Robust Token Gradient and Frequency-Aware Transferable Adversarial Attacks on Vision Transformers

Vision Transformers (ViTs) have achieved remarkable performance in computer vision tasks but are vulnerable to adversarial attacks. Recent studies have demonstrated the feasibility of crafting transferable adversarial examples based on ViT models. However, the adversarial examples generated by ViTs exhibit poor generalization, primarily due to structural differences between models and the tendency to overfit, which significantly hinders cross-architecture transferability. In this paper, we propose a novel framework to improve the generalization and transferability of adversarial attacks across diverse models, focusing on two key strategies: Token Gradient Divergence (TGD) and Multi-level Frequency-aware Attack (MFA). TGD, as a gradient regularization method, addresses the structural gradient issue of surrogate models, which is one of the causes of overfitting. By increasing the gradient divergence between tokens and eliminating the influence of the class token gradient, TGD enhances the transferability of adversarial examples across models. Meanwhile, MFA employs an implicit ensemble approach to enhance attack generalization. Through multiple spectral augmentations, it increases input diversity and simulates ensemble learning. By targeting critical frequency regions across models, MFA enhances adversarial example adaptability to different architectures, significantly boosting cross-architecture transferability. Extensive experiments on both ViTs and CNNs demonstrate that TGD-MFA significantly outperforms state-of-the-art transfer-based attacks, achieving substantial improvements in adversarial transferability and robustness.