Evidence map›Paper›PMID 42230715›Full record

ArticleScientific reports2026

A strong robust multi level database image watermark embedding scheme based on the Chinese remainder theorem.

Huizheng Geng, Sixu Guo, Yingqing Liu, Chunmei Li

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Huizheng GengDepartment of Security Technology, China Mobile Research Institute, Beijing, 100053, China.
Sixu GuoDepartment of Security Technology, China Mobile Research Institute, Beijing, 100053, China. guosixu@chinamobile.com.
Yingqing LiuDepartment of Security Technology, China Mobile Research Institute, Beijing, 100053, China.
Chunmei LiDepartment of Security Technology, China Mobile Research Institute, Beijing, 100053, China.

Funding

Research and Verification of Key Technologies for Secure and Efficient Federated Learning U22B2038
6 · The paper itself

Abstract

Due to the fact that most existing digital watermarking schemes can only add watermark information once, repeated additions will overwrite the original watermark information, making it impossible to achieve multi-level traceability of data. In response to the above issues, this paper proposes a multi-level database image watermark embedding scheme (MIWC) based on the Chinese remainder theorem. Taking image watermarks as carriers, MIWC preprocesses images using methods such as Haar wavelet transform and Bloom filter to form a pixel. It then performs secret segmentation on watermark information in accordance with the properties of the Chinese Remainder Theorem, enabling hierarchical and item-by-item addition of the watermark information. Furthermore, it records and traces the entire data flow chain. Functional analysis demonstrates that MIWC possesses the multi-level watermark embedding capability that existing schemes lack, thus holding high practical application value. Experimental results indicate that MIWC is reversible, with both watermark embedding and extraction exhibiting high efficiency. Even with the embedding of multi-level watermarks, MIWC remains highly efficient and lightweight. Meanwhile, MIWC also demonstrates strong robustness, being capable of resisting geometric attacks (including rotation and scaling) and common attacks (including JPEG compression, JPEG2000, Gaussian white noise, and salt-and-pepper noise) targeting the watermark.

Indexed as

Bloom filterChinese remainder theoremDatabase watermarkHaar wavelet transformWatermark robustness

Identifiers

PMID42230715
PMCPMC13469092

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.