Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders
Authors: Partha Ghosh, Arpan Losalka, Michael J Black | Published: 2018-05-31 | Updated: 2018-12-10
敵対的サンプル
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary
Authors: Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi | Published: 2018-05-15 | Updated: 2018-12-14
Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing
Authors: Jingyi Wang, Jun Sun, Peixin Zhang, Xinyu Wang | Published: 2018-05-14 | Updated: 2018-05-17
Black-box Adversarial Attacks with Limited Queries and Information
Authors: Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin | Published: 2018-04-23 | Updated: 2018-07-11
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks
Authors: Pu Zhao, Sijia Liu, Yanzhi Wang, Xue Lin | Published: 2018-04-09
Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks
Authors: Octavian Suciu, Radu Mărginean, Yiğitcan Kaya, Hal Daumé III, Tudor Dumitraş | Published: 2018-03-19 | Updated: 2019-03-08
Vulnerability of Deep Learning
Authors: Richard Kenway | Published: 2018-03-16
Detecting Adversarial Examples – A Lesson from Multimedia Forensics
Authors: Pascal Schöttle, Alexander Schlögl, Cecilia Pasquini, Rainer Böhme | Published: 2018-03-09
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
Certified Robustness to Adversarial Examples with Differential Privacy
Authors: Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Suman Jana | Published: 2018-02-09 | Updated: 2019-05-29