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
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Robust Classification using Robust Feature Augmentation
Authors: Kevin Eykholt, Swati Gupta, Atul Prakash, Amir Rahmati, Pratik Vaishnavi, Haizhong Zheng | Published: 2019-05-26 | Updated: 2019-09-18
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
Shredder: Learning Noise Distributions to Protect Inference Privacy
Authors: Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani, Dean Tullsen, Hadi Esmaeilzadeh | Published: 2019-05-26 | Updated: 2020-10-27
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
Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders
Authors: Hebi Li, Qi Xiao, Shixin Tian, Jin Tian | Published: 2019-05-26
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