most citedRobust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks

9 citations · 10 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CR2019★ 1 cited

Reducing audio membership inference attack accuracy to chance: 4 defenses

Michael Lomnitz, Nina Lopatina, Paul Gamble +4

It is critical to understand the privacy and robustness vulnerabilities of machine learning models, as their implementation expands in scope. In membership inference attacks, adver…

cs.LG2019★ 9 cited

Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks

Felipe A. Mejia, Paul Gamble, Zigfried Hampel-Arias +4

Adversarial training was introduced as a way to improve the robustness of deep learning models to adversarial attacks. This training method improves robustness against adversarial…