Constrained Gradient Descent: A Powerful and Principled Evasion Attack
Against Neural Networks

Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks

28 December 2021

Michael K. Reiter

Papers citing "Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks"

19 / 19 papers shown

Title
Differentiable Adversarial Attacks for Marked Temporal Point Processes Pritish Chakraborty Vinayak Gupta R. Raj Srikanta J. Bedathur A. De AAML 416 0 0 17 Jan 2025
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 280 689 0 19 Oct 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 39 328 0 07 Oct 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 196 1,821 0 03 Mar 2020
Square Attack: a query-efficient black-box adversarial attack via random search Maksym Andriushchenko Francesco Croce Nicolas Flammarion Matthias Hein AAML 56 977 0 29 Nov 2019
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack Francesco Croce Matthias Hein AAML 82 483 0 03 Jul 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 98 752 0 31 May 2019
Adversarial Training for Free! Ali Shafahi Mahyar Najibi Amin Ghiasi Zheng Xu John P. Dickerson Christoph Studer L. Davis Gavin Taylor Tom Goldstein AAML 107 1,238 0 29 Apr 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 104 2,018 0 08 Feb 2019
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei Maya Kabkab Rama Chellappa AAML GAN 67 1,172 0 17 May 2018
On the Suitability of $L_p$ -norms for Creating and Preventing Adversarial Examples Mahmood Sharif Lujo Bauer Michael K. Reiter AAML 109 138 0 27 Feb 2018
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks J. Uesato Brendan O'Donoghue Aaron van den Oord Pushmeet Kohli AAML 128 600 0 15 Feb 2018
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 101 2,142 0 21 Aug 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 103 1,851 0 20 May 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 74 892 0 01 Mar 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 49 947 0 14 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 450 3,124 0 04 Nov 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 170 8,513 0 16 Aug 2016
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 185 14,831 1 21 Dec 2013

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