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PRIME: Protect Your Videos From Malicious Editing
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PRIME: Protect Your Videos From Malicious Editing
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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.
Forward citations
Cited by 6 Pith papers
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Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezing
Vid-Freeze immunizes images by adding perturbations that target attention dynamics in I2V models to enforce temporal freezing and suppress motion synthesis.
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Delving into the Temporal Challenges of Unified Video Protection Against Image-to-Video and Fine-Tuning-based Customization
TC-UAP learns a shared multi-frame adversarial perturbation that protects videos of the same identity from both fine-tuning-based and reference-based video customization, remaining effective on unseen clips and under ...
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Anti-Prompt: Image Protection against Text-Guided Image-to-Video Generation
Imperceptible image perturbations that suppress text-conditioned attention and boost visual-only pathways disrupt text-guided I2V generation more efficiently than prior I2V protection.
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Anti-Prompt: Image Protection against Text-Guided Image-to-Video Generation
Anti-Prompt protects images from text-guided image-to-video generation by suppressing text-conditioned attention during denoising, producing visible generation failures.
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Anti-Prompt: Image Protection against Text-Guided Image-to-Video Generation
Anti-Prompt adds imperceptible perturbations to images that disrupt text-guided I2V generation by attenuating text-conditioned pathways, achieving protection on two model architectures with a new Video-LLM evaluation ...
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