Imperceptible Black-box Attack via Refining in Salient Region

Zeyu Dai, Shengcai Liu, Ke Tang, Qing Li

Abstract: Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker only has query access to the model output. Recent studies have devised successful black-box attacks with high query efficiency. However, such performance often comes at the cost of the imperceptibility of adversarial attacks, which is essential for attackers. To address this issue, in this paper we propose to use segmentation priors for black-box attacks such that the perturbations are limited in the salient region. We find that state-of-the-art black-box attacks equipped with segmentation priors can achieve much better imperceptibility performance with little reduction in query efficiency and success rate. We further propose the Saliency Attack, a new gradient-free black-box attack that can further improve the imperceptibility by refining perturbations in the salient region. Experimental results show that the perturbations generated by our approach are much more imperceptible than the ones generated by other attacks, and are interpretable to some extent. Furthermore, our approach is found to be more robust to detection-based defense, which demonstrates its efficacy as well.