Adversarial Examples: Attacks and Defenses for Deep Learning
Authors: Xiaoyong Yuan, Pan He, Qile Zhu, Xiaolin Li | Published: 2017-12-19 | Updated: 2018-07-07
Adversarial Learning
DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation
Authors: Catherine Wong | Published: 2017-12-14
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Authors: Wieland Brendel, Jonas Rauber, Matthias Bethge | Published: 2017-12-12 | Updated: 2018-02-16
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Authors: Battista Biggio, Fabio Roli | Published: 2017-12-08 | Updated: 2018-07-19
Improving Network Robustness against Adversarial Attacks with Compact Convolution
Authors: Rajeev Ranjan, Swami Sankaranarayanan, Carlos D. Castillo, Rama Chellappa | Published: 2017-12-03 | Updated: 2018-03-22
Where Classification Fails, Interpretation Rises
Authors: Chanh Nguyen, Georgi Georgiev, Yujie Ji, Ting Wang | Published: 2017-12-02
Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples
Authors: Jihun Hamm, Akshay Mehra | Published: 2017-11-12 | Updated: 2018-06-27
Intriguing Properties of Adversarial Examples
Authors: Ekin D. Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le | Published: 2017-11-08
Adversarial Frontier Stitching for Remote Neural Network Watermarking
Authors: Erwan Le Merrer, Patrick Perez, Gilles Trédan | Published: 2017-11-06 | Updated: 2019-08-07
Implicit Weight Uncertainty in Neural Networks
Authors: Nick Pawlowski, Andrew Brock, Matthew C. H. Lee, Martin Rajchl, Ben Glocker | Published: 2017-11-03 | Updated: 2018-05-25