Dynamics of watermark position in audio watermarked files using neural networks

Previous researches on digital audio watermarking has shown that effective techniques ensure inaudibility, reliability, robustness and protection against signal degradation. Crucial to this is the appropriate position of the watermark in the files. There is a risk of perceivable distortion in the...

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Main Authors:	Abubakar, Adamu, Haruna, Chiroma, Zeki, Akram M., Khan, Abdullah, Uddin, Mueen, Herawan, Tutut
Format:	Article
Language:	English English
Published:	Natural Sciences Publishing 2017
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/56194/ http://irep.iium.edu.my/56194/ http://irep.iium.edu.my/56194/ http://irep.iium.edu.my/56194/1/AMIS%20WAT.pdf http://irep.iium.edu.my/56194/7/56194-Dynamics%20of%20watermark%20position%20in%20audio%20watermarked%20files%20using%20neural%20networks_SCOPUS.pdf

id	iium-56194
recordtype	eprints
spelling	iium-561942018-03-19T13:37:24Z http://irep.iium.edu.my/56194/ Dynamics of watermark position in audio watermarked files using neural networks Abubakar, Adamu Haruna, Chiroma Zeki, Akram M. Khan, Abdullah Uddin, Mueen Herawan, Tutut QA75 Electronic computers. Computer science Previous researches on digital audio watermarking has shown that effective techniques ensure inaudibility, reliability, robustness and protection against signal degradation. Crucial to this is the appropriate position of the watermark in the files. There is a risk of perceivable distortion in the audio signal when the watermark is spread in the audio spectrum, which may result in the loss of the watermark. This paper addresses the lack of an optimal position for the watermark when spread spectrum watermarking techniques are used. In an attempt to solve this problem, we model various positions on the audio spectrum for embedding the watermark and use a neural network (feed forward neural network) to predict the best positions for the watermark in the host audio streams. We are able to determine optimal position. The result of the neural network experiment formulated within the spread spectrum watermarking technique enables us to determine the best position for embedding. After embedding, further experimental results on the strength of the watermarking technique utilizing the outcome of the neural network show a high level of robustness against a variety of signal degradations. The contribution of this work is to show that audio signals contain patterns which help determine the most appropriate points at which watermarks should be embedded. Natural Sciences Publishing 2017-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/56194/1/AMIS%20WAT.pdf application/pdf en http://irep.iium.edu.my/56194/7/56194-Dynamics%20of%20watermark%20position%20in%20audio%20watermarked%20files%20using%20neural%20networks_SCOPUS.pdf Abubakar, Adamu and Haruna, Chiroma and Zeki, Akram M. and Khan, Abdullah and Uddin, Mueen and Herawan, Tutut (2017) Dynamics of watermark position in audio watermarked files using neural networks. Applied Mathematics & Information Sciences, 11 (3). pp. 703-715. ISSN 1935-0090 E-ISSN 2325-0399 http://www.naturalspublishing.com/files/published/ma61b18xm1o7z9.pdf 10.18576/amis/110309
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Abubakar, Adamu Haruna, Chiroma Zeki, Akram M. Khan, Abdullah Uddin, Mueen Herawan, Tutut Dynamics of watermark position in audio watermarked files using neural networks
description	Previous researches on digital audio watermarking has shown that effective techniques ensure inaudibility, reliability, robustness and protection against signal degradation. Crucial to this is the appropriate position of the watermark in the files. There is a risk of perceivable distortion in the audio signal when the watermark is spread in the audio spectrum, which may result in the loss of the watermark. This paper addresses the lack of an optimal position for the watermark when spread spectrum watermarking techniques are used. In an attempt to solve this problem, we model various positions on the audio spectrum for embedding the watermark and use a neural network (feed forward neural network) to predict the best positions for the watermark in the host audio streams. We are able to determine optimal position. The result of the neural network experiment formulated within the spread spectrum watermarking technique enables us to determine the best position for embedding. After embedding, further experimental results on the strength of the watermarking technique utilizing the outcome of the neural network show a high level of robustness against a variety of signal degradations. The contribution of this work is to show that audio signals contain patterns which help determine the most appropriate points at which watermarks should be embedded.
format	Article
author	Abubakar, Adamu Haruna, Chiroma Zeki, Akram M. Khan, Abdullah Uddin, Mueen Herawan, Tutut
author_facet	Abubakar, Adamu Haruna, Chiroma Zeki, Akram M. Khan, Abdullah Uddin, Mueen Herawan, Tutut
author_sort	Abubakar, Adamu
title	Dynamics of watermark position in audio watermarked files using neural networks
title_short	Dynamics of watermark position in audio watermarked files using neural networks
title_full	Dynamics of watermark position in audio watermarked files using neural networks
title_fullStr	Dynamics of watermark position in audio watermarked files using neural networks
title_full_unstemmed	Dynamics of watermark position in audio watermarked files using neural networks
title_sort	dynamics of watermark position in audio watermarked files using neural networks
publisher	Natural Sciences Publishing
publishDate	2017
url	http://irep.iium.edu.my/56194/ http://irep.iium.edu.my/56194/ http://irep.iium.edu.my/56194/ http://irep.iium.edu.my/56194/1/AMIS%20WAT.pdf http://irep.iium.edu.my/56194/7/56194-Dynamics%20of%20watermark%20position%20in%20audio%20watermarked%20files%20using%20neural%20networks_SCOPUS.pdf
first_indexed	2023-09-18T21:19:17Z
last_indexed	2023-09-18T21:19:17Z
_version_	1777411772757573632

Dynamics of watermark position in audio watermarked files using neural networks

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