Towards Physical World Backdoor Attacks against Skeleton Action Recognition
<|reference_start|>Towards Physical World Backdoor Attacks against Skeleton Action Recognition: Skeleton Action Recognition (SAR) has attracted significant interest for its efficient representation of the human skeletal structure. Despite its advancements, recent studies have raised security concerns in SAR models, particularly their vulnerability to adversarial attacks. However, such strategies are limited to digital scenarios and ineffective in physical attacks, limiting their real-world applicability. To investigate the vulnerabilities of SAR in the physical world, we introduce the Physical Skeleton Backdoor Attacks (PSBA), the first exploration of physical backdoor attacks against SAR. Considering the practicalities of physical execution, we introduce a novel trigger implantation method that integrates infrequent and imperceivable actions as triggers into the original skeleton data. By incorporating a minimal amount of this manipulated data into the training set, PSBA enables the system misclassify any skeleton sequences into the target class when the trigger action is present. We examine the resilience of PSBA in both poisoned and clean-label scenarios, demonstrating its efficacy across a range of datasets, poisoning ratios, and model architectures. Additionally, we introduce a trigger-enhancing strategy to strengthen attack performance in the clean label setting. The robustness of PSBA is tested against three distinct backdoor defenses, and the stealthiness of PSBA is evaluated using two quantitative metrics. Furthermore, by employing a Kinect V2 camera, we compile a dataset of human actions from the real world to mimic physical attack situations, with our findings confirming the effectiveness of our proposed attacks. Our project website can be found at https://qichenzheng.github.io/psba-website.<|reference_end|>
@article{zheng2024towards,
    title={Towards Physical World Backdoor Attacks against Skeleton Action
  Recognition},
    author={Qichen Zheng, Yi Yu, Siyuan Yang, Jun Liu, Kwok-Yan Lam, and Alex Kot},
    journal={arXiv preprint arXiv:2408.08671},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08671},
    primaryClass={cs.CR cs.CV}
}