survey

A Survey on Reversible Data Hiding for Uncompressed Images

Authors:
Cheng Zhang

Hunan University, Changsha, China

Hunan University, Changsha, China

0000-0001-5669-162X
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,
Bo Ou

Hunan University, Changsha, China

Hunan University, Changsha, China

0000-0001-6936-9955
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,
Fei Peng

Guangzhou University, Guangzhou, China

Guangzhou University, Guangzhou, China

0000-0001-8053-4587
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,
Yao Zhao

Beijing Jiaotong University, Beijing, China

Beijing Jiaotong University, Beijing, China

0000-0002-8581-9554
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,
Keqin Li

State University of New York, New Paltz, USA

State University of New York, New Paltz, USA

0000-0001-5224-4048
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Authors Info & Claims

ACM Computing Surveys Volume 56 Issue 7Article No.: 180pp 1–33https://doi.org/10.1145/3645105

Published:09 April 2024Publication History

ACM Computing Surveys

Abstract

Reversible data hiding (RDH) has developed various theories and algorithms since the early 1990s. The existing works involve a large amount of specialized knowledge, making it difficult for researchers, especially primary learners, to have a good grounding in the basic ideas. In this survey, we will review the mainstream RDH algorithms in uncompressed images and analyze their unique features to provide readers with an introduction to basic topics in RDH. We analyze the most effective RDH frameworks and their common extensions. The classic techniques, including lossless compression-based RDH, difference expansion, integer transform, histogram shifting, prediction-error expansion (PEE), and their extensions, will be reviewed first. Then, three currently popular investigated schemes, i.e., multiple histograms modification, pairwise PEE, and pixel-value-ordering, are presented in detail. Four aspects of these mainstream techniques are reviewed and analyzed, including the evolution of embedding frameworks, detailed technological features, extensions, and the current state of the art. Furthermore, we look forward the possible future research based on early-age motivations.

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Index Terms

A Survey on Reversible Data Hiding for Uncompressed Images
1. Security and privacy
  1. Security services
    1. Authentication
    2. Digital rights management

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Published in
ACM Computing Surveys Volume 56, Issue 7
July 2024
1006 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3613612
Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
,
Michela Milano
University of Bologna, Italy
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
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Publication History
- Published: 9 April 2024
- Online AM: 8 February 2024
- Accepted: 31 January 2024
- Revised: 17 December 2023
- Received: 6 February 2023
Published in csur Volume 56, Issue 7

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Author Tags
Reversible data hiding
histogram shifting
prediction-error expansion
multiple histograms modification
pairwise prediction-error expansion
pixel-value-ordering
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A Survey on Reversible Data Hiding for Uncompressed Images

ACM Computing Surveys

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Improved PVO-based reversible data hiding

Reversible Data Hiding in Encrypted Images Based on Image Partition and Spatial Correlation

High-fidelity reversible data hiding scheme based on pixel-value-ordering and prediction-error expansion

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A Survey on Reversible Data Hiding for Uncompressed Images

ACM Computing Surveys

Abstract

REFERENCES

Cited By

Index Terms

Recommendations

Improved PVO-based reversible data hiding

Reversible Data Hiding in Encrypted Images Based on Image Partition and Spatial Correlation

High-fidelity reversible data hiding scheme based on pixel-value-ordering and prediction-error expansion

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