My Video Stays Mine: Temporally Consistent Universal Adversarial Perturbations against Video Customization

Recent diffusion-based video generation models have enabled high-quality video customization through both tuning-based pipelines, which fine-tune a video diffusion model, and reference-based pipelines such as image-to-video generation. However, these capabilities raise serious concerns about facial privacy and security. Existing anti-diffusion protections focus on the image domain or on reference-based I2V pipelines, leaving the tuning-based video customization unexplored. Protecting videos in this setting raises three challenges: image-level perturbations are erased by temporal operations, a perturbation tied to one clip fails to generalize across videos and lengths, and inconsistent temporal noise is easily removed by temporal attack. To address these challenges, we propose Temporally Consistent Universal Adversarial Perturbations (TC-UAP), the first protection method against both reference- and tuning-based video customization. TC-UAP learns a multi-frame universal adversarial perturbation over a set of videos of the same identity, so that a single perturbation can transfer to unseen videos and arbitrary clip lengths of that identity. Besides, we further enforce consistency through intrinsic temporal modeling and an extrinsic surrogate temporal-attack loss, ensuring robustness against temporal attacks. Extensive quantitative and qualitative experiments show that TC-UAP degrades identity preservation more than existing baselines under both tuning-based and reference-based video customization, and remains robust under three unseen temporal attacks.