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Extreme learning machine based optimal embedding location finder for image steganography

In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. T...

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Main Authors: Hayfaa, Abdulzahra Atee, Ahmad, Robiah, Mohd. Noor, Norliza, S. Rahma, Abdul Monem, Aljeroudi, Yazan
Format: Article
Published: Public Library Science 2017
Subjects:
T Technology
Online Access:http://eprints.utm.my/id/eprint/66139/
http://dx.doi.org/10.1371/journal.pone.0170329
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spelling my.utm.661392017-07-11T07:52:26Z http://eprints.utm.my/id/eprint/66139/ Extreme learning machine based optimal embedding location finder for image steganography Hayfaa, Abdulzahra Atee Ahmad, Robiah Mohd. Noor, Norliza S. Rahma, Abdul Monem Aljeroudi, Yazan T Technology In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM) algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM) index, fusion matrices, and mean square error (MSE). The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods. Public Library Science 2017-01-02 Article PeerReviewed Hayfaa, Abdulzahra Atee and Ahmad, Robiah and Mohd. Noor, Norliza and S. Rahma, Abdul Monem and Aljeroudi, Yazan (2017) Extreme learning machine based optimal embedding location finder for image steganography. Plos One, 12 (2). E0170329-E0170329. ISSN 1932-6203 http://dx.doi.org/10.1371/journal.pone.0170329 DOI:10.1371/journal.pone.0170329
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology
spellingShingle T Technology
Hayfaa, Abdulzahra Atee
Ahmad, Robiah
Mohd. Noor, Norliza
S. Rahma, Abdul Monem
Aljeroudi, Yazan
Extreme learning machine based optimal embedding location finder for image steganography
description In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM) algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM) index, fusion matrices, and mean square error (MSE). The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods.
format Article
author Hayfaa, Abdulzahra Atee
Ahmad, Robiah
Mohd. Noor, Norliza
S. Rahma, Abdul Monem
Aljeroudi, Yazan
author_facet Hayfaa, Abdulzahra Atee
Ahmad, Robiah
Mohd. Noor, Norliza
S. Rahma, Abdul Monem
Aljeroudi, Yazan
author_sort Hayfaa, Abdulzahra Atee
title Extreme learning machine based optimal embedding location finder for image steganography
title_short Extreme learning machine based optimal embedding location finder for image steganography
title_full Extreme learning machine based optimal embedding location finder for image steganography
title_fullStr Extreme learning machine based optimal embedding location finder for image steganography
title_full_unstemmed Extreme learning machine based optimal embedding location finder for image steganography
title_sort extreme learning machine based optimal embedding location finder for image steganography
publisher Public Library Science
publishDate 2017
url http://eprints.utm.my/id/eprint/66139/
http://dx.doi.org/10.1371/journal.pone.0170329
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score 13.211869

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