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Verifiable medical images for E-healthcare: A novel watermarking approach using robust bit-wise association of self-mutating offsprings of pixels
Microprocessors and Microsystems ; : 104483, 2022.
Article in English | ScienceDirect | ID: covidwho-1693104
ABSTRACT
In the current difficult time of COVID-19 pandemic, social distancing is a common practice adopted by healthy and infected people all over the world. Because of this, electronic healthcare and Telemedicine are coming up a big way. One of the most vital challenges in E-healthcare or telemedicine is the authentication and secure transmission of medical images received by an expert(doctor) at a far-off location from the sender(patient). To address the critical authentication issue, this paper proposes a blind pixel-based self-embedding fragile watermarking approach that is effective enough to localize the tampered region with more than 90% accuracy. The self-embedding approach plays a significant role in fragile watermarking for detecting the tampered region at the receiver side. Most of the self-embedding-based fragile watermarking approaches available in the literature are block-based which has a high False Positive Rate(FPR). In the proposed approach, every pixel of the image dynamically generates eight self-mutating offsprings and gets associated with them tightly at bit level. After a systematic set of procedures on that offsprings with a triple layer of security, a single authentication bit is selected using 16 × 1 multiplexer for each pixel intensity of the image. That’s why the proposed approach has a high imperceptibility level (more than 60 dB PSNR). Experimental results confirm the efficacy of the proposed approach in terms of imperceptibility and tamper detection rate.
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Full text: Available Collection: Databases of international organizations Database: ScienceDirect Language: English Journal: Microprocessors and Microsystems Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ScienceDirect Language: English Journal: Microprocessors and Microsystems Year: 2022 Document Type: Article