A DTCWT-SVD Based Video Watermarking resistant to frame rate conversion
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Abstract (arXiv)
Videos can be easily tampered, copied and redistributed by attackers for illegal and monetary usage. Such behaviors severely jeopardize the interest of content owners. Despite huge efforts made in digital video watermarking for copyright protection, typical distortions in video transmission including signal attacks, geometric attacks and temporal synchronization attacks can still easily erase the embedded signal. Among them, temporal synchronization attacks which include frame dropping, frame insertion and frame rate conversion is one of the most prevalent attacks. To address this issue, we present a new video watermarking based on joint Dual-Tree Cosine Wavelet Transformation (DTCWT) and Singular Value Decomposition (SVD), which is resistant to frame rate conversion. We first extract a set of candidate coefficient by applying SVD decomposition after DTCWT transform. Then, we simulate the watermark embedding by adjusting the shape of candidate coefficient. Finally, we perform group-level watermarking that includes moderate temporal redundancy to resist temporal desynchronization attacks. Extensive experimental results show that the proposed scheme is more resilient to temporal desynchronization attacks and performs better than the existing blind video watermarking schemes.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 23 references that are in the map. First posted on arXiv 2 Jun 2022.
Builds on
5 of 23 references.
References · 5
- 2018HiDDeN: Hiding Data With Deep Networkscited by 397
- 2018YouTube-VOS: Sequence-to-Sequence Video Object Segmentationcited by 232
- 2021DVMark: A Deep Multiscale Framework for Video Watermarkingcited by 46
- 2021From Image to Imuge: Immunized Image Generationcited by 11
- 2021Hiding Images into Images with Real-world Robustnesscited by 3
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2025The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fieldscited by 10
- 2022Robust Watermarking for Video Forgery Detection with Improved Imperceptibility and Robustnesscited by 3
- 2026Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Renderingcited by 0
- 2026Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering6 Oct 2026
- 2025The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields26 Feb 2025
- 2022Robust Watermarking for Video Forgery Detection with Improved Imperceptibility and Robustness7 Jul 2022
Similar, not cited
Close in wording, no citation either way
- 2025VideoMark: A Distortion-Free Robust Watermarking Framework for Video Diffusion Models
- 2024LVMark: Robust Watermark for Latent Video Diffusion Models
- 2024Video Seal: Open and Efficient Video Watermarking
- 2026An Efficient and Effective Watermarking Scheme for the Protection of the Intellectual Property Rights of Video Generative Models
- 2025LoT-Pass: Long-term-robust Image Watermarking for Image to Video Generation
- 2025Video Signature: Implicit Watermarking for Video Diffusion Models
- 2026FlowMark: Mask-Guided Video Watermarking
- 2025SPDMark: Selective Parameter Displacement for Robust Video Watermarking