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.

翻译：随着生成模型的发展，生成内容的质量持续提升。近期，开源模型使得仅需简单提示便可极其容易地操纵和编辑照片及视频。尽管这些前沿技术广受欢迎，却也引发了人们对个人隐私和肖像权的担忧。恶意用户可能利用这些工具实施欺骗或非法行为。虽然先前已有一些工作专注于保护照片免受生成模型侵害，但我们发现在保护视频和图像之间，效率与效果方面仍存在差距。因此，我们提出保护方法PRIME，以显著降低时间成本并提升保护性能。此外，为评估所提出的保护方法，我们同时考虑了客观指标和人类主观指标。评估结果表明，PRIME仅消耗先前最优方法8.3%的GPU计算时间，并在人类评估和客观指标上均取得了更优的保护效果。代码可在https://github.com/GuanlinLee/prime获取。