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Bio inspired optimization for universal spatial image steganalysis
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DOI：10.1016/j.jocs.2017.06.014.png)
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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 1000 x 34671 features. Ant Lion Optimization (ALO) is used to get best image features (1000 x 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. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Image steganalysis
Fusion classifiers
Ant lion optimization
Support vector machines
Bayes fusion classifier
Decision template fusion
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