Adversarial attack on skeletal motion is a hot topic. However, existing researches only consider part of dynamic features when measuring distance between skeleton graph sequences, which results in poor imperceptibility. To this end, we propose a novel adversarial attack method to attack action recognizers for skeletal motions. Firstly, our method systematically proposes a dynamic distance function to measure the difference between skeletal motions. Meanwhile, we innovatively introduce emotional features for complementary information. In addition, we use Alternating Direction Method of Multipliers(ADMM) to solve the constrained optimization problem, which generates adversarial samples with better imperceptibility to deceive the classifiers. Experiments show that our method is effective on multiple action classifiers and datasets. When the perturbation magnitude measured by l norms is the same, the dynamic perturbations generated by our method are much lower than that of other methods. What's more, we are the first to prove the effectiveness of emotional features, and provide a new idea for measuring the distance between skeletal motions.

翻译：骨架运动对抗攻击是当前研究热点。然而，现有研究在衡量骨架图序列间距离时仅考虑部分动态特征，导致攻击隐蔽性不足。为此，本文提出一种新颖的对抗攻击方法，针对骨架动作识别器进行攻击。首先，本方法系统性地提出一种动态距离函数以量化骨架运动间的差异。同时，我们创新性地引入情感特征作为补充信息。此外，采用交替方向乘子法（ADMM）求解约束优化问题，从而生成具有更强隐蔽性的对抗样本以欺骗分类器。实验表明，本方法在多种动作分类器和数据集上均有效。当以l范数衡量的扰动幅度相同时，本方法生成的动态扰动显著低于其他方法。更重要的是，我们首次证明了情感特征的有效性，并为衡量骨架运动间距离提供了新思路。