On Adversarial Vulnerability of Activation Functions in Automatic Speaker verification System

Abstract

Automatic Speaker verification (ASV) is the task of authenticating the claimed identity of a speaker from his/her voice characteristics. Even though deep neural network (DNN) based ASV systems achieved improved performance, recent investigations revealed that they are vulnerable to adversarial attacks. Several defense strategies have been proposed in the literature, which operates either at data augmentation or network architecture levels. Activation functions with local competition and stochasticity were proposed to improve the adversarial robustness of the DNN models. This work critically analyzes the adversarial vulnerability of the winner takes all (WTA), and its stochastic counterpart stochastic local winner takes all (SLWTA) activation functions. It is observed that while the SLWTA activation function offers improved robustness to the white box attacks, it is equally vulnerable to transferable black box attacks. We show that the adversarial robustness of SLWTA activation to white-box attacks can be attributed to its inability to generate effective adversarial examples rather than built-in robustness against adversarial attacks.