MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness

Vision Transformers (ViTs) achieve excellent performance in various tasks, but they are also vulnerable to adversarial attacks. Building robust ViTs is highly dependent on dedicated Adversarial Training (AT) strategies. However, current ViTs' adversarial training only employs well-established training approaches from convolutional neural network (CNN) training, where pre-training provides the basis for AT fine-tuning with the additional help of tailored data augmentations. In this paper, we take a closer look at the adversarial robustness of ViTs by providing a novel theoretical Mutual Information (MI) analysis in its autoencoder-based self-supervised pre-training. Specifically, we show that MI between the adversarial example and its latent representation in ViT-based autoencoders should be constrained by utilizing the MI bounds. Based on this finding, we propose a masked autoencoder-based pre-training method, MIMIR, that employs an MI penalty to facilitate the adversarial training of ViTs. Extensive experiments show that MIMIR outperforms state-of-the-art adversarially trained ViTs on benchmark datasets with higher natural and robust accuracy, indicating that ViTs can substantially benefit from exploiting MI. In addition, we consider two adaptive attacks by assuming that the adversary is aware of the MIMIR design, which further verifies the provided robustness.