Lightweight Lipschitz Margin Training for Certified Defense against Adversarial Examples

20 November 2018

Papers citing "Lightweight Lipschitz Margin Training for Certified Defense against Adversarial Examples"

7 / 7 papers shown

Title
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks Yusuke Tsuzuku Issei Sato Masashi Sugiyama AAML 105 307 0 12 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 224 3,186 0 01 Feb 2018
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 307 12,069 0 19 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,725 0 19 May 2017
Parseval Networks: Improving Robustness to Adversarial Examples Moustapha Cissé Piotr Bojanowski Edouard Grave Yann N. Dauphin Nicolas Usunier AAML 138 808 0 28 Apr 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 472 3,144 0 04 Nov 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 340 7,985 0 23 May 2016