Adversarially Robust Learning Could Leverage Computational Hardness
Authors: Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody | Published: 2019-05-28 | Updated: 2019-12-19
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Non-Determinism in Neural Networks for Adversarial Robustness
Authors: Daanish Ali Khan, Linhong Li, Ninghao Sha, Zhuoran Liu, Abelino Jimenez, Bhiksha Raj, Rita Singh | Published: 2019-05-26
Rearchitecting Classification Frameworks For Increased Robustness
Authors: Varun Chandrasekaran, Brian Tang, Nicolas Papernot, Kassem Fawaz, Somesh Jha, Xi Wu | Published: 2019-05-26 | Updated: 2019-12-03
Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling
Authors: Avishek Joey Bose, Andre Cianflone, William L. Hamilton | Published: 2019-05-26 | Updated: 2020-01-20
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
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
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