Step Restriction for Improving Adversarial Attacks

We propose an algorithm to automatically restrict the step size in the iterative optimization process with an application to adversarial attacks on speaker verification models. The proposed algorithm dynamically determines a subspace with a restriction radius r to which the Taylor approximation is applied at each iteration and then solves a linear problem within the subspace by using the projected gradient method. In experiments, we demonstrate adversarial attacks on three speaker verification models: i-vectors, SE-ResNet-34, and ECAPA-TDNN. We show that the degree of adversarial perturbations generated by the proposed algorithm is smaller than that generated by the conventional attack method.