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Article

Content-Adaptive Reversible Data Hiding with Multi-Stage Prediction Schemes

by
Hsiang-Cheh Huang
1,
Feng-Cheng Chang
2,* and
Hong-Yi Li
1
1
Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung City 811726, Taiwan
2
Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 251301, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(19), 6228; https://doi.org/10.3390/s25196228
Submission received: 10 September 2025 / Revised: 3 October 2025 / Accepted: 6 October 2025 / Published: 8 October 2025

Abstract

:With the proliferation of image-capturing and display-enabled IoT devices, ensuring the authenticity and integrity of visual data has become increasingly critical, especially in light of emerging cybersecurity threats and powerful generative AI tools. One of the major challenges in such sensor-based systems is the ability to protect privacy while maintaining data usability. Reversible data hiding has attracted growing attention due to its reversibility and ease of implementation, making it a viable solution for secure image communication in IoT environments. In this paper, we propose reversible data hiding techniques tailored to the content characteristics of images. Our approach leverages subsampling and quadtree partitioning, combined with multi-stage prediction schemes, to generate a predicted image aligned with the original. Secret information is embedded by analyzing the difference histogram between the original and predicted images, and enhanced through multi-round rotation techniques and a multi-level embedding strategy to boost capacity. By employing both subsampling and quadtree decomposition, the embedding strategy dynamically adapts to the inherent characteristics of the input image. Furthermore, we investigate the trade-off between embedding capacity and marked image quality. Experimental results demonstrate improved embedding performance, high visual fidelity, and low implementation complexity, highlighting the method’s suitability for resource-constrained IoT applications.
Keywords: reversible data hiding; content inherent characteristics; weighted average prediction; difference histogram; quadtree decomposition reversible data hiding; content inherent characteristics; weighted average prediction; difference histogram; quadtree decomposition

Share and Cite

MDPI and ACS Style

Huang, H.-C.; Chang, F.-C.; Li, H.-Y. Content-Adaptive Reversible Data Hiding with Multi-Stage Prediction Schemes. Sensors 2025, 25, 6228. https://doi.org/10.3390/s25196228

AMA Style

Huang H-C, Chang F-C, Li H-Y. Content-Adaptive Reversible Data Hiding with Multi-Stage Prediction Schemes. Sensors. 2025; 25(19):6228. https://doi.org/10.3390/s25196228

Chicago/Turabian Style

Huang, Hsiang-Cheh, Feng-Cheng Chang, and Hong-Yi Li. 2025. "Content-Adaptive Reversible Data Hiding with Multi-Stage Prediction Schemes" Sensors 25, no. 19: 6228. https://doi.org/10.3390/s25196228

APA Style

Huang, H.-C., Chang, F.-C., & Li, H.-Y. (2025). Content-Adaptive Reversible Data Hiding with Multi-Stage Prediction Schemes. Sensors, 25(19), 6228. https://doi.org/10.3390/s25196228

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