Learning to Transform Dynamically for Better Adversarial Transferability
<|reference_start|>Learning to Transform Dynamically for Better Adversarial Transferability: Adversarial examples, crafted by adding perturbations imperceptible to humans, can deceive neural networks. Recent studies identify the adversarial transferability across various models, \textit{i.e.}, the cross-model attack ability of adversarial samples. To enhance such adversarial transferability, existing input transformation-based methods diversify input data with transformation augmentation. However, their effectiveness is limited by the finite number of available transformations. In our study, we introduce a novel approach named Learning to Transform (L2T). L2T increases the diversity of transformed images by selecting the optimal combination of operations from a pool of candidates, consequently improving adversarial transferability. We conceptualize the selection of optimal transformation combinations as a trajectory optimization problem and employ a reinforcement learning strategy to effectively solve the problem. Comprehensive experiments on the ImageNet dataset, as well as practical tests with Google Vision and GPT-4V, reveal that L2T surpasses current methodologies in enhancing adversarial transferability, thereby confirming its effectiveness and practical significance. The code is available at https://github.com/RongyiZhu/L2T.<|reference_end|>
@article{zhu2024learning,
    title={Learning to Transform Dynamically for Better Adversarial Transferability},
    author={Rongyi Zhu, Zeliang Zhang, Susan Liang, Zhuo Liu, Chenliang Xu},
    journal={arXiv preprint arXiv:2405.14077},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14077},
    primaryClass={cs.CV cs.AI}
}