34 citations · 44 across the 3 of their papers we have counts for
4 papers
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…
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…
The VOiCES from a Distance Challenge 2019 Evaluation Plan
Mahesh Kumar Nandwana, Julien van Hout, Mitchell McLaren +3
The "VOiCES from a Distance Challenge 2019" is designed to foster research in the area of speaker recognition and automatic speech recognition (ASR) with the special focus on singl…
Deep Speech Denoising with Vector Space Projections
Jeff Hetherly, Paul Gamble, Maria Barrios +2
We propose an algorithm to denoise speakers from a single microphone in the presence of non-stationary and dynamic noise. Our approach is inspired by the recent success of neural n…