Summary of Uvcg: Leveraging Temporal Consistency For Universal Video Protection, by Kaizhou Li et al.
UVCG: Leveraging Temporal Consistency for Universal Video Protection
by KaiZhou Li, Jindong Gu, Xinchun Yu, Junjie Cao, Yansong Tang, Xiao-Ping Zhang
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Universal Video Consistency Guard (UVCG) tackles the security risks of AI-driven video editing by leveraging temporal consistency in videos. Unlike image-based methods, UVCG embeds another video’s content within a protected video through imperceptible perturbations, making it difficult for editing models to generate consistent outputs. Additionally, a perturbation-reuse strategy improves computational efficiency. The approach is tested across various Latent Diffusion Models (LDM) and multiple LDM-based editing pipelines, demonstrating effectiveness, transferability, and efficiency in safeguarding video content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI can now help protect videos from unauthorized edits! Researchers developed a new method called Universal Video Consistency Guard (UVCG). It makes it hard for AI to create fake videos by adding tiny changes to the original video. This helps keep videos safe from being altered without permission. The team tested this approach with different AI models and showed that it works well across many scenarios. |
Keywords
» Artificial intelligence » Diffusion » Transferability
