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Adversarial training is one of the most popular ways to learn robust models but is usually attack-dependent and time costly.
Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, and Tom Goldstein · 1904
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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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Provably robust deep learning via adversarially trained smoothed classifiers
Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, Huan Zhang, Ilya P. Razenshteyn, and Sébastien Bubeck · 1906
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Adversarially robust generalization just requires more unlabeled data
Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John E. Hopcroft, and Liwei Wang · 1906
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Towards stable and efficient training of verifiably robust neural networks
Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Duane Boning, and Cho-Jui Hsieh · 1906
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Empirical Bernstein Bounds and Sample Variance Penalization
Andreas Maurer and Massimiliano Pontil · 2009
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Learning with a strong adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvári · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2015
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2016
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Towards the science of security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2016
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
Foolbox: A python toolbox to benchmark the robustness of machine learning models
Jonas Rauber, Wieland Brendel, and Matthias Bethge · 2017
Cited alongside, same era.
Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Mixtrain: Scalable training of formally robust neural networks
Shiqi Wang, Yizheng Chen, Ahmed Abdou, and Suman Jana · 2018
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Towards fast computation of certified robustness for ReLU networks
Lily Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane Boning, and Inderjit Dhillon · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and J. Zico Kolter · 2018
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Efficient neural network robustness certification with general activation functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel · 2018
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Anish Athalye, Nicholas Carlini, and David A. Wagner · 2018
Cited alongside, same era.
Ai2: Safety and robustness certification of neural networks with abstract interpretation
Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev · 2018
Cited alongside, same era.
On the effectiveness of interval bound propagation for training verifiably robust models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli · 2018
Cited alongside, same era.
Harini Kannan, Alexey Kurakin, and Ian J. Goodfellow · 2018
Cited alongside, same era.
Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2018
Cited alongside, same era.
Second-order adversarial attack and certifiable robustness
Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin · 2018
Cited alongside, same era.
Towards robust neural networks via random self-ensemble
Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh · 2018
Cited alongside, same era.
Differentiable abstract interpretation for provably robust neural networks
Matthew Mirman, Timon Gehr, and Martin Vechev · 2018
Cited alongside, same era.
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John C Duchi · 2019
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Query-efficient hard-label black-box attack: An optimization-based approach
Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang, and Cho-Jui Hsieh · 2019
Later among the works it cites.
Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
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Convergence of adversarial training in overparametrized neural networks
Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, and Jason D Lee · 2019
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Adversarial robustness through local linearization
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli, et al · 2019
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Are labels required for improving adversarial robustness?
Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli, et al · 2019
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Mma training: Direct input space margin maximization through adversarial training
Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, and Ruitong Huang · 2020
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