2023
On Adversarial Robustness of Audio Classifiers
ICASSP 2023accepted
We make three contributions to improve adversarial robustness of audio classifiers. First, most existing works focus on ℓ<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf>-norm bounded adversarial perturbations. Instead, we consider signal-to-noise…