SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks
<|reference_start|>SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks: 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}{https://github.com/Brittany-Chen/SVASTIN}.<|reference_end|>
@article{pan2024svastin:,
    title={SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible
  Neural Networks},
    author={Yi Pan, Jun-Jie Huang, Zihan Chen, Wentao Zhao, Ziyue Wang},
    journal={arXiv preprint arXiv:2406.01894},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01894},
    primaryClass={cs.CV}
}