PRIME: Protect Your Videos From Malicious Editing
Guanlin Li Shuai Yang Jie Zhang Tianwei Zhang
With the development of generative models, the quality of generated content
keeps increasing. Recently, open-source models have made it surprisingly easy
to manipulate and edit photos and videos, with just a few simple prompts. While
these cutting-edge technologies have gained popularity, they have also given
rise to concerns regarding the privacy and portrait rights of individuals.
Malicious users can exploit these tools for deceptive or illegal purposes.
Although some previous works focus on protecting photos against generative
models, we find there are still gaps between protecting videos and images in
the aspects of efficiency and effectiveness. Therefore, we introduce our
protection method, PRIME, to significantly reduce the time cost and improve the
protection performance. Moreover, to evaluate our proposed protection method,
we consider both objective metrics and human subjective metrics. Our evaluation
results indicate that PRIME only costs 8.3% GPU hours of the cost of the
previous state-of-the-art method and achieves better protection results on both
human evaluation and objective metrics. Code can be found in
https://github.com/GuanlinLee/prime.