research-article

Multi-dimensional constraints-based PPVO for high fidelity reversible data hiding

Authors:

Wenbo WanAuthors Info & Claims

Volume 213, Issue PC

https://doi.org/10.1016/j.eswa.2022.119190

Published: 01 March 2023 Publication History

Abstract

Reversible data hiding (RDH) based on pixel value ordering (PVO) is an effective information security hiding technique that allows complete recovery of the cover image after data extraction. Current methods mainly adopt PVO-based prediction error histograms to obtain better embedding performance in terms of block prediction or pixel prediction, regardless of the correlation between image textures and different prediction contexts. In this work, we propose a multi-dimensional constraints (MDCs) method to explore prediction mechanism across three dimensions using a pixel-based PVO (PPVO) scheme for high-fidelity RDH. The method involves (a) a local complexity (LC) Constraint that focuses on pixels containing similar local complexity instances with more different quantization intervals, (b) an enclosing location (EL) constraint that discovers the most relevant pixels within the fully enclosed context for prediction error, and (c) an optimal mode (OM) constraint that fuses fully enclosed context adaptively based on optimal search during different subsets of prediction modes. In addition, we introduce the multi-dimensional constraints to determine the prediction context in the PPVO-based RDH method, which helps the prediction mechanism to obtain predictions that are more accurate. The proposed MDCs can be easily applied to existing PPVO-based RDH frameworks. Extensive experiments on test-image data sets show that the proposed method outperforms the current state-of-the-art PPVO approaches.

Highlights

•

A reversible information hiding method for PPVO based on Multi-Dimensional constraints is proposed.

•

The method proposes three constraints in order to obtain the optimal mode.

•

The method generates multiple prediction error histograms to achieve information hiding.

•

The method is superior to state-of-the-art PPVO-based RDH methods.

References

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Cited By

Meng FWu G(2024)A color image encryption and decryption scheme based on extended DNA coding and fractional-order 5D hyper-chaotic systemExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124413254:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124413
Zhang HSun HMeng F(2024)Adaptive reversible data hiding scheme based on difference division interpolationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124338254:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124338

Index Terms

Multi-dimensional constraints-based PPVO for high fidelity reversible data hiding
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image compression
    2. Image manipulation
2. Security and privacy

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 213, Issue PC

Mar 2023

1402 pages

ISSN:0957-4174

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2023

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Cited By

Meng FWu G(2024)A color image encryption and decryption scheme based on extended DNA coding and fractional-order 5D hyper-chaotic systemExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124413254:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124413
Zhang HSun HMeng F(2024)Adaptive reversible data hiding scheme based on difference division interpolationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124338254:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124338

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