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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"

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Adversarial Attack and Defense in Deep Ranking Mo Zhou Le Wang Zhenxing Niu Qilin Zhang N. Zheng G. Hua OOD 83 15 0 07 Jun 2021
A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification Kevin Roth AAML 29 2 0 06 Jun 2021
Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness Renjue Li Hanwei Zhang Pengfei Yang Cheng-Chao Huang Aimin Zhou Bai Xue Lijun Zhang FedML AAML 40 4 0 05 Jun 2021
Improving Neural Network Robustness via Persistency of Excitation Kaustubh Sridhar O. Sokolsky Insup Lee James Weimer AAML 97 20 0 03 Jun 2021
Exploring Memorization in Adversarial Training Yinpeng Dong Ke Xu Xiao Yang Tianyu Pang Zhijie Deng Hang Su Jun Zhu TDI 58 74 0 03 Jun 2021
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack Alexander Matyasko Lap-Pui Chau AAML 47 8 0 03 Jun 2021
Query Attack by Multi-Identity Surrogates Sizhe Chen Zhehao Huang Qinghua Tao Xiaolin Huang AAML 86 4 0 31 May 2021
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart Tianyu Pang Huishuai Zhang Di He Yinpeng Dong Hang Su Wei Chen Jun Zhu Tie-Yan Liu AAML 49 18 0 31 May 2021
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels Jingfeng Zhang Xilie Xu Bo Han Tongliang Liu Gang Niu Li-zhen Cui Masashi Sugiyama NoLa AAML 87 9 0 31 May 2021
Analysis and Applications of Class-wise Robustness in Adversarial Training Qi Tian Kun Kuang Ke Jiang Leilei Gan Yisen Wang AAML 99 47 0 29 May 2021
Robust Regularization with Adversarial Labelling of Perturbed Samples Xiaohui Guo Richong Zhang Yaowei Zheng Yongyi Mao AAML 34 0 0 28 May 2021
Adversarial Robustness against Multiple and Single $l_p$ -Threat Models via Quick Fine-Tuning of Robust Classifiers Francesco Croce Matthias Hein OOD AAML 67 18 0 26 May 2021
Deep Repulsive Prototypes for Adversarial Robustness A. Serban E. Poll Joost Visser OOD 52 3 0 26 May 2021
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training Yatong Bai Tanmay Gautam Yujie Gai Somayeh Sojoudi AAML 93 3 0 25 May 2021
Improved OOD Generalization via Adversarial Training and Pre-training Mingyang Yi Lu Hou Jiacheng Sun Lifeng Shang Xin Jiang Qun Liu Zhi-Ming Ma VLM 79 84 0 24 May 2021
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks Leo Schwinn René Raab A. Nguyen Dario Zanca Bjoern M. Eskofier AAML 86 61 0 21 May 2021
Certification of Iterative Predictions in Bayesian Neural Networks Matthew Wicker Luca Laurenti A. Patané Nicola Paoletti Alessandro Abate Marta Z. Kwiatkowska 140 11 0 21 May 2021
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks Hang Wang David J. Miller G. Kesidis GAN AAML 50 13 0 21 May 2021
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks Cong Xu Xiang Li Min Yang AAML 60 15 0 19 May 2021
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks Dequan Wang An Ju Evan Shelhamer David Wagner Trevor Darrell AAML 122 27 0 18 May 2021
Adversarial Examples Detection with Bayesian Neural Network Yao Li Tongyi Tang Cho-Jui Hsieh T. C. Lee GAN AAML 73 3 0 18 May 2021
Sparta: Spatially Attentive and Adversarially Robust Activation Qing Guo Felix Juefei Xu Changqing Zhou Wei Feng Yang Liu Song Wang AAML 70 4 0 18 May 2021
Adversarial Training for Gradient Descent: Analysis Through its Continuous-time Approximation Haotian Gu Xin Guo Xinyu Li 57 1 0 17 May 2021
Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning Franziska Boenisch Philip Sperl Konstantin Böttinger AAML 50 13 0 17 May 2021
High-Robustness, Low-Transferability Fingerprinting of Neural Networks Siyue Wang Xiao Wang Pin-Yu Chen Pu Zhao Xue Lin AAML 69 2 0 14 May 2021
XAI Handbook: Towards a Unified Framework for Explainable AI Sebastián M. Palacio Adriano Lucieri Mohsin Munir Jörn Hees Sheraz Ahmed Andreas Dengel 58 32 0 14 May 2021
Automated Decision-based Adversarial Attacks Qi-An Fu Yinpeng Dong Hang Su Jun Zhu AAML 26 1 0 09 May 2021
Understanding Catastrophic Overfitting in Adversarial Training Peilin Kang Seyed-Mohsen Moosavi-Dezfooli AAML 63 16 0 06 May 2021
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model Ruoxi Qin Linyuan Wang Xing-yuan Chen Xuehui Du Bin Yan AAML 69 5 0 06 May 2021
Multi-Robot Coordination and Planning in Uncertain and Adversarial Environments Lifeng Zhou Pratap Tokekar 107 44 0 02 May 2021
Adversarial Example Detection for DNN Models: A Review and Experimental Comparison Ahmed Aldahdooh W. Hamidouche Sid Ahmed Fezza Olivier Déforges AAML 239 128 0 01 May 2021
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense Haoxi Zhan Xiaobing Pei AAML 49 9 0 30 Apr 2021
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety Sebastian Houben Stephanie Abrecht Maram Akila Andreas Bär Felix Brockherde ... Serin Varghese Michael Weber Sebastian J. Wirkert Tim Wirtz Matthias Woehrle AAML 130 58 0 29 Apr 2021
Random Noise Defense Against Query-Based Black-Box Attacks Zeyu Qin Yanbo Fan H. Zha Baoyuan Wu AAML 137 60 0 23 Apr 2021
Dual Head Adversarial Training Yujing Jiang Xingjun Ma S. Erfani James Bailey AAML 52 4 0 21 Apr 2021
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations Kaidi Xu Chenan Wang Hao-Ran Cheng B. Kailkhura Xue Lin R. Goldhahn 55 3 0 21 Apr 2021
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? Vikash Sehwag Saeed Mahloujifar Tinashe Handina Sihui Dai Chong Xiang M. Chiang Prateek Mittal OOD 108 131 0 19 Apr 2021
Removing Adversarial Noise in Class Activation Feature Space Dawei Zhou N. Wang Chunlei Peng Xinbo Gao Xiaoyu Wang Jun Yu Tongliang Liu AAML 66 29 0 19 Apr 2021
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems Yue Gao Ilia Shumailov Kassem Fawaz AAML 134 11 0 18 Apr 2021
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition Alessandro De Palma Rudy Bunel Alban Desmaison Krishnamurthy Dvijotham Pushmeet Kohli Philip Torr M. P. Kumar 76 52 0 14 Apr 2021
Sparse Coding Frontend for Robust Neural Networks Can Bakiskan Metehan Cekic Ahmet Dundar Sezer Upamanyu Madhow AAML 21 0 0 12 Apr 2021
Connecting Attributions and QA Model Behavior on Realistic Counterfactuals Xi Ye Rohan Nair Greg Durrett 61 24 0 09 Apr 2021
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 105 67 0 09 Apr 2021
A single gradient step finds adversarial examples on random two-layers neural networks Sébastien Bubeck Yeshwanth Cherapanamjeri Gauthier Gidel Rémi Tachet des Combes MLT 82 28 0 08 Apr 2021
Universal Spectral Adversarial Attacks for Deformable Shapes Arianna Rampini Franco Pestarini Luca Cosmo Simone Melzi Emanuele Rodolà AAML 120 18 0 07 Apr 2021
Universal Adversarial Training with Class-Wise Perturbations Philipp Benz Chaoning Zhang Adil Karjauv In So Kweon AAML 58 27 0 07 Apr 2021
The art of defense: letting networks fool the attacker Jinlai Zhang Lyvjie Chen Binbin Liu Bojun Ouyang Jihong Zhu Minchi Kuang Houqing Wang Yanmei Meng AAML 3DPC 78 16 0 07 Apr 2021
$Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model$ Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model Payam Delgosha Hamed Hassani Ramtin Pedarsani AAML 66 8 0 05 Apr 2021
Reliably fast adversarial training via latent adversarial perturbation Geon Yeong Park Sang Wan Lee AAML 73 28 0 04 Apr 2021
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression Rehana Mahfuz R. Sahay Aly El Gamal AAML 36 3 0 03 Apr 2021