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Over recent years, devising classification algorithms that are robust to adversarial perturbations has emerged as a challenging problem.
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The Concentration of Measure Phenomenon
Michel Ledoux · 2001
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On cryptographic assumptions and challenges
Moni Naor · 2003
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Sequences of games: a tool for taming complexity in security proofs
Victor Shoup · 2004
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Hardness amplification of weakly verifiable puzzles
Ran Canetti, Shai Halevi, and Michael Steiner · 2005
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Oded Goldreich · 2007
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Omkant Pandey, Rafael Pass, and Vinod Vaikuntanathan · 2008
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General hardness amplification of predicates and puzzles
Thomas Holenstein and Grant Schoenebeck · 2011
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Evasion Attacks against Machine Learning at Test Time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World
Leslie Valiant · 2013
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Introduction to modern cryptography
Jonathan Katz and Yehuda Lindell · 2014
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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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Learning and inference in the presence of corrupted inputs
Uriel Feige, Yishay Mansour, and Robert Schapire · 2015
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Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Cryptographic assumptions: A position paper
Shafi Goldwasser and Yael Tauman Kalai · 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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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 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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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Can adversarially robust learning leverage computational hardness?
Saeed Mahloujifar 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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Simplifying game-based definitions
Phillip Rogaway and Yusi Zhang · 2018
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Are adversarial examples inevitable?
Ali Shafahi, W Ronny Huang, Christoph Studer, Soheil Feizi, and Tom Goldstein · 2018
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Idan Attias, Aryeh Kontorovich, and Yishay Mansour · 2018
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
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Pac-learning in the presence of adversaries
Daniel Cullina, Arjun Nitin Bhagoji, and Prateek Mittal · 2018
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Adversarial risk and robustness: General definitions and implications for the uniform distribution
Dimitrios Diochnos, Saeed Mahloujifar, and Mohammad Mahmoody · 2018
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Limitations of adversarial robustness: strong no free lunch theorem
Elvis Dohmatob · 2018
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Adversarial vulnerability for any classifier
Alhussein Fawzi, Hamza Fawzi, and Omar Fawzi · 2018
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Robust inference for multiclass classification
Uriel Feige, Yishay Mansour, and Robert E Schapire · 2018
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Certifiable distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
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On adversarial risk and training
Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, and Pradeep Ravikumar · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and J Zico Kolter · 2018
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Rademacher complexity for adversarially robust generalization
Dong Yin, Kannan Ramchandran, and Peter Bartlett · 2018
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Computational limitations in robust classification and win-win results
Akshay Degwekar and Vinod Vaikuntanathan · 2019
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Lower bounds for adversarially robust pac learning
Dimitrios I Diochnos, Saeed Mahloujifar, and Mohammad Mahmoody · 2019
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Can adversarially robust learning leveragecomputational hardness?
Saeed Mahloujifar and Mohammad Mahmoody · 2019
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The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure
Saeed Mahloujifar, Dimitrios I. Diochnos, and Mohammad Mahmoody · 2019
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Vc classes are adversarially robustly learnable, but only improperly
Omar Montasser, Steve Hanneke, and Nathan Srebro · 2019
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