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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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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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 |
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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 |
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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 |
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Universiti Utara Malaysia Press |
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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 |
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13.209306 |