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Current SOTA adversarially robust models are mostly based on adversarial training (AT) and differ only by some regularizers either at inner maximization or outer minimization steps.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Generative adversarial networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 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 · 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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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Curriculum adversarial training
Qi-Zhi Cai, Min Du, Chang Liu, and Dawn Song · 2018
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Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2018
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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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Harnessing the vulnerability of latent layers in adversarially trained models
Mayank Singh, Abhishek Sinha, Nupur Kumari, Harshitha Machiraju, Balaji Krishnamurthy, and Vineeth N Balasubramanian · 2019
Cited alongside, same era.
On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2019
Cited alongside, same era.
Improving adversarial robustness requires revisiting misclassified examples
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu · 2019
Cited alongside, same era.
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
Yogesh Balaji, Tom Goldstein, and Judy Hoffman · 2019
Cited alongside, same era.
Improving adversarial robustness via promoting ensemble diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu · 2019
Cited alongside, same era.
What it thinks is important is important: Robustness transfers through input gradients
Alvin Chan, Yi Tay, and Yew-Soon Ong · 2020
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Jacobian adversarially regularized networks for robustness
Alvin Chan, Yi Tay, Yew Soon Ong, and Jie Fu · 2020
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Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-tao Xia, and Yisen Wang · 2020
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Improving adversarial robustness through progressive hardening
Chawin Sitawarin, Supriyo Chakraborty, and David Wagner · 2020
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Geometry-aware instance-reweighted adversarial training
Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, and Mohan Kankanhalli · 2020
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Adversarial robustness through local linearization
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, and Pushmeet Kohli · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
Cited alongside, same era.
On the connection between adversarial robustness and saliency map interpretability
Christian Etmann, Sebastian Lunz, Peter Maass, and Carola-Bibiane Schönlieb · 2019
Cited alongside, same era.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
Cited alongside, same era.
On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
Cited alongside, same era.
Attacks which do not kill training make adversarial learning stronger
Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, and Mohan Kankanhalli · 2020
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2020
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Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J Zico Kolter · 2020
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Single-step adversarial training with dropout scheduling
BS Vivek and R Venkatesh Babu · 2020
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Enforcing linearity in dnn succours robustness and adversarial image generation
Anindya Sarkar and Raghu Iyengar · 2020
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Single-step adversarial training with dropout scheduling
R Venkatesh Babu · 2020
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