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%.

本研究提出了预训练模型引导的对抗微调（PMG-AFT）方法，通过精心设计辅助分支，利用原始预训练模型的监督来增强模型的零样本对抗鲁棒性。对15个零样本数据集进行的广泛实验证明，PMG-AFT在顶部1的抗干扰准确性方面显著优于最先进的方法，平均提高了4.99%。此外，我们的方法还能持续改善干净准确性，平均提高了8.72%。

预训练模型引导的零样本对抗鲁棒性微调