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Adversarial training has been demonstrated as one of the most effective methods for training robust models to defend against adversarial examples.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Y. Ng, and Honglak Lee · 2011
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Computer Vision - Algorithms and Applications
Richard Szeliski · 2011
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Deep neural networks for acoustic modeling in speech recognition
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Brian Kingsbury, et al · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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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 · 2014
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David A. Wagner · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
Earlier work this paper cites.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David A. Wagner · 2018
Cited alongside, same era.
Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel, and Ian J. Goodfellow · 2018
Cited alongside, same era.
Stochastic activation pruning for robust adversarial defense
Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, and Animashree Anandkumar · 2018
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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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, and Boqing Gong · 2019
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Improving the generalization of adversarial training with domain adaptation
Chuanbiao Song, Kun He, Liwei Wang, and John E. Hopcroft · 2019
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A direct approach to robust deep learning using adversarial networks
Huaxia Wang and Chun-Nam Yu · 2019
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AT-GAN: A generative attack model for adversarial transferring on generative adversarial nets
Xiaosen Wang, Kun He, and John E. Hopcroft · 2019
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
Cited alongside, same era.
Adversarial risk and the dangers of evaluating against weak attacks
Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli, and Aäron van den Oord · 2018
Cited alongside, same era.
On the sensitivity of adversarial robustness to input data distributions
Gavin Weiguang Ding, Kry Yik Chau Lui, Xiaomeng Jin, Luyu Wang, and Ruitong Huang · 2019
Cited alongside, same era.
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
Cited alongside, same era.
Adversarially robust generalization just requires more unlabeled data
Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John E. Hopcroft, and Liwei Wang · 2019
Closest in time.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, and Michael I. Jordan · 2019
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The limitations of adversarial training and the blind-spot attack
Huan Zhang, Hongge Chen, Zhao Song, Duane S. Boning, Inderjit S. Dhillon, and Cho-Jui Hsieh · 2019
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Interpreting adversarially trained convolutional neural networks
Tianyuan Zhang and Zhanxing Zhu · 2019
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Nesterov accelerated gradient and scale invariance for adversarial attacks
Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E. Hopcroft · 2020
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