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Multiple Perspective of Multipredictor Mechanism and Multihistogram Modification for High-Fidelity Reversible Data Hiding

by Kai Gao1, Chin-Chen Chang1,*, Chia-Chen Lin2,*

1 Department of Information Engineering and Computer Science, Feng Chia University, Taichung, 407, Taiwan
2 Department of Computer Science and Information Engineering, National Chin-Yin University of Technology, Taichung, 411, Taiwan

* Corresponding Authors: Chin-Chen Chang. Email: email; Chia-Chen Lin. Email: email

Computer Systems Science and Engineering 2024, 48(3), 813-833. https://doi.org/10.32604/csse.2024.038308

Abstract

Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics, which allows us to hide sensitive data in image files. In this paper, we propose a novel high-fidelity reversible data hiding scheme. Based on the advantage of the multipredictor mechanism, we combine two effective prediction schemes to improve prediction accuracy. In addition, the multihistogram technique is utilized to further improve the image quality of the stego image. Moreover, a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram. Experimental results show that the quality of the stego image of our scheme outperforms state-of-the-art schemes in most cases.

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APA Style
Gao, K., Chang, C., Lin, C. (2024). Multiple perspective of multipredictor mechanism and multihistogram modification for high-fidelity reversible data hiding. Computer Systems Science and Engineering, 48(3), 813-833. https://doi.org/10.32604/csse.2024.038308
Vancouver Style
Gao K, Chang C, Lin C. Multiple perspective of multipredictor mechanism and multihistogram modification for high-fidelity reversible data hiding. Comput Syst Sci Eng. 2024;48(3):813-833 https://doi.org/10.32604/csse.2024.038308
IEEE Style
K. Gao, C. Chang, and C. Lin, “Multiple Perspective of Multipredictor Mechanism and Multihistogram Modification for High-Fidelity Reversible Data Hiding,” Comput. Syst. Sci. Eng., vol. 48, no. 3, pp. 813-833, 2024. https://doi.org/10.32604/csse.2024.038308



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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