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?:abstract
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Internet of Medical Things (IoMT) driven Smart-health and emotional care is revolutionizing the healthcare industry by embracing several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing The authentication and secure exchange of Electronic Health Records (EHR), comprising of patient data collected using wearable sensors and laboratory investigations, is of paramount importance In this paper, we present a novel high payload and reversible EHR embedding framework to secure the patient information successfully and authenticate the received content The proposed approach is based on novel Left Data Mapping (LDM), Pixel Repetition Method (PRM), RC4 encryption, and checksum computation The input image of size M×N is upscaled by using PRM which guarantees reversibility with lesser computational complexity The binary secret data is encrypted using the RC4 encryption algorithm and then the encrypted data is grouped into 3-bit chunks and converted into decimal equivalents Before embedding, these decimal digits are encoded by LDM To embed the shifted data, the cover image is divided into 2×2 blocks and then in each block, two digits are embedded into the counter diagonal pixels For tamper detection and localization, a checksum digit computed from the block is embedded into one of the main diagonal pixels A fragile logo is embedded into the cover images in addition to EHR to facilitate early tamper detection The average Peak Signal to Noise Ratio (PSNR) of the stego images obtained is 41 95 dB for a very high embedding capacity of 2 25 bpp Further, the embedding time is less than 0 2 Sec Experimental results reveal that our approach outperforms many state-of-the-art techniques in terms of payload, imperceptibility, computational complexity, and capability to detect and localize tamper All the attributes affirm that the proposed scheme is a potential candidate for providing better security and authentication solutions for IoMT based smart health IEEE
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