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We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 1901
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The limitations of adversarial training and the blind-spot attack
Huan Zhang, Hongge Chen, Zhao Song, Duane Boning, Inderjit S Dhillon, and Cho-Jui Hsieh · 1901
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AdverTorch v0.1: An adversarial robustness toolbox based on pytorch
Gavin Weiguang Ding, Luyu Wang, and Xiaomeng Jin · 1902
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You only propagate once: Accelerating adversarial training via maximal principle
Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, and Bin Dong · 1905
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The differentiability of the upper envelop
Yao-Liang Yu · 2012
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Evasion attacks against machine learning at test time
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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The nature of statistical learning theory
Vladimir Vapnik · 2013
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Learning with a strong adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvári · 2015
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
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Towards deep learning models resistant to adversarial attacks
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Margin maximization for robust classification using deep learning
Alexander Matyasko and Lap-Pui Chau · 2017
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Andrew Slavin Ross and Finale Doshi-Velez · 2017
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Robust large margin deep neural networks
Jure Sokolic, Raja Giryes, Guillermo Sapiro, and Miguel RD Rodrigues · 2017
Adversarial risk and the dangers of evaluating against weak attacks
Jonathan Uesato, Brendan O’Donoghue, Aaron van den Oord, and Pushmeet Kohli · 2018
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Bayesian adversarial learning
Nanyang Ye and Zhanxing Zhu · 2018
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John C Duchi · 2019
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Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
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Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika · 2019
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Provable robustness of relu networks via maximization of linear regions
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Large margin deep networks for classification
Gamaleldin F Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio · 2018
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Low frequency adversarial perturbation
Chuan Guo, Jared S Frank, and Kilian Q Weinberger · 2018
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Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses
Jérôme Rony, Luiz G Hafemann, Luis S Oliveira, Ismail Ben Ayed, Robert Sabourin, and Eric Granger · 2018
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Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks
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On the sensitivity of adversarial robustness to input data distributions
Gavin Weiguang Ding, Kry Yik-Chau Lui, Xiaomeng Jin, Luyu Wang, and Ruitong Huang
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Adversarial training for free!
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On the effectiveness of low frequency perturbations
Yash Sharma, Gavin Weiguang Ding, and Marcus A Brubaker · 2019
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Are labels required for improving adversarial robustness?
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On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2019
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Adversarial margin maximization networks
Ziang Yan, Yiwen Guo, and Changshui Zhang · 2019
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Macer: Attack-free and scalable robust training via maximizing certified radius
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