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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

1 February 2018

Papers citing "Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples"

50 / 1,929 papers shown

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Vulnerabilities of Connectionist AI Applications: Evaluation and Defence Christian Berghoff Matthias Neu Arndt von Twickel AAML 109 25 0 18 Mar 2020
Anomalous Example Detection in Deep Learning: A Survey Saikiran Bulusu B. Kailkhura Yue Liu P. Varshney Basel Alomair AAML 163 47 0 16 Mar 2020
Towards Face Encryption by Generating Adversarial Identity Masks Xiao Yang Yinpeng Dong Tianyu Pang Hang Su Jun Zhu YueFeng Chen H. Xue AAML PICV 151 75 0 15 Mar 2020
Certified Defenses for Adversarial Patches Ping Yeh-Chiang Renkun Ni Ahmed Abdelkader Chen Zhu Christoph Studer Tom Goldstein AAML 67 172 0 14 Mar 2020
Minimum-Norm Adversarial Examples on KNN and KNN-Based Models Chawin Sitawarin David Wagner AAML 70 20 0 14 Mar 2020
Topological Effects on Attacks Against Vertex Classification B. A. Miller Mustafa Çamurcu Alexander J. Gomez Kevin S. Chan Tina Eliassi-Rad AAML 46 2 0 12 Mar 2020
Manifold Regularization for Locally Stable Deep Neural Networks Charles Jin Martin Rinard AAML 94 15 0 09 Mar 2020
On the Robustness of Cooperative Multi-Agent Reinforcement Learning Jieyu Lin Kristina Dzeparoska Shanghang Zhang A. Leon-Garcia Nicolas Papernot AAML 132 69 0 08 Mar 2020
Adversarial Machine Learning: Bayesian Perspectives D. Insua Roi Naveiro Víctor Gallego Jason Poulos AAML 27 21 0 07 Mar 2020
Exploiting Verified Neural Networks via Floating Point Numerical Error Kai Jia Martin Rinard AAML 97 37 0 06 Mar 2020
Confusing and Detecting ML Adversarial Attacks with Injected Attractors Jiyi Zhang E. Chang H. Lee AAML 60 1 0 05 Mar 2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee Hyungyu Lee Sungroh Yoon AAML 252 119 0 05 Mar 2020
Colored Noise Injection for Training Adversarially Robust Neural Networks Evgenii Zheltonozhskii Chaim Baskin Yaniv Nemcovsky Brian Chmiel A. Mendelson A. Bronstein AAML 32 5 0 04 Mar 2020
Deep Neural Network Perception Models and Robust Autonomous Driving Systems M. Shafiee Ahmadreza Jeddi Amir Nazemi Paul Fieguth A. Wong OOD 62 16 0 04 Mar 2020
Denoised Smoothing: A Provable Defense for Pretrained Classifiers Hadi Salman Mingjie Sun Greg Yang Ashish Kapoor J. Zico Kolter 94 23 0 04 Mar 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 302 1,866 0 03 Mar 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 129 67 0 02 Mar 2020
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets Thu Dinh Bao Wang Andrea L. Bertozzi Stanley J. Osher AAML 34 17 0 02 Mar 2020
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models Xiao Zhang Jinghui Chen Quanquan Gu David Evans 76 17 0 01 Mar 2020
Improving Certified Robustness via Statistical Learning with Logical Reasoning Zhuolin Yang Zhikuan Zhao Wei Ping Jiawei Zhang Linyi Li ... Bojan Karlas Ji Liu Heng Guo Ce Zhang Yue Liu AAML 140 13 0 28 Feb 2020
Are L2 adversarial examples intrinsically different? Mingxuan Li Jingyuan Wang Yufan Wu AAML 16 0 0 28 Feb 2020
Detecting Patch Adversarial Attacks with Image Residuals Marius Arvinte Ahmed H. Tewfik S. Vishwanath AAML 29 5 0 28 Feb 2020
TSS: Transformation-Specific Smoothing for Robustness Certification Linyi Li Maurice Weber Xiaojun Xu Luka Rimanic B. Kailkhura Tao Xie Ce Zhang Yue Liu AAML 147 57 0 27 Feb 2020
On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks Yue Zhao Yuwei Wu Caihua Chen A. Lim 3DPC 97 72 0 27 Feb 2020
Improving Robustness of Deep-Learning-Based Image Reconstruction Ankit Raj Y. Bresler Yue Liu OOD AAML 96 51 0 26 Feb 2020
Revisiting Ensembles in an Adversarial Context: Improving Natural Accuracy Aditya Saligrama Guillaume Leclerc AAML 25 1 0 26 Feb 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 167 812 0 26 Feb 2020
Randomization matters. How to defend against strong adversarial attacks Rafael Pinot Raphael Ettedgui Geovani Rizk Y. Chevaleyre Jamal Atif AAML 130 60 0 26 Feb 2020
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping Sanghyun Hong Varun Chandrasekaran Yigitcan Kaya Tudor Dumitras Nicolas Papernot AAML 90 137 0 26 Feb 2020
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks Sandesh Kamath Amit Deshpande Subrahmanyam Kambhampati Venkata V. Balasubramanian 88 12 0 26 Feb 2020
Adversarial Ranking Attack and Defense Mo Zhou Zhenxing Niu Le Wang Qilin Zhang G. Hua 150 39 0 26 Feb 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang Xilie Xu Bo Han Gang Niu Li-zhen Cui Masashi Sugiyama Mohan S. Kankanhalli AAML 69 406 0 26 Feb 2020
Wireless Fingerprinting via Deep Learning: The Impact of Confounding Factors Metehan Cekic S. Gopalakrishnan Upamanyu Madhow 41 11 0 25 Feb 2020
HYDRA: Pruning Adversarially Robust Neural Networks Vikash Sehwag Shiqi Wang Prateek Mittal Suman Jana AAML 69 25 0 24 Feb 2020
Lagrangian Decomposition for Neural Network Verification Rudy Bunel Alessandro De Palma Alban Desmaison Krishnamurthy Dvijotham Pushmeet Kohli Philip Torr M. P. Kumar 81 50 0 24 Feb 2020
Using Single-Step Adversarial Training to Defend Iterative Adversarial Examples Guanxiong Liu Issa M. Khalil Abdallah Khreishah AAML 58 19 0 22 Feb 2020
Polarizing Front Ends for Robust CNNs Can Bakiskan S. Gopalakrishnan Metehan Cekic Upamanyu Madhow Ramtin Pedarsani AAML 45 4 0 22 Feb 2020
UnMask: Adversarial Detection and Defense Through Robust Feature Alignment Scott Freitas Shang-Tse Chen Zijie J. Wang Duen Horng Chau AAML 61 23 0 21 Feb 2020
Robustness from Simple Classifiers Sharon Qian Dimitris Kalimeris Gal Kaplun Yaron Singer AAML 18 1 0 21 Feb 2020
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework Dinghuai Zhang Mao Ye Chengyue Gong Zhanxing Zhu Qiang Liu AAML 99 64 0 21 Feb 2020
Boosting Adversarial Training with Hypersphere Embedding Tianyu Pang Xiao Yang Yinpeng Dong Kun Xu Jun Zhu Hang Su AAML 89 156 0 20 Feb 2020
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks Tianlin Li Siyue Wang Pin-Yu Chen Xinyu Lin Peter Chin AAML 44 3 0 19 Feb 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 297 840 0 19 Feb 2020
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example Serena Booth Yilun Zhou Ankit J. Shah J. Shah BDL 42 2 0 19 Feb 2020
Randomized Smoothing of All Shapes and Sizes Greg Yang Tony Duan J. E. Hu Hadi Salman Ilya P. Razenshteyn Jungshian Li AAML 103 216 0 19 Feb 2020
Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks Tsubasa Takahashi GNN AAML 150 37 0 19 Feb 2020
Deflecting Adversarial Attacks Yao Qin Nicholas Frosst Colin Raffel G. Cottrell Geoffrey E. Hinton AAML 64 15 0 18 Feb 2020
Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness Huijie Feng Chunpeng Wu Guoyang Chen Weifeng Zhang Y. Ning AAML 71 11 0 17 Feb 2020
CAT: Customized Adversarial Training for Improved Robustness Minhao Cheng Qi Lei Pin-Yu Chen Inderjit Dhillon Cho-Jui Hsieh OOD AAML 102 117 0 17 Feb 2020
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality Yi Zhang Orestis Plevrakis S. Du Xingguo Li Zhao Song Sanjeev Arora 127 53 0 16 Feb 2020