Evidence map›Paper›PMID 32469877›Full record

ArticlePloS one2020

Reversible data hiding techniques with high message embedding capacity in images.

Furqan Aziz, Taeeb Ahmad, Abdul Haseeb Malik, M Irfan Uddin, Shafiq Ahmad, Mohamed Sharaf

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

The trial behind it

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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

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

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

6 authors.

Furqan AzizCenter of Excellence in IT, Institute of Management Sciences, Peshawar, Pakistan.
Taeeb AhmadCenter of Excellence in IT, Institute of Management Sciences, Peshawar, Pakistan.
Abdul Haseeb MalikDepartment of Computer Science, University of Peshawar, Peshawar, Pakistan.
M Irfan UddinInstitute of Computing, Kohat University of Science and Technology, Kohat, Pakistan.ORCID 0000-0002-1355-3881
Shafiq AhmadDepartment of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Kingdom of Saudi Arabia.
Mohamed SharafDepartment of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Kingdom of Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reversible Data Hiding (RDH) techniques have gained popularity over the last two decades, where data is embedded in an image in such a way that the original image can be restored. Earlier works on RDH was based on the Image Histogram Modification that uses the peak point to embed data in the image. More recent works focus on the Difference Image Histogram Modification that exploits the fact that the neighbouring pixels of an image are highly correlated and therefore the difference of image makes more space to embed large amount of data. In this paper we propose a framework to increase the embedding capacity of reversible data hiding techniques that use a difference of image to embed data. The main idea is that, instead of taking the difference of the neighboring pixels, we rearrange the columns (or rows) of the image in a way that enhances the smooth regions of an image. Any difference based technique to embed data can then be used in the transformed image. The proposed method is applied on different types of images including textures, patterns and publicly available images. Experimental results demonstrate that the proposed method not only increases the message embedding capacity of a given image by more than 50% but also the visual quality of the marked image containing the message is more than the visual quality obtained by existing state-of-the-art reversible data hiding technique. The proposed technique is also verified by Pixel Difference Histogram (PDH) Stegoanalysis and results demonstrate that marked images generated by proposed method is undetectable by PDH analysis.

Indexed as

AlgorithmsComputer SimulationDatabases, FactualComputer SecurityHistological TechniquesHumansImage Processing, Computer-AssistedRecords

Identifiers

PMID32469877
PMCPMC7259517

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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.