MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense

19 May 2017

Papers citing "MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense"

4 / 4 papers shown

Title
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning Ethan Rathbun Kaleel Mahmood Sohaib Ahmad Caiwen Ding Marten van Dijk AAML 24 4 0 26 Nov 2022
StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection Aqib Rashid Jose Such AAML 24 5 0 15 Feb 2022
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective G. R. Machado Eugênio Silva R. Goldschmidt AAML 33 156 0 08 Sep 2020
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 50 226 0 18 Jul 2018