VideoGuard: Protecting Video Content from Unauthorized Editing

TL;DR

VideoGuard introduces a novel method to protect videos from unauthorized editing by applying subtle, optimized perturbations that disrupt generative diffusion models, leveraging joint frame optimization and motion information.

Contribution

The paper presents a new video protection technique that effectively defends against malicious editing by integrating inter-frame information and joint optimization, surpassing existing image-based methods.

Findings

VideoGuard significantly reduces unauthorized editing success.

The method outperforms baseline protection techniques.

Protection effectiveness is validated through both objective and subjective metrics.

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 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 perturbations that interfere with the functioning of the intended generative diffusion models. 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, we adopt joint frame optimization, treating all video frames as an optimization entity. Furthermore, we extract video motion information and fuse it into optimization objectives. Thus, these alterations can effectively force the models to produce outputs that are implausible and inconsistent. We provide a pipeline to optimize this 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.