# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
## 5 Conclusions  
In this paper, we proposed a dual partial reversible data hiding (PRDH) method based on an enhanced Hamming Code (HC(7,4)) to improve embedding capacity while maintaining high image quality. By constructing virtual pixels from two cover images and applying optimized error correction, our method achieves full reversibility with respect to the cover images and significantly increases embedding capacity. Experimental results demonstrate that the proposed approach achieves an embedding rate of up to 1.5 bpp with PSNR values consistently exceeding 48 dB, outperforming existing dual-image RDH techniques. While the proposed scheme guarantees accurate recovery of the generated cover images after data extraction, it does not support reconstruction of the original image itself. This limitation results from the intentional omission of auxiliary data during the cover image generation phase. Transformation from the original image to the cover image is deterministic but not reversible; as such, the proposed method offers partial reversibility. This tradeoff enables a more efficient embedding process but limits the applicability of the scheme in domains that require exact preservation of the original image, such as legal evidence processing or lossless medical archiving. In addition, we evaluated the computational complexity and runtime of the proposed method. The algorithm runs in linear time with respect to image size and achieves embedding and extraction within approximately one second for a \(512\times 512\) image on a standard laptop, demonstrating its lightweight design and practical applicability to latency-sensitive environments. Future work will explore the extension of the proposed framework to color images and video sequences as well as the integration of adaptive error correction and cryptographic mechanisms to further enhance robustness. The application of this approach to compressed image formats and its performance under advanced steganalysis attacks also remain promising directions. As with typical LSB-based RDH techniques, the proposed method is inherently fragile under lossy transformations such as JPEG compression. Because the embedded data reside in the least significant bits, such operations can distort or remove the hidden information. Therefore, the proposed scheme is best suited for uncompressed or lightly compressed domains where exact data reversibility is required. Future research will explore transform-domain extensions and robustness-enhancing strategies to improve resilience against compression and modern steganalysis attacks, including those based on neural networks. Although the proposed method is theoretical in scope, its efficient encoding structure and high visual fidelity suggest potential utility in practical domains such as medical image management and se cure communications. Future research will explore its integration into application-specific frameworks, addressing requirements such as compatibility with standardized formats (e.g., DICOM) and clinical distortion tolerances. While the proposed method shows promise for secure communication and metadata-preserving archiving, its applicability to clinical or forensic systems requiring perfect image reconstruction remains limited, and integration into existing pipelines is suggested as future work. Each author discussed the details of the manuscript. C.K. designed and wrote the manuscript. C.K. implemented the proposed technique and provided the experimental results. C.-N.Y. and L.L. reviewed and revised the article. C.K. drafted and revised the manuscript. All authors have read and agreed to the published version of the manuscript. This research was supported in part by National Science and Technology Council (NSTC) under Grant No. 112-2221-E-259-007-MY2 (C.Y) and by the National Natural Science Foundation of China (No. 62466038), the Technology Innovation Guidance Program Project (Special Project of Technology Cooperation, Science, and Technology Department of Jiangxi Province) (No.