  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.
