no code implementations • 25 Feb 2019 • Yi Han, David Hubczenko, Paul Montague, Olivier De Vel, Tamas Abraham, Benjamin I. P. Rubinstein, Christopher Leckie, Tansu Alpcan, Sarah Erfani
Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting.
no code implementations • 17 Aug 2018 • Yi Han, Benjamin I. P. Rubinstein, Tamas Abraham, Tansu Alpcan, Olivier De Vel, Sarah Erfani, David Hubczenko, Christopher Leckie, Paul Montague
Despite the successful application of machine learning (ML) in a wide range of domains, adaptability---the very property that makes machine learning desirable---can be exploited by adversaries to contaminate training and evade classification.