Blockchain-Based Reversible Data Hiding for Securing Medical Images

Abstract

Medical images carry a lot of important information for making a medical diagnosis. Since the medical images need to be communicated frequently to allow timely and accurate diagnosis, it has become a target for malicious attacks. Hence, medical images are protected through encryption algorithms. Recently, reversible data hiding on the encrypted images (RDHEI) schemes are employed to embed private information into the medical images. This allows effective and secure communication, wherein the privately embedded information (e.g., medical records and personal information) is very useful to the medical diagnosis. However, existing RDHEI schemes still suffer from low embedding capacity, which limits their applicability. Besides, such solution still lacks a good mechanism to ensure its integrity and traceability. To resolve these issues, a novel approach based on image block-wise encryption and histogram shifting is proposed to provide more embedding capacity in the encrypted images. The embedding rate is over 0.8 bpp for typical medical images. On top of that, a blockchain-based system for RDHEI is proposed to resolve the traceability. The private information is stored on the blockchain together with the hash value of the original medical image. This allows traceability of all the medical images communicated over the proposed blockchain network.

SSE_CIB: Secure storage and exchange of copyrighted images using blockchain

“…For a reliable and trustful ecosystem, the papers 67,69,73 apply an access-control mechanism for the participants connected to a sharing platform of medical images. Various techniques such as uniform quantization, 68 bat algorithm and data augmentation, 70 edge histogram descriptor extraction, 66 and block-wise encryption and histogram shifting 71 are deployed as pre-processing steps before ingesting the image-related data in the chain. For prediction analysis in the healthcare sector, Priya et al 72 suggest recording the weights from a trained model in the blockchain.…”

“…However, the paper presents a precise overview of the constraints necessary in blockchain‐IPFS collaborative model. Horng et al 71 propose a new scheme for reversible data hiding inside medical images, where the prime objective of the work is to increase embedding capacity in encrypted images. Their work comprises image block‐wise encryption and histogram shifting to accomplish their goals.…”

“…Research works such as References 54,59,63 have introduced models primarily focused on detecting copyright infringement as opposed to creating a mechanism for sharing and exchanging content. The image sharing management frameworks proposed by References 68,69,71 may prove to be better suited for a collaborative ecosystem. Nevertheless, there exists room for enhancement, including the incorporation of compressed images with varying quality levels and the inclusion of performance metrics to provide a more comprehensive assessment of the shared images.…”

A Novel and Robust Hybrid Blockchain and Steganography Scheme

“…Horng et al [26] encoded the cover images they determined with the RDHEI method they proposed using block permutation. They also concealed the encrypted patient data in the cover image, which they encrypted using the histogram shifting method.…”

“…Although the RDHEI algorithm proposed by Horng et al [26] is difficult to implement in real life, it offers 0.8 bpp embedding rate success. In the OTA algorithm, the embedding rate is 100%.…”