Pre-Trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness

Abstract

Large-scale pre-trained vision-language models like CLIP have demonstrated impressive performance across various tasks, and exhibit remarkable zero-shot generalization capability, while they are also vulnerable to impercep-tible adversarial examples. Existing works typically em-ploy adversarial training (fine-tuning) as a defense method against adversarial examples. However, direct application to the CLIP model may result in overfitting, compromising the model's capacity for generalization. In this paper, we propose Pre-trained Model Guided Adversarial Fine-Tuning (PMG-AFT) method, which leverages supervision from the original pre-trained model by carefully designing an auxiliary branch, to enhance the model's zero-shot ad-versarial robustness. Specifically, PMG-AFT minimizes the distance between the features of adversarial examples in the target model and those in the pre-trained model, aiming to preserve the generalization features already captured by the pre-trained model. Extensive Experiments on 15 zero-shot datasets demonstrate that PMG-AFT significantly outper-forms the state-of-the-art method, improving the top-1 ro-bust accuracy by an average of 4.99%. Furthermore, our approach consistently improves clean accuracy by an aver-age of 8.72%. Our code is available at here.1