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AN OPTIMIZED ENCRYPTION BASED STEGANOGRAPHY MODEL TO SECURE MEDICAL IMAGES USING METAHEURISTIC TECHNIQUE.
Publication year - 2020
Publication title -
international journal of engineering, sciences and research technology
Language(s) - English
Resource type - Journals
ISSN - 2277-9655
DOI - 10.29121/ijesrt.v9.i10.2020.13
Subject(s) - steganography , computer science , encryption , particle swarm optimization , cryptography , peak signal to noise ratio , steganography tools , information hiding , data mining , artificial intelligence , theoretical computer science , algorithm , image (mathematics) , computer security
The implementation of a secret data sharing algorithm along with water marking, steganography and cryptography can have various applications besides medical data privacy. It can be used for improving the authentication ability of confidential data too, so the demand of this type of approaches increases rapidly. We know that, Steganography is a scientific technique that is used to provide safe communication through multimedia carrier, for example, a combination of confidential information might be in the form of images, audio, and video files. If this feature is visible, the attack point is open, so the goal here is always to hide the existence of relevant information. Steganography has a variety of useful applications. But like any science, it can be used for bad intentions. In this research, medial image steganography model is designed to provide the security while transmitting the information in the form of a medical image by utilizing the concept of Discrete Wavelet transformation (DWT) as a decomposition approach with Modified Jamal Encryption Algorithm (MJEA) encryption. In addition the concept of Particle Swarm Optimization (PSO) as an optimization technique used to find out the better hiding location in the medical images. To provide high security different processes are implemented such as pre-processing that is used to resize and conversion of the image with image decomposition. At last, the performance parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), entropy and correlation coefficients are measured and compared with the existing work to validate the proposed model.

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