43 citations · 87 across the 6 of their papers we have counts for
Showing 2019Show all
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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…