A High-Capacity Reversible Data Hiding Scheme for Encrypted Hyperspectral Images Using Multi-Layer MSB Block Labeling and ERLE Compression

Yijie Lin, Chia-Chen Lin (), Zhe-Min Yeh, Ching-Chun Chang and Chin-Chen Chang ()
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Yijie Lin: Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan
Chia-Chen Lin: Department of Computer Science and Information Engineering, National of Chin-Yi University of Technology, Taichung 411, Taiwan
Zhe-Min Yeh: Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan
Ching-Chun Chang: Information and Communication Security Research Center, Feng Chia University, Taichung 407, Taiwan
Chin-Chen Chang: Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan

Future Internet, 2025, vol. 17, issue 8, 1-21

Abstract: In the context of secure and efficient data transmission over the future Internet, particularly for remote sensing and geospatial applications, reversible data hiding (RDH) in encrypted hyperspectral images (HSIs) has emerged as a critical technology. This paper proposes a novel RDH scheme specifically designed for encrypted HSIs, offering enhanced embedding capacity without compromising data security or reversibility. The approach introduces a multi-layer block labeling mechanism that leverages the similarity of most significant bits (MSBs) to accurately locate embeddable regions. To minimize auxiliary information overhead, we incorporate an Extended Run-Length Encoding (ERLE) algorithm for effective label map compression. The proposed method achieves embedding rates of up to 3.79 bits per pixel per band (bpppb), while ensuring high-fidelity reconstruction, as validated by strong PSNR metrics. Comprehensive security evaluations using NPCR, UACI, and entropy confirm the robustness of the encryption. Extensive experiments across six standard hyperspectral datasets demonstrate the superiority of our method over existing RDH techniques in terms of capacity, embedding rate, and reconstruction quality. These results underline the method’s potential for secure data embedding in next-generation Internet-based geospatial and remote sensing systems.

Keywords: reversible data hiding; encrypted hyperspectral images; MSB prediction; Extended Run-Length Encoding; high-capacity embedding; image recovery (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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