Enhancing Adversarial Defense by k-Winners-Take-All
Authors: Chang Xiao, Peilin Zhong, Changxi Zheng | Published: 2019-05-25 | Updated: 2019-10-29
The advantages of multiple classes for reducing overfitting from test set reuse
Authors: Vitaly Feldman, Roy Frostig, Moritz Hardt | Published: 2019-05-24
Devil in the Detail: Attack Scenarios in Industrial Applications
Authors: Simon D. Duque Anton, Alexander Hafner, Hans Dieter Schotten | Published: 2019-05-24
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
Authors: Liwei Song, Reza Shokri, Prateek Mittal | Published: 2019-05-24 | Updated: 2019-08-25
Partially Encrypted Machine Learning using Functional Encryption
Authors: Theo Ryffel, Edouard Dufour-Sans, Romain Gay, Francis Bach, David Pointcheval | Published: 2019-05-24 | Updated: 2021-09-23
Power up! Robust Graph Convolutional Network via Graph Powering
Authors: Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi | Published: 2019-05-24 | Updated: 2021-09-21
Robust Attribution Regularization
Authors: Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha | Published: 2019-05-23 | Updated: 2019-10-26
Thwarting finite difference adversarial attacks with output randomization
Authors: Haidar Khan, Daniel Park, Azer Khan, Bülent Yener | Published: 2019-05-23
Adversarially Robust Distillation
Authors: Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein | Published: 2019-05-23 | Updated: 2019-12-02
A Direct Approach to Robust Deep Learning Using Adversarial Networks
Authors: Huaxia Wang, Chun-Nam Yu | Published: 2019-05-23