Universiti Teknologi Malaysia Institutional Repository

Extreme learning machine based optimal embedding location finder for image steganography

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

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Official URL: http://dx.doi.org/10.1371/journal.pone.0170329


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.

Item Type:Article
Additional Information:RADIS System Ref No:PB/2017/11442
Uncontrolled Keywords:embedding, machine learning
Subjects:T Technology
Divisions:Razak School of Engineering and Advanced Technology
ID Code:66139
Deposited By: Widya Wahid
Deposited On:11 Jul 2017 07:52
Last Modified:11 Jul 2017 07:52

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