Prompt-Driven Contrastive Learning for Transferable Adversarial Attacks

Abstract:

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 \textbf{PDCL-Attack}, which leverages CLIP to enhance the transferability of adversarial perturbations generated within a generative model-based attack framework. Specifically, we exploit the joint vision-language space to formulate an effective prompt-driven feature guidance by harnessing the semantic representation power of text, particularly from the input ground truth. 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.