MixTrain: Scalable Training of Verifiably Robust Neural Networks
Authors: Shiqi Wang, Yizheng Chen, Ahmed Abdou, Suman Jana | Published: 2018-11-06 | Updated: 2018-12-01
Robustness Improvement
SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters
Authors: Hassan Ali, Faiq Khalid, Hammad Tariq, Muhammad Abdullah Hanif, Semeen Rehman, Rehan Ahmed, Muhammad Shafique | Published: 2018-11-04 | Updated: 2020-05-15
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks
Authors: Siyue Wang, Xiao Wang, Pu Zhao, Wujie Wen, David Kaeli, Peter Chin, Xue Lin | Published: 2018-09-13
Enhanced Attacks on Defensively Distilled Deep Neural Networks
Authors: Yujia Liu, Weiming Zhang, Shaohua Li, Nenghai Yu | Published: 2017-11-16
Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples
Authors: Jihun Hamm, Akshay Mehra | Published: 2017-11-12 | Updated: 2018-06-27
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks
Authors: Thilo Strauss, Markus Hanselmann, Andrej Junginger, Holger Ulmer | Published: 2017-09-11 | Updated: 2018-02-08
Towards Proving the Adversarial Robustness of Deep Neural Networks
Authors: Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, Mykel J. Kochenderfer | Published: 2017-09-08