Extreme learning machine based optimal embedding location finder for image steganography

Hayfaa Abdulzahra Atee, Robiah Ahmad, Norliza Mohd Noor, Abdul Monem S. Rahma, Yazan Aljeroudi, Zhaohong Deng
2017 PLoS ONE  
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.
more » ... s 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. Islam and Gupta 2014 Spatial-LSBM Better security than LSBR Conflicting for most of the model-preserving steganographic techniques Abdelwahab and Hassaan 2008 Frequency-DWT Does not require the original cover image to extract the embedded secret image. Did not tested for text into image. Prabakaran and Bhavani 2012 Frequency-DWT Hiding a large-size secret image into a small-size cover image. The quality of stego-image is not satisfied. Extreme learning machine based optimal embedding location finder for image steganography PLOS ONE | Extreme learning machine based optimal embedding location finder for image steganography PLOS ONE | Fig 4. Relationship of the correlation metric to the texture features (a) contrast, (b): energy, (c) homogeneity, (d) entropy, (e) correlation, (f) mean, and (g) standard deviation for Sails image. Extreme learning machine based optimal embedding location finder for image steganography PLOS ONE | Extreme learning machine based optimal embedding location finder for image steganography PLOS ONE | Extreme learning machine based optimal embedding location finder for image steganography PLOS ONE |
doi:10.1371/journal.pone.0170329 pmid:28196080 pmcid:PMC5308843 fatcat:7y6loyxa7vdtzodtk3jj3stkxa