  The Vision Transformer has emerged as a powerful tool for image
classification tasks, surpassing the performance of convolutional neural
networks (CNNs). Recently, many researchers have attempted to understand the
robustness of Transformers against adversarial attacks. However, previous
researches have focused solely on perturbations in the spatial domain. This
paper proposes an additional perspective that explores the adversarial
robustness of Transformers against frequency-selective perturbations in the
spectral domain. To facilitate comparison between these two domains, an attack
framework is formulated as a flexible tool for implementing attacks on images
in the spatial and spectral domains. The experiments reveal that Transformers
rely more on phase and low frequency information, which can render them more
vulnerable to frequency-selective attacks than CNNs. This work offers new
insights into the properties and adversarial robustness of Transformers.
