ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack

• URL: http://arxiv.org/abs/2408.05479v1
• Date: Sat, 10 Aug 2024 08:10:30 GMT
• Title: ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack
• Authors: Ziyi Gao, Kai Chen, Zhipeng Wei, Tingshu Mou, Jingjing Chen, Zhiyu Tan, Hao Li, Yu-Gang Jiang,
• Abstract summary: We propose the Recursive Token Merging for Video Diffusion-based Unrestricted Adrial Attack (ReToMe-VA) To achieve spatial imperceptibility, ReToMe-VA adopts a Timestep-wise Adrial Latent Optimization (TALO) strategy. To achieve temporal imperceptibility, ReToMe-VA introduces a Recursive Token Merging (ReToMe) mechanism by matching and merging tokens across video frames.
• Score: 71.2286719703198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent diffusion-based unrestricted attacks generate imperceptible adversarial examples with high transferability compared to previous unrestricted attacks and restricted attacks. However, existing works on diffusion-based unrestricted attacks are mostly focused on images yet are seldom explored in videos. In this paper, we propose the Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack (ReToMe-VA), which is the first framework to generate imperceptible adversarial video clips with higher transferability. Specifically, to achieve spatial imperceptibility, ReToMe-VA adopts a Timestep-wise Adversarial Latent Optimization (TALO) strategy that optimizes perturbations in diffusion models' latent space at each denoising step. TALO offers iterative and accurate updates to generate more powerful adversarial frames. TALO can further reduce memory consumption in gradient computation. Moreover, to achieve temporal imperceptibility, ReToMe-VA introduces a Recursive Token Merging (ReToMe) mechanism by matching and merging tokens across video frames in the self-attention module, resulting in temporally consistent adversarial videos. ReToMe concurrently facilitates inter-frame interactions into the attack process, inducing more diverse and robust gradients, thus leading to better adversarial transferability. Extensive experiments demonstrate the efficacy of ReToMe-VA, particularly in surpassing state-of-the-art attacks in adversarial transferability by more than 14.16% on average.

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