Revisiting the Robust Generalization of Adversarial Prompt Tuning
<|reference_start|>Revisiting the Robust Generalization of Adversarial Prompt Tuning: Understanding the vulnerability of large-scale pre-trained vision-language models like CLIP against adversarial attacks is key to ensuring zero-shot generalization capacity on various downstream tasks. State-of-the-art defense mechanisms generally adopt prompt learning strategies for adversarial fine-tuning to improve the adversarial robustness of the pre-trained model while keeping the efficiency of adapting to downstream tasks. Such a setup leads to the problem of over-fitting which impedes further improvement of the model's generalization capacity on both clean and adversarial examples. In this work, we propose an adaptive Consistency-guided Adversarial Prompt Tuning (i.e., CAPT) framework that utilizes multi-modal prompt learning to enhance the alignment of image and text features for adversarial examples and leverage the strong generalization of pre-trained CLIP to guide the model-enhancing its robust generalization on adversarial examples while maintaining its accuracy on clean ones. We also design a novel adaptive consistency objective function to balance the consistency of adversarial inputs and clean inputs between the fine-tuning model and the pre-trained model. We conduct extensive experiments across 14 datasets and 4 data sparsity schemes (from 1-shot to full training data settings) to show the superiority of CAPT over other state-of-the-art adaption methods. CAPT demonstrated excellent performance in terms of the in-distribution performance and the generalization under input distribution shift and across datasets.<|reference_end|>
@article{yang2024revisiting,
    title={Revisiting the Robust Generalization of Adversarial Prompt Tuning},
    author={Fan Yang, Mingxuan Xia, Sangzhou Xia, Chicheng Ma, Hui Hui},
    journal={arXiv preprint arXiv:2405.11154},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11154},
    primaryClass={cs.CV cs.AI}
}