Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking
Zihan Su, Xuerui Qiu, Hongbin Xu Xu, Tangyu Jiang, Jun-hao Zhuang, Chun Yuan, Ming Li, Shengfeng He, Fei Yu
The explosive growth of generative video models has amplified the demand forreliable copyright preservation of AI-generated content. Despite its popularity inimage synthesis, invisible generative watermarking remains largely underexploredin video generation. To address this gap, we propose Safe-Sora, the first frameworkto embed graphical watermarks directly into the video generation process. Motivated by the observation that watermarking performance is closely tied to the visualsimilarity between the watermark and cover content, we introduce a hierarchicalcoarse-to-fine adaptive matching mechanism. Specifically, the watermark image isdivided into patches, each assigned to the most visually similar video frame, andfurther localized to the optimal spatial region for seamless embedding. To enablespatiotemporal fusion of watermark patches across video frames, we develop a 3Dwavelet transform-enhanced Mamba architecture with a novel scanning strategy,effectively modeling long-range dependencies during watermark embedding andretrieval. To the best of our knowledge, this is the first attempt to apply state spacemodels to watermarking, opening new avenues for efficient and robust watermarkprotection. Extensive experiments demonstrate that Safe-Sora achieves state-of-the-art performance in terms of video quality, watermark fidelity, and robustness, whichis largely attributed to our proposals. Code and additional supporting materials areprovided in the supplementary.