Multilayer Reversible Information Hiding with Prediction-Error Expansion and Dynamic Threshold Analysis.

I-Hui Pan, Ping-Sheng Huang, Te-Jen Chang, Hsiang-Hsiung Chen

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Content-Adaptive Reversible Data Hiding with Multi-Stage Prediction Schemes.Sensors (Basel, Switzerland) · 2025
Article

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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.

I-Hui PanAir Command and Staff College, National Defense University, Taoyuan 335, Taiwan.ORCID 0000-0002-6220-5357

Ping-Sheng HuangDepartment of Electronic Engineering, Ming Chuan University, Taoyuan 333, Taiwan.

Te-Jen ChangDepartment of Electrical and Electronic Engineering, Chung-Cheng Institute of Technology, National Defense University, Taoyuan 335, Taiwan.ORCID 0000-0002-0272-1712

Hsiang-Hsiung ChenDepartment of Electrical and Electronic Engineering, Chung-Cheng Institute of Technology, National Defense University, Taoyuan 335, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid development of internet and social media has driven the great requirement for information sharing and intelligent property protection. Therefore, reversible information embedding theory has marked some approaches for information security. Assuming reversibility, the original and embedded data must be completely restored. In this paper, a high-capacity and multilayer reversible information hiding technique for digital images was presented. First, the integer Haar wavelet transform scheme converted the cover image from the spatial into the frequency domain that was used. Furthermore, we applied dynamic threshold analysis, the parameters of the predicted model, the location map, and the multilayer embedding method to improve the quality of the stego image and restore the cover image. In comparison with current algorithms, the proposed algorithm often had better embedding capacity versus image quality performance.

Indexed as

dynamic threshold analysishigh-capacitymultilayerpredicted parameters adjustmentreversible information hiding

Identifiers

PMID35808367

DOI10.3390/s22134872

PMCPMC9269020

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