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Motivated by safety-critical applications, test-time attacks on classifiers via adversarial examples has recently received a great deal of attention.
Nearest neighbor pattern classification
T. Cover and P.E. Hart · 1967
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Luc P Devroye and Terry J Wagner · 1977
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Consistent nonparametric regression
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Luc Devroye, Laszlo Gyorfi, Adam Krzyzak, and Gabor Lugosi · 1994
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Michael Mitzenmacher and Eli Upfal · 2005
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Rates of convergence for the cluster tree
Kamalika Chaudhuri and Sanjoy Dasgupta · 2010
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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UCI machine learning repository, 2013
M. Lichman · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Rates of convergence for nearest neighbor classification
Kamalika Chaudhuri and Sanjoy Dasgupta · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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A bayes consistent 1-nn classifier
Aryeh Kontorovich and Roi Weiss · 2015
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The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2016
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Matthias Hein and Maksym Andriushchenko · 2017
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Towards proving the adversarial robustness of deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
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Provable defenses against adversarial examples via the convex outer adversarial polytope
J Zico Kolter and Eric Wong · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Mądry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Laurent Amsaleg, James Bailey, Sarah Erfani, Teddy Furon, Michael E Houle, Miloš Radovanović, and Nguyen Xuan Vinh · 2016
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Robustness of classifiers: from adversarial to random noise
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2016
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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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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
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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, and Yen-Chen Lin · 2017
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Practical black-box attacks against deep learning systems using adversarial examples
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Berkay Celik, and Ananthram Swami · 2017
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Certifiable distributional robustness with principled adversarial training
John Duchi Aman Sinha, Hongseok Namkoong · 2018
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Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S Schoenholz, Maithra Raghu, Martin Wattenberg, and Ian Goodfellow · 2018
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