Structure-Aware Reversible Data Hiding for Images with Functional Modules
- 1School of Artificial Intelligence, Hainan Normal University, Haikou 571158, China
- 2Key Laboratory of Data Science and Smart Education, Ministry of Education, Haikou 571158, China
Abstract
Reversible data hiding (RDH) in structured images remains challenging: most deep-learning methods target natural images, where distortions can be spread across the entire picture. When the carrier contains functionally critical regions—the finder and alignment patterns of a QR code, for instance—a uniform allocation of distortion directly hurts machine readability and, as a result, reversibility. This paper presents AR-WeNet, a structure-aware reversible data hiding framework for images with rigid functional structures. It brings together two components: (1) a Regulatory Information Encryption Module (RIEM) that turns a structured regulatory record into an encrypted binary pattern through UTF-8 encoding, QR error-correction coding, and Arnold scrambling; and (2) a DWT-domain invertible neural network backbone enhanced by Multi-Scale Wavelet Subband Attention (MSWA). A region-adaptive embedding (RAE) strategy, steered by a predefined structural weight map, concentrates embedding distortions in redundant data modules while leaving functional modules intact. On the self-constructed E-Voucher-500 benchmark, AR-WeNet reaches 63.29 dB PSNR, 0.9999 SSIM, a 0.000% pre-correction bit error rate, and 100% exact QR decoding success—an 8.00 dB improvement over the uniform-weighting WeNet baseline. Robustness tests under JPEG compression, Gaussian noise, and cropping confirm resilience at both the bit level and the decoding level. On DIV2K, the INN backbone achieves 55.27 dB, surpassing HiNet by 10.41 dB, showing that the design extends beyond structured images to natural-image hiding.
Related
- Structure of irreducible homomorphisms to/from free and injective modules
- On the Structure of Higher Zhu Induced Modules in Positive Characteristic
- The Fundamental Limits of Structure-Agnostic Functional Estimation
- One-Shot Information Hiding and Compound Wiretap Channels
- Dense Weak Hiding: Closing Complexity Gaps in Nonconvex and PL Finite-Sum Optimization under Individual Smoothness
BibTeX
@article{li2026structureaware,
title = {{Structure-Aware Reversible Data Hiding for Images with Functional Modules}},
author = {Xiyan Li and Bo Liang and Zhengjie Deng},
journal = {Mathematics},
year = {2026},
month = oct,
doi = {10.3390/math14203845},
url = {https://www.mdpi.com/2227-7390/14/20/3845},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Structure-Aware Reversible Data Hiding for Images with Functional Modules
AU - Xiyan Li
AU - Bo Liang
AU - Zhengjie Deng
PY - 2026
DA - 2026/10/21
JO - Mathematics
DO - 10.3390/math14203845
UR - https://www.mdpi.com/2227-7390/14/20/3845
AB - Reversible data hiding (RDH) in structured images remains challenging: most deep-learning methods target natural images, where distortions can be spread across the entire picture. When the carrier contains functionally critical regions—the finder and alignment patterns of a QR code, for instance—a uniform allocation of distortion directly hurts machine readability and, as a result, reversibility. This paper presents AR-WeNet, a structure-aware reversible data hiding framework for images with rigid functional structures. It brings together two components: (1) a Regulatory Information Encryption Module (RIEM) that turns a structured regulatory record into an encrypted binary pattern through UTF-8 encoding, QR error-correction coding, and Arnold scrambling; and (2) a DWT-domain invertible neural network backbone enhanced by Multi-Scale Wavelet Subband Attention (MSWA). A region-adaptive embedding (RAE) strategy, steered by a predefined structural weight map, concentrates embedding distortions in redundant data modules while leaving functional modules intact. On the self-constructed E-Voucher-500 benchmark, AR-WeNet reaches 63.29 dB PSNR, 0.9999 SSIM, a 0.000% pre-correction bit error rate, and 100% exact QR decoding success—an 8.00 dB improvement over the uniform-weighting WeNet baseline. Robustness tests under JPEG compression, Gaussian noise, and cropping confirm resilience at both the bit level and the decoding level. On DIV2K, the INN backbone achieves 55.27 dB, surpassing HiNet by 10.41 dB, showing that the design extends beyond structured images to natural-image hiding.
SN - 2227-7390
ER -