Saliency Attack: Towards Imperceptible Black-box Adversarial Attack

TL;DR

This paper introduces the Saliency Attack, a black-box adversarial attack method that restricts perturbations to salient regions, significantly improving imperceptibility while maintaining high attack success rates.

Contribution

It proposes a novel saliency-based perturbation restriction method compatible with existing attacks, enhancing imperceptibility without sacrificing success rate, and introduces the Saliency Attack for refined perturbations.

Findings

Achieves better imperceptibility scores (MAD, $L_0$, $L_2$)

Maintains high attack success rates

Perturbations are interpretable and robust to defenses

Abstract

Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker is only accessible to the model output. Recent studies have devised effective black-box attacks with high query efficiency. However, such performance is often accompanied by compromises in attack imperceptibility, hindering the practical use of these approaches. In this paper, we propose to restrict the perturbations to a small salient region to generate adversarial examples that can hardly be perceived. This approach is readily compatible with many existing black-box attacks and can significantly improve their imperceptibility with little degradation in attack success rate. Further, we propose the Saliency Attack, a new black-box attack aiming to refine the perturbations in the salient region to achieve even better imperceptibility. Extensive experiments show that compared to the state-of-the-art black-box attacks, our approach achieves much better imperceptibility scores, including most apparent distortion (MAD), $L_0$ and $L_2$ distances, and also obtains significantly higher success rates judged by a human-like threshold on MAD. Importantly, the perturbations generated by our approach are interpretable to some extent. Finally, it is also demonstrated to be robust to different detection-based defenses.