Concept-based Adversarial Attack: a Probabilistic Perspective

TL;DR

This paper introduces a probabilistic, concept-based adversarial attack framework that generates diverse, concept-preserving adversarial examples by operating on concept distributions, improving attack diversity and efficiency.

Contribution

It extends adversarial attacks to operate on concept distributions, enabling more diverse and concept-preserving adversarial examples with a principled probabilistic approach.

Findings

More diverse adversarial examples generated

Higher attack efficiency achieved

Effective preservation of original concept

Abstract

We propose a concept-based adversarial attack framework that extends beyond single-image perturbations by adopting a probabilistic perspective. Rather than modifying a single image, our method operates on an entire concept - represented by a distribution - to generate diverse adversarial examples. Preserving the concept is essential, as it ensures that the resulting adversarial images remain identifiable as instances of the original underlying category or identity. By sampling from this concept-based adversarial distribution, we generate images that maintain the original concept but vary in pose, viewpoint, or background, thereby misleading the classifier. Mathematically, this framework remains consistent with traditional adversarial attacks in a principled manner. Our theoretical and empirical results demonstrate that concept-based adversarial attacks yield more diverse adversarial examples and effectively preserve the underlying concept, while achieving higher attack efficiency.