[2509.23279] Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezing

Computer Science > Computer Vision and Pattern Recognition arXiv:2509.23279 (cs) [Submitted on 27 Sep 2025 (v1), last revised 4 Apr 2026 (this version, v2)] Title:Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezing Authors:Rohit Chowdhury, Aniruddha Bala, Rohan Jaiswal, Siddharth Roheda View a PDF of the paper titled Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezing, by Rohit Chowdhury and 3 other authors View PDF HTML (experimental) Abstract:The rapid progress of image-to-video (I2V) generation models has introduced significant risks by enabling deceptive or malicious video synthesis from a single image. Prior defenses such as I2VGuard attempt to immunize images by inducing spatio-temporal degradation, which does not necessarily provide meaningful protection, since residual motion can still convey malicious intent. In this work, we introduce Vid-Freeze -- a novel adversarial defense that adds imperceptible perturbations to enforce temporal freezing in generated videos. Our method explicitly targets attention dynamics in I2V models to suppress motion synthesis. As a result, immunized images produce standstill or near-static videos, effectively blocking malicious content generation. Experiments demonstrate strong protection across models and support temporal freezing as a promising direction for proactive and meaningful defense against I2V misuse. Comments: Subjects: Computer Vision and Patt...