Summary of Prime: Protect Your Videos From Malicious Editing, by Guanlin Li et al.
PRIME: Protect Your Videos From Malicious Editing
by Guanlin Li, Shuai Yang, Jie Zhang, Tianwei Zhang
First submitted to arxiv on: 2 Feb 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 This research proposes a method called PRIME to protect videos and images from manipulation by generative models. The increasing ease of editing photos and videos using open-source models has raised concerns about privacy and portrait rights. While some previous works focus on protecting images, there is a gap in protecting videos efficiently and effectively. PRIME aims to bridge this gap by reducing the time cost and improving protection performance. Evaluation results show that PRIME only requires 8.3% of the GPU hours needed for the state-of-the-art method while achieving better protection results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a favorite photo or video, but someone else can easily change it to make it look fake. This is happening more often because of new technologies that let people edit photos and videos with just a few words. While this sounds cool, it also raises concerns about privacy and who gets to control how we are represented in pictures and videos. Some smart folks have been working on ways to keep our media safe from manipulation, but they haven’t focused as much on protecting videos yet. This new method, PRIME, tries to fix that by making it more efficient and effective at keeping our videos and images authentic. |
