Robust and imperceptible adversarial video attack is challenging due to the
spatial and temporal characteristics of videos. The existing video adversarial
attack methods mainly take a gradient-based approach and generate adversarial
videos with noticeable perturbations. In this paper, we propose a novel Sparse
Adversarial Video Attack via Spatio-Temporal Invertible Neural Networks
(SVASTIN) to generate adversarial videos through spatio-temporal feature space
information exchanging. It consists of a Guided Target Video Learning (GTVL)
module to balance the perturbation budget and optimization speed and a
Spatio-Temporal Invertible Neural Network (STIN) module to perform
spatio-temporal feature space information exchanging between a source video and
the target feature tensor learned by GTVL module. Extensive experiments on
UCF-101 and Kinetics-400 demonstrate that our proposed SVASTIN can generate
adversarial examples with higher imperceptibility than the state-of-the-art
methods with the higher fooling rate. Code is available at
\href{https://github.com/Brittany-Chen/SVASTIN}{this https URL}.

使用稀疏对抗性视频攻击通过时空可逆神经网络在时空特征空间信息交换中生成对抗性视频，实验证明该方法比现有方法生成的对抗性示例具有更高的隐秘性和愚弄性。