 face similar security risks from adversarial attacks as deep convolutional neural networks (CNNs). However%2C directly applying attack methodology on CNNs to ViTs has been demonstrated to be ineffective since the ViTs typically work on patch-wise encoding. This article explores the vulnerability of ViTs against adversarial attacks under a black-box scenario%2C and proposes a novel query-efficient hard-label adversarial attack method called AdvViT. Specifically%2C considering that ViTs are highly sensitive to patch modification%2C we propose to optimize the adversarial perturbation on the individual patches. To reduce the dimension of perturbation search space%2C we modify only a handful of low-frequency components of each patch. Moreover%2C we design a weight mask matrix for all patches to further optimize the perturbation on different regions of a whole image. We test six mainstream ViT backbones on the ImageNet-1k dataset. Experimental results show that compared with the state-of-the-art attacks on CNNs%2C our AdvViT achieves much lower %24L_2%24-norm distortion under the same query budget%2C sufficiently validating the vulnerability of ViTs against adversarial attacks.?quality=80&w=1200)
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