3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.LG2020
Improving Adversarial Robustness by Enforcing Local and Global Compactness
Anh Bui, Trung Le, He Zhao +4
The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application. Among many developed defens…
cs.LG2019★ 3 cited
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
He Zhao, Trung Le, Paul Montague +3
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier. Recently, v…
stat.ML2019
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence
Yi Han, David Hubczenko, Paul Montague +6
Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supe…