论文标题
探索视觉变压器的对抗性鲁棒性从光谱的角度来看
Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective
论文作者
论文摘要
视觉变压器已成为图像分类任务的强大工具，超过了卷积神经网络（CNN）的性能。最近，许多研究人员试图了解变压器对对抗攻击的鲁棒性。但是，以前的研究仅集中在空间域中的扰动上。本文提出了一个额外的观点，探讨了变压器对光谱域中频率选择性扰动的对抗鲁棒性。为了促进这两个域之间的比较，将攻击框架作为一种灵活的工具，用于实施对空间和光谱域中图像的攻击。实验表明，变压器更多地依赖于相位和低频信息，这可能使它们比CNN更容易受到频率选择性攻击的影响。这项工作为变压器的属性和对抗性鲁棒性提供了新的见解。
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.
