Meta Adversarial Perturbations

Abstract

A plethora of attack methods have been proposed to generate adversarial examples, among which the iterative methods have been demonstrated the ability to find a strong attack. However, the computation of an adversarial perturbation for a new data point requires solving a time-consuming optimization problem from scratch. To generate a stronger attack, it normally requires updating a data point with more iterations. In this paper, we show the existence of a meta adversarial perturbation (MAP), a better initialization that causes natural images to be misclassified with high probability after being updated through only a one-step gradient ascent update, and propose an algorithm for computing such perturbations. We conduct extensive experiments, and the empirical results demonstrate that state-of-the-art deep neural networks are vulnerable to meta perturbations. We further show that these perturbations are not only image-agnostic, but also model-agnostic, as a single perturbation generalizes well across unseen data points and different neural network architectures.