.org/abs/1902.06626
Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces.
Mohsen Imani; Mohammad Saidur Rahman; Nate Mathews; Matthew Wright

http://arxiv.org/abs/1902.08034
Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training.
Silvija Kokalj-Filipovic; Rob Miller; Nicholas Chang; Chi Leung Lau

http://arxiv.org/abs/1902.06044
Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness.
Silvija Kokalj-Filipovic; Rob Miller

http://arxiv.org/abs/1902.05974
DeepFault: Fault Localization for Deep Neural Networks.
Hasan Ferit Eniser; Simos Gerasimou; Alper Sen

http://arxiv.org/abs/1902.05586
Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?
Cody Burkard; Brent Lagesse

http://arxiv.org/abs/1902.04818
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples.
Kevin Roth; Yannic Kilcher; Thomas Hofmann

http://arxiv.org/abs/1902.04416
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems.
Ahmed Abusnaina; Aminollah Khormali; Hisham Alasmary; Jeman Park; Afsah Anwar; Ulku Meteriz; Aziz Mohaisen

http://arxiv.org/abs/1902.04238
Adversarial Samples on Android Malware Detection Systems for IoT Systems.
Xiaolei Liu; Xiaojiang Du; Xiaosong Zhang; Qingxin Zhu; Mohsen Guizani

http://arxiv.org/abs/1902.07285
A Survey: Towards a Robust Deep Neural Network in Text Domain.
Wenqi Wang; Lina Wang; Benxiao Tang; Run Wang; Aoshuang Ye

http://arxiv.org/abs/1902.03538
Model Compression with Adversarial Robustness: A Unified Optimization Framework.
Shupeng University of Rochester Gui; Haotao Texas A&M University Wang; Chen University of Rochester Yu; Haichuan University of Rochester Yang; Zhangyang Texas A&M University Wang; Ji Ytech Seattle AI lab, FeDA lab, AI platform, Kwai Inc Liu

http://arxiv.org/abs/1902.03380
When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks.
Chao-Han Huck Yang; Yi-Chieh Liu; Pin-Yu Chen; Xiaoli Ma; Yi-Chang James Tsai

http://arxiv.org/abs/1902.03227
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images.
Sanjana Srivastava; Guy Ben-Yosef; Xavier Boix

http://arxiv.org/abs/1902.02947
Understanding the One-Pixel Attack: Propagation Maps and Locality Analysis.
Danilo Vasconcellos Vargas; Jiawei Su

http://arxiv.org/abs/1902.03151
Discretization based Solutions for Secure Machine Learning against Adversarial Attacks.
Priyadarshini Panda; Indranil Chakraborty; Kaushik Roy

http://arxiv.org/abs/1902.02826
Robustness Of Saak Transform Against Adversarial Attacks.
Thiyagarajan Ramanathan; Abinaya Manimaran; Suya You; C-C Jay Kuo

http://arxiv.org/abs/1902.02918
Certified Adversarial Robustness via Randomized Smoothing.
Jeremy M Cohen; Elan Rosenfeld; J. Zico Kolter

http://arxiv.org/abs/1902.02041
Fooling Neural Network Interpretations via Adversarial Model Manipulation.
Juyeon Heo; Sunghwan Joo; Taesup Moon

http://arxiv.org/abs/1902.02067
Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples.
Derui Wang; Chaoran Li; Sheng Wen; Xiaojun Chang; Surya Nepal; Yang Xiang

http://arxiv.org/abs/1902.01686
Fatal Brain Damage.
El Mahdi El Mhamdi; Rachid Guerraoui; Sergei Volodin

http://arxiv.org/abs/1902.01148
Theoretical evidence for adversarial robustness through randomization.
Rafael Pinot; Laurent Meunier; Alexandre Araujo; Hisashi Kashima; Florian Yger; Cédric Gouy-Pailler; Jamal Atif

http://arxiv.org/abs/1902.01080
Predictive Uncertainty Quantification with Compound Density Networks.
Agustinus Kristiadi; Sina Däubener; Asja Fischer

http://arxiv.org/abs/1902.01147
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks.
Alberto Marchisio; Giorgio Nanfa; Faiq Khalid; Muhammad Abdullah Hanif; Maurizio Martina; Muhammad Shafique

http://arxiv.org/abs/1902.01235
Robustness Certificates Against Adversarial Examples for ReLU Networks.
Sahil Singla; Soheil Feizi

http://arxiv.org/abs/1902.00236
Natural and Adversarial Error Detection using Invariance to Image Transformations.
Yuval Bahat; Michal Irani; Gregory Shakhnarovich

http://arxiv.org/abs/1902.01220
Adaptive Gradient for Adversarial Perturbations Generation.
Yatie Xiao; Chi-Man Pun

http://arxiv.org/abs/1902.00577
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks.
Sascha Saralajew; Lars Holdijk; Maike Rees; Thomas Villmann

http://arxiv.org/abs/1902.00541
The Efficacy of SHIELD under Different Threat Models.
Cory Cornelius; Nilaksh Das; Shang-Tse Chen; Li Chen; Michael E. Kounavis; Duen Horng Chau

http://arxiv.org/abs/1902.01208
A New Family of Neural Networks Provably Resistant to Adversarial Attacks.
Rakshit Agrawal; Alfaro Luca de; David Helmbold

http://arxiv.org/abs/1902.00358
Training Artificial Neural Networks by Generalized Likelihood Ratio Method: Exploring Brain-like Learning to Improve Robustness.
Li Xiao; Yijie Peng; Jeff Hong; Zewu Ke; Shuhuai Yang

http://arxiv.org/abs/1901.10861
A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance.
Adi Shamir; Itay Safran; Eyal Ronen; Orr Dunkelman

http://arxiv.org/abs/1901.11188
Augmenting Model Robustness with Transformation-Invariant Attacks.
Houpu Yao; Zhe Wang; Guangyu Nie; Yassine Mazboudi; Yezhou Yang; Yi Ren

http://arxiv.org/abs/1901.10513
Adversarial Examples Are a Natural Consequence of Test Error in Noise.
Nic Ford; Justin Gilmer; Nicolas Carlini; Dogus Cubuk

http://arxiv.org/abs/1901.10258
RED-Attack: Resource Efficient Decision based Attack for Machine Learning.
Faiq Khalid; Hassan Ali; Muhammad Abdullah Hanif; Semeen Rehman; Rehan Ahmed; Muhammad Shafique

http://arxiv.org/abs/1901.10622
Reliable Smart Road Signs.
Muhammed O. Sayin; Chung-Wei Lin; Eunsuk Kang; Shinichi Shiraishi; Tamer Basar

http://arxiv.org/abs/1901.10371
On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks.
Peter Langenberg; Emilio Rafael Balda; Arash Behboodi; Rudolf Mathar

http://arxiv.org/abs/1901.10650
Adversarial Metric Attack and Defense for Person Re-identification.
Song Bai; Yingwei Li; Yuyin Zhou; Qizhu Li; Philip H. S. Torr

http://arxiv.org/abs/1901.09981
Improving Adversarial Robustness of Ensembles with Diversity Training.
Sanjay Kariyappa; Moinuddin K. Qureshi

http://arxiv.org/abs/1901.09878
CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks.
Alberto Marchisio; Giorgio Nanfa; Faiq Khalid; Muhammad Abdullah Hanif; Maurizio Martina; Muhammad Shafique

http://arxiv.org/abs/1901.09963
Defense Methods Against Adversarial Examples for Recurrent Neural Networks.
Ishai Rosenberg; Asaf Shabtai; Yuval Elovici; Lior Rokach

http://arxiv.org/abs/1901.09960
Using Pre-Training Can Improve Model Robustness and Uncertainty.
Dan Hendrycks; Kimin Lee; Mantas Mazeika

http://arxiv.org/abs/1901.09863
Efficient Multiparty Interactive Coding for Insertions, Deletions and Substitutions. (1%)
Ran Gelles; Yael T. Kalai; Govind Ramnarayan

http://arxiv.org/abs/1901.09413
An Information-Theoretic Explanation for the Adversarial Fragility of AI Classifiers.
Hui Xie; Jirong Yi; Weiyu Xu; Raghu Mudumbai

http://arxiv.org/abs/1901.09496
Characterizing the Shape of Activation Space in Deep Neural Networks.
Thomas Gebhart; Paul Schrater; Alan Hylton

http://arxiv.org/abs/1901.09493
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models.
Anshuman Chhabra; Abhishek Roy; Prasant Mohapatra

http://arxiv.org/abs/1901.09892
A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm.
Xiaolei Liu; Yuheng Luo; Xiaosong Zhang; Qingxin Zhu

http://arxiv.org/abs/1901.10300
Weighted-Sampling Audio Adversarial Example Attack.
Xiaolei Liu; Xiaosong Zhang; Kun Wan; Qingxin Zhu; Yufei Ding

http://arxiv.org/abs/1901.09113
Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data.
Yi Shi; Yalin E. Sagduyu; Kemal Davaslioglu; Jason H. Li

http://arxiv.org/abs/1901.08846
Improving Adversarial Robustness via Promoting Ensemble Diversity.
Tianyu Pang; Kun Xu; Chao Du; Ning Chen; Jun Zhu

http://arxiv.org/abs/1901.08873
Chapter: Vulnerability of Quantum Information Systems to Collective Manipulation. (1%)
Fernando J. Gómez-Ruiz; Ferney J. Rodríguez; Luis Quiroga; Neil F. Johnson

http://arxiv.org/abs/1901.09035
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples.
Yinpeng Dong; Fan Bao; Hang Su; Jun Zhu

http://arxiv.org/abs/1901.08360
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples.
Kamil Nar; Orhan Ocal; S. Shankar Sastry; Kannan Ramchandran

http://arxiv.org/abs/1901.08573
Theoretically Principled Trade-off between Robustness and Accuracy.
Hongyang Zhang; Yaodong Yu; Jiantao Jiao; Eric P. Xing; Laurent El Ghaoui; Michael I. Jordan

http://arxiv.org/abs/1901.07846
SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems.
Tianyu Du; Shouling Ji; Jinfeng Li; Qinchen Gu; Ting Wang; Raheem Beyah

http://arxiv.org/abs/1901.08121
Sitatapatra: Blocking the Transfer of Adversarial Samples.
Ilia Shumailov; Xitong Gao; Yiren Zhao; Robert Mullins; Ross Anderson; Cheng-Zhong Xu

http://arxiv.org/abs/1901.07132
Universal Rules for Fooling Deep Neural Networks based Text Classification.
Di Li; Danilo Vasconcellos Vargas; Sakurai Kouichi

http://arxiv.org/abs/1901.06796
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey.
Wei Emma Zhang; Quan Z. Sheng; Ahoud Alhazmi; Chenliang Li

http://arxiv.org/abs/1901.07152
Sensitivity Analysis of Deep Neural Networks.
Hai Shu; Hongtu Zhu

http://arxiv.org/abs/1901.06834
Perception-in-the-Loop Adversarial Examples.
Mahmoud Salamati; Sadegh Soudjani; Rupak Majumdar

http://arxiv.org/abs/1901.05674
Easy to Fool? Testing the Anti-evasion Capabilities of PDF Malware Scanners.
Saeed TU Darmstadt Ehteshamifar; Antonio xorlab Barresi; Thomas R. ETH Zurich Gross; Michael TU Darmstadt Pradel

http://arxiv.org/abs/1901.04684
The Limitations of Adversarial Training and the Blind-Spot Attack.
Huan Zhang; Hongge Chen; Zhao Song; Duane Boning; Inderjit S. Dhillon; Cho-Jui Hsieh

http://arxiv.org/abs/1901.03706
Generating Adversarial Perturbation with Root Mean Square Gradient.
Yatie Xiao; Chi-Man Pun; Jizhe Zhou

http://arxiv.org/abs/1901.03808
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System.
Huangxun Chen; Chenyu Huang; Qianyi Huang; Qian Zhang; Wei Wang

http://arxiv.org/abs/1901.03583
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries.
Luca Demetrio; Battista Biggio; Giovanni Lagorio; Fabio Roli; Alessandro Armando

http://arxiv.org/abs/1901.03398
Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification.
Luiz G. Hafemann; Robert Sabourin; Luiz S. Oliveira

http://arxiv.org/abs/1901.03037
Image Transformation can make Neural Networks more robust against Adversarial Examples.
Dang Duy Thang; Toshihiro Matsui

http://arxiv.org/abs/1901.03006
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers.
Daniel Liu; Ronald Yu; Hao Su

http://arxiv.org/abs/1901.02229
Interpretable BoW Networks for Adversarial Example Detection.
Krishna Kanth Nakka; Mathieu Salzmann

http://arxiv.org/abs/1901.01677
Image Super-Resolution as a Defense Against Adversarial Attacks.
Aamir Mustafa; Salman H. Khan; Munawar Hayat; Jianbing Shen; Ling Shao

http://arxiv.org/abs/1901.09657
Fake News Detection via NLP is Vulnerable to Adversarial Attacks.
Zhixuan Zhou; Huankang Guan; Meghana Moorthy Bhat; Justin Hsu

http://arxiv.org/abs/1901.01223
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study.
Xurong Li; Shouling Ji; Meng Han; Juntao Ji; Zhenyu Ren; Yushan Liu; Chunming Wu

http://arxiv.org/abs/1901.00546
Multi-Label Adversarial Perturbations.
Qingquan Song; Haifeng Jin; Xiao Huang; Xia Hu

http://arxiv.org/abs/1901.00532
Adversarial Robustness May Be at Odds With Simplicity.
Preetum Nakkiran

http://arxiv.org/abs/1901.00054
A Noise-Sensitivity-Analysis-Based Test Prioritization Technique for Deep Neural Networks.
Long Zhang; Xuechao Sun; Yong Li; Zhenyu Zhang

http://arxiv.org/abs/1812.10812
DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems.
Husheng Zhou; Wei Li; Yuankun Zhu; Yuqun Zhang; Bei Yu; Lingming Zhang; Cong Liu

http://arxiv.org/abs/1812.10528
Adversarial Attack and Defense on Graph Data: A Survey.
Lichao Sun; Yingtong Dou; Carl Yang; Ji Wang; Yixin Liu; Philip S. Yu; Lifang He; Bo Li

http://arxiv.org/abs/1812.10061
Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition.
Krishan Rajaratnam; Jugal Kalita

http://arxiv.org/abs/1812.10049
PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning.
Mehdi Jafarnia-Jahromi; Tasmin Chowdhury; Hsin-Tai Wu; Sayandev Mukherjee

http://arxiv.org/abs/1812.10199
A Multiversion Programming Inspired Approach to Detecting Audio Adversarial Examples.
Qiang Zeng; Jianhai Su; Chenglong Fu; Golam Kayas; Lannan Luo

http://arxiv.org/abs/1812.10085
A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome.
Li Chen; Qi Li; Weiye Chen; Zeyu Wang; Haifeng Li

http://arxiv.org/abs/1812.10217
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors.
Yue Zhao; Hong Zhu; Ruigang Liang; Qintao Shen; Shengzhi Zhang; Kai Chen

http://arxiv.org/abs/1812.11017
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense.
Hang Zhou; Kejiang Chen; Weiming Zhang; Han Fang; Wenbo Zhou; Nenghai Yu

http://arxiv.org/abs/1812.09660
Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks.
Ankur Chowdhary; Sailik Sengupta; Dijiang Huang; Subbarao Kambhampati

http://arxiv.org/abs/1812.09803
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks.
Thomas Brunner; Frederik Diehl; Michael Truong Le; Alois Knoll

http://arxiv.org/abs/1812.09638
Exploiting the Inherent Limitation of L0 Adversarial Examples.
Fei Zuo; Bokai Yang; Xiaopeng Li; Lannan Luo; Qiang Zeng

http://arxiv.org/abs/1812.09431
Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain.
Chi Zhang; Xiaohan Duan; Linyuan Wang; Yongli Li; Bin Yan; Guoen Hu; Ruyuan Zhang; Li Tong

http://arxiv.org/abs/1812.08108
Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge.
Deqiang Li; Qianmu Li; Yanfang Ye; Shouhuai Xu

http://arxiv.org/abs/1812.08329
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach.
Tsui-Wei Weng; Pin-Yu Chen; Lam M. Nguyen; Mark S. Squillante; Ivan Oseledets; Luca Daniel

http://arxiv.org/abs/1812.06815
Spartan Networks: Self-Feature-Squeezing Neural Networks for increased robustness in adversarial settings.
François Menet; Paul Berthier; José M. Fernandez; Michel Gagnon

http://arxiv.org/abs/1812.06626
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering.
Kevin Eykholt; Atul Prakash

http://arxiv.org/abs/1812.08342
A Survey of Safety and Trustworthiness of Deep Neural Networks.
Xiaowei Huang; Daniel Kroening; Wenjie Ruan; James Sharp; Youcheng Sun; Emese Thamo; Min Wu; Xinping Yi

http://arxiv.org/abs/1812.06570
Defense-VAE: A Fast and Accurate Defense against Adversarial Attacks.
Xiang Li; Shihao Ji

http://arxiv.org/abs/1812.07385
Perturbation Analysis of Learning Algorithms: A Unifying Perspective on Generation of Adversarial Examples.
Emilio Rafael Balda; Arash Behboodi; Rudolf Mathar

http://arxiv.org/abs/1812.06371
Trust Region Based Adversarial Attack on Neural Networks.
Zhewei Yao; Amir Gholami; Peng Xu; Kurt Keutzer; Michael Mahoney

http://arxiv.org/abs/1812.05793
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing.
Jingyi Wang; Guoliang Dong; Jun Sun; Xinyu Wang; Peixin Zhang

http://arxiv.org/abs/1812.05271
TextBugger: Generating Adversarial Text Against Real-world Applications.
Jinfeng Li; Shouling Ji; Tianyu Du; Bo Li; Ting Wang

http://arxiv.org/abs/1812.05720
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem.
Matthias Hein; Maksym Andriushchenko; Julian Bitterwolf

http://arxiv.org/abs/1812.05447
Generating Hard Examples for Pixel-wise Classification. (4%)
Hyungtae Lee; Heesung Kwon; Wonkook Kim

http://arxiv.org/abs/1812.05013
Thwarting Adversarial Examples: An $L_0$-RobustSparse Fourier Transform.
Mitali Bafna; Jack Murtagh; Nikhil Vyas

http://arxiv.org/abs/1812.04293
On the Security of Randomized Defenses Against Adversarial Samples.
Kumar Sharad; Giorgia Azzurra Marson; Hien Thi Thu Truong; Ghassan Karame

http://arxiv.org/abs/1812.04599
Adversarial Framing for Image and Video Classification.
Konrad Zolna; Michal Zajac; Negar Rostamzadeh; Pedro O. Pinheiro

http://arxiv.org/abs/1812.03705
Defending Against Universal Perturbations With Shared Adversarial Training.
Chaithanya Kumar Mummadi; Thomas Brox; Jan Hendrik Metzen

http://arxiv.org/abs/1812.03411
Feature Denoising for Improving Adversarial Robustness.
Cihang Xie; Yuxin Wu; der Maaten Laurens van; Alan Yuille; Kaiming He

http://arxiv.org/abs/1812.03405
AutoGAN: Robust Classifier Against Adversarial Attacks.
Blerta Lindqvist; Shridatt Sugrim; Rauf Izmailov

http://arxiv.org/abs/1812.03303
Detecting Adversarial Examples in Convolutional Neural Networks.
Stefanos Pertigkiozoglou; Petros Maragos

http://arxiv.org/abs/1812.03413
Learning Transferable Adversarial Examples via Ghost Networks.
Yingwei Li; Song Bai; Yuyin Zhou; Cihang Xie; Zhishuai Zhang; Alan Yuille

http://arxiv.org/abs/1812.03190
Deep-RBF Networks Revisited: Robust Classification with Rejection.
Pourya Habib Zadeh; Reshad Hosseini; Suvrit Sra

http://arxiv.org/abs/1812.03087
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach.
Rajeev Sahay; Rehana Mahfuz; Aly El Gamal

http://arxiv.org/abs/1812.02891
Adversarial Defense of Image Classification Using a Variational Auto-Encoder.
Yi Luo; Henry Pfister

http://arxiv.org/abs/1812.02885
Adversarial Attacks, Regression, and Numerical Stability Regularization.
Andre T. Nguyen; Edward Raff

http://arxiv.org/abs/1812.02575
Prior Networks for Detection of Adversarial Attacks.
Andrey Malinin; Mark Gales

http://arxiv.org/abs/1812.02524
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack.
Jingyang Zhang; Hsin-Pai Cheng; Chunpeng Wu; Hai Li; Yiran Chen

http://arxiv.org/abs/1812.02843
Fooling Network Interpretation in Image Classification.
Akshayvarun Subramanya; Vipin Pillai; Hamed Pirsiavash

http://arxiv.org/abs/1812.02606
The Limitations of Model Uncertainty in Adversarial Settings.
Kathrin Grosse; David Pfaff; Michael Thomas Smith; Michael Backes

http://arxiv.org/abs/1812.02637
MMA Training: Direct Input Space Margin Maximization through Adversarial Training.
Gavin Weiguang Ding; Yash Sharma; Kry Yik Chau Lui; Ruitong Huang

http://arxiv.org/abs/1812.02737
On Configurable Defense against Adversarial Example Attacks.
Bo Luo; Min Li; Yu Li; Qiang Xu

http://arxiv.org/abs/1812.01821
Regularized Ensembles and Transferability in Adversarial Learning.
Yifan Chen; Yevgeniy Vorobeychik

http://arxiv.org/abs/1812.02132
SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications.
Abdullah Hamdi; Matthias Müller; Bernard Ghanem

http://arxiv.org/abs/1812.01647
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures.
Jonathan Dj Uesato; Ananya Dj Kumar; Csaba Dj Szepesvari; Tom Dj Erez; Avraham Dj Ruderman; Keith Dj Anderson; Dj Krishmamurthy; Dvijotham; Nicolas Heess; Pushmeet Kohli

http://arxiv.org/abs/1812.01804
Random Spiking and Systematic Evaluation of Defenses Against Adversarial Examples.
Huangyi Ge; Sze Yiu Chau; Bruno Ribeiro; Ninghui Li

http://arxiv.org/abs/1812.00740
Disentangling Adversarial Robustness and Generalization.
David Stutz; Matthias Hein; Bernt Schiele

http://arxiv.org/abs/1812.00891
Interpretable Deep Learning under Fire.
Xinyang Zhang; Ningfei Wang; Hua Shen; Shouling Ji; Xiapu Luo; Ting Wang

http://arxiv.org/abs/1812.01198
Adversarial Example Decomposition.
Horace He; Aaron Lou; Qingxuan Jiang; Isay Katsman; Serge Belongie; Ser-Nam Lim

http://arxiv.org/abs/1812.00483
Model-Reuse Attacks on Deep Learning Systems.
Yujie Ji; Xinyang Zhang; Shouling Ji; Xiapu Luo; Ting Wang

http://arxiv.org/abs/1812.00552
Universal Perturbation Attack Against Image Retrieval.
Jie Li; Rongrong Ji; Hong Liu; Xiaopeng Hong; Yue Gao; Qi Tian

http://arxiv.org/abs/1812.01713
FineFool: Fine Object Contour Attack via Attention.
Jinyin Chen; Haibin Zheng; Hui Xiong; Mengmeng Su

http://arxiv.org/abs/1812.00239
Building robust classifiers through generation of confident out of distribution examples.
Kumar Sricharan; Ashok Srivastava

http://arxiv.org/abs/1812.00151
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification.
Qi Lei; Lingfei Wu; Pin-Yu Chen; Alexandros G. Dimakis; Inderjit S. Dhillon; Michael Witbrock

http://arxiv.org/abs/1812.00181
Effects of Loss Functions And Target Representations on Adversarial Robustness.
Sean Saito; Sujoy Roy

http://arxiv.org/abs/1812.00292
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems.
Edward Chou; Florian Tramèr; Giancarlo Pellegrino

http://arxiv.org/abs/1811.12641
Transferable Adversarial Attacks for Image and Video Object Detection.
Xingxing Wei; Siyuan Liang; Xiaochun Cao; Jun Zhu

http://arxiv.org/abs/1811.12673
ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples.
Xiaojun Jia; Xingxing Wei; Xiaochun Cao; Hassan Foroosh

http://arxiv.org/abs/1812.00037
Adversarial Defense by Stratified Convolutional Sparse Coding.
Bo Sun; Nian-hsuan Tsai; Fangchen Liu; Ronald Yu; Hao Su

http://arxiv.org/abs/1811.12395
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks.
Akhilan Boopathy; Tsui-Wei Weng; Pin-Yu Chen; Sijia Liu; Luca Daniel

http://arxiv.org/abs/1811.12335
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting.
Artur Bekasov; Iain Murray

http://arxiv.org/abs/1811.12601
Adversarial Examples as an Input-Fault Tolerance Problem.
Angus Galloway; Anna Golubeva; Graham W. Taylor

http://arxiv.org/abs/1811.12470
Analyzing Federated Learning through an Adversarial Lens.
Arjun Nitin Bhagoji; Supriyo Chakraborty; Prateek Mittal; Seraphin Calo

http://arxiv.org/abs/1811.11875
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers.
Nathan Inkawhich; Matthew Inkawhich; Yiran Chen; Hai Li

http://arxiv.org/abs/1811.11553
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects.
Michael A. Alcorn; Qi Li; Zhitao Gong; Chengfei Wang; Long Mai; Wei-Shinn Ku; Anh Nguyen

http://arxiv.org/abs/1811.11493
A randomized gradient-free attack on ReLU networks.
Francesco Croce; Matthias Hein

http://arxiv.org/abs/1811.11402
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness.
Siddique Latif; Rajib Rana; Junaid Qadir

http://arxiv.org/abs/1811.11079
Robust Classification of Financial Risk.
Suproteem K. Sarkar; Kojin Oshiba; Daniel Giebisch; Yaron Singer

http://arxiv.org/abs/1811.11304
Universal Adversarial Training.
Ali Shafahi; Mahyar Najibi; Zheng Xu; John Dickerson; Larry S. Davis; Tom Goldstein

http://arxiv.org/abs/1811.11310
Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry.
Kevin McCloskey; Ankur Taly; Federico Monti; Michael P. Brenner; Lucy Colwell

http://arxiv.org/abs/1811.10828
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks.
Jinghui Chen; Dongruo Zhou; Jinfeng Yi; Quanquan Gu

http://arxiv.org/abs/1811.10745
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies.
Bao Wang; Binjie Yuan; Zuoqiang Shi; Stanley J. Osher

http://arxiv.org/abs/1811.10716
Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks.
Jianyu Wang; Haichao Zhang

http://arxiv.org/abs/1811.09982
Is Data Clustering in Adversarial Settings Secure?
Battista Biggio; Ignazio Pillai; Samuel Rota Bulò; Davide Ariu; Marcello Pelillo; Fabio Roli

http://arxiv.org/abs/1811.09831
Attention, Please! Adversarial Defense via Activation Rectification and Preservation.
Shangxi Wu; Jitao Sang; Kaiyuan Xu; Jiaming Zhang; Yanfeng Sun; Liping Jing; Jian Yu

http://arxiv.org/abs/1811.09716
Robustness via curvature regularization, and vice versa.
Seyed-Mohsen Moosavi-Dezfooli; Alhussein Fawzi; Jonathan Uesato; Pascal Frossard

http://arxiv.org/abs/1811.09600
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses.
Jérôme Rony; Luiz G. Hafemann; Luiz S. Oliveira; Ismail Ben Ayed; Robert Sabourin; Eric Granger

http://arxiv.org/abs/1811.09310
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack.
Adnan Siraj Rakin; Zhezhi He; Deliang Fan

http://arxiv.org/abs/1811.09300
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles.
Edward Grefenstette; Robert Stanforth; Brendan O'Donoghue; Jonathan Uesato; Grzegorz Swirszcz; Pushmeet Kohli

http://arxiv.org/abs/1811.09043
Detecting Adversarial Perturbations Through Spatial Behavior in Activation Spaces.
Ziv Katzir; Yuval Elovici

http://arxiv.org/abs/1811.09020
Task-generalizable Adversarial Attack based on Perceptual Metric.
Muzammal Naseer; Salman H. Khan; Shafin Rahman; Fatih Porikli

http://arxiv.org/abs/1811.09008
Towards Robust Neural Networks with Lipschitz Continuity.
Muhammad Usama; Dong Eui Chang

http://arxiv.org/abs/1811.08577
How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples.
Utku Ozbulak; Neve Wesley De; Messem Arnout Van

http://arxiv.org/abs/1811.08484
MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial Defense.
Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Timo Bremer

http://arxiv.org/abs/1811.08458
Intermediate Level Adversarial Attack for Enhanced Transferability.
Qian Huang; Zeqi Gu; Isay Katsman; Horace He; Pian Pawakapan; Zhiqiu Lin; Serge Belongie; Ser-Nam Lim

http://arxiv.org/abs/1811.08080
Lightweight Lipschitz Margin Training for Certified Defense against Adversarial Examples.
Hajime Ono; Tsubasa Takahashi; Kazuya Kakizaki

http://arxiv.org/abs/1812.02622
Convolutional Neural Networks with Transformed Input based on Robust Tensor Network Decomposition.
Jenn-Bing Ong; Wee-Keong Ng; C. -C. Jay Kuo

http://arxiv.org/abs/1811.07950
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding.
Yao Li; Martin Renqiang Min; Wenchao Yu; Cho-Jui Hsieh; Thomas C. M. Lee; Erik Kruus

http://arxiv.org/abs/1811.07457
Generalizable Adversarial Training via Spectral Normalization.
Farzan Farnia; Jesse M. Zhang; David Tse

http://arxiv.org/abs/1811.07311
Regularized adversarial examples for model interpretability.
Yoel Shoshan; Vadim Ratner

http://arxiv.org/abs/1811.07375
The Taboo Trap: Behavioural Detection of Adversarial Samples.
Ilia Shumailov; Yiren Zhao; Robert Mullins; Ross Anderson

http://arxiv.org/abs/1811.07266
DeepConsensus: using the consensus of features from multiple layers to attain robust image classification.
Yuchen Li; Safwan Hossain; Kiarash Jamali; Frank Rudzicz

http://arxiv.org/abs/1811.07211
Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples.
Jacob M. Springer; Charles S. Strauss; Austin M. Thresher; Edward Kim; Garrett T. Kenyon

http://arxiv.org/abs/1811.07108
Boosting the Robustness Verification of DNN by Identifying the Achilles's Heel.
Chengdong Feng; Zhenbang Chen; Weijiang Hong; Hengbiao Yu; Wei Dong; Ji Wang

http://arxiv.org/abs/1811.07018
Protecting Voice Controlled Systems Using Sound Source Identification Based on Acoustic Cues.
Yuan Gong; Christian Poellabauer

http://arxiv.org/abs/1811.06969
DARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsules.
Nicholas Frosst; Sara Sabour; Geoffrey Hinton

http://arxiv.org/abs/1811.06609
A Spectral View of Adversarially Robust Features.
Shivam Garg; Vatsal Sharan; Brian Hu Zhang; Gregory Valiant

http://arxiv.org/abs/1811.06539
A note on hyperparameters in black-box adversarial examples.
Jamie Hayes

http://arxiv.org/abs/1811.06492
Mathematical Analysis of Adversarial Attacks.
Zehao Dou; Stanley J. Osher; Bao Wang

http://arxiv.org/abs/1811.06418
Adversarial Examples from Cryptographic Pseudo-Random Generators.
Sébastien Bubeck; Yin Tat Lee; Eric Price; Ilya Razenshteyn

http://arxiv.org/abs/1811.06029
Verification of Recurrent Neural Networks Through Rule Extraction.
Qinglong Wang; Kaixuan Zhang; Xue Liu; C. Lee Giles

http://arxiv.org/abs/1811.05808
Robustness of spectral methods for community detection.
Ludovic Stephan; Laurent Massoulié

http://arxiv.org/abs/1811.05521
Deep Q learning for fooling neural networks.
Mandar Kulkarni

http://arxiv.org/abs/1811.03733
Universal Decision-Based Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses.
Thomas A. Hogan; Bhavya Kailkhura

http://arxiv.org/abs/1811.03685
New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling.
Ian Goodfellow

http://arxiv.org/abs/1811.03531
A Geometric Perspective on the Transferability of Adversarial Directions.
Zachary Charles; Harrison Rosenberg; Dimitris Papailiopoulos

http://arxiv.org/abs/1811.03456
CAAD 2018: Iterative Ensemble Adversarial Attack.
Jiayang Liu; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/1811.03194
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning.
Florian Tramèr; Pascal Dupré; Gili Rusak; Giancarlo Pellegrino; Dan Boneh

http://arxiv.org/abs/1811.02625
MixTrain: Scalable Training of Verifiably Robust Neural Networks.
Shiqi Wang; Yizheng Chen; Ahmed Abdou; Suman Jana

http://arxiv.org/abs/1811.02248
SparseFool: a few pixels make a big difference.
Apostolos Modas; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard

http://arxiv.org/abs/1811.01811
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls.
Yi Shi; Yalin E. Sagduyu; Kemal Davaslioglu; Jason H. Li

http://arxiv.org/abs/1811.01749
FUNN: Flexible Unsupervised Neural Network.
David Vigouroux; Sylvain Picard

http://arxiv.org/abs/1811.01629
On the Transferability of Adversarial Examples Against CNN-Based Image Forensics.
Mauro Barni; Kassem Kallas; Ehsan Nowroozi; Benedetta Tondi

http://arxiv.org/abs/1811.01444
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning.
Faiq Khalid; Muhammmad Abdullah Hanif; Semeen Rehman; Junaid Qadir; Muhammad Shafique

http://arxiv.org/abs/1811.01437
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks.
Faiq Khalid; Hassan Ali; Hammad Tariq; Muhammad Abdullah Hanif; Semeen Rehman; Rehan Ahmed; Muhammad Shafique

http://arxiv.org/abs/1811.01443
SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters.
Hassan Ali; Faiq Khalid; Hammad Tariq; Muhammad Abdullah Hanif; Semeen Rehman; Rehan Ahmed; Muhammad Shafique

http://arxiv.org/abs/1811.01302
Adversarial Gain.
Peter Henderson; Koustuv Sinha; Rosemary Nan Ke; Joelle Pineau

http://arxiv.org/abs/1811.01225
CAAD 2018: Powerful None-Access Black-Box Attack Based on Adversarial Transformation Network.
Xiaoyi Dong; Weiming Zhang; Nenghai Yu

http://arxiv.org/abs/1811.01312
Adversarial Black-Box Attacks on Automatic Speech Recognition Systems using Multi-Objective Evolutionary Optimization.
Shreya Khare; Rahul Aralikatte; Senthil Mani

http://arxiv.org/abs/1811.01213
Learning to Defense by Learning to Attack.
Haoming Jiang; Zhehui Chen; Yuyang Shi; Bo Dai; Tuo Zhao

http://arxiv.org/abs/1811.01134
A Marauder's Map of Security and Privacy in Machine Learning.
Nicolas Papernot

http://arxiv.org/abs/1811.01057
Semidefinite relaxations for certifying robustness to adversarial examples.
Aditi Raghunathan; Jacob Steinhardt; Percy Liang

http://arxiv.org/abs/1811.00866
Efficient Neural Network Robustness Certification with General Activation Functions.
Huan Zhang; Tsui-Wei Weng; Pin-Yu Chen; Cho-Jui Hsieh; Luca Daniel

http://arxiv.org/abs/1811.00830
Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks.
Davide Maiorca; Battista Biggio; Giorgio Giacinto

http://arxiv.org/abs/1811.01031
TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks.
Faiq Khalid; Muhammad Abdullah Hanif; Semeen Rehman; Rehan Ahmed; Muhammad Shafique

http://arxiv.org/abs/1811.00621
Improving Adversarial Robustness by Encouraging Discriminative Features.
Chirag Agarwal; Anh Nguyen; Dan Schonfeld

http://arxiv.org/abs/1811.00525
On the Geometry of Adversarial Examples.
Marc Khoury; Dylan Hadfield-Menell

http://arxiv.org/abs/1811.00401
Excessive Invariance Causes Adversarial Vulnerability.
Jörn-Henrik Jacobsen; Jens Behrmann; Richard Zemel; Matthias Bethge

http://arxiv.org/abs/1811.02658
When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers.
Yujia Wang; David J. Miller; George Kesidis

http://arxiv.org/abs/1811.00189
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example.
Jiayang Liu; Weiming Zhang; Kazuto Fukuchi; Youhei Akimoto; Jun Sakuma

http://arxiv.org/abs/1810.12576
Improved Network Robustness with Adversary Critic.
Alexander Matyasko; Lap-Pui Chau

http://arxiv.org/abs/1810.12715
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models.
Sven Gowal; Krishnamurthy Dvijotham; Robert Stanforth; Rudy Bunel; Chongli Qin; Jonathan Uesato; Relja Arandjelovic; Timothy Mann; Pushmeet Kohli

http://arxiv.org/abs/1810.12272
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution.
Dimitrios I. Diochnos; Saeed Mahloujifar; Mohammad Mahmoody

http://arxiv.org/abs/1810.12042
Logit Pairing Methods Can Fool Gradient-Based Attacks.
Marius Mosbach; Maksym Andriushchenko; Thomas Trost; Matthias Hein; Dietrich Klakow

http://arxiv.org/abs/1810.11783
RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications.
Huan Zhang; Pengchuan Zhang; Cho-Jui Hsieh

http://arxiv.org/abs/1810.11914
Rademacher Complexity for Adversarially Robust Generalization.
Dong Yin; Kannan Ramchandran; Peter Bartlett

http://arxiv.org/abs/1810.11793
Robust Audio Adversarial Example for a Physical Attack.
Hiromu Yakura; Jun Sakuma

http://arxiv.org/abs/1810.11726
Towards Robust Deep Neural Networks.
Timothy E. Wang; Yiming Gu; Dhagash Mehta; Xiaojun Zhao; Edgar A. Bernal

http://arxiv.org/abs/1810.11711
Regularization Effect of Fast Gradient Sign Method and its Generalization.
Chandler Zuo

http://arxiv.org/abs/1810.11580
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples.
Guanhong Tao; Shiqing Ma; Yingqi Liu; Xiangyu Zhang

http://arxiv.org/abs/1810.10731
Law and Adversarial Machine Learning.
Ram Shankar Siva Kumar; David R. O'Brien; Kendra Albert; Salome Vilojen

http://arxiv.org/abs/1810.10751
Attack Graph Convolutional Networks by Adding Fake Nodes.
Xiaoyun Wang; Minhao Cheng; Joe Eaton; Cho-Jui Hsieh; Felix Wu

http://arxiv.org/abs/1810.10939
Evading classifiers in discrete domains with provable optimality guarantees.
Bogdan Kulynych; Jamie Hayes; Nikita Samarin; Carmela Troncoso

http://arxiv.org/abs/1810.10625
Robust Adversarial Learning via Sparsifying Front Ends.
Soorya Gopalakrishnan; Zhinus Marzi; Metehan Cekic; Upamanyu Madhow; Ramtin Pedarsani

http://arxiv.org/abs/1810.10031
Stochastic Substitute Training: A Gray-box Approach to Craft Adversarial Examples Against Gradient Obfuscation Defenses.
Mohammad Hashemi; Greg Cusack; Eric Keller

http://arxiv.org/abs/1810.09650
One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy.
Jingkang Wang; Ruoxi Jia; Gerald Friedland; Bo Li; Costas Spanos

http://arxiv.org/abs/1810.10109
Et Tu Alexa? When Commodity WiFi Devices Turn into Adversarial Motion Sensors.
Yanzi Zhu; Zhujun Xiao; Yuxin Chen; Zhijing Li; Max Liu; Ben Y. Zhao; Haitao Zheng

http://arxiv.org/abs/1810.09519
Adversarial Risk Bounds via Function Transformation.
Justin Khim; Po-Ling Loh

http://arxiv.org/abs/1810.09225
Cost-Sensitive Robustness against Adversarial Examples.
Xiao Zhang; David Evans

http://arxiv.org/abs/1810.09619
Sparse DNNs with Improved Adversarial Robustness.
Yiwen Guo; Chao Zhang; Changshui Zhang; Yurong Chen

http://arxiv.org/abs/1810.08640
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm.
Tsui-Wei Weng; Huan Zhang; Pin-Yu Chen; Aurelie Lozano; Cho-Jui Hsieh; Luca Daniel

http://arxiv.org/abs/1810.08280
Exploring Adversarial Examples in Malware Detection.
Octavian Suciu; Scott E. Coull; Jeffrey Johns

http://arxiv.org/abs/1810.08070
A Training-based Identification Approach to VIN Adversarial Examples.
Yingdi Wang; Wenjia Niu; Tong Chen; Yingxiao Xiang; Jingjing Liu; Gang Li; Jiqiang Liu

http://arxiv.org/abs/1810.07481
Provable Robustness of ReLU networks via Maximization of Linear Regions.
Francesco University of Tübingen Croce; Maksym Saarland University Andriushchenko; Matthias University of Tübingen Hein

http://arxiv.org/abs/1810.10337
Projecting Trouble: Light Based Adversarial Attacks on Deep Learning Classifiers.
Nicole Nichols; Robert Jasper

http://arxiv.org/abs/1810.07339
Security Matters: A Survey on Adversarial Machine Learning.
Guofu Li; Pengjia Zhu; Jin Li; Zhemin Yang; Ning Cao; Zhiyi Chen

http://arxiv.org/abs/1810.06583
Concise Explanations of Neural Networks using Adversarial Training.
Prasad Chalasani; Jiefeng Chen; Amrita Roy Chowdhury; Somesh Jha; Xi Wu

http://arxiv.org/abs/1810.05162
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation.
Chaowei Xiao; Ruizhi Deng; Bo Li; Fisher Yu; Mingyan Liu; Dawn Song

http://arxiv.org/abs/1810.05206
MeshAdv: Adversarial Meshes for Visual Recognition.
Chaowei Xiao; Dawei Yang; Bo Li; Jia Deng; Mingyan Liu

http://arxiv.org/abs/1810.05665
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only.
Tianhang Zheng; Changyou Chen; Kui Ren

http://arxiv.org/abs/1810.03913
Analyzing the Noise Robustness of Deep Neural Networks.
Mengchen Liu; Shixia Liu; Hang Su; Kelei Cao; Jun Zhu

http://arxiv.org/abs/1810.03806
The Adversarial Attack and Detection under the Fisher Information Metric.
Chenxiao Zhao; P. Thomas Fletcher; Mixue Yu; Yaxin Peng; Guixu Zhang; Chaomin Shen

http://arxiv.org/abs/1810.04065
Limitations of adversarial robustness: strong No Free Lunch Theorem.
Elvis Dohmatob

http://arxiv.org/abs/1810.03739
Efficient Two-Step Adversarial Defense for Deep Neural Networks.
Ting-Jui Chang; Yukun He; Peng Li

http://arxiv.org/abs/1810.03538
Combinatorial Attacks on Binarized Neural Networks.
Elias B. Khalil; Amrita Gupta; Bistra Dilkina

http://arxiv.org/abs/1810.03773
Average Margin Regularization for Classifiers.
Matt Olfat; Anil Aswani

http://arxiv.org/abs/1810.02424
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness.
Chihuang Liu; Joseph JaJa

http://arxiv.org/abs/1810.02180
Improved Generalization Bounds for Robust Learning.
Idan Attias; Aryeh Kontorovich; Yishay Mansour

http://arxiv.org/abs/1810.01407
Can Adversarially Robust Learning Leverage Computational Hardness?
Saeed Mahloujifar; Mohammad Mahmoody

http://arxiv.org/abs/1810.01185
Adversarial Examples - A Complete Characterisation of the Phenomenon.
Alexandru Constantin Serban; Erik Poll; Joost Visser

http://arxiv.org/abs/1810.01110
Link Prediction Adversarial Attack.
Jinyin Chen; Ziqiang Shi; Yangyang Wu; Xuanheng Xu; Haibin Zheng

http://arxiv.org/abs/1810.01279
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network.
Xuanqing Liu; Yao Li; Chongruo Wu; Cho-Jui Hsieh

http://arxiv.org/abs/1810.00740
Improving the Generalization of Adversarial Training with Domain Adaptation.
Chuanbiao Song; Kun He; Liwei Wang; John E. Hopcroft

http://arxiv.org/abs/1810.01021
Large batch size training of neural networks with adversarial training and second-order information.
Zhewei Yao; Amir Gholami; Daiyaan Arfeen; Richard Liaw; Joseph Gonzalez; Kurt Keutzer; Michael Mahoney

http://arxiv.org/abs/1810.00953
Improved robustness to adversarial examples using Lipschitz regularization of the loss.
Chris Finlay; Adam Oberman; Bilal Abbasi

http://arxiv.org/abs/1810.00470
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks.
Kenneth T. Co; Luis Muñoz-González; Maupeou Sixte de; Emil C. Lupu

http://arxiv.org/abs/1810.01268
CAAD 2018: Generating Transferable Adversarial Examples.
Yash Sharma; Tien-Dung Le; Moustafa Alzantot

http://arxiv.org/abs/1810.00144
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space.
Fuxun Yu; Chenchen Liu; Yanzhi Wang; Liang Zhao; Xiang Chen

http://arxiv.org/abs/1810.00208
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression.
Yiren Zhao; Ilia Shumailov; Robert Mullins; Ross Anderson

http://arxiv.org/abs/1809.10875
Characterizing Audio Adversarial Examples Using Temporal Dependency.
Zhuolin Yang; Bo Li; Pin-Yu Chen; Dawn Song

http://arxiv.org/abs/1810.00069
Adversarial Attacks and Defences: A Survey.
Anirban Chakraborty; Manaar Alam; Vishal Dey; Anupam Chattopadhyay; Debdeep Mukhopadhyay

http://arxiv.org/abs/1810.00024
Explainable Black-Box Attacks Against Model-based Authentication.
Washington Garcia; Joseph I. Choi; Suman K. Adari; Somesh Jha; Kevin R. B. Butler

http://arxiv.org/abs/1810.07242
Adversarial Attacks on Cognitive Self-Organizing Networks: The Challenge and the Way Forward.
Muhammad Usama; Junaid Qadir; Ala Al-Fuqaha

http://arxiv.org/abs/1809.09262
Neural Networks with Structural Resistance to Adversarial Attacks.
Alfaro Luca de

http://arxiv.org/abs/1809.08999
Fast Geometrically-Perturbed Adversarial Faces.
Ali Dabouei; Sobhan Soleymani; Jeremy Dawson; Nasser M. Nasrabadi

http://arxiv.org/abs/1809.08986
On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces.
Chia-Yi Hsu; Pei-Hsuan Lu; Pin-Yu Chen; Chia-Mu Yu

http://arxiv.org/abs/1809.08758
Low Frequency Adversarial Perturbation.
Chuan Guo; Jared S. Frank; Kilian Q. Weinberger

http://arxiv.org/abs/1809.08706
Is Ordered Weighted $\ell_1$ Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCAR.
Pin-Yu Chen; Bhanukiran Vinzamuri; Sijia Liu

http://arxiv.org/abs/1809.08516
Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization.
Bao Wang; Alex T. Lin; Wei Zhu; Penghang Yin; Andrea L. Bertozzi; Stanley J. Osher

http://arxiv.org/abs/1809.08352
Unrestricted Adversarial Examples.
Tom B. Brown; Nicholas Carlini; Chiyuan Zhang; Catherine Olsson; Paul Christiano; Ian Goodfellow

http://arxiv.org/abs/1809.08316
Adversarial Binaries for Authorship Identification.
Xiaozhu Meng; Barton P. Miller; Somesh Jha

http://arxiv.org/abs/1809.07802
Playing the Game of Universal Adversarial Perturbations.
Julien Perolat; Mateusz Malinowski; Bilal Piot; Olivier Pietquin

http://arxiv.org/abs/1809.08098
Efficient Formal Safety Analysis of Neural Networks.
Shiqi Wang; Kexin Pei; Justin Whitehouse; Junfeng Yang; Suman Jana

http://arxiv.org/abs/1809.07062
Adversarial Training Towards Robust Multimedia Recommender System.
Jinhui Tang; Xiaoyu Du; Xiangnan He; Fajie Yuan; Qi Tian; Tat-Seng Chua

http://arxiv.org/abs/1809.07016
Generating 3D Adversarial Point Clouds.
Chong Xiang; Charles R. Qi; Bo Li

http://arxiv.org/abs/1809.06498
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples.
Deqiang Li; Ramesh Baral; Tao Li; Han Wang; Qianmu Li; Shouhuai Xu

http://arxiv.org/abs/1809.06452
Robustness Guarantees for Bayesian Inference with Gaussian Processes.
Luca Cardelli; Marta Kwiatkowska; Luca Laurenti; Andrea Patane

http://arxiv.org/abs/1809.05966
Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches.
Yuezun Li; Xiao Bian; Ming-ching Chang; Siwei Lyu

http://arxiv.org/abs/1809.05962
Robust Adversarial Perturbation on Deep Proposal-based Models.
Yuezun Li; Daniel Tian; Ming-Ching Chang; Xiao Bian; Siwei Lyu

http://arxiv.org/abs/1809.05165
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks.
Siyue Wang; Xiao Wang; Pu Zhao; Wujie Wen; David Kaeli; Peter Chin; Xue Lin

http://arxiv.org/abs/1809.04913
Query-Efficient Black-Box Attack by Active Learning.
Pengcheng Li; Jinfeng Yi; Lijun Zhang

http://arxiv.org/abs/1809.04790
Adversarial Examples: Opportunities and Challenges.
Jiliang Zhang; Chen Li

http://arxiv.org/abs/1809.04098
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions.
Yusuke Tsuzuku; Issei Sato

http://arxiv.org/abs/1809.04397
Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition.
Krishan Rajaratnam; Kunal Shah; Jugal Kalita

http://arxiv.org/abs/1809.04120
Humans can decipher adversarial images.
Zhenglong Zhou; Chaz Firestone

http://arxiv.org/abs/1809.03740
Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System. (22%)
Barbara Rychalska; Dominika Basaj; Przemyslaw Biecek; Anna Wroblewska

http://arxiv.org/abs/1809.03063
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure.
Saeed Mahloujifar; Dimitrios I. Diochnos; Mohammad Mahmoody

http://arxiv.org/abs/1809.03008
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability.
Kai Y. Xiao; Vincent Tjeng; Nur Muhammad Shafiullah; Aleksander Madry

http://arxiv.org/abs/1809.03113
Certified Adversarial Robustness with Additive Noise.
Bai Li; Changyou Chen; Wenlin Wang; Lawrence Carin

http://arxiv.org/abs/1809.02918
Towards Query Efficient Black-box Attacks: An Input-free Perspective.
Yali Du; Meng Fang; Jinfeng Yi; Jun Cheng; Dacheng Tao

http://arxiv.org/abs/1809.02797
Fast Gradient Attack on Network Embedding.
Jinyin Chen; Yangyang Wu; Xuanheng Xu; Yixian Chen; Haibin Zheng; Qi Xuan

http://arxiv.org/abs/1809.02786
Structure-Preserving Transformation: Generating Diverse and Transferable Adversarial Examples.
Dan Peng; Zizhan Zheng; Xiaofeng Zhang

http://arxiv.org/abs/1809.02861
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks.
Ambra Demontis; Marco Melis; Maura Pintor; Matthew Jagielski; Battista Biggio; Alina Oprea; Cristina Nita-Rotaru; Fabio Roli

http://arxiv.org/abs/1809.02560
A Deeper Look at 3D Shape Classifiers.
Jong-Chyi Su; Matheus Gadelha; Rui Wang; Subhransu Maji

http://arxiv.org/abs/1809.02444
Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computer.
Alvin Chan; Lei Ma; Felix Juefei-Xu; Xiaofei Xie; Yang Liu; Yew Soon Ong

http://arxiv.org/abs/1809.02701
Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for Question Answering.
Eric Wallace; Pedro Rodriguez; Shi Feng; Ikuya Yamada; Jordan Boyd-Graber

http://arxiv.org/abs/1809.02681
Query Attack via Opposite-Direction Feature:Towards Robust Image Retrieval.
Zhedong Zheng; Liang Zheng; Yi Yang; Fei Wu

http://arxiv.org/abs/1809.02079
Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models.
Tong Niu; Mohit Bansal

http://arxiv.org/abs/1809.02104
Are adversarial examples inevitable?
Ali Shafahi; W. Ronny Huang; Christoph Studer; Soheil Feizi; Tom Goldstein

http://arxiv.org/abs/1809.02077
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection.
Zilong Lin; Yong Shi; Zhi Xue

http://arxiv.org/abs/1809.01829
Adversarial Reprogramming of Text Classification Neural Networks.
Paarth Neekhara; Shehzeen Hussain; Shlomo Dubnov; Farinaz Koushanfar

http://arxiv.org/abs/1809.01715
Bridging machine learning and cryptography in defence against adversarial attacks.
Olga Taran; Shideh Rezaeifar; Slava Voloshynovskiy

http://arxiv.org/abs/1809.01093
Adversarial Attacks on Node Embeddings.
Aleksandar Bojchevski; Stephan Günnemann

http://arxiv.org/abs/1809.01697
HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition.
Zirui Xu; Fuxun Yu; Chenchen Liu; Xiang Chen

http://arxiv.org/abs/1809.00594
Adversarial Attack Type I: Cheat Classifiers by Significant Changes.
Sanli Tang; Xiaolin Huang; Mingjian Chen; Chengjin Sun; Jie Yang

http://arxiv.org/abs/1809.00065
MULDEF: Multi-model-based Defense Against Adversarial Examples for Neural Networks.
Siwakorn Srisakaokul; Yuhao Zhang; Zexuan Zhong; Wei Yang; Tao Xie; Bo Li

http://arxiv.org/abs/1808.09413
DLFuzz: Differential Fuzzing Testing of Deep Learning Systems.
Jianmin Guo; Yu Jiang; Yue Zhao; Quan Chen; Jiaguang Sun

http://arxiv.org/abs/1808.09115
All You Need is "Love": Evading Hate-speech Detection.
Tommi Gröndahl; Luca Pajola; Mika Juuti; Mauro Conti; N. Asokan

http://arxiv.org/abs/1808.09540
Lipschitz regularized Deep Neural Networks generalize and are adversarially robust.
Chris Finlay; Jeff Calder; Bilal Abbasi; Adam Oberman

http://arxiv.org/abs/1809.00958
Targeted Nonlinear Adversarial Perturbations in Images and Videos.
Roberto Rey-de-Castro; Herschel Rabitz

http://arxiv.org/abs/1808.08750
Generalisation in humans and deep neural networks.
Robert Geirhos; Carlos R. Medina Temme; Jonas Rauber; Heiko H. Schütt; Matthias Bethge; Felix A. Wichmann

http://arxiv.org/abs/1808.08609
Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge.
Pasquale Minervini; Sebastian Riedel

http://arxiv.org/abs/1808.08426
Analysis of adversarial attacks against CNN-based image forgery detectors.
Diego Gragnaniello; Francesco Marra; Giovanni Poggi; Luisa Verdoliva

http://arxiv.org/abs/1808.08444
Guiding Deep Learning System Testing using Surprise Adequacy.
Jinhan Kim; Robert Feldt; Shin Yoo

http://arxiv.org/abs/1808.08197
Is Machine Learning in Power Systems Vulnerable?
Yize Chen; Yushi Tan; Deepjyoti Deka

http://arxiv.org/abs/1808.07945
Maximal Jacobian-based Saliency Map Attack.
Rey Wiyatno; Anqi Xu

http://arxiv.org/abs/1808.07713
Adversarial Attacks on Deep-Learning Based Radio Signal Classification.
Meysam Sadeghi; Erik G. Larsson

http://arxiv.org/abs/1808.08282
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection.
Mahdieh Abbasi; Arezoo Rajabi; Azadeh Sadat Mozafari; Rakesh B. Bobba; Christian Gagne

http://arxiv.org/abs/1808.06645
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples.
George A. Adam; Petr Smirnov; David Duvenaud; Benjamin Haibe-Kains; Anna Goldenberg

http://arxiv.org/abs/1808.05770
Reinforcement Learning for Autonomous Defence in Software-Defined Networking.
Yi Han; Benjamin I. P. Rubinstein; Tamas Abraham; Tansu Alpcan; Vel Olivier De; Sarah Erfani; David Hubczenko; Christopher Leckie; Paul Montague

http://arxiv.org/abs/1808.05705
Mitigation of Adversarial Attacks through Embedded Feature Selection.
Ziyi Bao; Luis Muñoz-González; Emil C. Lupu

http://arxiv.org/abs/1808.05665
Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding.
Lea Schönherr; Katharina Kohls; Steffen Zeiler; Thorsten Holz; Dorothea Kolossa

http://arxiv.org/abs/1808.05537
Distributionally Adversarial Attack.
Tianhang Zheng; Changyou Chen; Kui Ren

http://arxiv.org/abs/1808.03601
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks.
Fan Yang; Zhiyuan Chen

http://arxiv.org/abs/1808.04218
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection.
Xiao Chen; Chaoran Li; Derui Wang; Sheng Wen; Jun Zhang; Surya Nepal; Yang Xiang; Kui Ren

http://arxiv.org/abs/1808.02651
Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer.
Hsueh-Ti Derek Liu; Michael Tao; Chun-Liang Li; Derek Nowrouzezahrai; Alec Jacobson

http://arxiv.org/abs/1808.02455
Data augmentation using synthetic data for time series classification with deep residual networks.
Hassan Ismail Fawaz; Germain Forestier; Jonathan Weber; Lhassane Idoumghar; Pierre-Alain Muller

http://arxiv.org/abs/1808.01976
Adversarial Vision Challenge.
Wieland Brendel; Jonas Rauber; Alexey Kurakin; Nicolas Papernot; Behar Veliqi; Marcel Salathé; Sharada P. Mohanty; Matthias Bethge

http://arxiv.org/abs/1808.01785
Defense Against Adversarial Attacks with Saak Transform.
Sibo Song; Yueru Chen; Ngai-Man Cheung; C. -C. Jay Kuo

http://arxiv.org/abs/1808.01753
Gray-box Adversarial Training.
Vivek B. S.; Konda Reddy Mopuri; R. Venkatesh Babu

http://arxiv.org/abs/1808.01688
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models.
Dong Su; Huan Zhang; Hongge Chen; Jinfeng Yi; Pin-Yu Chen; Yupeng Gao

http://arxiv.org/abs/1808.01664
Structured Adversarial Attack: Towards General Implementation and Better Interpretability.
Kaidi Xu; Sijia Liu; Pu Zhao; Pin-Yu Chen; Huan Zhang; Quanfu Fan; Deniz Erdogmus; Yanzhi Wang; Xue Lin

http://arxiv.org/abs/1808.01452
Traits & Transferability of Adversarial Examples against Instance Segmentation & Object Detection.
Raghav Gurbaxani; Shivank Mishra

http://arxiv.org/abs/1808.01546
ATMPA: Attacking Machine Learning-based Malware Visualization Detection Methods via Adversarial Examples.
Xinbo Liu; Jiliang Zhang; Yaping Lin; He Li

http://arxiv.org/abs/1808.01153
Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions.
Konda Reddy Mopuri; Phani Krishna Uppala; R. Venkatesh Babu

http://arxiv.org/abs/1808.01352
DeepCloak: Adversarial Crafting As a Defensive Measure to Cloak Processes.
Mehmet Sinan Inci; Thomas Eisenbarth; Berk Sunar

http://arxiv.org/abs/1808.00123
EagleEye: Attack-Agnostic Defense against Adversarial Inputs (Technical Report).
Yujie Ji; Xinyang Zhang; Ting Wang

http://arxiv.org/abs/1807.10454
Rob-GAN: Generator, Discriminator, and Adversarial Attacker.
Xuanqing Liu; Cho-Jui Hsieh

http://arxiv.org/abs/1807.10335
A general metric for identifying adversarial images.
Siddharth Krishna Kumar

http://arxiv.org/abs/1807.10272
Evaluating and Understanding the Robustness of Adversarial Logit Pairing.
Logan Engstrom; Andrew Ilyas; Anish Athalye

http://arxiv.org/abs/1807.09937
HiDDeN: Hiding Data With Deep Networks.
Jiren Zhu; Russell Kaplan; Justin Johnson; Li Fei-Fei

http://arxiv.org/abs/1807.09705
Limitations of the Lipschitz constant as a defense against adversarial examples.
Todd Huster; Cho-Yu Jason Chiang; Ritu Chadha

http://arxiv.org/abs/1807.09443
Unbounded Output Networks for Classification.
Stefan Elfwing; Eiji Uchibe; Kenji Doya

http://arxiv.org/abs/1807.09380
Contrastive Video Representation Learning via Adversarial Perturbations.
Jue Wang; Anoop Cherian

http://arxiv.org/abs/1807.08108
Simultaneous Adversarial Training - Learn from Others Mistakes.
Zukang Liao

http://arxiv.org/abs/1807.07978
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors.
Andrew Ilyas; Logan Engstrom; Aleksander Madry

http://arxiv.org/abs/1807.07769
Physical Adversarial Examples for Object Detectors.
Kevin Eykholt; Ivan Evtimov; Earlence Fernandes; Bo Li; Amir Rahmati; Florian Tramer; Atul Prakash; Tadayoshi Kohno; Dawn Song

http://arxiv.org/abs/1807.10590
Harmonic Adversarial Attack Method.
Wen Heng; Shuchang Zhou; Tingting Jiang

http://arxiv.org/abs/1807.06752
Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding.
Tong Chen; Wenjia Niu; Yingxiao Xiang; Xiaoxuan Bai; Jiqiang Liu; Zhen Han; Gang Li

http://arxiv.org/abs/1807.06732
Motivating the Rules of the Game for Adversarial Example Research.
Justin Gilmer; Ryan P. Adams; Ian Goodfellow; David Andersen; George E. Dahl

http://arxiv.org/abs/1807.06714
Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions.
Adnan Siraj Rakin; Jinfeng Yi; Boqing Gong; Deliang Fan

http://arxiv.org/abs/1807.06064
Online Robust Policy Learning in the Presence of Unknown Adversaries.
Aaron J. Havens; Zhanhong Jiang; Soumik Sarkar

http://arxiv.org/abs/1807.05832
Manifold Adversarial Learning.
Shufei Zhang; Kaizhu Huang; Jianke Zhu; Yang Liu

http://arxiv.org/abs/1807.04457
Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach.
Minhao Cheng; Thong Le; Pin-Yu Chen; Jinfeng Yi; Huan Zhang; Cho-Jui Hsieh

http://arxiv.org/abs/1807.04200
With Friends Like These, Who Needs Adversaries?
Saumya Jetley; Nicholas A. Lord; Philip H. S. Torr

http://arxiv.org/abs/1807.03888
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks.
Kimin Lee; Kibok Lee; Honglak Lee; Jinwoo Shin

http://arxiv.org/abs/1807.03571
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees.
Min Wu; Matthew Wicker; Wenjie Ruan; Xiaowei Huang; Marta Kwiatkowska

http://arxiv.org/abs/1807.04270
Attack and defence in cellular decision-making: lessons from machine learning.
Thomas J. Rademaker; Emmanuel Bengio; Paul François

http://arxiv.org/abs/1807.03326
Adaptive Adversarial Attack on Scene Text Recognition.
Xiaoyong Yuan; Pan He; Xiaolin Andy Li; Dapeng Oliver Wu

http://arxiv.org/abs/1807.02905
Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks.
Saeid Asgari Taghanaki; Arkadeep Das; Ghassan Hamarneh

http://arxiv.org/abs/1807.02188
Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness.
Priyadarshini Panda; Kaushik Roy

http://arxiv.org/abs/1807.01697
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations.
Dan Hendrycks; Thomas G. Dietterich

http://arxiv.org/abs/1807.01216
Local Gradients Smoothing: Defense against localized adversarial attacks.
Muzammal Naseer; Salman H. Khan; Fatih Porikli

http://arxiv.org/abs/1807.01069
Adversarial Robustness Toolbox v1.0.0.
Maria-Irina Nicolae; Mathieu Sinn; Minh Ngoc Tran; Beat Buesser; Ambrish Rawat; Martin Wistuba; Valentina Zantedeschi; Nathalie Baracaldo; Bryant Chen; Heiko Ludwig; Ian M. Molloy; Ben Edwards

http://arxiv.org/abs/1807.00458
Adversarial Perturbations Against Real-Time Video Classification Systems.
Shasha Li; Ajaya Neupane; Sujoy Paul; Chengyu Song; Srikanth V. Krishnamurthy; Amit K. Roy Chowdhury; Ananthram Swami

http://arxiv.org/abs/1807.00340
Towards Adversarial Training with Moderate Performance Improvement for Neural Network Classification.
Xinhan Di; Pengqian Yu; Meng Tian

http://arxiv.org/abs/1807.00051
Adversarial Examples in Deep Learning: Characterization and Divergence.
Wenqi Wei; Ling Liu; Margaret Loper; Stacey Truex; Lei Yu; Mehmet Emre Gursoy; Yanzhao Wu

http://arxiv.org/abs/1806.11146
Adversarial Reprogramming of Neural Networks.
Gamaleldin F. Elsayed; Ian Goodfellow; Jascha Sohl-Dickstein

http://arxiv.org/abs/1806.10707
Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning.
Jasjeet Dhaliwal; Saurabh Shintre

http://arxiv.org/abs/1806.10496
Customizing an Adversarial Example Generator with Class-Conditional GANs.
Shih-hong Tsai

http://arxiv.org/abs/1806.09410
Exploring Adversarial Examples: Patterns of One-Pixel Attacks.
David Kügler; Alexander Distergoft; Arjan Kuijper; Anirban Mukhopadhyay

http://arxiv.org/abs/1806.09035
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions.
Alex Kouzemtchenko

http://arxiv.org/abs/1806.09030
On Adversarial Examples for Character-Level Neural Machine Translation.
Javid Ebrahimi; Daniel Lowd; Dejing Dou

http://arxiv.org/abs/1806.08970
Evaluation of Momentum Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) Based Attack Method on MCS 2018 Adversarial Attacks on Black Box Face Recognition System.
Md Ashraful Alam Milton

http://arxiv.org/abs/1806.09186
Detection based Defense against Adversarial Examples from the Steganalysis Point of View.
Jiayang Liu; Weiming Zhang; Yiwei Zhang; Dongdong Hou; Yujia Liu; Hongyue Zha; Nenghai Yu

http://arxiv.org/abs/1806.08028
Gradient Adversarial Training of Neural Networks.
Ayan Sinha; Zhao Chen; Vijay Badrinarayanan; Andrew Rabinovich

http://arxiv.org/abs/1806.07723
Combinatorial Testing for Deep Learning Systems.
Lei Ma; Fuyuan Zhang; Minhui Xue; Bo Li; Yang Liu; Jianjun Zhao; Yadong Wang

http://arxiv.org/abs/1806.07492
On the Learning of Deep Local Features for Robust Face Spoofing Detection.
Souza Gustavo Botelho de; João Paulo Papa; Aparecido Nilceu Marana

http://arxiv.org/abs/1806.07409
Built-in Vulnerabilities to Imperceptible Adversarial Perturbations.
Thomas Tanay; Jerone T. A. Andrews; Lewis D. Griffin

http://arxiv.org/abs/1806.06108
Non-Negative Networks Against Adversarial Attacks.
William Fleshman; Edward Raff; Jared Sylvester; Steven Forsyth; Mark McLean

http://arxiv.org/abs/1806.05476
Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data.
Jacson Rodrigues Correia-Silva; Rodrigo F. Berriel; Claudine Badue; Souza Alberto F. de; Thiago Oliveira-Santos

http://arxiv.org/abs/1806.05337
Hierarchical interpretations for neural network predictions.
Chandan Singh; W. James Murdoch; Bin Yu

http://arxiv.org/abs/1806.05236
Manifold Mixup: Better Representations by Interpolating Hidden States.
Vikas Verma; Alex Lamb; Christopher Beckham; Amir Najafi; Ioannis Mitliagkas; Aaron Courville; David Lopez-Paz; Yoshua Bengio

http://arxiv.org/abs/1806.04646
Adversarial Attacks on Variational Autoencoders.
George Gondim-Ribeiro; Pedro Tabacof; Eduardo Valle

http://arxiv.org/abs/1806.04425
Ranking Robustness Under Adversarial Document Manipulations.
Gregory Goren; Oren Kurland; Moshe Tennenholtz; Fiana Raiber

http://arxiv.org/abs/1806.04169
Defense Against the Dark Arts: An overview of adversarial example security research and future research directions.
Ian Goodfellow

http://arxiv.org/abs/1806.02977
Monge blunts Bayes: Hardness Results for Adversarial Training.
Zac Cranko; Aditya Krishna Menon; Richard Nock; Cheng Soon Ong; Zhan Shi; Christian Walder

http://arxiv.org/abs/1806.02924
Revisiting Adversarial Risk.
Arun Sai Suggala; Adarsh Prasad; Vaishnavh Nagarajan; Pradeep Ravikumar

http://arxiv.org/abs/1806.02782
Training Augmentation with Adversarial Examples for Robust Speech Recognition.
Sining Sun; Ching-Feng Yeh; Mari Ostendorf; Mei-Yuh Hwang; Lei Xie

http://arxiv.org/abs/1806.02371
Adversarial Attack on Graph Structured Data.
Hanjun Dai; Hui Li; Tian Tian; Xin Huang; Lin Wang; Jun Zhu; Le Song

http://arxiv.org/abs/1806.02256
Adversarial Regression with Multiple Learners.
Liang Tong; Sixie Yu; Scott Alfeld; Yevgeniy Vorobeychik

http://arxiv.org/abs/1806.02032
Killing four birds with one Gaussian process: the relation between different test-time attacks.
Kathrin Grosse; Michael T. Smith; Michael Backes

http://arxiv.org/abs/1806.02299
DPatch: An Adversarial Patch Attack on Object Detectors.
Xin Liu; Huanrui Yang; Ziwei Liu; Linghao Song; Hai Li; Yiran Chen

http://arxiv.org/abs/1806.02190
Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise.
Vahid Behzadan; Arslan Munir

http://arxiv.org/abs/1806.01477
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks.
Chirag Agarwal; Bo Dong; Dan Schonfeld; Anthony Hoogs

http://arxiv.org/abs/1806.01471
PAC-learning in the presence of evasion adversaries.
Daniel Cullina; Arjun Nitin Bhagoji; Prateek Mittal

http://arxiv.org/abs/1806.00667
Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks.
Yarin Gal; Lewis Smith

http://arxiv.org/abs/1806.00580
Detecting Adversarial Examples via Key-based Network.
Pinlong Zhao; Zhouyu Fu; Ou wu; Qinghua Hu; Jun Wang

http://arxiv.org/abs/1806.00088
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks.
Jan Svoboda; Jonathan Masci; Federico Monti; Michael M. Bronstein; Leonidas Guibas

http://arxiv.org/abs/1806.00081
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders.
Partha Ghosh; Arpan Losalka; Michael J Black

http://arxiv.org/abs/1805.12514
Scaling provable adversarial defenses.
Eric Wong; Frank R. Schmidt; Jan Hendrik Metzen; J. Zico Kolter

http://arxiv.org/abs/1805.12487
Sequential Attacks on Agents for Long-Term Adversarial Goals.
Edgar Tretschk; Seong Joon Oh; Mario Fritz

http://arxiv.org/abs/1805.12316
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data.
Puyudi Yang; Jianbo Chen; Cho-Jui Hsieh; Jane-Ling Wang; Michael I. Jordan

http://arxiv.org/abs/1805.12302
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization.
Avishek Joey Bose; Parham Aarabi

http://arxiv.org/abs/1805.11852
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio.
Nilaksh Das; Madhuri Shanbhogue; Shang-Tse Chen; Li Chen; Michael E. Kounavis; Duen Horng Chau

http://arxiv.org/abs/1805.12017
Robustifying Models Against Adversarial Attacks by Langevin Dynamics.
Vignesh Srinivasan; Arturo Marban; Klaus-Robert Müller; Wojciech Samek; Shinichi Nakajima

http://arxiv.org/abs/1805.12152
Robustness May Be at Odds with Accuracy.
Dimitris Tsipras; Shibani Santurkar; Logan Engstrom; Alexander Turner; Aleksander Madry

http://arxiv.org/abs/1805.11770
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks.
Chun-Chen Tu; Paishun Ting; Pin-Yu Chen; Sijia Liu; Huan Zhang; Jinfeng Yi; Cho-Jui Hsieh; Shin-Ming Cheng

http://arxiv.org/abs/1805.11596
Adversarial Noise Attacks of Deep Learning Architectures -- Stability Analysis via Sparse Modeled Signals.
Yaniv Romano; Aviad Aberdam; Jeremias Sulam; Michael Elad

http://arxiv.org/abs/1805.11666
Why Botnets Work: Distributed Brute-Force Attacks Need No Synchronization.
Salman Salamatian; Wasim Huleihel; Ahmad Beirami; Asaf Cohen; Muriel Médard

http://arxiv.org/abs/1805.10997
Adversarial Examples in Remote Sensing.
Wojciech Czaja; Neil Fendley; Michael Pekala; Christopher Ratto; I-Jeng Wang

http://arxiv.org/abs/1805.11090
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization.
Moustafa Alzantot; Yash Sharma; Supriyo Chakraborty; Huan Zhang; Cho-Jui Hsieh; Mani Srivastava

http://arxiv.org/abs/1805.10652
Defending Against Adversarial Attacks by Leveraging an Entire GAN.
Gokula Krishnan Santhanam; Paulina Grnarova

http://arxiv.org/abs/1805.10265
Training verified learners with learned verifiers.
Krishnamurthy Dvijotham; Sven Gowal; Robert Stanforth; Relja Arandjelovic; Brendan O'Donoghue; Jonathan Uesato; Pushmeet Kohli

http://arxiv.org/abs/1805.10204
Adversarial examples from computational constraints.
Sébastien Bubeck; Eric Price; Ilya Razenshteyn

http://arxiv.org/abs/1805.10133
Laplacian Networks: Bounding Indicator Function Smoothness for Neural Network Robustness.
Carlos Eduardo Rosar Kos Lassance; Vincent Gripon; Antonio Ortega

http://arxiv.org/abs/1805.09380
Anonymizing k-Facial Attributes via Adversarial Perturbations.
Saheb Chhabra; Richa Singh; Mayank Vatsa; Gaurav Gupta

http://arxiv.org/abs/1805.09370
Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients.
Fuxun Yu; Zirui Xu; Yanzhi Wang; Chenchen Liu; Xiang Chen

http://arxiv.org/abs/1805.09190
Towards the first adversarially robust neural network model on MNIST.
Lukas Schott; Jonas Rauber; Matthias Bethge; Wieland Brendel

http://arxiv.org/abs/1805.08736
Adversarially Robust Training through Structured Gradient Regularization.
Kevin Roth; Aurelien Lucchi; Sebastian Nowozin; Thomas Hofmann

http://arxiv.org/abs/1805.08000
Adversarial Noise Layer: Regularize Neural Network By Adding Noise.
Zhonghui You; Jinmian Ye; Kunming Li; Zenglin Xu; Ping Wang

http://arxiv.org/abs/1805.07894
Constructing Unrestricted Adversarial Examples with Generative Models.
Yang Song; Rui Shu; Nate Kushman; Stefano Ermon

http://arxiv.org/abs/1805.08006
Bidirectional Learning for Robust Neural Networks.
Sidney Pontes-Filho; Marcus Liwicki

http://arxiv.org/abs/1805.07984
Adversarial Attacks on Neural Networks for Graph Data.
Daniel Zügner; Amir Akbarnejad; Stephan Günnemann

http://arxiv.org/abs/1805.07862
Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference.
Ruying Bao; Sihang Liang; Qingcan Wang

http://arxiv.org/abs/1805.07816
Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks.
Jiefeng Chen; Xi Wu; Vaibhav Rastogi; Yingyu Liang; Somesh Jha

http://arxiv.org/abs/1805.07820
Targeted Adversarial Examples for Black Box Audio Systems.
Rohan Taori; Amog Kamsetty; Brenton Chu; Nikita Vemuri

http://arxiv.org/abs/1805.06605
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models.
Pouya Samangouei; Maya Kabkab; Rama Chellappa

http://arxiv.org/abs/1805.06130
Towards Robust Neural Machine Translation.
Yong Cheng; Zhaopeng Tu; Fandong Meng; Junjie Zhai; Yang Liu

http://arxiv.org/abs/1805.05010
Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing.
Jingyi Wang; Jun Sun; Peixin Zhang; Xinyu Wang

http://arxiv.org/abs/1805.04807
Curriculum Adversarial Training.
Qi-Zhi Cai; Min Du; Chang Liu; Dawn Song

http://arxiv.org/abs/1805.04810
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning.
Jinyuan Jia; Neil Zhenqiang Gong

http://arxiv.org/abs/1805.04613
Breaking Transferability of Adversarial Samples with Randomness.
Yan Zhou; Murat Kantarcioglu; Bowei Xi

http://arxiv.org/abs/1805.03553
On Visual Hallmarks of Robustness to Adversarial Malware.
Alex Huang; Abdullah Al-Dujaili; Erik Hemberg; Una-May O'Reilly

http://arxiv.org/abs/1805.03438
Robust Classification with Convolutional Prototype Learning.
Hong-Ming Yang; Xu-Yao Zhang; Fei Yin; Cheng-Lin Liu

http://arxiv.org/abs/1805.02917
Interpretable Adversarial Perturbation in Input Embedding Space for Text.
Motoki Sato; Jun Suzuki; Hiroyuki Shindo; Yuji Matsumoto

http://arxiv.org/abs/1805.02131
A Counter-Forensic Method for CNN-Based Camera Model Identification.
David Güera; Yu Wang; Luca Bondi; Paolo Bestagini; Stefano Tubaro; Edward J. Delp

http://arxiv.org/abs/1805.01431
Siamese networks for generating adversarial examples.
Mandar Kulkarni; Aria Abubakar

http://arxiv.org/abs/1805.00089
Concolic Testing for Deep Neural Networks.
Youcheng Sun; Min Wu; Wenjie Ruan; Xiaowei Huang; Marta Kwiatkowska; Daniel Kroening

http://arxiv.org/abs/1804.11313
How Robust are Deep Neural Networks?
Biswa Sengupta; Karl J. Friston

http://arxiv.org/abs/1804.11285
Adversarially Robust Generalization Requires More Data.
Ludwig Schmidt; Shibani Santurkar; Dimitris Tsipras; Kunal Talwar; Aleksander Mądry

http://arxiv.org/abs/1804.11022
Adversarial Regression for Detecting Attacks in Cyber-Physical Systems.
Amin Ghafouri; Yevgeniy Vorobeychik; Xenofon Koutsoukos

http://arxiv.org/abs/1804.10829
Formal Security Analysis of Neural Networks using Symbolic Intervals.
Shiqi Wang; Kexin Pei; Justin Whitehouse; Junfeng Yang; Suman Jana

http://arxiv.org/abs/1804.09699
Towards Fast Computation of Certified Robustness for ReLU Networks.
Tsui-Wei Weng; Huan Zhang; Hongge Chen; Zhao Song; Cho-Jui Hsieh; Duane Boning; Inderjit S. Dhillon; Luca Daniel

http://arxiv.org/abs/1804.08794
Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning.
Mahdieh Abbasi; Arezoo Rajabi; Christian Gagné; Rakesh B. Bobba

http://arxiv.org/abs/1804.08757
Siamese Generative Adversarial Privatizer for Biometric Data.
Witold Oleszkiewicz; Peter Kairouz; Karol Piczak; Ram Rajagopal; Tomasz Trzcinski

http://arxiv.org/abs/1804.08598
Black-box Adversarial Attacks with Limited Queries and Information.
Andrew Ilyas; Logan Engstrom; Anish Athalye; Jessy Lin

http://arxiv.org/abs/1804.08529
VectorDefense: Vectorization as a Defense to Adversarial Examples.
Vishaal Munusamy Kabilan; Brandon Morris; Anh Nguyen

http://arxiv.org/abs/1804.08778
Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers.
Ishai Rosenberg; Asaf Shabtai; Yuval Elovici; Lior Rokach

http://arxiv.org/abs/1804.07998
Generating Natural Language Adversarial Examples.
Moustafa Alzantot; Yash Sharma; Ahmed Elgohary; Bo-Jhang Ho; Mani Srivastava; Kai-Wei Chang

http://arxiv.org/abs/1804.07870
Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size.
Ian Goodfellow

http://arxiv.org/abs/1804.07757
Learning More Robust Features with Adversarial Training.
Shuangtao Li; Yuanke Chen; Yanlin Peng; Lin Bai

http://arxiv.org/abs/1804.07729
ADef: an Iterative Algorithm to Construct Adversarial Deformations.
Rima Alaifari; Giovanni S. Alberti; Tandri Gauksson

http://arxiv.org/abs/1804.07062
Attacking Convolutional Neural Network using Differential Evolution.
Jiawei Su; Danilo Vasconcellos Vargas; Kouichi Sakurai

http://arxiv.org/abs/1804.07045
Semantic Adversarial Deep Learning.
Tommaso Dreossi; Somesh Jha; Sanjit A. Seshia

http://arxiv.org/abs/1804.06760
Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components.
Cumhur Erkan Tuncali; Georgios Fainekos; Hisahiro Ito; James Kapinski

http://arxiv.org/abs/1804.06898
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input.
Youmna Farag; Helen Yannakoudakis; Ted Briscoe

http://arxiv.org/abs/1804.06473
Robust Machine Comprehension Models via Adversarial Training.
Yicheng Wang; Mohit Bansal

http://arxiv.org/abs/1804.06059
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks.
Mohit Iyyer; John Wieting; Kevin Gimpel; Luke Zettlemoyer

http://arxiv.org/abs/1804.05805
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm.
Wenjie Ruan; Min Wu; Youcheng Sun; Xiaowei Huang; Daniel Kroening; Marta Kwiatkowska

http://arxiv.org/abs/1804.05810
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector.
Shang-Tse Chen; Cory Cornelius; Jason Martin; Duen Horng Chau

http://arxiv.org/abs/1805.00310
On the Limitation of MagNet Defense against $L_1$-based Adversarial Examples.
Pei-Hsuan Lu; Pin-Yu Chen; Kang-Cheng Chen; Chia-Mu Yu

http://arxiv.org/abs/1804.05296
Adversarial Attacks Against Medical Deep Learning Systems.
Samuel G. Finlayson; Hyung Won Chung; Isaac S. Kohane; Andrew L. Beam

http://arxiv.org/abs/1804.04177
Detecting Malicious PowerShell Commands using Deep Neural Networks.
Danny Hendler; Shay Kels; Amir Rubin

http://arxiv.org/abs/1804.03286
On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses.
Anish Athalye; Nicholas Carlini

http://arxiv.org/abs/1804.03308
Adversarial Training Versus Weight Decay.
Angus Galloway; Thomas Tanay; Graham W. Taylor

http://arxiv.org/abs/1804.03193
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks.
Pu Zhao; Sijia Liu; Yanzhi Wang; Xue Lin

http://arxiv.org/abs/1804.02691
Adaptive Spatial Steganography Based on Probability-Controlled Adversarial Examples.
Sai Ma; Qingxiao Guan; Xianfeng Zhao; Yaqi Liu

http://arxiv.org/abs/1804.02485
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations.
Alex Lamb; Jonathan Binas; Anirudh Goyal; Dmitriy Serdyuk; Sandeep Subramanian; Ioannis Mitliagkas; Yoshua Bengio

http://arxiv.org/abs/1804.01635
Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks.
Neale Ratzlaff; Li Fuxin

http://arxiv.org/abs/1804.00097
Adversarial Attacks and Defences Competition.
Alexey Kurakin; Ian Goodfellow; Samy Bengio; Yinpeng Dong; Fangzhou Liao; Ming Liang; Tianyu Pang; Jun Zhu; Xiaolin Hu; Cihang Xie; Jianyu Wang; Zhishuai Zhang; Zhou Ren; Alan Yuille; Sangxia Huang; Yao Zhao; Yuzhe Zhao; Zhonglin Han; Junjiajia Long; Yerkebulan Berdibekov; Takuya Akiba; Seiya Tokui; Motoki Abe

http://arxiv.org/abs/1803.11157
Security Consideration For Deep Learning-Based Image Forensics.
Wei Zhao; Pengpeng Yang; Rongrong Ni; Yao Zhao; Haorui Wu

http://arxiv.org/abs/1803.10840
Defending against Adversarial Images using Basis Functions Transformations.
Uri Shaham; James Garritano; Yutaro Yamada; Ethan Weinberger; Alex Cloninger; Xiuyuan Cheng; Kelly Stanton; Yuval Kluger

http://arxiv.org/abs/1803.10418
The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples.
Ayse Elvan Aydemir; Alptekin Temizel; Tugba Taskaya Temizel

http://arxiv.org/abs/1803.09868
Bypassing Feature Squeezing by Increasing Adversary Strength.
Yash Sharma; Pin-Yu Chen

http://arxiv.org/abs/1803.09638
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples.
Pei-Hsuan Lu; Pin-Yu Chen; Chia-Mu Yu

http://arxiv.org/abs/1803.09468
Clipping free attacks against artificial neural networks.
Boussad Addad; Jerome Kodjabachian; Christophe Meyer

http://arxiv.org/abs/1803.09163
Security Theater: On the Vulnerability of Classifiers to Exploratory Attacks.
Tegjyot Singh Sethi; Mehmed Kantardzic; Joung Woo Ryu

http://arxiv.org/abs/1803.09162
A Dynamic-Adversarial Mining Approach to the Security of Machine Learning.
Tegjyot Singh Sethi; Mehmed Kantardzic; Lingyu Lyua; Jiashun Chen

http://arxiv.org/abs/1803.09156
An Overview of Vulnerabilities of Voice Controlled Systems.
Yuan Gong; Christian Poellabauer

http://arxiv.org/abs/1804.00504
Generalizability vs. Robustness: Adversarial Examples for Medical Imaging.
Magdalini Paschali; Sailesh Conjeti; Fernando Navarro; Nassir Navab

http://arxiv.org/abs/1803.09043
CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography.
Weixuan Tang; Bin Li; Shunquan Tan; Mauro Barni; Jiwu Huang

http://arxiv.org/abs/1803.08773
Detecting Adversarial Perturbations with Saliency.
Chiliang Zhang; Zhimou Yang; Zuochang Ye

http://arxiv.org/abs/1803.08680
Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization.
Daniel Jakubovitz; Raja Giryes

http://arxiv.org/abs/1803.08533
Understanding Measures of Uncertainty for Adversarial Example Detection.
Lewis Smith; Yarin Gal

http://arxiv.org/abs/1803.07994
Adversarial Defense based on Structure-to-Signal Autoencoders.
Joachim Folz; Sebastian Palacio; Joern Hees; Damian Borth; Andreas Dengel

http://arxiv.org/abs/1803.08134
Task dependent Deep LDA pruning of neural networks.
Qing Tian; Tal Arbel; James J. Clark

http://arxiv.org/abs/1803.07519
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems.
Lei Ma; Felix Juefei-Xu; Fuyuan Zhang; Jiyuan Sun; Minhui Xue; Bo Li; Chunyang Chen; Ting Su; Li Li; Yang Liu; Jianjun Zhao; Yadong Wang

http://arxiv.org/abs/1803.06975
Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks.
Octavian Suciu; Radu Mărginean; Yiğitcan Kaya; Hal III Daumé; Tudor Dumitraş

http://arxiv.org/abs/1803.06978
Improving Transferability of Adversarial Examples with Input Diversity.
Cihang Xie; Zhishuai Zhang; Yuyin Zhou; Song Bai; Jianyu Wang; Zhou Ren; Alan Yuille

http://arxiv.org/abs/1803.06567
A Dual Approach to Scalable Verification of Deep Networks.
Dj Krishnamurthy; Dvijotham; Robert Stanforth; Sven Gowal; Timothy Mann; Pushmeet Kohli

http://arxiv.org/abs/1803.06373
Adversarial Logit Pairing.
Harini Kannan; Alexey Kurakin; Ian Goodfellow

http://arxiv.org/abs/1804.00499
Semantic Adversarial Examples.
Hossein Hosseini; Radha Poovendran

http://arxiv.org/abs/1803.05598
Large Margin Deep Networks for Classification.
Gamaleldin F. Elsayed; Dilip Krishnan; Hossein Mobahi; Kevin Regan; Samy Bengio

http://arxiv.org/abs/1803.05787
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples.
Zihao Liu; Qi Liu; Tao Liu; Nuo Xu; Xue Lin; Yanzhi Wang; Wujie Wen

http://arxiv.org/abs/1803.04765
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning.
Nicolas Papernot; Patrick McDaniel

http://arxiv.org/abs/1803.04683
Invisible Mask: Practical Attacks on Face Recognition with Infrared.
Zhe Zhou; Di Tang; Xiaofeng Wang; Weili Han; Xiangyu Liu; Kehuan Zhang

http://arxiv.org/abs/1803.05123
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training.
Derek Wang; Chaoran Li; Sheng Wen; Surya Nepal; Yang Xiang

http://arxiv.org/abs/1803.04173
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables.
Bojan Kolosnjaji; Ambra Demontis; Battista Biggio; Davide Maiorca; Giorgio Giacinto; Claudia Eckert; Fabio Roli

http://arxiv.org/abs/1803.03880
Combating Adversarial Attacks Using Sparse Representations.
Soorya Gopalakrishnan; Zhinus Marzi; Upamanyu Madhow; Ramtin Pedarsani

http://arxiv.org/abs/1803.03870
Detecting Adversarial Examples via Neural Fingerprinting.
Sumanth Dathathri; Stephan Zheng; Tianwei Yin; Richard M. Murray; Yisong Yue

http://arxiv.org/abs/1803.03613
Detecting Adversarial Examples - A Lesson from Multimedia Forensics.
Pascal Schöttle; Alexander Schlögl; Cecilia Pasquini; Rainer Böhme

http://arxiv.org/abs/1803.03607
On Generation of Adversarial Examples using Convex Programming.
Emilio Rafael Balda; Arash Behboodi; Rudolf Mathar

http://arxiv.org/abs/1803.03544
Explaining Black-box Android Malware Detection.
Marco Melis; Davide Maiorca; Battista Biggio; Giorgio Giacinto; Fabio Roli

http://arxiv.org/abs/1803.02988
Rethinking Feature Distribution for Loss Functions in Image Classification.
Weitao Wan; Yuanyi Zhong; Tianpeng Li; Jiansheng Chen

http://arxiv.org/abs/1803.02536
Sparse Adversarial Perturbations for Videos.
Xingxing Wei; Jun Zhu; Hang Su

http://arxiv.org/abs/1803.01442
Stochastic Activation Pruning for Robust Adversarial Defense.
Guneet S. Dhillon; Kamyar Azizzadenesheli; Zachary C. Lipton; Jeremy Bernstein; Jean Kossaifi; Aran Khanna; Anima Anandkumar

http://arxiv.org/abs/1803.01128
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples.
Minhao Cheng; Jinfeng Yi; Pin-Yu Chen; Huan Zhang; Cho-Jui Hsieh

http://arxiv.org/abs/1803.00940
Protecting JPEG Images Against Adversarial Attacks.
Aaditya Prakash; Nick Moran; Solomon Garber; Antonella DiLillo; James Storer

http://arxiv.org/abs/1802.09707
Understanding and Enhancing the Transferability of Adversarial Examples.
Lei Wu; Zhanxing Zhu; Cheng Tai; Weinan E

http://arxiv.org/abs/1802.09653
On the Suitability of $L_p$-norms for Creating and Preventing Adversarial Examples.
Mahmood Sharif; Lujo Bauer; Michael K. Reiter

http://arxiv.org/abs/1802.09502
Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples.
Jake Zhao; Kyunghyun Cho

http://arxiv.org/abs/1802.09308
Max-Mahalanobis Linear Discriminant Analysis Networks.
Tianyu Pang; Chao Du; Jun Zhu

http://arxiv.org/abs/1803.00404
Deep Defense: Training DNNs with Improved Adversarial Robustness.
Ziang Yan; Yiwen Guo; Changshui Zhang

http://arxiv.org/abs/1802.08760
Sensitivity and Generalization in Neural Networks: an Empirical Study.
Roman Novak; Yasaman Bahri; Daniel A. Abolafia; Jeffrey Pennington; Jascha Sohl-Dickstein

http://arxiv.org/abs/1802.08686
Adversarial vulnerability for any classifier.
Alhussein Fawzi; Hamza Fawzi; Omar Fawzi

http://arxiv.org/abs/1802.08678
Verifying Controllers Against Adversarial Examples with Bayesian Optimization.
Shromona Ghosh; Felix Berkenkamp; Gireeja Ranade; Shaz Qadeer; Ashish Kapoor

http://arxiv.org/abs/1803.00401
Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks.
Gaurav Goswami; Nalini Ratha; Akshay Agarwal; Richa Singh; Mayank Vatsa

http://arxiv.org/abs/1802.08241
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries.
Zhewei Yao; Amir Gholami; Qi Lei; Kurt Keutzer; Michael W. Mahoney

http://arxiv.org/abs/1802.08195
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans.
Gamaleldin F. Elsayed; Shreya Shankar; Brian Cheung; Nicolas Papernot; Alex Kurakin; Ian Goodfellow; Jascha Sohl-Dickstein

http://arxiv.org/abs/1802.08567
Adversarial Training for Probabilistic Spiking Neural Networks.
Alireza Bagheri; Osvaldo Simeone; Bipin Rajendran

http://arxiv.org/abs/1802.07896
L2-Nonexpansive Neural Networks.
Haifeng Qian; Mark N. Wegman

http://arxiv.org/abs/1802.07770
Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch.
João Monteiro; Isabela Albuquerque; Zahid Akhtar; Tiago H. Falk

http://arxiv.org/abs/1802.07295
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning.
Christopher Frederickson; Michael Moore; Glenn Dawson; Robi Polikar

http://arxiv.org/abs/1802.07124
Out-distribution training confers robustness to deep neural networks.
Mahdieh Abbasi; Christian Gagné

http://arxiv.org/abs/1802.06927
On Lyapunov exponents and adversarial perturbation.
Vinay Uday Prabhu; Nishant Desai; John Whaley

http://arxiv.org/abs/1802.06816
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression.
Nilaksh Das; Madhuri Shanbhogue; Shang-Tse Chen; Fred Hohman; Siwei Li; Li Chen; Michael E. Kounavis; Duen Horng Chau

http://arxiv.org/abs/1802.06806
Divide, Denoise, and Defend against Adversarial Attacks.
Seyed-Mohsen Moosavi-Dezfooli; Ashish Shrivastava; Oncel Tuzel

http://arxiv.org/abs/1802.06627
Robustness of Rotation-Equivariant Networks to Adversarial Perturbations.
Beranger Dumont; Simona Maggio; Pablo Montalvo

http://arxiv.org/abs/1802.06552
Are Generative Classifiers More Robust to Adversarial Attacks?
Yingzhen Li; John Bradshaw; Yash Sharma

http://arxiv.org/abs/1802.06430
DARTS: Deceiving Autonomous Cars with Toxic Signs.
Chawin Sitawarin; Arjun Nitin Bhagoji; Arsalan Mosenia; Mung Chiang; Prateek Mittal

http://arxiv.org/abs/1802.05763
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction.
Fuxun Yu; Qide Dong; Xiang Chen

http://arxiv.org/abs/1802.05666
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks.
Jonathan Uesato; Brendan O'Donoghue; Aaron van den Oord; Pushmeet Kohli

http://arxiv.org/abs/1802.05385
Fooling OCR Systems with Adversarial Text Images.
Congzheng Song; Vitaly Shmatikov

http://arxiv.org/abs/1802.05193
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks.
Qi Liu; Tao Liu; Zihao Liu; Yanzhi Wang; Yier Jin; Wujie Wen

http://arxiv.org/abs/1802.09900
Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints.
Di Tang; XiaoFeng Wang; Kehuan Zhang

http://arxiv.org/abs/1802.04822
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models.
Mengying Sun; Fengyi Tang; Jinfeng Yi; Fei Wang; Jiayu Zhou

http://arxiv.org/abs/1802.04528
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples.
Felix Kreuk; Assi Barak; Shir Aviv-Reuven; Moran Baruch; Benny Pinkas; Joseph Keshet

http://arxiv.org/abs/1802.04034
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks.
Yusuke Tsuzuku; Issei Sato; Masashi Sugiyama

http://arxiv.org/abs/1802.04457
Predicting Adversarial Examples with High Confidence.
Angus Galloway; Graham W. Taylor; Medhat Moussa

http://arxiv.org/abs/1802.03471
Certified Robustness to Adversarial Examples with Differential Privacy.
Mathias Lecuyer; Vaggelis Atlidakis; Roxana Geambasu; Daniel Hsu; Suman Jana

http://arxiv.org/abs/1802.03041
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection.
Andrea Paudice; Luis Muñoz-González; Andras Gyorgy; Emil C. Lupu

http://arxiv.org/abs/1802.01549
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples.
Adnan Siraj Rakin; Zhezhi He; Boqing Gong; Deliang Fan

http://arxiv.org/abs/1802.01421
First-order Adversarial Vulnerability of Neural Networks and Input Dimension.
Carl-Johann Simon-Gabriel; Yann Ollivier; Léon Bottou; Bernhard Schölkopf; David Lopez-Paz

http://arxiv.org/abs/1802.00573
Secure Detection of Image Manipulation by means of Random Feature Selection.
Zhipeng Chen; Benedetta Tondi; Xiaolong Li; Rongrong Ni; Yao Zhao; Mauro Barni

http://arxiv.org/abs/1802.01448
Hardening Deep Neural Networks via Adversarial Model Cascades.
Deepak Vijaykeerthy; Anshuman Suri; Sameep Mehta; Ponnurangam Kumaraguru

http://arxiv.org/abs/1802.00420
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples.
Anish Athalye; Nicholas Carlini; David Wagner

http://arxiv.org/abs/1801.10578
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach.
Tsui-Wei Weng; Huan Zhang; Pin-Yu Chen; Jinfeng Yi; Dong Su; Yupeng Gao; Cho-Jui Hsieh; Luca Daniel

http://arxiv.org/abs/1801.09827
Robustness of classification ability of spiking neural networks.
Jie Yang; Pingping Zhang; Yan Liu

http://arxiv.org/abs/1801.09344
Certified Defenses against Adversarial Examples.
Aditi Raghunathan; Jacob Steinhardt; Percy Liang

http://arxiv.org/abs/1801.09097
Towards an Understanding of Neural Networks in Natural-Image Spaces.
Yifei Fan; Anthony Yezzi

http://arxiv.org/abs/1801.08926
Deflecting Adversarial Attacks with Pixel Deflection.
Aaditya Prakash; Nick Moran; Solomon Garber; Antonella DiLillo; James Storer

http://arxiv.org/abs/1801.08917
Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning.
Hyrum S. Anderson; Anant Kharkar; Bobby Filar; David Evans; Phil Roth

http://arxiv.org/abs/1801.08535
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition.
Xuejing Yuan; Yuxuan Chen; Yue Zhao; Yunhui Long; Xiaokang Liu; Kai Chen; Shengzhi Zhang; Heqing Huang; Xiaofeng Wang; Carl A. Gunter

http://arxiv.org/abs/1801.08092
Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations.
Konda Reddy Mopuri; Aditya Ganeshan; R. Venkatesh Babu

http://arxiv.org/abs/1801.07175
Adversarial Texts with Gradient Methods.
Zhitao Gong; Wenlu Wang; Bo Li; Dawn Song; Wei-Shinn Ku

http://arxiv.org/abs/1801.05420
A Comparative Study of Rule Extraction for Recurrent Neural Networks.
Qinglong Wang; Kaixuan Zhang; Alexander G. II Ororbia; Xinyu Xing; Xue Liu; C. Lee Giles

http://arxiv.org/abs/1801.04695
Sparsity-based Defense against Adversarial Attacks on Linear Classifiers.
Zhinus Marzi; Soorya Gopalakrishnan; Upamanyu Madhow; Ramtin Pedarsani

http://arxiv.org/abs/1801.04693
Towards Imperceptible and Robust Adversarial Example Attacks against Neural Networks.
Bo Luo; Yannan Liu; Lingxiao Wei; Qiang Xu

http://arxiv.org/abs/1801.04354
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers.
Ji Gao; Jack Lanchantin; Mary Lou Soffa; Yanjun Qi

http://arxiv.org/abs/1801.04055
A3T: Adversarially Augmented Adversarial Training.
Akram Erraqabi; Aristide Baratin; Yoshua Bengio; Simon Lacoste-Julien

http://arxiv.org/abs/1801.03339
Fooling End-to-end Speaker Verification by Adversarial Examples.
Felix Kreuk; Yossi Adi; Moustapha Cisse; Joseph Keshet

http://arxiv.org/abs/1801.02950
Adversarial Deep Learning for Robust Detection of Binary Encoded Malware.
Abdullah Al-Dujaili; Alex Huang; Erik Hemberg; Una-May O'Reilly

http://arxiv.org/abs/1801.02850
Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks.
Yongshuai Liu; Jiyu Chen; Hao Chen

http://arxiv.org/abs/1801.02780
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos.
Chawin Sitawarin; Arjun Nitin Bhagoji; Arsalan Mosenia; Prateek Mittal; Mung Chiang

http://arxiv.org/abs/1801.02774
Adversarial Spheres.
Justin Gilmer; Luke Metz; Fartash Faghri; Samuel S. Schoenholz; Maithra Raghu; Martin Wattenberg; Ian Goodfellow

http://arxiv.org/abs/1801.02613
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality.
Xingjun Ma; Bo Li; Yisen Wang; Sarah M. Erfani; Sudanthi Wijewickrema; Grant Schoenebeck; Dawn Song; Michael E. Houle; James Bailey

http://arxiv.org/abs/1801.02612
Spatially Transformed Adversarial Examples.
Chaowei Xiao; Jun-Yan Zhu; Bo Li; Warren He; Mingyan Liu; Dawn Song

http://arxiv.org/abs/1801.02610
Generating Adversarial Examples with Adversarial Networks.
Chaowei Xiao; Bo Li; Jun-Yan Zhu; Warren He; Mingyan Liu; Dawn Song

http://arxiv.org/abs/1801.02608
LaVAN: Localized and Visible Adversarial Noise.
Danny Karmon; Daniel Zoran; Yoav Goldberg

http://arxiv.org/abs/1801.02384
Attacking Speaker Recognition With Deep Generative Models.
Wilson Cai; Anish Doshi; Rafael Valle

http://arxiv.org/abs/1801.02318
HeNet: A Deep Learning Approach on Intel$^\circledR$ Processor Trace for Effective Exploit Detection.
Li Chen; Salmin Sultana; Ravi Sahita

http://arxiv.org/abs/1801.02257
Denoising Dictionary Learning Against Adversarial Perturbations.
John Mitro; Derek Bridge; Steven Prestwich

http://arxiv.org/abs/1801.01953
Adversarial Perturbation Intensity Achieving Chosen Intra-Technique Transferability Level for Logistic Regression.
Martin Gubri

http://arxiv.org/abs/1801.01944
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text.
Nicholas Carlini; David Wagner

http://arxiv.org/abs/1801.01828
Shielding Google's language toxicity model against adversarial attacks.
Nestor Rodriguez; Sergio Rojas-Galeano

http://arxiv.org/abs/1801.02480
Facial Attributes: Accuracy and Adversarial Robustness.
Andras Rozsa; Manuel Günther; Ethan M. Rudd; Terrance E. Boult

http://arxiv.org/abs/1801.00905
Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space.
Mayank Singh; Abhishek Sinha; Balaji Krishnamurthy

http://arxiv.org/abs/1801.00634
High Dimensional Spaces, Deep Learning and Adversarial Examples.
Simant Dube

http://arxiv.org/abs/1801.00554
Did you hear that? Adversarial Examples Against Automatic Speech Recognition.
Moustafa Alzantot; Bharathan Balaji; Mani Srivastava

http://arxiv.org/abs/1801.00553
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey.
Naveed Akhtar; Ajmal Mian

http://arxiv.org/abs/1801.00349
A General Framework for Adversarial Examples with Objectives.
Mahmood Sharif; Sruti Bhagavatula; Lujo Bauer; Michael K. Reiter

http://arxiv.org/abs/1712.09936
Gradient Regularization Improves Accuracy of Discriminative Models.
Dániel Varga; Adrián Csiszárik; Zsolt Zombori

http://arxiv.org/abs/1712.09665
Adversarial Patch.
Tom B. Brown; Dandelion Mané; Aurko Roy; Martín Abadi; Justin Gilmer

http://arxiv.org/abs/1712.09491
Exploring the Space of Black-box Attacks on Deep Neural Networks.
Arjun Nitin Bhagoji; Warren He; Bo Li; Dawn Song

http://arxiv.org/abs/1712.09327
Building Robust Deep Neural Networks for Road Sign Detection.
Arkar Min Aung; Yousef Fadila; Radian Gondokaryono; Luis Gonzalez

http://arxiv.org/abs/1712.09196
The Robust Manifold Defense: Adversarial Training using Generative Models.
Ajil Jalal; Andrew Ilyas; Constantinos Daskalakis; Alexandros G. Dimakis

http://arxiv.org/abs/1712.08996
Android Malware Detection using Deep Learning on API Method Sequences.
ElMouatez Billah Karbab; Mourad Debbabi; Abdelouahid Derhab; Djedjiga Mouheb

http://arxiv.org/abs/1712.09344
Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger.
Vahid Behzadan; Arslan Munir

http://arxiv.org/abs/1712.08713
Query-limited Black-box Attacks to Classifiers.
Fnu Suya; Yuan Tian; David Evans; Paolo Papotti

http://arxiv.org/abs/1712.08263
Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks.
Siqi Yang; Arnold Wiliem; Shaokang Chen; Brian C. Lovell

http://arxiv.org/abs/1712.08250
ReabsNet: Detecting and Revising Adversarial Examples.
Jiefeng Chen; Zihang Meng; Changtian Sun; Wei Tang; Yinglun Zhu

http://arxiv.org/abs/1712.08062
Note on Attacking Object Detectors with Adversarial Stickers.
Kevin Eykholt; Ivan Evtimov; Earlence Fernandes; Bo Li; Dawn Song; Tadayoshi Kohno; Amir Rahmati; Atul Prakash; Florian Tramer

http://arxiv.org/abs/1712.07805
Wolf in Sheep's Clothing - The Downscaling Attack Against Deep Learning Applications.
Qixue Xiao; Kang Li; Deyue Zhang; Yier Jin

http://arxiv.org/abs/1712.07113
Query-Efficient Black-box Adversarial Examples (superceded).
Andrew Ilyas; Logan Engstrom; Anish Athalye; Jessy Lin

http://arxiv.org/abs/1712.07107
Adversarial Examples: Attacks and Defenses for Deep Learning.
Xiaoyong Yuan; Pan He; Qile Zhu; Xiaolin Li

http://arxiv.org/abs/1712.06751
HotFlip: White-Box Adversarial Examples for Text Classification.
Javid Ebrahimi; Anyi Rao; Daniel Lowd; Dejing Dou

http://arxiv.org/abs/1712.06646
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time.
David J. Miller; Yulia Wang; George Kesidis

http://arxiv.org/abs/1712.06174
Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study.
Matteo Fischetti; Jason Jo

http://arxiv.org/abs/1712.06131
Super-sparse Learning in Similarity Spaces.
Ambra Demontis; Marco Melis; Battista Biggio; Giorgio Fumera; Fabio Roli

http://arxiv.org/abs/1712.05919
Attack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Classification Models.
Jack W. Stokes; De Wang; Mady Marinescu; Marc Marino; Brian Bussone

http://arxiv.org/abs/1712.05419
DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation.
Catherine Wong

http://arxiv.org/abs/1712.04248
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models.
Wieland Brendel; Jonas Rauber; Matthias Bethge

http://arxiv.org/abs/1712.04006
Training Ensembles to Detect Adversarial Examples.
Alexander Bagnall; Razvan Bunescu; Gordon Stewart

http://arxiv.org/abs/1712.03632
Robust Deep Reinforcement Learning with Adversarial Attacks.
Anay Pattanaik; Zhenyi Tang; Shuijing Liu; Gautham Bommannan; Girish Chowdhary

http://arxiv.org/abs/1712.03390
NAG: Network for Adversary Generation.
Konda Reddy Mopuri; Utkarsh Ojha; Utsav Garg; R. Venkatesh Babu

http://arxiv.org/abs/1712.03141
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning.
Battista Biggio; Fabio Roli

http://arxiv.org/abs/1712.02976
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser.
Fangzhou Liao; Ming Liang; Yinpeng Dong; Tianyu Pang; Xiaolin Hu; Jun Zhu

http://arxiv.org/abs/1712.02494
Adversarial Examples that Fool Detectors.
Jiajun Lu; Hussein Sibai; Evan Fabry

http://arxiv.org/abs/1712.02779
Exploring the Landscape of Spatial Robustness.
Logan Engstrom; Brandon Tran; Dimitris Tsipras; Ludwig Schmidt; Aleksander Madry

http://arxiv.org/abs/1712.02328
Generative Adversarial Perturbations.
Omid Poursaeed; Isay Katsman; Bicheng Gao; Serge Belongie

http://arxiv.org/abs/1712.02051
Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning.
Hongge Chen; Huan Zhang; Pin-Yu Chen; Jinfeng Yi; Cho-Jui Hsieh

http://arxiv.org/abs/1712.01785
Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems.
Kexin Pei; Linjie Zhu; Yinzhi Cao; Junfeng Yang; Carl Vondrick; Suman Jana

http://arxiv.org/abs/1712.00699
Improving Network Robustness against Adversarial Attacks with Compact Convolution.
Rajeev Ranjan; Swami Sankaranarayanan; Carlos D. Castillo; Rama Chellappa

http://arxiv.org/abs/1712.00673
Towards Robust Neural Networks via Random Self-ensemble.
Xuanqing Liu; Minhao Cheng; Huan Zhang; Cho-Jui Hsieh

http://arxiv.org/abs/1712.00558
Where Classification Fails, Interpretation Rises.
Chanh Nguyen; Georgi Georgiev; Yujie Ji; Ting Wang

http://arxiv.org/abs/1711.11561
Measuring the tendency of CNNs to Learn Surface Statistical Regularities.
Jason Jo; Yoshua Bengio

http://arxiv.org/abs/1711.10056
Adversary Detection in Neural Networks via Persistent Homology.
Thomas Gebhart; Paul Schrater

http://arxiv.org/abs/1711.09856
On the Robustness of Semantic Segmentation Models to Adversarial Attacks.
Anurag Arnab; Ondrej Miksik; Philip H. S. Torr

http://arxiv.org/abs/1711.09681
Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation.
YoungJoon Yoo; Seonguk Park; Junyoung Choi; Sangdoo Yun; Nojun Kwak

http://arxiv.org/abs/1711.09404
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients.
Andrew Slavin Ross; Finale Doshi-Velez

http://arxiv.org/abs/1711.09115
Geometric robustness of deep networks: analysis and improvement.
Can Kanbak; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard

http://arxiv.org/abs/1711.08534
Safer Classification by Synthesis.
William Wang; Angelina Wang; Aviv Tamar; Xi Chen; Pieter Abbeel

http://arxiv.org/abs/1711.08478
MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples.
Nicholas Carlini; David Wagner

http://arxiv.org/abs/1711.08244
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning.
Ambrish Rawat; Martin Wistuba; Maria-Irina Nicolae

http://arxiv.org/abs/1711.08001
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training.
Xi Wu; Uyeong Jang; Jiefeng Chen; Lingjiao Chen; Somesh Jha

http://arxiv.org/abs/1711.07356
Evaluating Robustness of Neural Networks with Mixed Integer Programming.
Vincent Tjeng; Kai Xiao; Russ Tedrake

http://arxiv.org/abs/1711.07183
Adversarial Attacks Beyond the Image Space.
Xiaohui Zeng; Chenxi Liu; Yu-Siang Wang; Weichao Qiu; Lingxi Xie; Yu-Wing Tai; Chi Keung Tang; Alan L. Yuille

http://arxiv.org/abs/1711.06598
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models.
Kathrin Grosse; David Pfaff; Michael Thomas Smith; Michael Backes

http://arxiv.org/abs/1711.05934
Enhanced Attacks on Defensively Distilled Deep Neural Networks.
Yujia Liu; Weiming Zhang; Shaohua Li; Nenghai Yu

http://arxiv.org/abs/1711.05929
Defense against Universal Adversarial Perturbations.
Naveed Akhtar; Jian Liu; Ajmal Mian

http://arxiv.org/abs/1711.05475
The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels.
Yannic Kilcher; Thomas Hofmann

http://arxiv.org/abs/1711.04368
Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples.
Jihun Hamm; Akshay Mehra

http://arxiv.org/abs/1711.03280
Crafting Adversarial Examples For Speech Paralinguistics Applications.
Yuan Gong; Christian Poellabauer

http://arxiv.org/abs/1711.02846
Intriguing Properties of Adversarial Examples.
Ekin D. Cubuk; Barret Zoph; Samuel S. Schoenholz; Quoc V. Le

http://arxiv.org/abs/1711.01991
Mitigating Adversarial Effects Through Randomization.
Cihang Xie; Jianyu Wang; Zhishuai Zhang; Zhou Ren; Alan Yuille

http://arxiv.org/abs/1711.01791
HyperNetworks with statistical filtering for defending adversarial examples.
Zhun Sun; Mete Ozay; Takayuki Okatani

http://arxiv.org/abs/1711.01768
Towards Reverse-Engineering Black-Box Neural Networks.
Seong Joon Oh; Max Augustin; Bernt Schiele; Mario Fritz

http://arxiv.org/abs/1711.00867
The (Un)reliability of saliency methods.
Pieter-Jan Kindermans; Sara Hooker; Julius Adebayo; Maximilian Alber; Kristof T. Schütt; Sven Dähne; Dumitru Erhan; Been Kim

http://arxiv.org/abs/1711.00851
Provable defenses against adversarial examples via the convex outer adversarial polytope.
Eric Wong; J. Zico Kolter

http://arxiv.org/abs/1711.00449
Attacking Binarized Neural Networks.
Angus Galloway; Graham W. Taylor; Medhat Moussa

http://arxiv.org/abs/1711.00117
Countering Adversarial Images using Input Transformations.
Chuan Guo; Mayank Rana; Moustapha Cisse; der Maaten Laurens van

http://arxiv.org/abs/1710.11469
Conditional Variance Penalties and Domain Shift Robustness.
Christina Heinze-Deml; Nicolai Meinshausen

http://arxiv.org/abs/1710.11342
Generating Natural Adversarial Examples.
Zhengli Zhao; Dheeru Dua; Sameer Singh

http://arxiv.org/abs/1710.10766
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples.
Yang Song; Taesup Kim; Sebastian Nowozin; Stefano Ermon; Nate Kushman

http://arxiv.org/abs/1710.10733
Attacking the Madry Defense Model with $L_1$-based Adversarial Examples.
Yash Sharma; Pin-Yu Chen

http://arxiv.org/abs/1710.10571
Certifying Some Distributional Robustness with Principled Adversarial Training.
Aman Sinha; Hongseok Namkoong; Riccardo Volpi; John Duchi

http://arxiv.org/abs/1710.10547
Interpretation of Neural Networks is Fragile.
Amirata Ghorbani; Abubakar Abid; James Zou

http://arxiv.org/abs/1710.10225
Adversarial Detection of Flash Malware: Limitations and Open Issues.
Davide Maiorca; Ambra Demontis; Battista Biggio; Fabio Roli; Giorgio Giacinto

http://arxiv.org/abs/1710.09412
mixup: Beyond Empirical Risk Minimization.
Hongyi Zhang; Moustapha Cisse; Yann N. Dauphin; David Lopez-Paz

http://arxiv.org/abs/1710.08864
One pixel attack for fooling deep neural networks.
Jiawei Su; Danilo Vasconcellos Vargas; Sakurai Kouichi

http://arxiv.org/abs/1710.07859
Feature-Guided Black-Box Safety Testing of Deep Neural Networks.
Matthew Wicker; Xiaowei Huang; Marta Kwiatkowska

http://arxiv.org/abs/1710.06081
Boosting Adversarial Attacks with Momentum.
Yinpeng Dong; Fangzhou Liao; Tianyu Pang; Hang Su; Jun Zhu; Xiaolin Hu; Jianguo Li

http://arxiv.org/abs/1710.04677
Game-Theoretic Design of Secure and Resilient Distributed Support Vector Machines with Adversaries.
Rui Zhang; Quanyan Zhu

http://arxiv.org/abs/1710.03337
Standard detectors aren't (currently) fooled by physical adversarial stop signs.
Jiajun Lu; Hussein Sibai; Evan Fabry; David Forsyth

http://arxiv.org/abs/1710.03107
Verification of Binarized Neural Networks via Inter-Neuron Factoring.
Chih-Hong Cheng; Georg Nührenberg; Chung-Hao Huang; Harald Ruess

http://arxiv.org/abs/1710.00814
Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight.
Yen-Chen Lin; Ming-Yu Liu; Min Sun; Jia-Bin Huang

http://arxiv.org/abs/1710.00486
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks.
Divya Gopinath; Guy Katz; Corina S. Pasareanu; Clark Barrett

http://arxiv.org/abs/1709.10207
Provably Minimally-Distorted Adversarial Examples.
Nicholas Carlini; Guy Katz; Clark Barrett; David L. Dill

http://arxiv.org/abs/1709.09917
DR.SGX: Hardening SGX Enclaves against Cache Attacks with Data Location Randomization.
Ferdinand Technische Universität Darmstadt, Germany Brasser; Srdjan ETH Zurich, Switzerland Capkun; Alexandra University of Würzburg Dmitrienko; Tommaso Technische Universität Darmstadt, Germany Frassetto; Kari ETH Zurich, Switzerland Kostiainen; Ahmad-Reza Technische Universität Darmstadt, Germany Sadeghi

http://arxiv.org/abs/1709.09130
Output Range Analysis for Deep Neural Networks.
Souradeep Dutta; Susmit Jha; Sriram Sanakaranarayanan; Ashish Tiwari

http://arxiv.org/abs/1709.08693
Fooling Vision and Language Models Despite Localization and Attention Mechanism.
Xiaojun Xu; Xinyun Chen; Chang Liu; Anna Rohrbach; Trevor Darrell; Dawn Song

http://arxiv.org/abs/1709.06662
Verifying Properties of Binarized Deep Neural Networks.
Nina Narodytska; Shiva Prasad Kasiviswanathan; Leonid Ryzhyk; Mooly Sagiv; Toby Walsh

http://arxiv.org/abs/1709.05583
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification.
Xiaoyu Cao; Neil Zhenqiang Gong

http://arxiv.org/abs/1709.04447
A Learning and Masking Approach to Secure Learning.
Linh Nguyen; Sky Wang; Arunesh Sinha

http://arxiv.org/abs/1709.04137
Models and Framework for Adversarial Attacks on Complex Adaptive Systems.
Vahid Behzadan; Arslan Munir

http://arxiv.org/abs/1709.04114
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples.
Pin-Yu Chen; Yash Sharma; Huan Zhang; Jinfeng Yi; Cho-Jui Hsieh

http://arxiv.org/abs/1709.03582
Art of singular vectors and universal adversarial perturbations.
Valentin Khrulkov; Ivan Oseledets

http://arxiv.org/abs/1709.03423
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks.
Thilo Strauss; Markus Hanselmann; Andrej Junginger; Holger Ulmer

http://arxiv.org/abs/1709.02802
Towards Proving the Adversarial Robustness of Deep Neural Networks.
Guy Stanford University Katz; Clark Stanford University Barrett; David L. Stanford University Dill; Kyle Stanford University Julian; Mykel J. Stanford University Kochenderfer

http://arxiv.org/abs/1709.02538
DeepFense: Online Accelerated Defense Against Adversarial Deep Learning.
Bita Darvish Rouhani; Mohammad Samragh; Mojan Javaheripi; Tara Javidi; Farinaz Koushanfar

http://arxiv.org/abs/1709.00609
Security Evaluation of Pattern Classifiers under Attack.
Battista Biggio; Giorgio Fumera; Fabio Roli

http://arxiv.org/abs/1709.00045
On Security and Sparsity of Linear Classifiers for Adversarial Settings.
Ambra Demontis; Paolo Russu; Battista Biggio; Giorgio Fumera; Fabio Roli

http://arxiv.org/abs/1708.09790
Be Selfish and Avoid Dilemmas: Fork After Withholding (FAW) Attacks on Bitcoin.
Yujin Kwon; Dohyun Kim; Yunmok Son; Eugene Vasserman; Yongdae Kim

http://arxiv.org/abs/1708.09056
Practical Attacks Against Graph-based Clustering.
Yizheng Chen; Yacin Nadji; Athanasios Kountouras; Fabian Monrose; Roberto Perdisci; Manos Antonakakis; Nikolaos Vasiloglou

http://arxiv.org/abs/1708.08559
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars.
Yuchi Tian; Kexin Pei; Suman Jana; Baishakhi Ray

http://arxiv.org/abs/1708.08327
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features.
Liang Tong; Bo Li; Chen Hajaj; Chaowei Xiao; Ning Zhang; Yevgeniy Vorobeychik

http://arxiv.org/abs/1708.06939
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid.
Marco Melis; Ambra Demontis; Battista Biggio; Gavin Brown; Giorgio Fumera; Fabio Roli

http://arxiv.org/abs/1708.06670
CNN Fixations: An unraveling approach to visualize the discriminative image regions.
Konda Reddy Mopuri; Utsav Garg; R. Venkatesh Babu

http://arxiv.org/abs/1708.06131
Evasion Attacks against Machine Learning at Test Time.
Battista Biggio; Igino Corona; Davide Maiorca; Blaine Nelson; Nedim Srndic; Pavel Laskov; Giorgio Giacinto; Fabio Roli

http://arxiv.org/abs/1708.05493
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples.
Yinpeng Dong; Hang Su; Jun Zhu; Fan Bao

http://arxiv.org/abs/1708.05207
Learning Universal Adversarial Perturbations with Generative Models.
Jamie Hayes; George Danezis

http://arxiv.org/abs/1708.04301
Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API.
Hossein Hosseini; Baicen Xiao; Andrew Clark; Radha Poovendran

http://arxiv.org/abs/1708.03999
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models.
Pin-Yu Chen; Huan Zhang; Yash Sharma; Jinfeng Yi; Cho-Jui Hsieh

http://arxiv.org/abs/1708.02582
Cascade Adversarial Machine Learning Regularized with a Unified Embedding.
Taesik Na; Jong Hwan Ko; Saibal Mukhopadhyay

http://arxiv.org/abs/1708.01697
Adversarial Robustness: Softmax versus Openmax.
Andras Rozsa; Manuel Günther; Terrance E. Boult

http://arxiv.org/abs/1708.00807
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning.
Andrew P. Norton; Yanjun Qi

http://arxiv.org/abs/1707.08945
Robust Physical-World Attacks on Deep Learning Models.
Kevin Eykholt; Ivan Evtimov; Earlence Fernandes; Bo Li; Amir Rahmati; Chaowei Xiao; Atul Prakash; Tadayoshi Kohno; Dawn Song

http://arxiv.org/abs/1707.07397
Synthesizing Robust Adversarial Examples.
Anish Athalye; Logan Engstrom; Andrew Ilyas; Kevin Kwok

http://arxiv.org/abs/1707.07328
Adversarial Examples for Evaluating Reading Comprehension Systems.
Robin Jia; Percy Liang

http://arxiv.org/abs/1707.07013
Confidence estimation in Deep Neural networks via density modelling.
Akshayvarun Subramanya; Suraj Srinivas; R. Venkatesh Babu

http://arxiv.org/abs/1707.06728
Efficient Defenses Against Adversarial Attacks.
Valentina Zantedeschi; Maria-Irina Nicolae; Ambrish Rawat

http://arxiv.org/abs/1707.05970
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers.
Ishai Rosenberg; Asaf Shabtai; Lior Rokach; Yuval Elovici

http://arxiv.org/abs/1707.05572
Fast Feature Fool: A data independent approach to universal adversarial perturbations.
Konda Reddy Mopuri; Utsav Garg; R. Venkatesh Babu

http://arxiv.org/abs/1707.05474
APE-GAN: Adversarial Perturbation Elimination with GAN.
Shiwei Shen; Guoqing Jin; Ke Gao; Yongdong Zhang

http://arxiv.org/abs/1707.05373
Houdini: Fooling Deep Structured Prediction Models.
Moustapha Cisse; Yossi Adi; Natalia Neverova; Joseph Keshet

http://arxiv.org/abs/1707.04131
Foolbox: A Python toolbox to benchmark the robustness of machine learning models.
Jonas Rauber; Wieland Brendel; Matthias Bethge

http://arxiv.org/abs/1707.03501
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles.
Jiajun Lu; Hussein Sibai; Evan Fabry; David Forsyth

http://arxiv.org/abs/1707.03184
A Survey on Resilient Machine Learning.
Atul Kumar; Sameep Mehta

http://arxiv.org/abs/1707.02812
Towards Crafting Text Adversarial Samples.
Suranjana Samanta; Sameep Mehta

http://arxiv.org/abs/1707.01159
UPSET and ANGRI : Breaking High Performance Image Classifiers.
Sayantan Sarkar; Ankan Bansal; Upal Mahbub; Rama Chellappa

http://arxiv.org/abs/1706.06969
Comparing deep neural networks against humans: object recognition when the signal gets weaker.
Robert Geirhos; David H. J. Janssen; Heiko H. Schütt; Jonas Rauber; Matthias Bethge; Felix A. Wichmann

http://arxiv.org/abs/1706.06083
Towards Deep Learning Models Resistant to Adversarial Attacks.
Aleksander Madry; Aleksandar Makelov; Ludwig Schmidt; Dimitris Tsipras; Adrian Vladu

http://arxiv.org/abs/1706.04701
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong.
Warren He; James Wei; Xinyun Chen; Nicholas Carlini; Dawn Song

http://arxiv.org/abs/1706.03922
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples.
Yizhen Wang; Somesh Jha; Kamalika Chaudhuri

http://arxiv.org/abs/1706.01763
Adversarial-Playground: A Visualization Suite for Adversarial Sample Generation.
Andrew Norton; Yanjun Qi

http://arxiv.org/abs/1706.00633
Towards Robust Detection of Adversarial Examples.
Tianyu Pang; Chao Du; Yinpeng Dong; Jun Zhu

http://arxiv.org/abs/1705.10686
Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples.
Weilin Xu; David Evans; Yanjun Qi

http://arxiv.org/abs/1705.09764
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks.
Chang Song; Hsin-Pai Cheng; Huanrui Yang; Sicheng Li; Chunpeng Wu; Qing Wu; Hai Li; Yiran Chen

http://arxiv.org/abs/1705.09552
Classification regions of deep neural networks.
Alhussein Fawzi; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard; Stefano Soatto

http://arxiv.org/abs/1705.09554
Robustness of classifiers to universal perturbations: a geometric perspective.
Seyed-Mohsen Moosavi-Dezfooli; Alhussein Fawzi; Omar Fawzi; Pascal Frossard; Stefano Soatto

http://arxiv.org/abs/1705.09064
MagNet: a Two-Pronged Defense against Adversarial Examples.
Dongyu Meng; Hao Chen

http://arxiv.org/abs/1705.08475
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation.
Matthias Hein; Maksym Andriushchenko

http://arxiv.org/abs/1705.08378
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction.
Bin Liang; Hongcheng Li; Miaoqiang Su; Xirong Li; Wenchang Shi; Xiaofeng Wang

http://arxiv.org/abs/1705.08131
Black-Box Attacks against RNN based Malware Detection Algorithms.
Weiwei Hu; Ying Tan

http://arxiv.org/abs/1705.07819
Regularizing deep networks using efficient layerwise adversarial training.
Swami Sankaranarayanan; Arpit Jain; Rama Chellappa; Ser Nam Lim

http://arxiv.org/abs/1705.07535
Evading Classifiers by Morphing in the Dark.
Hung Dang; Yue Huang; Ee-Chien Chang

http://arxiv.org/abs/1705.07263
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods.
Nicholas Carlini; David Wagner

http://arxiv.org/abs/1705.07204
Ensemble Adversarial Training: Attacks and Defenses.
Florian Tramèr; Alexey Kurakin; Nicolas Papernot; Ian Goodfellow; Dan Boneh; Patrick McDaniel

http://arxiv.org/abs/1705.07213
MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense.
Sailik Sengupta; Tathagata Chakraborti; Subbarao Kambhampati

http://arxiv.org/abs/1705.06640
DeepXplore: Automated Whitebox Testing of Deep Learning Systems.
Kexin Pei; Yinzhi Cao; Junfeng Yang; Suman Jana

http://arxiv.org/abs/1705.06452
Delving into adversarial attacks on deep policies.
Jernej Kos; Dawn Song

http://arxiv.org/abs/1705.05264
Extending Defensive Distillation.
Nicolas Papernot; Patrick McDaniel

http://arxiv.org/abs/1705.03387
Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN.
Hyeungill Lee; Sungyeob Han; Jungwoo Lee

http://arxiv.org/abs/1705.02900
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression.
Nilaksh Das; Madhuri Shanbhogue; Shang-Tse Chen; Fred Hohman; Li Chen; Michael E. Kounavis; Duen Horng Chau

http://arxiv.org/abs/1705.02224
Detecting Adversarial Samples Using Density Ratio Estimates.
Lovedeep Gondara

http://arxiv.org/abs/1704.08996
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection.
Ambra Demontis; Marco Melis; Battista Biggio; Davide Maiorca; Daniel Arp; Konrad Rieck; Igino Corona; Giorgio Giacinto; Fabio Roli

http://arxiv.org/abs/1704.08847
Parseval Networks: Improving Robustness to Adversarial Examples.
Moustapha Cisse; Piotr Bojanowski; Edouard Grave; Yann Dauphin; Nicolas Usunier

http://arxiv.org/abs/1704.08006
Deep Text Classification Can be Fooled.
Bin Liang; Hongcheng Li; Miaoqiang Su; Pan Bian; Xirong Li; Wenchang Shi

http://arxiv.org/abs/1704.05712
Universal Adversarial Perturbations Against Semantic Image Segmentation.
Jan Hendrik Metzen; Mummadi Chaithanya Kumar; Thomas Brox; Volker Fischer

http://arxiv.org/abs/1704.04960
Adversarial and Clean Data Are Not Twins.
Zhitao Gong; Wenlu Wang; Wei-Shinn Ku

http://arxiv.org/abs/1704.05051
Google's Cloud Vision API Is Not Robust To Noise.
Hossein Hosseini; Baicen Xiao; Radha Poovendran

http://arxiv.org/abs/1704.03453
The Space of Transferable Adversarial Examples.
Florian Tramèr; Nicolas Papernot; Ian Goodfellow; Dan Boneh; Patrick McDaniel

http://arxiv.org/abs/1704.03296
Interpretable Explanations of Black Boxes by Meaningful Perturbation. (1%)
Ruth Fong; Andrea Vedaldi

http://arxiv.org/abs/1704.02654
Enhancing Robustness of Machine Learning Systems via Data Transformations.
Arjun Nitin Bhagoji; Daniel Cullina; Chawin Sitawarin; Prateek Mittal

http://arxiv.org/abs/1704.01704
Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks.
Yi Han; Benjamin I. P. Rubinstein

http://arxiv.org/abs/1704.01547
Comment on "Biologically inspired protection of deep networks from adversarial attacks".
Wieland Brendel; Matthias Bethge

http://arxiv.org/abs/1704.01155
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks.
Weilin Xu; David Evans; Yanjun Qi

http://arxiv.org/abs/1704.00103
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly.
Jiajun Lu; Theerasit Issaranon; David Forsyth

http://arxiv.org/abs/1703.09387
Adversarial Transformation Networks: Learning to Generate Adversarial Examples.
Shumeet Baluja; Ian Fischer

http://arxiv.org/abs/1703.09202
Biologically inspired protection of deep networks from adversarial attacks.
Aran Nayebi; Surya Ganguli

http://arxiv.org/abs/1703.09793
Deceiving Google's Cloud Video Intelligence API Built for Summarizing Videos.
Hossein Hosseini; Baicen Xiao; Radha Poovendran

http://arxiv.org/abs/1703.08603
Adversarial Examples for Semantic Segmentation and Object Detection.
Cihang Xie; Jianyu Wang; Zhishuai Zhang; Yuyin Zhou; Lingxi Xie; Alan Yuille

http://arxiv.org/abs/1703.07928
Self corrective Perturbations for Semantic Segmentation and Classification.
Swami Sankaranarayanan; Arpit Jain; Ser Nam Lim

http://arxiv.org/abs/1703.07909
Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains.
Tegjyot Singh Sethi; Mehmed Kantardzic

http://arxiv.org/abs/1703.06857
On the Limitation of Convolutional Neural Networks in Recognizing Negative Images.
Hossein Hosseini; Baicen Xiao; Mayoore Jaiswal; Radha Poovendran

http://arxiv.org/abs/1703.05561
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking.
Erwin Quiring; Daniel Arp; Konrad Rieck

http://arxiv.org/abs/1703.04318
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems.
Hossein Hosseini; Yize Chen; Sreeram Kannan; Baosen Zhang; Radha Poovendran

http://arxiv.org/abs/1703.06748
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents.
Yen-Chen Lin; Zhang-Wei Hong; Yuan-Hong Liao; Meng-Li Shih; Ming-Yu Liu; Min Sun

http://arxiv.org/abs/1703.01101
Adversarial Examples for Semantic Image Segmentation.
Volker Fischer; Mummadi Chaithanya Kumar; Jan Hendrik Metzen; Thomas Brox

http://arxiv.org/abs/1703.00978
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components.
Tommaso Dreossi; Alexandre Donzé; Sanjit A. Seshia

http://arxiv.org/abs/1703.00410
Detecting Adversarial Samples from Artifacts.
Reuben Feinman; Ryan R. Curtin; Saurabh Shintre; Andrew B. Gardner

http://arxiv.org/abs/1702.08138
Deceiving Google's Perspective API Built for Detecting Toxic Comments.
Hossein Hosseini; Sreeram Kannan; Baosen Zhang; Radha Poovendran

http://arxiv.org/abs/1702.06856
Robustness to Adversarial Examples through an Ensemble of Specialists.
Mahdieh Abbasi; Christian Gagné

http://arxiv.org/abs/1702.06832
Adversarial examples for generative models.
Jernej Kos; Ian Fischer; Dawn Song

http://arxiv.org/abs/1702.06763
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples.
Ji Gao; Beilun Wang; Zeming Lin; Weilin Xu; Yanjun Qi

http://arxiv.org/abs/1702.06280
On the (Statistical) Detection of Adversarial Examples.
Kathrin Grosse; Praveen Manoharan; Nicolas Papernot; Michael Backes; Patrick McDaniel

http://arxiv.org/abs/1702.05983
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN.
Weiwei Hu; Ying Tan

http://arxiv.org/abs/1702.04267
On Detecting Adversarial Perturbations.
Jan Hendrik Metzen; Tim Genewein; Volker Fischer; Bastian Bischoff

http://arxiv.org/abs/1702.02284
Adversarial Attacks on Neural Network Policies.
Sandy Huang; Nicolas Papernot; Ian Goodfellow; Yan Duan; Pieter Abbeel

http://arxiv.org/abs/1702.01135
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks.
Guy Katz; Clark Barrett; David Dill; Kyle Julian; Mykel Kochenderfer

http://arxiv.org/abs/1701.04143
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks.
Vahid Behzadan; Arslan Munir

http://arxiv.org/abs/1701.00939
Dense Associative Memory is Robust to Adversarial Inputs.
Dmitry Krotov; John J Hopfield

http://arxiv.org/abs/1612.07767
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics.
Xin Li; Fuxin Li

http://arxiv.org/abs/1612.06299
Simple Black-Box Adversarial Perturbations for Deep Networks.
Nina Narodytska; Shiva Prasad Kasiviswanathan

http://arxiv.org/abs/1612.01401
Learning Adversary-Resistant Deep Neural Networks.
Qinglong Wang; Wenbo Guo; Kaixuan Zhang; Alexander G. II Ororbia; Xinyu Xing; Xue Liu; C. Lee Giles

http://arxiv.org/abs/1612.00334
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Examples.
Beilun Wang; Ji Gao; Yanjun Qi

http://arxiv.org/abs/1612.00155
Adversarial Images for Variational Autoencoders.
Pedro Tabacof; Julia Tavares; Eduardo Valle

http://arxiv.org/abs/1612.00410
Deep Variational Information Bottleneck.
Alexander A. Alemi; Ian Fischer; Joshua V. Dillon; Kevin Murphy

http://arxiv.org/abs/1612.00138
Towards Robust Deep Neural Networks with BANG.
Andras Rozsa; Manuel Gunther; Terrance E. Boult

http://arxiv.org/abs/1611.06179
LOTS about Attacking Deep Features.
Andras Rozsa; Manuel Günther; Terrance E. Boult

http://arxiv.org/abs/1611.04786
AdversariaLib: An Open-source Library for the Security Evaluation of Machine Learning Algorithms Under Attack.
Igino Corona; Battista Biggio; Davide Maiorca

http://arxiv.org/abs/1611.03814
Towards the Science of Security and Privacy in Machine Learning.
Nicolas Papernot; Patrick McDaniel; Arunesh Sinha; Michael Wellman

http://arxiv.org/abs/1611.02770
Delving into Transferable Adversarial Examples and Black-box Attacks.
Yanpei Liu; Xinyun Chen; Chang Liu; Dawn Song

http://arxiv.org/abs/1611.01236
Adversarial Machine Learning at Scale.
Alexey Kurakin; Ian Goodfellow; Samy Bengio

http://arxiv.org/abs/1610.08401
Universal adversarial perturbations.
Seyed-Mohsen Moosavi-Dezfooli; Alhussein Fawzi; Omar Fawzi; Pascal Frossard

http://arxiv.org/abs/1610.06940
Safety Verification of Deep Neural Networks.
Xiaowei Huang; Marta Kwiatkowska; Sen Wang; Min Wu

http://arxiv.org/abs/1610.04563
Are Accuracy and Robustness Correlated?
Andras Rozsa; Manuel Günther; Terrance E. Boult

http://arxiv.org/abs/1610.04256
Assessing Threat of Adversarial Examples on Deep Neural Networks.
Abigail Graese; Andras Rozsa; Terrance E. Boult

http://arxiv.org/abs/1610.01934
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks.
Qinglong Wang; Wenbo Guo; Alexander G. II Ororbia; Xinyu Xing; Lin Lin; C. Lee Giles; Xue Liu; Peng Liu; Gang Xiong

http://arxiv.org/abs/1610.01239
Adversary Resistant Deep Neural Networks with an Application to Malware Detection.
Qinglong Wang; Wenbo Guo; Kaixuan Zhang; Alexander G. II Ororbia; Xinyu Xing; C. Lee Giles; Xue Liu

http://arxiv.org/abs/1610.00768
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library.
Nicolas Papernot; Fartash Faghri; Nicholas Carlini; Ian Goodfellow; Reuben Feinman; Alexey Kurakin; Cihang Xie; Yash Sharma; Tom Brown; Aurko Roy; Alexander Matyasko; Vahid Behzadan; Karen Hambardzumyan; Zhishuai Zhang; Yi-Lin Juang; Zhi Li; Ryan Sheatsley; Abhibhav Garg; Jonathan Uesato; Willi Gierke; Yinpeng Dong; David Berthelot; Paul Hendricks; Jonas Rauber; Rujun Long; Patrick McDaniel

http://arxiv.org/abs/1609.01461
Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems.
Battista Biggio; Giorgio Fumera; Gian Luca Marcialis; Fabio Roli

http://arxiv.org/abs/1609.00804
Randomized Prediction Games for Adversarial Machine Learning.
Samuel Rota Bulò; Battista Biggio; Ignazio Pillai; Marcello Pelillo; Fabio Roli

http://arxiv.org/abs/1608.08967
Robustness of classifiers: from adversarial to random noise.
Alhussein Fawzi; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard

http://arxiv.org/abs/1608.07690
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples.
Thomas Tanay; Lewis Griffin

http://arxiv.org/abs/1608.04644
Towards Evaluating the Robustness of Neural Networks.
Nicholas Carlini; David Wagner

http://arxiv.org/abs/1608.00853
A study of the effect of JPG compression on adversarial images.
Gintare Karolina Dziugaite; Zoubin Ghahramani; Daniel M. Roy

http://arxiv.org/abs/1608.00530
Early Methods for Detecting Adversarial Images.
Dan Hendrycks; Kevin Gimpel

http://arxiv.org/abs/1607.05113
On the Effectiveness of Defensive Distillation.
Nicolas Papernot; Patrick McDaniel

http://arxiv.org/abs/1607.04311
Defensive Distillation is Not Robust to Adversarial Examples.
Nicholas Carlini; David Wagner

http://arxiv.org/abs/1607.02533
Adversarial examples in the physical world.
Alexey Kurakin; Ian Goodfellow; Samy Bengio

http://arxiv.org/abs/1606.04435
Adversarial Perturbations Against Deep Neural Networks for Malware Classification.
Kathrin Grosse; Nicolas Papernot; Praveen Manoharan; Michael Backes; Patrick McDaniel

http://arxiv.org/abs/1605.07277
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.
Nicolas Papernot; Patrick McDaniel; Ian Goodfellow

http://arxiv.org/abs/1605.07262
Measuring Neural Net Robustness with Constraints.
Osbert Bastani; Yani Ioannou; Leonidas Lampropoulos; Dimitrios Vytiniotis; Aditya Nori; Antonio Criminisi

http://arxiv.org/abs/1605.05411
Are Facial Attributes Adversarially Robust?
Andras Rozsa; Manuel Günther; Ethan M. Rudd; Terrance E. Boult

http://arxiv.org/abs/1605.01775
Adversarial Diversity and Hard Positive Generation.
Andras Rozsa; Ethan M. Rudd; Terrance E. Boult

http://arxiv.org/abs/1604.08275
Crafting Adversarial Input Sequences for Recurrent Neural Networks.
Nicolas Papernot; Patrick McDaniel; Ananthram Swami; Richard Harang

http://arxiv.org/abs/1604.04326
Improving the Robustness of Deep Neural Networks via Stability Training.
Stephan Zheng; Yang Song; Thomas Leung; Ian Goodfellow

http://arxiv.org/abs/1604.02606
A General Retraining Framework for Scalable Adversarial Classification.
Bo Li; Yevgeniy Vorobeychik; Xinyun Chen

http://arxiv.org/abs/1603.05145
Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions.
Qiyang Zhao; Lewis D Griffin

http://arxiv.org/abs/1602.05973
Breaking Symmetric Cryptosystems using Quantum Period Finding. (1%)
Marc Kaplan; Gaëtan Leurent; Anthony Leverrier; María Naya-Plasencia

http://arxiv.org/abs/1602.02697
Practical Black-Box Attacks against Machine Learning.
Nicolas Papernot; Patrick McDaniel; Ian Goodfellow; Somesh Jha; Z. Berkay Celik; Ananthram Swami

http://arxiv.org/abs/1602.02389
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms.
Tom Zahavy; Bingyi Kang; Alex Sivak; Jiashi Feng; Huan Xu; Shie Mannor

http://arxiv.org/abs/1601.07213
Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization.
Alexander G. II Ororbia; C. Lee Giles; Daniel Kifer

http://arxiv.org/abs/1511.07528
The Limitations of Deep Learning in Adversarial Settings.
Nicolas Papernot; Patrick McDaniel; Somesh Jha; Matt Fredrikson; Z. Berkay Celik; Ananthram Swami

http://arxiv.org/abs/1511.06385
A Unified Gradient Regularization Family for Adversarial Examples.
Chunchuan Lyu; Kaizhu Huang; Hai-Ning Liang

http://arxiv.org/abs/1511.06381
Manifold Regularized Deep Neural Networks using Adversarial Examples.
Taehoon Lee; Minsuk Choi; Sungroh Yoon

http://arxiv.org/abs/1511.06306
Robust Convolutional Neural Networks under Adversarial Noise.
Jonghoon Jin; Aysegul Dundar; Eugenio Culurciello

http://arxiv.org/abs/1511.06292
Foveation-based Mechanisms Alleviate Adversarial Examples.
Yan Luo; Xavier Boix; Gemma Roig; Tomaso Poggio; Qi Zhao

http://arxiv.org/abs/1511.06233
Towards Open Set Deep Networks.
Abhijit Bendale; Terrance Boult

http://arxiv.org/abs/1511.05432
Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization.
Uri Shaham; Yutaro Yamada; Sahand Negahban

http://arxiv.org/abs/1511.05122
Adversarial Manipulation of Deep Representations.
Sara Sabour; Yanshuai Cao; Fartash Faghri; David J. Fleet

http://arxiv.org/abs/1511.04599
DeepFool: a simple and accurate method to fool deep neural networks.
Seyed-Mohsen Moosavi-Dezfooli; Alhussein Fawzi; Pascal Frossard

http://arxiv.org/abs/1511.04508
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks.
Nicolas Papernot; Patrick McDaniel; Xi Wu; Somesh Jha; Ananthram Swami

http://arxiv.org/abs/1511.03034
Learning with a Strong Adversary.
Ruitong Huang; Bing Xu; Dale Schuurmans; Csaba Szepesvari

http://arxiv.org/abs/1510.05328
Exploring the Space of Adversarial Images.
Pedro Tabacof; Eduardo Valle

http://arxiv.org/abs/1510.04189
Improving Back-Propagation by Adding an Adversarial Gradient.
Arild Nøkland

http://arxiv.org/abs/1507.04761
Deep Learning and Music Adversaries.
Corey Kereliuk; Bob L. Sturm; Jan Larsen

http://arxiv.org/abs/1502.02590
Analysis of classifiers' robustness to adversarial perturbations.
Alhussein Fawzi; Omar Fawzi; Pascal Frossard

http://arxiv.org/abs/1412.6572
Explaining and Harnessing Adversarial Examples.
Ian J. Goodfellow; Jonathon Shlens; Christian Szegedy

http://arxiv.org/abs/1412.5068
Towards Deep Neural Network Architectures Robust to Adversarial Examples.
Shixiang Gu; Luca Rigazio

http://arxiv.org/abs/1412.1897
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images.
Anh Nguyen; Jason Yosinski; Jeff Clune

http://arxiv.org/abs/1401.7727
Security Evaluation of Support Vector Machines in Adversarial Environments.
Battista Biggio; Igino Corona; Blaine Nelson; Benjamin I. P. Rubinstein; Davide Maiorca; Giorgio Fumera; Giorgio Giacinto; and Fabio Roli

http://arxiv.org/abs/1312.6199
Intriguing properties of neural networks.
Christian Szegedy; Wojciech Zaremba; Ilya Sutskever; Joan Bruna; Dumitru Erhan; Ian Goodfellow; Rob Fergus