Implanting Robust Watermarks in Latent Diffusion Models for Video Generation

作者：Xiaohang Liu, Heng Chang, Jinfu Wei, Lei Zhu, Emily Liu, Likun Li, Shiji Zhou, Chengyuan Li, Di Xu, Wei Gao · 年份：2025 · DOI：10.1109/icassp49660.2025.10888991 · 被引用次数：2 · 研究领域：Computer Graphics and Visualization Techniques、Generative Adversarial Networks and Image Synthesis、Music and Audio Processing

In the dynamic realm of digital media, latent diffusion models (LDM) have revolutionized the generation of videos, surpassing the capabilities of traditional generative models. This paper presents Stable Video Signature, a pioneering watermarking framework for LDM in video generation. Addressing the pressing need for copyright and model protection, our approach is the first to implant watermarks directly into the generation process of LDM based on video through a novel two-stage process. We first encode watermarks into the video’s latent space embedding, ensuring a holistic temporal decoding mechanism of LDM. Then watermark is integrated into the LDM’s decoder. In this process, our method can maintain frame consistency, preserving the quality and robustness of generated videos during watermark implantation. We further show that the framework embeds watermarks seamlessly into LDM, maintaining the original functionality of the models and exhibiting resilience against a spectrum of watermark attacks. Our comprehensive experiments on both text-to-video and image-to-video generation tasks substantiate the efficacy of Stable Video Signature. This work not only pioneers watermarking in video generation LDM, but also sets a precedent for safeguarding intellectual property in the age of advanced media synthesis.