Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition
This addresses security concerns for deploying skeleton-based action recognition in real-world systems like surveillance, but it is incremental as it builds on existing adversarial attack and defense research.
The paper tackles the problem of adversarial vulnerability in skeleton-based action recognition, which currently achieves about 90% accuracy in benign environments, by formulating adversarial skeleton generation as a constrained optimization problem and proposing an efficient defense method, with extensive evaluations showing effectiveness.
Skeleton-based action recognition has attracted increasing attention due to its strong adaptability to dynamic circumstances and potential for broad applications such as autonomous and anonymous surveillance. With the help of deep learning techniques, it has also witnessed substantial progress and currently achieved around 90\% accuracy in benign environment. On the other hand, research on the vulnerability of skeleton-based action recognition under different adversarial settings remains scant, which may raise security concerns about deploying such techniques into real-world systems. However, filling this research gap is challenging due to the unique physical constraints of skeletons and human actions. In this paper, we attempt to conduct a thorough study towards understanding the adversarial vulnerability of skeleton-based action recognition. We first formulate generation of adversarial skeleton actions as a constrained optimization problem by representing or approximating the physiological and physical constraints with mathematical formulations. Since the primal optimization problem with equality constraints is intractable, we propose to solve it by optimizing its unconstrained dual problem using ADMM. We then specify an efficient plug-in defense, inspired by recent theories and empirical observations, against the adversarial skeleton actions. Extensive evaluations demonstrate the effectiveness of the attack and defense method under different settings.