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Adversarial examples pose a unique challenge for deep learning systems.
A fourier perspective on model robustness in computer vision
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D. Cubuk, and Justin Gilmer · 1906
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Double backpropagation increasing generalization performance
H. Drucker and Y. Le Cun · 1991
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Frequency-tuned universal adversarial attacks
Yingpeng Deng and Lina J. Karam · 2003
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Towards frequency-based explanation for robust CNN
Zifan Wang, Yilin Yang, Ankit Shrivastava, Varun Rawal, and Zihao Ding · 2005
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Towards frequency-based explanation for robust cnn
Zifan Wang, Yilin Yang, Ankit Shrivastava, Varun Rawal, and Zihao Ding · 2005
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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Explaining and harnessing adversarial examples
I. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2016
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A study of the effect of JPG compression on adversarial images
Gintare Karolina Dziugaite, Zoubin Ghahramani, and Daniel M. Roy · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, P. Mcdaniel, Xi Wu, S. Jha, and A. Swami · 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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Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, L. Chen, M. Kounavis, and Duen Horng Chau · 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.
Harini Kannan, A. Kurakin, and I. Goodfellow · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
J. Z. Kolter and Eric Wong · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
On the effectiveness of low frequency perturbations
Yash Sharma, Gavin Weiguang Ding, and Marcus A. Brubaker · 2019
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and K. Sakurai · 2019
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On the structural sensitivity of deep convolutional networks to the directions of fourier basis functions
Yusuke Tsuzuku and Issei Sato · 2019
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Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, L. V. D. Maaten, A. Yuille, and Kaiming He · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, E. Xing, L. Ghaoui, and Michael I. Jordan · 2019
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, A. Kurakin, Nicolas Papernot, D. Boneh, and P. Mcdaniel · 2018
Cited alongside, same era.
Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2018
Cited alongside, same era.
Low frequency adversarial perturbation
Chuan Guo, Jared S. Frank, and Kilian Q. Weinberger · 2019
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, D. Tsipras, Logan Engstrom, Brandon Tran, and A. Madry · 2019
Cited alongside, same era.
Principal component properties of adversarial samples
Malhar Jere, Sandro Herbig, Christine H. Lind, and F. Koushanfar · 2019
Cited alongside, same era.
Adversarial training for free!
A. Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, L. Davis, G. Taylor, and T. Goldstein · 2019
Cited alongside, same era.
Toward few-step adversarial training from a frequency perspective
H. Wang, Cory Cornelius, Brandon Edwards, and Jason Martin
Cited in the paper.
Francesco Croce and Matthias Hein · 2020
Later among the works it cites.
A singular value perspective on model robustness
Malhar Jere, Maghav Kumar, and F. Koushanfar · 2020
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Hold me tight! Influence of discriminative features on deep network boundaries
Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2020
Later among the works it cites.
Impact of spatial frequency based constraints on adversarial robustness
Rémi Bernhard, Pierre-Alain Moëllic, Martial Mermillod, Yannick Bourrier, Romain Cohendet, Miguel Solinas, and Marina Reyboz · 2021
Closest in time.
Detecting autoattack perturbations in the frequency domain
P. Lorenz, Paula Harder, Dominik Strassel, Margret Keuper, and Janis Keuper · 2021
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