VideoGuard: Protecting Video Content from Unauthorized Editing

2025-08-05

AI Summary

Existing image-level protection methods fail to mitigate malicious video editing enabled by generative AI, due to the unique temporal redundancy and cross-frame attention mechanisms in diffusion-based video generation. Method: We propose the first adversarial watermarking framework tailored for diffusion models in video. It jointly optimizes per-frame perturbations under optical flow-guided motion-aware loss, injecting imperceptible spatiotemporal noise to disrupt inter-frame attention during diffusion sampling. Contribution/Results: Our approach introduces optical-flow-constrained motion-consistency regularization and a novel inter-frame attention interference module, balancing robust defense and visual fidelity. Experiments demonstrate superior disruption of generative consistency against all baselines, while preserving video quality—achieving ΔPSNR < 0.1 dB and near-identical SSIM. A user study confirms subjective imperceptibility, validating both objective and perceptual transparency.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Adversarial Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Protect videos from unauthorized malicious editing
• Prevent generative models from manipulating video content
• Optimize perturbations to disrupt video diffusion models

Innovation

• Joint frame optimization for video protection
• Fusing motion information into optimization objectives
• Nearly unnoticeable perturbations disrupt diffusion models