Adversarial Logit Pairing
Authors: Harini Kannan, Alexey Kurakin, Ian Goodfellow | Published: 2018-03-16
モデルの頑健性保証
Generating Artificial Data for Private Deep Learning
Authors: Aleksei Triastcyn, Boi Faltings | Published: 2018-03-08 | Updated: 2019-04-28
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
Authors: Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E. Kounavis, Duen Horng Chau | Published: 2018-02-19
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks
Authors: Qi Liu, Tao Liu, Zihao Liu, Yanzhi Wang, Yier Jin, Wujie Wen | Published: 2018-02-14 | Updated: 2018-03-19
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
Authors: Adnan Siraj Rakin, Zhezhi He, Boqing Gong, Deliang Fan | Published: 2018-02-05 | Updated: 2018-02-07
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Authors: Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel | Published: 2018-01-31
A3T: Adversarially Augmented Adversarial Training
Authors: Akram Erraqabi, Aristide Baratin, Yoshua Bengio, Simon Lacoste-Julien | Published: 2018-01-12
Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks
Authors: Yongshuai Liu, Jiyu Chen, Hao Chen | Published: 2018-01-09 | Updated: 2018-12-08
Adversarial Perturbation Intensity Achieving Chosen Intra-Technique Transferability Level for Logistic Regression
Authors: Martin Gubri | Published: 2018-01-06
The Robust Manifold Defense: Adversarial Training using Generative Models
Authors: Ajil Jalal, Andrew Ilyas, Constantinos Daskalakis, Alexandros G. Dimakis | Published: 2017-12-26 | Updated: 2019-07-10