Meta Adversarial Perturbations

• URL: http://arxiv.org/abs/2111.10291v1
• Date: Fri, 19 Nov 2021 16:01:45 GMT
• Title: Meta Adversarial Perturbations
• Authors: Chia-Hung Yuan, Pin-Yu Chen, Chia-Mu Yu
• Abstract summary: We show the existence of a meta adversarial perturbation (MAP) MAP causes natural images to be misclassified with high probability after being updated through only a one-step gradient ascent update. We 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.
• Score: 66.43754467275967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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