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Sridevi Arumugham

Researcher at Shanmugha Arts, Science, Technology & Research Academy

Publications -  16
Citations -  126

Sridevi Arumugham is an academic researcher from Shanmugha Arts, Science, Technology & Research Academy. The author has contributed to research in topics: Encryption & Confusion and diffusion. The author has an hindex of 5, co-authored 15 publications receiving 99 citations.

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Journal ArticleDOI

Networked medical data sharing on secure medium – A web publishing mode for DICOM viewer with three layer authentication

TL;DR: In this paper, a security enhanced DICOM image sharing over a LAN addressing confidentiality, integrity and authentication has been proposed, where the AES encrypted patient history was combined along with the thumb impression and Quick Response (QR) code of patient ID as watermark.
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Networked hardware assisted key image and chaotic attractors for secure RGB image communication

TL;DR: The proposed approach is a hardware – software codesign which possesses a good keyspace, improved key sensitivity and satisfies the various statistical parameters thus offering substantial resistance to differential, occlusion and chosen plaintext attacks on RGB images.
Journal ArticleDOI

YRBS coding with logistic map – a novel Sanskrit aphorism and chaos for image encryption

TL;DR: YRBS coding has been employed along with simple one dimensional logistic map for encrypting 256 × 256 grayscale test images and this approach offers a good resistance to chosen cipher text attack which was a challenge in DNA coding.
Journal ArticleDOI

Tamper-Resistant Secure Medical Image Carrier: An IWT–SVD–Chaos–FPGA Combination

TL;DR: In the proposed work, the important credentials of the patient such as their treatment history, ID and thumb impression are integrated in the form of a 256 × 256 watermark image which overcomes the False Positive Problem.
Proceedings ArticleDOI

IoT Framework for Secure Medical Image Transmission

TL;DR: The proposed server - client model of authenticated medical image communication is proposed, developed using python 2.7 and validated by performing entropy, correlation, differential, error metric and NIST test analyses.