Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders
Authors: Hebi Li, Qi Xiao, Shixin Tian, Jin Tian | Published: 2019-05-26
文献データベース
Adversarial Distillation for Ordered Top-k Attacks
Authors: Zekun Zhang, Tianfu Wu | Published: 2019-05-25
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks
Authors: Jirong Yi, Hui Xie, Leixin Zhou, Xiaodong Wu, Weiyu Xu, Raghuraman Mudumbai | Published: 2019-05-25
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
Authors: Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu | Published: 2019-05-25 | Updated: 2020-02-20
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