Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4950986 | Journal of Computational Science | 2017 | 17 Pages |
Abstract
Universal Image steganalysis is a two class optimization problem. This research uses S-UNIWARD spatial steganographic algorithm to create stego images from 500 cover images. The image features extracted in spatial domain are noise models of neighbouring pixels giving 1000Â ÃÂ 34671 features. Ant Lion Optimization (ALO) is used to get best image features (1000Â ÃÂ 381 features). The classifiers used are Single (SVM and MLP) and Fusion classifiers (Bayes, Decision Template, Dempster Schafer). All fusion classifiers and SVM give classification accuracy of 99.3%. Thus Fusion classifiers with ALO act as best universal steganalyser in spatial domain.
Related Topics
Physical Sciences and Engineering
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Computational Theory and Mathematics
Authors
J. Anita Christaline, R. Ramesh, C. Gomathy, D. Vaishali,