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
モデルの頑健性保証
CycleGAN, a Master of Steganography
Authors: Casey Chu, Andrey Zhmoginov, Mark Sandler | Published: 2017-12-08 | Updated: 2017-12-16
Generative Adversarial Perturbations
Authors: Omid Poursaeed, Isay Katsman, Bicheng Gao, Serge Belongie | Published: 2017-12-06 | Updated: 2018-07-06
Where Classification Fails, Interpretation Rises
Authors: Chanh Nguyen, Georgi Georgiev, Yujie Ji, Ting Wang | Published: 2017-12-02
Evaluating Robustness of Neural Networks with Mixed Integer Programming
Authors: Vincent Tjeng, Kai Xiao, Russ Tedrake | Published: 2017-11-20 | Updated: 2019-02-18
The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels
Authors: Yannic Kilcher, Thomas Hofmann | Published: 2017-11-15
LatentPoison – Adversarial Attacks On The Latent Space
Authors: Antonia Creswell, Anil A. Bharath, Biswa Sengupta | Published: 2017-11-08
Provable defenses against adversarial examples via the convex outer adversarial polytope
Authors: Eric Wong, J. Zico Kolter | Published: 2017-11-02 | Updated: 2018-06-08
Attacking Binarized Neural Networks
Authors: Angus Galloway, Graham W. Taylor, Medhat Moussa | Published: 2017-11-01 | Updated: 2018-01-31
Attacking the Madry Defense Model with $L_1$-based Adversarial Examples
Authors: Yash Sharma, Pin-Yu Chen | Published: 2017-10-30 | Updated: 2018-07-27