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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:English
English
Published: 2023
Subjects:
T58.5-58.64 Information technology
QA75 Electronic computers. Computer science
Online Access:https://etd.uum.edu.my/10713/1/s902694_01.pdf
https://etd.uum.edu.my/10713/2/s902694_02.pdf
https://etd.uum.edu.my/10713/
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spelling my.uum.etd.107132023-10-25T01:58:00Z https://etd.uum.edu.my/10713/ Optimization of medical image steganography using n-decomposition genetic algorithm Al-Sarayefi, Bushra Abdullah Shtayt 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 NonPeerReviewed text en https://etd.uum.edu.my/10713/1/s902694_01.pdf text en https://etd.uum.edu.my/10713/2/s902694_02.pdf Al-Sarayefi, Bushra Abdullah Shtayt (2023) Optimization of medical image steganography using n-decomposition genetic algorithm. Doctoral thesis, Universiti Utara Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
url_provider http://etd.uum.edu.my/
language English
English
topic T58.5-58.64 Information technology
QA75 Electronic computers. Computer science
spellingShingle T58.5-58.64 Information technology
QA75 Electronic computers. Computer science
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
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
publishDate 2023
url https://etd.uum.edu.my/10713/1/s902694_01.pdf
https://etd.uum.edu.my/10713/2/s902694_02.pdf
https://etd.uum.edu.my/10713/
_version_ 1781708409854230528
score 13.211869

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