Image Steganography by Modified Simple Linear Iterative Clustering
Ismail Kich1, El Bachir Ameur2, Youssef Taouil3
1Ismail Kich*, Research Team MSISI LaRIT, University Ibn Tofail, Morocco.
2El Bachir Ameur, Research Team MSISI – LaRIT, University Ibn Tofail, Morocco.
3Youssef Taouil, Research Team MSISI, LaRIT, University Ibn Tofail, Morocco.
Manuscript received on January 14, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 1640-1647 | Volume-9 Issue-4, February 2020. | Retrieval Number: C8903019320/2020©BEIESP | DOI: 10.35940/ijitee.C8903.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Steganography is an information security technique that consists of concealing secret data into digital medias including videos, texts, network protocols and images. In this paper, a steganography method to dissimulate the secret information in gray-scale images is proposed; the dissimulation is adapted to the cover image’s texture, data is hidden in the edge areas. The edge pixels are selected by over-segmentation using Modified Simple Linear Iterative Clustering (M-SLIC). This algorithm allows to decompose the cover image into K regions which we call superpixels. The image’s texture and the amount of the secret data are the factors that help to determine the value of the parameter K. Choosing the pixels of complex regions to conceal secret information is due to the fact that the human visual system is designed to notice changes in the pixels of smooth areas. Therefore, edge areas tolerate larger changes than smooth areas without causing detectable distortions. Experiment on a large set of images were carried out; results illustrate the good performance of the proposed work in terms of capacity, security and imperceptibility in comparison to recent works.
Keywords: Steganography, Data Hiding, Steganalysis, Superpixels, Edge Detection, SLIC.
Scope of the Article: Clustering