title: Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking
summary: The explosive growth of generative video models has amplified the demand for
  reliable copyright preservation of AI-generated content. Despite its popularity
  in image synthesis, invisible generative watermarking remains largely
  underexplored in video generation. To address this gap, we propose Safe-Sora,
  the first framework to embed graphical watermarks directly into the video
  generation process. Motivated by the observation that watermarking performance
  is closely tied to the visual similarity between the watermark and cover
  content, we introduce a hierarchical coarse-to-fine adaptive matching
  mechanism. Specifically, the watermark image is divided into patches, each
  assigned to the most visually similar video frame, and further localized to the
  optimal spatial region for seamless embedding. To enable spatiotemporal fusion
  of watermark patches across video frames, we develop a 3D wavelet
  transform-enhanced Mamba architecture with a novel spatiotemporal local
  scanning strategy, effectively modeling long-range dependencies during
  watermark embedding and retrieval. To the best of our knowledge, this is the
  first attempt to apply state space models to watermarking, opening new avenues
  for efficient and robust watermark protection. Extensive experiments
  demonstrate that Safe-Sora achieves state-of-the-art performance in terms of
  video quality, watermark fidelity, and robustness, which is largely attributed
  to our proposals. We will release our code upon publication.
paper_authors._id: 682dd41740c6417d995087de
paper_authors.name: Zihan Su
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T16:17:54.970Z
paper_authors._id: 648dca31385b84261811505d
paper_authors.avatarUrl: /avatars/dfd124e3b5ffed8b0d3f429be0f7bdc0.svg
paper_authors.fullname: Zihan Su
paper_authors.type: user
paper_authors.user: Sugewud
paper_authors._id: 682dd41740c6417d995087df
paper_authors.name: Xuerui Qiu
paper_authors._id: 682dd41740c6417d995087e0
paper_authors.name: Hongbin Xu
paper_authors._id: 682dd41740c6417d995087e1
paper_authors.name: Tangyu Jiang
paper_authors._id: 682dd41740c6417d995087e2
paper_authors.name: Junhao Zhuang
paper_authors._id: 682dd41740c6417d995087e3
paper_authors.name: Chun Yuan
paper_authors._id: 682dd41740c6417d995087e4
paper_authors.name: Ming Li
paper_authors._id: 682dd41740c6417d995087e5
paper_authors.name: Shengfeng He
paper_authors._id: 682dd41740c6417d995087e6
paper_authors.name: Fei Richard Yu
paper_title: Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking
paper_summary: The explosive growth of generative video models has amplified the demand for
  reliable copyright preservation of AI-generated content. Despite its popularity
  in image synthesis, invisible generative watermarking remains largely
  underexplored in video generation. To address this gap, we propose Safe-Sora,
  the first framework to embed graphical watermarks directly into the video
  generation process. Motivated by the observation that watermarking performance
  is closely tied to the visual similarity between the watermark and cover
  content, we introduce a hierarchical coarse-to-fine adaptive matching
  mechanism. Specifically, the watermark image is divided into patches, each
  assigned to the most visually similar video frame, and further localized to the
  optimal spatial region for seamless embedding. To enable spatiotemporal fusion
  of watermark patches across video frames, we develop a 3D wavelet
  transform-enhanced Mamba architecture with a novel spatiotemporal local
  scanning strategy, effectively modeling long-range dependencies during
  watermark embedding and retrieval. To the best of our knowledge, this is the
  first attempt to apply state space models to watermarking, opening new avenues
  for efficient and robust watermark protection. Extensive experiments
  demonstrate that Safe-Sora achieves state-of-the-art performance in terms of
  video quality, watermark fidelity, and robustness, which is largely attributed
  to our proposals. We will release our code upon publication.
paper_ai_summary: Safe-Sora embeds invisible watermarks into AI-generated videos using a hierarchical adaptive matching mechanism and a 3D wavelet transform-enhanced Mamba architecture, achieving top performance in video quality, watermark fidelity, and robustness.
paper_ai_keywords: generative watermarking hierarchical coarse-to-fine adaptive matching 3D wavelet transform Mamba architecture spatiotemporal local scanning state space models watermark embedding watermark retrieval