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A fundamental question in adversarial machine learning is whether a robust classifier exists for a given task.
Extremal properties of half-spaces for spherically invariant measures
Vladimir N Sudakov and Boris S Tsirelson · 1974
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The Brunn-Minkowski inequality in Gauss space
Christer Borell · 1975
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Concentration of measure and isoperimetric inequalities in product spaces
Michel Talagrand · 1995
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Isoperimetry and Gaussian analysis
Michel Ledoux · 1996
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The VC dimension of k k -fold union
David Eisenstat and Dana Angluin · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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A Probabilistic Theory of Pattern Recognition
Luc Devroye, László Györfi, and Gábor Lugosi · 2013
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 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 · 2014
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Label distribution learning
Xin Geng · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Deep label distribution learning with label ambiguity
Bin-Bin Gao, Chao Xing, Chen-Wei Xie, Jianxin Wu, and Xin Geng · 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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Adversarial vulnerability for any classifier
Alhussein Fawzi, Hamza Fawzi, and Omar Fawzi · 2018
Cited alongside, same era.
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S Schoenholz, Maithra Raghu, Martin Wattenberg, and Ian Goodfellow · 2018
Cited alongside, same era.
Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten · 2018
Cited alongside, same era.
Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Mądry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Certified defenses against adversarial examples
Generalized no free lunch theorem for adversarial robustness
Elvis Dohmatob · 2019
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Scalable verified training for provably robust image classification
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli · 2019
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O2U-Net: A simple noisy label detection approach for deep neural networks
Jinchi Huang, Lie Qu, Rongfei Jia, and Binqiang Zhao · 2019
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Understanding the (un)interpretability of natural image distributions using generative models
Ryen Krusinga, Sohil Shah, Matthias Zwicker, Tom Goldstein, and David Jacobs · 2019
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Certified adversarial robustness with additive noise
Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin · 2019
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Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
Cited alongside, same era.
Scaling provable adversarial defenses
Eric Wong, Frank R Schmidt, Jan Hendrik Metzen, and Zico Kolter · 2018
Cited alongside, same era.
Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2018
Cited alongside, same era.
Are labels required for improving adversarial robustness?
Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Alhussein Fawzi, Robert Stanforth, and Pushmeet Kohli · 2019
Cited alongside, same era.
Lower bounds on adversarial robustness from optimal transport
Arjun Nitin Bhagoji, Daniel Cullina, and Prateek Mittal · 2019
Cited alongside, same era.
Adversarial examples from computational constraints
Sebastien Bubeck, Yin Tat Lee, Eric Price, and Ilya Razenshteyn · 2019
Cited alongside, same era.
Human uncertainty makes classification more robust
Joshua C Peterson, Ruairidh M Battleday, Thomas L Griffiths, and Olga Russakovsky · 2019
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Are adversarial examples inevitable?
Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, and Tom Goldstein · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
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RobustBench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2020
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On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
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Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang · 2020
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Improved estimation of concentration under ℓ p \ell_{p} -norm distance metrics using half spaces
Jack Prescott, Xiao Zhang, and David Evans · 2021
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