Recent vision-language foundation models, such as CLIP, have demonstrated superior capabilities in learning representations that can be transferable across diverse range of downstream tasks and domains. With the emergence of such powerful models, it has become crucial to effectively leverage their capabilities in tackling challenging vision tasks. On the other hand, only a few works have focused on devising adversarial examples that transfer well to both unknown domains and model architectures. In this paper, we propose a novel transfer attack method called PDCL-Attack, which leverages the CLIP model to enhance the transferability of adversarial perturbations generated by a generative model-based attack framework. Specifically, we formulate an effective prompt-driven feature guidance by harnessing the semantic representation power of text, particularly from the ground-truth class labels of input images. To the best of our knowledge, we are the first to introduce prompt learning to enhance the transferable generative attacks. Extensive experiments conducted across various cross-domain and cross-model settings empirically validate our approach, demonstrating its superiority over state-of-the-art methods.

本研究针对现有对抗攻击方法在未知领域与模型架构下的转移性不足问题，提出了一种新颖的攻击方法PDCL-Attack。该方法利用CLIP模型和提示驱动的特征指导，提升生成对抗扰动的转移性，经过大量跨领域和跨模型的实验验证，显示出优于现有最先进方法的效果。

基于提示驱动的对比学习用于可转移的对抗攻击