Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm

Existing embedding techniques depend on cover audio selected by users. Unknowingly, users may make a poor cover audio selection that is not optimised in its capacity or imperceptibility features, which could reduce the effectiveness of any embedding technique. As a trade-off exists between capacity...

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Main Authors: Noor Azam, Muhammad Harith, Ridzuan, Farida, Mohd Sayuti, M Norazizi Sham
Format: Article
Language:English
Published: Universiti Utara Malaysia Press 2023
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Online Access:https://repo.uum.edu.my/id/eprint/29403/1/JICT%2022%2002%202023%20255-282.pdf
https://repo.uum.edu.my/id/eprint/29403/
https://doi.org/10.32890/jict2023.22.2.5
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spelling my.uum.repo.294032023-04-19T01:45:03Z https://repo.uum.edu.my/id/eprint/29403/ Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm Noor Azam, Muhammad Harith Ridzuan, Farida Mohd Sayuti, M Norazizi Sham QA75 Electronic computers. Computer science Existing embedding techniques depend on cover audio selected by users. Unknowingly, users may make a poor cover audio selection that is not optimised in its capacity or imperceptibility features, which could reduce the effectiveness of any embedding technique. As a trade-off exists between capacity and imperceptibility, producing a method focused on optimising both features is crucial. One of the search methods commonly used to find solutions for the trade-off problem in various fields is the Multi-Objective Evolutionary Algorithm (MOEA). Therefore, this research proposed a new method for optimising cover audio selection for audio steganography using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which falls under the MOEA Pareto dominance paradigm. The proposed method provided suggestions for cover audio to users based on imperceptibility and capacity features. The sample difference calculation was initially formulated to determine the maximum capacity for each cover audio defined in the cover audio database. Next, NSGA-II was implemented to determine the optimised solutions based on the parameters provided by each chromosome. The experimental results demonstrated the effectiveness of the proposed method as it managed to dominate the solutions from the previous method selected based on one criterion only. In addition, the proposed method considered that the trade-off managed to select the solution as the highest priority compared to the previous method, which put the same solution as low as 71 in the priority ranking. In conclusion, the method optimised the cover audio selected, thus, improving the effectiveness of the audio steganography used. It can be a response to help people whose computers and mobile devices continue to be unfamiliar with audio steganography in an age where information security is crucial. Universiti Utara Malaysia Press 2023 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/29403/1/JICT%2022%2002%202023%20255-282.pdf Noor Azam, Muhammad Harith and Ridzuan, Farida and Mohd Sayuti, M Norazizi Sham (2023) Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm. Journal of Information and Communication Technology, 22 (2). pp. 255-282. ISSN 2180-3862 https://doi.org/10.32890/jict2023.22.2.5
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Noor Azam, Muhammad Harith
Ridzuan, Farida
Mohd Sayuti, M Norazizi Sham
Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm
description Existing embedding techniques depend on cover audio selected by users. Unknowingly, users may make a poor cover audio selection that is not optimised in its capacity or imperceptibility features, which could reduce the effectiveness of any embedding technique. As a trade-off exists between capacity and imperceptibility, producing a method focused on optimising both features is crucial. One of the search methods commonly used to find solutions for the trade-off problem in various fields is the Multi-Objective Evolutionary Algorithm (MOEA). Therefore, this research proposed a new method for optimising cover audio selection for audio steganography using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which falls under the MOEA Pareto dominance paradigm. The proposed method provided suggestions for cover audio to users based on imperceptibility and capacity features. The sample difference calculation was initially formulated to determine the maximum capacity for each cover audio defined in the cover audio database. Next, NSGA-II was implemented to determine the optimised solutions based on the parameters provided by each chromosome. The experimental results demonstrated the effectiveness of the proposed method as it managed to dominate the solutions from the previous method selected based on one criterion only. In addition, the proposed method considered that the trade-off managed to select the solution as the highest priority compared to the previous method, which put the same solution as low as 71 in the priority ranking. In conclusion, the method optimised the cover audio selected, thus, improving the effectiveness of the audio steganography used. It can be a response to help people whose computers and mobile devices continue to be unfamiliar with audio steganography in an age where information security is crucial.
format Article
author Noor Azam, Muhammad Harith
Ridzuan, Farida
Mohd Sayuti, M Norazizi Sham
author_facet Noor Azam, Muhammad Harith
Ridzuan, Farida
Mohd Sayuti, M Norazizi Sham
author_sort Noor Azam, Muhammad Harith
title Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm
title_short Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm
title_full Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm
title_fullStr Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm
title_full_unstemmed Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm
title_sort optimized cover selection for audio steganography using multi-objective evolutionary algorithm
publisher Universiti Utara Malaysia Press
publishDate 2023
url https://repo.uum.edu.my/id/eprint/29403/1/JICT%2022%2002%202023%20255-282.pdf
https://repo.uum.edu.my/id/eprint/29403/
https://doi.org/10.32890/jict2023.22.2.5
_version_ 1765299783619051520
score 13.209306