Foolbox: A Python toolbox to benchmark the robustness of machine learning models
Authors: Jonas Rauber, Wieland Brendel, Matthias Bethge | Published: 2017-07-13 | Updated: 2018-03-20
Certified Robustness
A Survey on Resilient Machine Learning
Authors: Atul Kumar, Sameep Mehta | Published: 2017-07-11
Towards Deep Learning Models Resistant to Adversarial Attacks
Authors: Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu | Published: 2017-06-19 | Updated: 2019-09-04
Adversarial-Playground: A Visualization Suite for Adversarial Sample Generation
Authors: Andrew Norton, Yanjun Qi | Published: 2017-06-06 | Updated: 2017-06-16
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Authors: Matthias Hein, Maksym Andriushchenko | Published: 2017-05-23 | Updated: 2017-11-05
Black-Box Attacks against RNN based Malware Detection Algorithms
Authors: Weiwei Hu, Ying Tan | Published: 2017-05-23
Ensemble Adversarial Training: Attacks and Defenses
Authors: Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel | Published: 2017-05-19 | Updated: 2020-04-26
Delving into adversarial attacks on deep policies
Authors: Jernej Kos, Dawn Song | Published: 2017-05-18
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
Authors: Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Li Chen, Michael E. Kounavis, Duen Horng Chau | Published: 2017-05-08
Universal Adversarial Perturbations Against Semantic Image Segmentation
Authors: Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox, Volker Fischer | Published: 2017-04-19 | Updated: 2017-07-31