A Mechanistic Analysis of Adversarial Fine-tuning of Vision Transformers
For practitioners using ViTs in high-risk applications, this work provides a mechanistic understanding of adversarial fine-tuning's limitations, though the findings are incremental.
The paper analyzes adversarial fine-tuning of Vision Transformers (ViTs) for robustness to image corruptions, finding that while it improves performance on seen corruptions, it does not transfer to unseen corruptions and does not fundamentally change sparse representations.
The widespread use of image classification models in high-risk, real-world situations necessitates making these models robust to slight disturbances or perturbations, such as blurring or sharpening, in the input images. While vision transformers (ViTs) play an integral role in many modern-day multi-modal models like Vision-Language-Models (VLMs) and Vision-Language-Action (VLA) models, they have received a lack of attention in the setting of robustness. In this work, we analyze the effects of adversarial fine-tuning, a popular method for improving model robustness to image perturbations, on a ViT's performance on perturbed and regular images through a mechanistic lens. We adversarially train a ViT on low-frequency and high-frequency image corruptions, and attempt to explain changes in downstream model performance through an examination of the model's attention mechanisms, internal representations, and knowledge evolution. Overall, our results suggest that, while fine-tuning on inputs with common corruptions improves model performance and certainty on new instances of corrupted data, these improvements do not transfer to other classes of corruptions not seen in the training. Additionally, despite observing changes in visual attention and knowledge evolution across layers, we found that adversarial training did not lead to fundamental changes in the sparse representations learned by ViTs.