On the Adversarial Robustness of Vision Transformers

TL;DR

This paper conducts a comprehensive study on the adversarial robustness of vision transformers, revealing their advantages over CNNs and MLP-Mixer, and analyzing factors influencing their robustness.

Contribution

It provides new insights into the robustness mechanisms of ViTs, compares them with CNNs, and explores methods to enhance their adversarial robustness.

Findings

ViTs are more robust to adversarial attacks than CNNs and MLP-Mixer.

High-frequency features correlate with model vulnerability.

Techniques from CNNs can improve ViT robustness.

Abstract

Following the success in advancing natural language processing and understanding, transformers are expected to bring revolutionary changes to computer vision. This work provides a comprehensive study on the robustness of vision transformers (ViTs) against adversarial perturbations. Tested on various white-box and transfer attack settings, we find that ViTs possess better adversarial robustness when compared with MLP-Mixer and convolutional neural networks (CNNs) including ConvNeXt, and this observation also holds for certified robustness. Through frequency analysis and feature visualization, we summarize the following main observations contributing to the improved robustness of ViTs: 1) Features learned by ViTs contain less high-frequency patterns that have spurious correlation, which helps explain why ViTs are less sensitive to high-frequency perturbations than CNNs and MLP-Mixer, and there is a high correlation between how much the model learns high-frequency features and its robustness against different frequency-based perturbations. 2) Introducing convolutional or tokens-to-token blocks for learning high-frequency features in ViTs can improve classification accuracy but at the cost of adversarial robustness. 3) Modern CNN designs that borrow techniques from ViTs including activation function, layer norm, larger kernel size to imitate the global attention, and patchify the images as inputs, etc., could help bridge the performance gap between ViTs and CNNs not only in terms of performance, but also certified and empirical adversarial robustness. Moreover, we show adversarial training is also applicable to ViT for training robust models, and sharpness-aware minimization can also help improve robustness, while pre-training with clean images on larger datasets does not significantly improve adversarial robustness.