Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking
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.
682dd41740c6417d995087de
Zihan Su
admin_assigned
2025-05-28T16:17:54.970Z
648dca31385b84261811505d
/avatars/dfd124e3b5ffed8b0d3f429be0f7bdc0.svg
Zihan Su
user
Sugewud
682dd41740c6417d995087df
Xuerui Qiu
682dd41740c6417d995087e0
Hongbin Xu
682dd41740c6417d995087e1
Tangyu Jiang
682dd41740c6417d995087e2
Junhao Zhuang
claimed_verified
2025-07-13T07:30:14.262Z
64970d3d9c3b29dca8633f87
/avatars/11e3c9c66d28490d6d09925f9aa47cd1.svg
JunhaoZhuang
user
JunhaoZhuang
682dd41740c6417d995087e3
Chun Yuan
682dd41740c6417d995087e4
Ming Li
682dd41740c6417d995087e5
Shengfeng He
682dd41740c6417d995087e6
Fei Richard Yu
Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking
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.
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.
generative watermarking hierarchical coarse-to-fine adaptive matching 3D wavelet transform Mamba architecture spatiotemporal local scanning state space models watermark embedding watermark retrieval