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Adversarial Training (AT) with Projected Gradient Descent (PGD) is an effective approach for improving the robustness of the deep neural networks.
80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T Freeman · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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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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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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A boundary tilting persepective on the phenomenon of adversarial examples
Thomas Tanay and Lewis Griffin · 2016
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Yfcc100m: The new data in multimedia research
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
Cited alongside, same era.
Adv-bnn: Improved adversarial defense through robust bayesian neural network
Xuanqing Liu, Yao Li, Chongruo Wu, and Cho-Jui Hsieh · 2018
Cited alongside, same era.
Characterizing adversarial subspaces using local intrinsic dimensionality
Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, and James Bailey · 2018
Cited alongside, same era.
Exploring the limits of weakly supervised pretraining
Certified adversarial robustness with additive noise
Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin · 2019
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
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Disentangling adversarial robustness and generalization
David Stutz, Matthias Hein, and Bernt Schiele · 2019
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Ke Sun, Zhanxing Zhu, and Zhouchen Lin · 2019
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Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L Yuille, and Kaiming He · 2019
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Billion-scale semi-supervised learning for image classification
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Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 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.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
Cited alongside, same era.
Are labels required for improving adversarial robustness?
Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Alhussein Fawzi, Robert Stanforth, and Pushmeet Kohli · 2019
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
Cited alongside, same era.
Parametric noise injection: Trainable randomness to improve deep neural network robustness against adversarial attack
Zhezhi He, Adnan Siraj Rakin, and Deliang Fan · 2019
Cited alongside, same era.
Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
Cited alongside, same era.
I Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, and Dhruv Mahajan · 2019
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Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li · 2019
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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 · 2019
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Defense against adversarial attacks using feature scattering-based adversarial training
Haichao Zhang and Jianyu Wang · 2019
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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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Learn2perturb: an end-to-end feature perturbation learning to improve adversarial robustness
Ahmadreza Jeddi, Mohammad Javad Shafiee, Michelle Karg, Christian Scharfenberger, and Alexander Wong · 2020
Closest in time.
Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J Zico Kolter · 2020
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Efficient adversarial training with transferable adversarial examples
Haizhong Zheng, Ziqi Zhang, Juncheng Gu, Honglak Lee, and Atul Prakash · 2020
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