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In this paper, we develop improved techniques for defending against adversarial examples at scale.
Creating artificial neural networks that generalize
Sietsma, J. and Dow, R · 1991
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Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob · 2013
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Explaining and harnessing adversarial examples
Goodfellow, Ian J, Shlens, Jonathon, and Szegedy, Christian · 2014
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Distilling the knowledge in a neural network
Hinton, Geoffrey, Vinyals, Oriol, and Dean, Jeff · 2015
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Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, Nicolas, McDaniel, Patrick, Wu, Xi, Jha, Somesh, and Swami, Ananthram · 2016
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Rethinking the inception architecture for computer vision
Szegedy, Christian, Vanhoucke, Vincent, Ioffe, Sergey, Shlens, Jon, and Wojna, Zbigniew · 2016
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Adversarial perturbation of deep neural networks
Warde-Farley, David and Goodfellow, Ian · 2016
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Brown, Tom B, Mané, Dandelion, Roy, Aurko, Abadi, Martín, and Gilmer, Justin · 2017
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Intriguing properties of adversarial examples
Cubuk, E. D., Zoph, B., Schoenholz, S., and Le, Q · 2017
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The reversible residual network: Backpropagation without storing activations
Gomez, Aidan, Ren, Mengye, Urtasun, Raquel, and Grosse, Roger · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
Katz, Guy, Barrett, Clark, Dill, David L, Julian, Kyle, and Kochenderfer, Mykel J · 2017
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Kolter, J Zico and Wong, Eric · 2017
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Adversarial machine learning at scale
Kurakin, Alexey, Goodfellow, Ian, and Bengio, Samy · 2017
Cited alongside, same era.
Feature squeezing: Detecting adversarial examples in deep neural networks
Xu, Weilin, Evans, David, and Qi, Yanjun · 2017
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Mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y., and Lopez-Paz, D · 2017
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, Percy Liang · 2018
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Certifiable distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, John Duchi · 2018
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, Anish, Carlini, Nicholas, and Wagner, David · 2018
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Kurakin, Alexey, Goodfellow, Ian, and Bengio, Samy · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Cited alongside, same era.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, Takeru, Maeda, Shin-ichi, Koyama, Masanori, and Ishii, Shin · 2017
Cited alongside, same era.
cleverhans v2.0.0: an adversarial machine learning library
Nicolas Papernot, Nicholas Carlini, Ian Goodfellow Reuben Feinman Fartash Faghri Alexander Matyasko Karen Hambardzumyan Yi-Lin Juang Alexey Kurakin Ryan Sheatsley Abhibhav Garg Yen-Chen Lin · 2017
Cited alongside, same era.
Thermometer encoding: One hot way to resist adversarial examples
Buckman, Jacob, Roy, Aurko, Raffel, Colin, and Goodfellow, Ian · 2018
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Gilmer, Justin, Metz, Luke, Faghri, Fartash, Schoenholz, Samuel S., Raghu, Maithra, Wattenberg, Martin, and Goodfellow, Ian · 2018
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Ensemble adversarial training: Attacks and defenses
Tramèr, F., Kurakin, A., Papernot, N., Boneh, D., and McDaniel, P · 2018
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