An Evolutionary Computing Enriched RS Attack Resilient Medical Image Steganography Model for Telemedicine Applications

TL;DR

This paper presents a robust, reversible medical image steganography model utilizing Discrete Ripplet Transformation and an adaptive genetic algorithm, enhancing secure telemedicine data transmission over insecure channels.

Contribution

It introduces a novel steganography scheme with DRT and AGA for optimal pixel adjustment, improving data hiding capacity and imperceptibility in medical images.

Findings

Outperforms wavelet-based methods in PSNR and embedding capacity

Achieves high imperceptibility and robustness in medical image steganography

Enhances secure telemedicine communication over insecure channels

Abstract

The recent advancement in computing technologies and resulting vision based applications have gives rise to a novel practice called telemedicine that requires patient diagnosis images or allied information to recommend or even perform diagnosis practices being located remotely. However, to ensure accurate and optimal telemedicine there is the requirement of seamless or flawless biomedical information about patient. On the contrary, medical data transmitted over insecure channel often remains prone to get manipulated or corrupted by attackers. The existing cryptosystems alone are not sufficient to deal with these issues and hence in this paper a highly robust reversible image steganography model has been developed for secret information hiding. Unlike traditional wavelet transform techniques, we incorporated Discrete Ripplet Transformation (DRT) technique for message embedding in the medical cover images. In addition, to assure seamless communication over insecure channel, a dual cryptosystem model containing proposed steganography scheme and RSA cryptosystem has been developed. One of the key novelties of the proposed research work is the use of adaptive genetic algorithm (AGA) for optimal pixel adjustment process (OPAP) that enriches data hiding capacity as well as imperceptibility features. The performance assessment reveals that the proposed steganography model outperforms other wavelet transformation based approaches in terms of high PSNR, embedding capacity, imperceptibility etc.