VIDEOGUARD: PROTECTING VIDEO CONTENT FROM UNAUTHORIZED EDITING

Junjie Cao, Hongxiang Li, Xinchun Yu, Jindong Gu, Li KaiZhou, Yansong Tang, Xiao-Ping Zhang

Keywords: Diffusion Models, Video Editing Protection

TL;DR: Propose a method for protecting videos from malicious editing

Abstract: With the rapid development of generative technology, current generative models can generate high-fidelity digital content and edit it in a controlled manner. However, there is a risk that malicious individuals might misuse these capabilities for misleading or unlawful activities. Although existing research has attempted to shield photographic images from being manipulated by generative models, there remains a significant disparity in the protection offered to video content editing. To bridge the gap, we propose a protection method named VideoGuard, which can effectively protect videos from unauthorized malicious editing. This protection is achieved through the subtle introduction of nearly unnoticeable perturbation that interferes with the functioning of the intended generative diffusion models. Different from images, videos consist of sequential frames, containing not only visual content but also motion dynamics. Due to the redundancy between video frames, and inter-frame attention mechanism in video diffusion models, simply applying image-based protection methods separately to every video frame can not shield video from unauthorized editing. To tackle the above challenge, rather than optimize perturbation in a frame-wise manner like image-based methods, we adopt joint frame optimization, treating all the video frames as an optimization entity. Furthermore, we extract video motion information and fuse it into optimization objectives. Thereby, these alterations can effectively compel the models to produce outputs that are implausible and inconsistent. We provide a pipeline to optimize such a perturbation. Finally, we use both objective metrics and subjective metrics to demonstrate the efficacy of our method, and the results show that the protection performance of VideoGuard is superior to all the baseline methods.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 6023