Enhancing Adversarial Defense by k-Winners-Take-All

25 May 2019

Papers citing "Enhancing Adversarial Defense by k-Winners-Take-All"

8 / 58 papers shown

Title
Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples Kaleel Mahmood Deniz Gurevin Marten van Dijk Phuong Ha Nguyen AAML 12 22 0 18 Jun 2020
Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks Hokuto Hirano K. Koga Kazuhiro Takemoto AAML 19 47 0 22 May 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 91 1,785 0 03 Mar 2020
Adversarial Distributional Training for Robust Deep Learning Yinpeng Dong Zhijie Deng Tianyu Pang Hang Su Jun Zhu OOD 22 121 0 14 Feb 2020
Training Provably Robust Models by Polyhedral Envelope Regularization Chen Liu Mathieu Salzmann Sabine Süsstrunk AAML 23 7 0 10 Dec 2019
One Man's Trash is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples Chang Xiao Changxi Zheng AAML 25 19 0 25 Nov 2019
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses Yingwei Li S. Bai Cihang Xie Zhenyu A. Liao Xiaohui Shen Alan Yuille AAML 39 50 0 01 Apr 2019
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,112 0 04 Nov 2016