Optimization of medical image steganography using n-decomposition genetic algorithm

Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixel...

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Main Author:	Al-Sarayefi, Bushra Abdullah Shtayt
Format:	Thesis
Language:	eng eng
Published:	2023
Subjects:	T58.5-58.64 Information technology
Online Access:	https://etd.uum.edu.my/10713/1/s902694_01.pdf https://etd.uum.edu.my/10713/2/s902694_02.pdf
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id	my-uum-etd.10713
record_format	uketd_dc
spelling	my-uum-etd.107132023-10-25T01:58:00Z Optimization of medical image steganography using n-decomposition genetic algorithm 2023 Al-Sarayefi, Bushra Abdullah Shtayt Zakaria, Nur Haryani Harun, Nor Hazlyna Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Art & Sciences T58.5-58.64 Information technology QA75 Electronic computers. Computer science Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications. 2023 Thesis https://etd.uum.edu.my/10713/ https://etd.uum.edu.my/10713/1/s902694_01.pdf text eng 2025-10-18 staffonly https://etd.uum.edu.my/10713/2/s902694_02.pdf text eng public other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Zakaria, Nur Haryani Harun, Nor Hazlyna
topic	T58.5-58.64 Information technology T58.5-58.64 Information technology
spellingShingle	T58.5-58.64 Information technology T58.5-58.64 Information technology Al-Sarayefi, Bushra Abdullah Shtayt Optimization of medical image steganography using n-decomposition genetic algorithm
description	Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Al-Sarayefi, Bushra Abdullah Shtayt
author_facet	Al-Sarayefi, Bushra Abdullah Shtayt
author_sort	Al-Sarayefi, Bushra Abdullah Shtayt
title	Optimization of medical image steganography using n-decomposition genetic algorithm
title_short	Optimization of medical image steganography using n-decomposition genetic algorithm
title_full	Optimization of medical image steganography using n-decomposition genetic algorithm
title_fullStr	Optimization of medical image steganography using n-decomposition genetic algorithm
title_full_unstemmed	Optimization of medical image steganography using n-decomposition genetic algorithm
title_sort	optimization of medical image steganography using n-decomposition genetic algorithm
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2023
url	https://etd.uum.edu.my/10713/1/s902694_01.pdf https://etd.uum.edu.my/10713/2/s902694_02.pdf
_version_	1783729409424883712

Optimization of medical image steganography using n-decomposition genetic algorithm

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