SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks

• URL: http://arxiv.org/abs/2406.01894v1
• Date: Tue, 4 Jun 2024 01:58:32 GMT
• Title: SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks
• Authors: Yi Pan, Jun-Jie Huang, Zihan Chen, Wentao Zhao, Ziyue Wang,
• Abstract summary: The existing adversarial attack methods mainly take a gradient-based approach and generate adversarial videos with noticeable perturbations. We propose a novel Sparse Adversarial Attack via S-Brittany Invertible Neural Networks (VASTIN) to generate adversarial videos through imperceptible feature space information exchanging. 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.
• Score: 14.87613382899623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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