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A wide range of defenses have been proposed to harden neural networks against adversarial attacks.
Problémes concrets d’analyse fonctionnelle
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Extremal properties of half-spaces for spherically invariant measures
Vladimir Sudakov and Boris Tsirelson · 1974
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The isoperimetric inequality
Robert Osserman et al · 1978
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Asymptotic Theory of Finite Dimensional Normed Spaces
Vitali D Milman and Gideon Schechtman · 1986
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On the method of bounded differences
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Concentration of measure and isoperimetric inequalities in product spaces
Michel Talagrand · 1995
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A new look at independence
Michel Talagrand · 1996
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An isoperimetric inequality on the discrete cube, and an elementary proof of the isoperimetric inequality in gauss space
Sergey G Bobkov et al · 1997
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The concentration of measure phenomenon
Michel Ledoux · 2001
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The isoperimetric problem
Antonio Ros · 2001
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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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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Defensive distillation is not robust to adversarial examples
Nicholas Carlini and David Wagner · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Cited alongside, same era.
Synthesizing robust adversarial examples
Anish Athalye and Ilya Sutskever · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten · 2017
Cited alongside, same era.
Efficient defenses against adversarial attacks
Valentina Zantedeschi, Maria-Irina Nicolae, and Ambrish Rawat · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
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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Shield: Fast, practical defense and vaccination for deep learning using JPEG compression
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E Kounavis, and Duen Horng Chau · 2018
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Stochastic activation pruning for robust adversarial defense
Guneet S Dhillon, Kamyar Azizzadenesheli, Zachary C Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, and Anima Anandkumar · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
J Zico Kolter and Eric Wong · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
MagNet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
Cited alongside, same era.
APE-GAN: Adversarial perturbation elimination with GAN
Shiwei Shen, Guoqing Jin, Ke Gao, and Yongdong Zhang · 2017
Cited alongside, same era.
PixelDefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2017
Cited alongside, same era.
One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Sakurai Kouichi · 2017
Cited alongside, same era.
High-Dimensional Probability
Roman Vershynin · 2017
Cited alongside, same era.
Closest in time.
Adversarial vulnerability for any classifier
Alhussein Fawzi, Hamza Fawzi, and Omar Fawzi · 2018
Closest in time.
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S Schoenholz, Maithra Raghu, Martin Wattenberg, and Ian Goodfellow · 2018
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Characterizing adversarial subspaces using local intrinsic dimensionality
Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Michael E Houle, Grant Schoenebeck, Dawn Song, and James Bailey · 2018
Closest in time.
Saeed Mahloujifar, Dimitrios I Diochnos, and Mohammad Mahmoody · 2018
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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Defense-GAN: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
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Adversarial vulnerability of neural networks increases with input dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Bernhard Schölkopf, Léon Bottou, and David Lopez-Paz · 2018
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Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
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Analyzing the robustness of nearest neighbors to adversarial examples
Yizhen Wang, Somesh Jha, and Kamalika Chaudhuri · 2018
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