UVCG: Leveraging Temporal Consistency for Universal Video Protection

TL;DR

UVCG is a novel method that uses imperceptible, temporally consistent perturbations to protect videos from malicious editing, effectively disrupting AI-based editing models while maintaining video quality.

Contribution

This paper introduces UVCG, a simple yet effective approach leveraging temporal consistency to safeguard videos against AI-driven editing, with a perturbation-reuse strategy for efficiency.

Findings

UVCG effectively prevents malicious video edits across multiple models.

The approach is highly transferable and maintains video quality.

Perturbation-reuse improves computational efficiency.

Abstract

The security risks of AI-driven video editing have garnered significant attention. Although recent studies indicate that adding perturbations to images can protect them from malicious edits, directly applying image-based methods to perturb each frame in a video becomes ineffective, as video editing techniques leverage the consistency of inter-frame information to restore individually perturbed content. To address this challenge, we leverage the temporal consistency of video content to propose a straightforward and efficient, yet highly effective and broadly applicable approach, Universal Video Consistency Guard (UVCG). UVCG embeds the content of another video(target video) within a protected video by introducing continuous, imperceptible perturbations which has the ability to force the encoder of editing models to map continuous inputs to misaligned continuous outputs, thereby inhibiting the generation of videos consistent with the intended textual prompts. Additionally leveraging similarity in perturbations between adjacent frames, we improve the computational efficiency of perturbation generation by employing a perturbation-reuse strategy. We applied UVCG across various versions of Latent Diffusion Models (LDM) and assessed its effectiveness and generalizability across multiple LDM-based editing pipelines. The results confirm the effectiveness, transferability, and efficiency of our approach in safeguarding video content from unauthorized modifications.