A Direct Approach to Robust Deep Learning Using Adversarial Networks
Authors: Huaxia Wang, Chun-Nam Yu | Published: 2019-05-23
敵対的サンプル
On Norm-Agnostic Robustness of Adversarial Training
Authors: Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin | Published: 2019-05-15
Interpreting and Evaluating Neural Network Robustness
Authors: Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen | Published: 2019-05-10
Adversarial Image Translation: Unrestricted Adversarial Examples in Face Recognition Systems
Authors: Kazuya Kakizaki, Kosuke Yoshida | Published: 2019-05-09 | Updated: 2020-01-28
Adaptive Generation of Unrestricted Adversarial Inputs
Authors: Isaac Dunn, Hadrien Pouget, Tom Melham, Daniel Kroening | Published: 2019-05-07 | Updated: 2019-10-01
Adversarial Examples Are Not Bugs, They Are Features
Authors: Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry | Published: 2019-05-06 | Updated: 2019-08-12
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples
Authors: Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal | Published: 2019-05-05
Transfer of Adversarial Robustness Between Perturbation Types
Authors: Daniel Kang, Yi Sun, Tom Brown, Dan Hendrycks, Jacob Steinhardt | Published: 2019-05-03
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Authors: Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong | Published: 2019-05-01 | Updated: 2019-12-09
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Authors: Francesco Crecchi, Davide Bacciu, Battista Biggio | Published: 2019-04-30 | Updated: 2019-05-01