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Recently, FGSM adversarial training is found to be able to train a robust model which is comparable to the one trained by PGD but an order of magnitude faster.
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“Countering Adversarial Images using Input Transformations”, 2017
Chuan Guo, Mayank Rana, Moustapha Cisse and Laurens van Maaten · 2017
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Eric Wong and J. Kolter · 2017
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Anish Athalye, Nicholas Carlini and David Wagner · 2018
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“Thermometer Encoding: One Hot Way To Resist Adversarial Examples”
Jacob Buckman, Aurko Roy, Colin Raffel and Ian Goodfellow · 2018
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“Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models”, 2018
Pouya Samangouei, Maya Kabkab and Rama Chellappa · 2018
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In Proceedings of the 2019 Conference of the North
Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2019
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“Towards Deep Learning Models Resistant to Adversarial Attacks”, 2019
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“Ensemble Adversarial Training: Attacks and Defenses”, 2020
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