  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 imperceptible
adversarial examples. Existing works typically employ 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 adversarial 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 outperforms the state-of-the-art method, improving
the top-1 robust accuracy by an average of 4.99%. Furthermore, our approach
consistently improves clean accuracy by an average of 8.72%. Our code is
available at
https://github.com/serendipity1122/Pre-trained-Model-Guided-Fine-Tuning-for-Zero-Shot-Adversarial-Robustness.
