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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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Multi-way Encoding for Robustness Donghyun Kim Sarah Adel Bargal Jianming Zhang Stan Sclaroff AAML 41 2 0 05 Jun 2019
Adversarial Training is a Form of Data-dependent Operator Norm Regularization Kevin Roth Yannic Kilcher Thomas Hofmann 58 13 0 04 Jun 2019
Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai Tianle Cai Di He Chen Dan Kun He John E. Hopcroft Liwei Wang 98 158 0 03 Jun 2019
Enhancing Transformation-based Defenses using a Distribution Classifier C. Kou H. Lee E. Chang Teck Khim Ng 67 3 0 01 Jun 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 130 754 0 31 May 2019
Are Labels Required for Improving Adversarial Robustness? J. Uesato Jean-Baptiste Alayrac Po-Sen Huang Robert Stanforth Alhussein Fawzi Pushmeet Kohli AAML 97 335 0 31 May 2019
Bypassing Backdoor Detection Algorithms in Deep Learning T. Tan Reza Shokri FedML AAML 108 152 0 31 May 2019
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness Adnan Siraj Rakin Zhezhi He Li Yang Yanzhi Wang Liqiang Wang Deliang Fan AAML 96 21 0 30 May 2019
Bandlimiting Neural Networks Against Adversarial Attacks Yuping Lin A. KasraAhmadiK. Hui Jiang AAML 42 6 0 30 May 2019
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness Saeed Mahloujifar Xiao Zhang Mohammad Mahmoody David Evans 67 22 0 29 May 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla Soheil Feizi FAtt AAML 93 65 0 28 May 2019
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation Yuzhe Yang Guo Zhang Dina Katabi Zhi Xu AAML 100 171 0 28 May 2019
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss Pengcheng Li Jinfeng Yi Bowen Zhou Lijun Zhang AAML 65 37 0 28 May 2019
Brain-inspired reverse adversarial examples Shaokai Ye S. Tan Kaidi Xu Yanzhi Wang Chenglong Bao Kaisheng Ma AAML 25 5 0 28 May 2019
Adversarially Robust Learning Could Leverage Computational Hardness Sanjam Garg S. Jha Saeed Mahloujifar Mohammad Mahmoody AAML 163 24 0 28 May 2019
GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification Xuwang Yin Soheil Kolouri Gustavo K. Rohde AAML 106 44 0 27 May 2019
Scaleable input gradient regularization for adversarial robustness Chris Finlay Adam M. Oberman AAML 101 79 0 27 May 2019
$Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$$ Provable robustness against all adversarial $l_p$ -perturbations for $p\geq 1$ Francesco Croce Matthias Hein OOD 78 75 0 27 May 2019
Non-Determinism in Neural Networks for Adversarial Robustness Daanish Ali Khan Linhong Li Ninghao Sha Zhuoran Liu Abelino Jiménez Bhiksha Raj Rita Singh OOD AAML 33 3 0 26 May 2019
Robust Classification using Robust Feature Augmentation Kevin Eykholt Swati Gupta Atul Prakash Amir Rahmati Pratik Vaishnavi Haizhong Zheng AAML 59 2 0 26 May 2019
Rearchitecting Classification Frameworks For Increased Robustness Varun Chandrasekaran Brian Tang Nicolas Papernot Kassem Fawaz S. Jha Xi Wu AAML OOD 100 8 0 26 May 2019
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 OOD 45 3 0 26 May 2019
Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders Hebi Li Qi Xiao Shixin Tian Jin Tian AAML 65 4 0 26 May 2019
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 R. Mudumbai AAML 75 6 0 25 May 2019
Enhancing Adversarial Defense by k-Winners-Take-All Chang Xiao Peilin Zhong Changxi Zheng AAML 80 99 0 25 May 2019
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks Yuanshun Yao Huiying Li Haitao Zheng Ben Y. Zhao AAML 55 13 0 24 May 2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples Liwei Song Reza Shokri Prateek Mittal SILM MIACV AAML 91 248 0 24 May 2019
Generative adversarial network based on chaotic time series Makoto Naruse Takashi Matsubara N. Chauvet Kazutaka Kanno Tianyu Yang Atsushi Uchida AI4TS GAN 72 19 0 24 May 2019
Thwarting finite difference adversarial attacks with output randomization Haidar Khan Daniel Park Azer Khan B. Yener SILM AAML 52 0 0 23 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang Zhanxing Zhu AAML GAN FAtt 123 161 0 23 May 2019
Adversarially robust transfer learning Ali Shafahi Parsa Saadatpanah Chen Zhu Amin Ghiasi Christoph Studer David Jacobs Tom Goldstein OOD 52 117 0 20 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 32 5 0 19 May 2019
Simple Black-box Adversarial Attacks Chuan Guo Jacob R. Gardner Yurong You A. Wilson Kilian Q. Weinberger AAML 78 581 0 17 May 2019
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models Nicholas Carlini ELM 68 14 0 17 May 2019
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization Seungyong Moon Gaon An Hyun Oh Song AAML MLAU 88 136 0 16 May 2019
On Norm-Agnostic Robustness of Adversarial Training Bai Li Changyou Chen Wenlin Wang Lawrence Carin OOD SILM 68 7 0 15 May 2019
Robustification of deep net classifiers by key based diversified aggregation with pre-filtering O. Taran Shideh Rezaeifar T. Holotyak Svyatoslav Voloshynovskiy AAML 52 1 0 14 May 2019
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models M. Singh Abhishek Sinha Nupur Kumari Harshitha Machiraju Balaji Krishnamurthy V. Balasubramanian AAML 56 61 0 13 May 2019
Moving Target Defense for Deep Visual Sensing against Adversarial Examples Qun Song Zhenyu Yan Rui Tan AAML 45 20 0 11 May 2019
Universal Adversarial Perturbations for Speech Recognition Systems Paarth Neekhara Shehzeen Samarah Hussain Prakhar Pandey Shlomo Dubnov Julian McAuley F. Koushanfar AAML 82 118 0 09 May 2019
Adversarial Defense Framework for Graph Neural Network Shen Wang Zhengzhang Chen Jingchao Ni Xiao Yu Zhichun Li Haifeng Chen Philip S. Yu AAML GNN 71 28 0 09 May 2019
An Empirical Evaluation of Adversarial Robustness under Transfer Learning Todor Davchev Timos Korres Stathi Fotiadis N. Antonopoulos S. Ramamoorthy AAML 38 0 0 07 May 2019
Adaptive Generation of Unrestricted Adversarial Inputs Isaac Dunn Hadrien Pouget T. Melham Daniel Kroening AAML 61 7 0 07 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 113 1,846 0 06 May 2019
Batch Normalization is a Cause of Adversarial Vulnerability A. Galloway A. Golubeva T. Tanay M. Moussa Graham W. Taylor ODL AAML 84 80 0 06 May 2019
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples Vikash Sehwag A. Bhagoji Liwei Song Chawin Sitawarin Daniel Cullina M. Chiang Prateek Mittal OODD 77 26 0 05 May 2019
Transfer of Adversarial Robustness Between Perturbation Types Daniel Kang Yi Sun Tom B. Brown Dan Hendrycks Jacob Steinhardt AAML 71 49 0 03 May 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang Tianyuan Zhang Yiping Lu Zhanxing Zhu Bin Dong AAML 130 362 0 02 May 2019
Adversarial Training with Voronoi Constraints Marc Khoury Dylan Hadfield-Menell AAML 63 24 0 02 May 2019
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 AAML 86 245 0 01 May 2019