  Gait recognition is widely used in social security applications due to its
advantages in long-distance human identification. Recently, sequence-based
methods have achieved high accuracy by learning abundant temporal and spatial
information. However, their robustness under adversarial attacks has not been
clearly explored. In this paper, we demonstrate that the state-of-the-art gait
recognition model is vulnerable to such attacks. To this end, we propose a
novel temporal sparse adversarial attack method. Different from previous
additive noise models which add perturbations on original samples, we employ a
generative adversarial network based architecture to semantically generate
adversarial high-quality gait silhouettes or video frames. Moreover, by
sparsely substituting or inserting a few adversarial gait silhouettes, the
proposed method ensures its imperceptibility and achieves a high attack success
rate. The experimental results show that if only one-fortieth of the frames are
attacked, the accuracy of the target model drops dramatically.
