Extreme learning machine based optimal embedding location finder for image steganography

• Published in: PloS one Vol. 12; no. 2; p. e0170329
• Main Authors: Atee, Hayfaa Abdulzahra, Ahmad, Robiah, Noor, Norliza Mohd, Rahma, Abdul Monem S., Aljeroudi, Yazan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.02.2017; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Communication; Computer and Information Sciences; Deformation; Diagnostic Imaging; Embedding; Engineering schools; Fourier transforms; Genetic algorithms; Homogeneity; Humans; Image Processing, Computer-Assisted; Internet; Learning algorithms; Machine Learning; Mathematical analysis; Mathematical models; Matrix methods; Models, Theoretical; Neural networks; Optimization; Physical Sciences; Research and Analysis Methods; Researchers; Science; Steganography; Training; Wavelet transforms; Baghdad Iraq; Kuala Lumpur Malaysia; Malaysia; Iraq
• ISSN: 1932-6203; 1932-6203
• DOI: 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.