An Evolutionary Computing Enriched RS Attack Resilient Medical Image Steganography Model for Telemedicine Applications
This work addresses security vulnerabilities in telemedicine for remote patient diagnosis, though it appears incremental as it builds on existing steganography and cryptosystem techniques.
The paper tackles the problem of securing medical images in telemedicine by developing a reversible steganography model that uses Discrete Ripplet Transformation and an adaptive genetic algorithm, resulting in improved performance metrics such as higher PSNR and embedding capacity compared to wavelet-based methods.
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.