Towards Deep Learning Models Resistant to Adversarial Attacks

19 June 2017

Papers citing "Towards Deep Learning Models Resistant to Adversarial Attacks"

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Title
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation Raphael Gontijo-Lopes Dong Yin Ben Poole Justin Gilmer E. D. Cubuk AAML 33 204 0 06 Jun 2019
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric Yujun Shi B. Liao Guangyong Chen Yun-Hai Liu Ming-Ming Cheng Jiashi Feng AAML 6 2 0 06 Jun 2019
Image Synthesis with a Single (Robust) Classifier Shibani Santurkar Dimitris Tsipras Brandon Tran Andrew Ilyas Logan Engstrom Aleksander Madry AAML 14 34 0 06 Jun 2019
Should Adversarial Attacks Use Pixel p-Norm? Ayon Sen Xiaojin Zhu Liam Marshall Robert D. Nowak 14 21 0 06 Jun 2019
Query-efficient Meta Attack to Deep Neural Networks Jiawei Du Hu Zhang Qiufeng Wang Yi Yang Jiashi Feng AAML 27 84 0 06 Jun 2019
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise Xuanqing Liu Tesi Xiao Si Si Qin Cao Sanjiv Kumar Cho-Jui Hsieh 24 133 0 05 Jun 2019
MNIST-C: A Robustness Benchmark for Computer Vision Norman Mu Justin Gilmer 27 203 0 05 Jun 2019
A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization Tyler Sypherd Mario Díaz J. Cava Gautam Dasarathy Peter Kairouz Lalitha Sankar 23 28 0 05 Jun 2019
Enhancing Gradient-based Attacks with Symbolic Intervals Shiqi Wang Yizheng Chen Ahmed Abdou Suman Jana AAML 31 15 0 05 Jun 2019
Do Image Classifiers Generalize Across Time? Vaishaal Shankar Achal Dave Rebecca Roelofs Deva Ramanan Benjamin Recht Ludwig Schmidt 20 82 0 05 Jun 2019
Multi-way Encoding for Robustness Donghyun Kim Sarah Adel Bargal Jianming Zhang Stan Sclaroff AAML 18 2 0 05 Jun 2019
Adversarial Training is a Form of Data-dependent Operator Norm Regularization Kevin Roth Yannic Kilcher Thomas Hofmann 17 13 0 04 Jun 2019
What do AI algorithms actually learn? - On false structures in deep learning L. Thesing Vegard Antun A. Hansen 8 21 0 04 Jun 2019
Architecture Selection via the Trade-off Between Accuracy and Robustness Zhun Deng Cynthia Dwork Jialiang Wang Yao-Min Zhao AAML 19 3 0 04 Jun 2019
Understanding the Limitations of Conditional Generative Models Ethan Fetaya J. Jacobsen Will Grathwohl R. Zemel 21 53 0 04 Jun 2019
Correctness Verification of Neural Networks Yichen Yang Martin Rinard AAML 19 12 0 03 Jun 2019
Adversarial Robustness as a Prior for Learned Representations Logan Engstrom Andrew Ilyas Shibani Santurkar Dimitris Tsipras Brandon Tran Aleksander Madry OOD AAML 29 63 0 03 Jun 2019
DAWN: Dynamic Adversarial Watermarking of Neural Networks S. Szyller B. Atli Samuel Marchal Nadarajah Asokan MLAU AAML 18 176 0 03 Jun 2019
Adversarial Risk Bounds for Neural Networks through Sparsity based Compression E. Balda Arash Behboodi Niklas Koep R. Mathar AAML 27 7 0 03 Jun 2019
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models P. Morawiecki Przemysław Spurek Marek Śmieja Jacek Tabor AAML OOD 27 8 0 03 Jun 2019
Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai Tianle Cai Di He Chen Dan Kun He J. Hopcroft Liwei Wang 20 154 0 03 Jun 2019
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness Nhathai Phan Minh Nhat Vu Yang Liu R. Jin Dejing Dou Xintao Wu My T. Thai AAML 22 51 0 02 Jun 2019
Adversarial Examples for Edge Detection: They Exist, and They Transfer Christian Cosgrove Alan Yuille AAML GAN 28 12 0 02 Jun 2019
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems Tianyi Lin Chi Jin Michael I. Jordan 30 500 0 02 Jun 2019
Enhancing Transformation-based Defenses using a Distribution Classifier C. Kou H. Lee E. Chang Teck Khim Ng 37 3 0 01 Jun 2019
Perceptual Evaluation of Adversarial Attacks for CNN-based Image Classification Sid Ahmed Fezza Yassine Bakhti W. Hamidouche Olivier Déforges AAML 22 31 0 01 Jun 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 63 746 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 36 331 0 31 May 2019
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness A. Malinin Mark Gales UQCV AAML 27 172 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 40 21 0 30 May 2019
Meta Dropout: Learning to Perturb Features for Generalization Haebeom Lee Taewook Nam Eunho Yang Sung Ju Hwang OOD 22 3 0 30 May 2019
Interpretable Adversarial Training for Text Samuel Barham S. Feizi AAML 21 17 0 30 May 2019
Bandlimiting Neural Networks Against Adversarial Attacks Yuping Lin A. KasraAhmadiK. Hui Jiang AAML 14 6 0 30 May 2019
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward A. Qayyum Muhammad Usama Junaid Qadir Ala I. Al-Fuqaha AAML 27 187 0 29 May 2019
Functional Adversarial Attacks Cassidy Laidlaw S. Feizi AAML 19 183 0 29 May 2019
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness Saeed Mahloujifar Xiao Zhang Mohammad Mahmoody David Evans 19 22 0 29 May 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 31 63 0 28 May 2019
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks Haohan Wang Xindi Wu Pengcheng Yin Eric Xing 22 513 0 28 May 2019
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks Pu Zhao Siyue Wang Cheng Gongye Yanzhi Wang Yunsi Fei Xinyu Lin AAML 19 75 0 28 May 2019
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation Yuzhe Yang Guo Zhang Dina Katabi Zhi Xu AAML 15 168 0 28 May 2019
Controlling Neural Level Sets Matan Atzmon Niv Haim Lior Yariv Ofer Israelov Haggai Maron Y. Lipman AI4CE 22 118 0 28 May 2019
Expected Tight Bounds for Robust Training Salman Alsubaihi Adel Bibi Modar Alfadly Abdullah Hamdi Guohao Li OOD AAML 17 0 0 28 May 2019
Brain-inspired reverse adversarial examples Shaokai Ye S. Tan Kaidi Xu Yanzhi Wang Chenglong Bao Kaisheng Ma AAML 8 5 0 28 May 2019
Adversarially Robust Learning Could Leverage Computational Hardness Sanjam Garg S. Jha Saeed Mahloujifar Mohammad Mahmoody AAML 23 24 0 28 May 2019
Label Universal Targeted Attack Naveed Akhtar M. Jalwana Bennamoun Ajmal Mian AAML 25 5 0 27 May 2019
Analyzing the Interpretability Robustness of Self-Explaining Models Haizhong Zheng Earlence Fernandes A. Prakash AAML LRM 24 7 0 27 May 2019
GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification Xuwang Yin Soheil Kolouri Gustavo K. Rohde AAML 33 43 0 27 May 2019
Scaleable input gradient regularization for adversarial robustness Chris Finlay Adam M. Oberman AAML 16 77 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 25 75 0 27 May 2019
Distributionally Robust Optimization and Generalization in Kernel Methods Matthew Staib Stefanie Jegelka 31 129 0 27 May 2019