EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
Authors: Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh | Published: 2017-09-13 | Updated: 2018-02-10
Certified Robustness
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
Learning Universal Adversarial Perturbations with Generative Models
Authors: Jamie Hayes, George Danezis | Published: 2017-08-17 | Updated: 2018-01-05
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
Authors: Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, Cho-Jui Hsieh | Published: 2017-08-14 | Updated: 2017-11-02
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
Authors: Ishai Rosenberg, Asaf Shabtai, Lior Rokach, Yuval Elovici | Published: 2017-07-19 | Updated: 2018-06-24
Houdini: Fooling Deep Structured Prediction Models
Authors: Moustapha Cisse, Yossi Adi, Natalia Neverova, Joseph Keshet | Published: 2017-07-17
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
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