Intriguing Properties of Input-dependent Randomized Smoothing

Intriguing Properties of Input-dependent Randomized Smoothing

11 October 2021

Peter Súkeník

Stephan Günnemann

Papers citing "Intriguing Properties of Input-dependent Randomized Smoothing"

19 / 19 papers shown

Title
Deep Learning Through the Lens of Example Difficulty R. Baldock Hartmut Maennel Behnam Neyshabur 72 160 0 17 Jun 2021
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy Chong Chen Kezhi Kong Peihong Yu J. Luque Tom Goldstein Furong Huang AAML 37 8 0 07 Mar 2021
Data-Dependent Randomized Smoothing Motasem Alfarra Adel Bibi Philip Torr Guohao Li UQCV 58 35 0 08 Dec 2020
Expressivity of Deep Neural Networks Ingo Gühring Mones Raslan Gitta Kutyniok 54 51 0 09 Jul 2020
Analyzing Accuracy Loss in Randomized Smoothing Defenses Yue Gao Harrison Rosenberg Kassem Fawaz S. Jha Justin Hsu AAML 47 6 0 03 Mar 2020
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 130 2,036 0 08 Feb 2019
Sorting out Lipschitz function approximation Cem Anil James Lucas Roger C. Grosse 86 321 0 13 Nov 2018
Certified Adversarial Robustness with Additive Noise Bai Li Changyou Chen Wenlin Wang Lawrence Carin AAML 91 348 0 10 Sep 2018
Towards Fast Computation of Certified Robustness for ReLU Networks Tsui-Wei Weng Huan Zhang Hongge Chen Zhao Song Cho-Jui Hsieh Duane S. Boning Inderjit S. Dhillon Luca Daniel AAML 89 691 0 25 Apr 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks Yusuke Tsuzuku Issei Sato Masashi Sugiyama AAML 103 307 0 12 Feb 2018
Certified Robustness to Adversarial Examples with Differential Privacy Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel J. Hsu Suman Jana SILM AAML 92 932 0 09 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 205 3,184 0 01 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 155 2,147 0 21 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 287 12,060 0 19 Jun 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 118 1,857 0 20 May 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 467 3,140 0 04 Nov 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 254 19,045 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 257 14,912 1 21 Dec 2013
Rényi Divergence and Kullback-Leibler Divergence T. Erven P. Harremoes 79 1,338 0 12 Jun 2012