Analyzing Adversarial Robustness of Vision Transformers against Spatial and Spectral Attacks
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Vision Transformers have emerged as a powerful architecture that can outperform convolutional neural networks (CNNs) in image classification tasks. Several attempts have been made to understand robustness of Transformers against adversarial attacks, but existing studies draw inconsistent results, i.e., some conclude that Transformers are more robust than CNNs, while some others find that they have similar degrees of robustness. In this paper, we address two issues unexplored in the existing studies examining adversarial robustness of Transformers. First, we argue that the image quality should be simultaneously considered in evaluating adversarial robustness. We find that the superiority of one architecture to another in terms of robustness can change depending on the attack strength expressed by the quality of the attacked images. Second, by noting that Transformers and CNNs rely on different types of information in images, we formulate an attack framework, called Fourier attack, as a tool for implementing flexible attacks, where an image can be attacked in the spectral domain as well as in the spatial domain. This attack perturbs the magnitude and phase information of particular frequency components selectively. Through extensive experiments, we find that Transformers tend to rely more on phase information and low frequency information than CNNs, and thus sometimes they are even more vulnerable under frequency-selective attacks. It is our hope that this work provides new perspectives in understanding the properties and adversarial robustness of Transformers.
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