Structure-Aware Reversible Data Hiding for Images with Functional Modules

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.