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Adversarial training is a popular defense strategy against attack threat models with bounded Lp norms.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton · 2012
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Fast r-cnn
Ross Girshick · 2015
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 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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The robust manifold defense: Adversarial training using generative models
Ajil Jalal, Andrew Ilyas, Constantinos Daskalakis, and Alexandros G Dimakis · 2017
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Emnist: an extension of mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 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
Earlier work this paper cites.
Adversarial vulnerability for any classifier
Alhussein Fawzi, Hamza Fawzi, and Omar Fawzi · 2018
Cited alongside, same era.
Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2018
Cited alongside, same era.
Defense-gan: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
Cited alongside, same era.
Semidefinite relaxations for certifying robustness to adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy S Liang · 2018
Cited alongside, same era.
Characterizing adversarial subspaces using local intrinsic dimensionality
Fine-grained synthesis of unrestricted adversarial examples, 2019
Omid Poursaeed, Tianxing Jiang, Harry Yang, Serge Belongie, and Ser-Nam Lim · 2019
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2019
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Generating diverse high-fidelity images with vq-vae-2
Oriol Vinyals Ali Razavi, Aaron van den Oord · 2019
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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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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.
Constructing unrestricted adversarial examples with generative models
Yang Song, Rui Shu, Nate Kushman, and Stefano Ermon · 2018
Cited alongside, same era.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Cited alongside, same era.
Resisting adversarial attacks by k k -winners-take-all
Chang Xiao, Peilin Zhong, and Changxi Zheng · 2019
Cited alongside, same era.
The odds are odd: A statistical test for detecting adversarial examples
Kevin Roth, Yannic Kilcher, and Thomas Hofmann · 2019
Cited alongside, same era.
Mixup inference: Better exploiting mixup to defend adversarial attacks
Tianyu Pang, Kun Xu, and Jun Zhu · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
Cited alongside, same era.
Disentangling adversarial robustness and generalization
David Stutz, Matthias Hein, and Bernt Schiele · 2019
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Robustness (python library), 2019
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, and Dimitris Tsipras · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Image2StyleGAN: How to embed images into the StyleGAN latent space?
Rameen Abdal, Yipeng Qin, and Peter Wonka · 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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Wasserstein smoothing: Certified robustness against wasserstein adversarial attacks
Alexander Levine and Soheil Feizi · 2020
Closest in time.
Certified defenses for adversarial patches
Ping-Yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studor, and Tom Goldstein · 2020
Closest in time.
Robustness certificates for sparse adversarial attacks by randomized ablation
Alexander Levine and Soheil Feizi · 2020
Closest in time.
Wasserstein smoothing: Certified robustness against wasserstein adversarial attacks
Alexander Levine and Soheil Feizi · 2020
Closest in time.
Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J. Zico Kolter · 2020
Closest in time.