Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space
<|reference_start|>Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space: Skeletal motion plays a pivotal role in human activity recognition (HAR). Recently, attack methods have been proposed to identify the universal vulnerability of skeleton-based HAR(S-HAR). However, the research of adversarial transferability on S-HAR is largely missing. More importantly, existing attacks all struggle in transfer across unknown S-HAR models. We observed that the key reason is that the loss landscape of the action recognizers is rugged and sharp. Given the established correlation in prior studies~\cite{qin2022boosting,wu2020towards} between loss landscape and adversarial transferability, we assume and empirically validate that smoothing the loss landscape could potentially improve adversarial transferability on S-HAR. This is achieved by proposing a new post-train Dual Bayesian strategy, which can effectively explore the model posterior space for a collection of surrogates without the need for re-training. Furthermore, to craft adversarial examples along the motion manifold, we incorporate the attack gradient with information of the motion dynamics in a Bayesian manner. Evaluated on benchmark datasets, e.g. HDM05 and NTU 60, the average transfer success rate can reach as high as 35.9\% and 45.5\% respectively. In comparison, current state-of-the-art skeletal attacks achieve only 3.6\% and 9.8\%. The high adversarial transferability remains consistent across various surrogate, victim, and even defense models. Through a comprehensive analysis of the results, we provide insights on what surrogates are more likely to exhibit transferability, to shed light on future research.<|reference_end|>
@article{diao2024boosting,
    title={Boosting Adversarial Transferability for Skeleton-based Action
  Recognition via Exploring the Model Posterior Space},
    author={Yunfeng Diao, Baiqi Wu, Ruixuan Zhang, Xun Yang, Meng Wang, He Wang},
    journal={arXiv preprint arXiv:2407.08572},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08572},
    primaryClass={cs.CV}
}