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ANOVA and Fisher Criterion based Feature Selection for Lower Dimensional Universal Image Steganalysis
Madhavi Bharatbhai Desai, S. V. Patel, Bhumi Prajapati
Pages - 145 - 160     |    Revised - 30-06-2016     |    Published - 31-07-2016
Volume - 10   Issue - 3    |    Publication Date - July 2016  Table of Contents
Steganalysis, SVM, ANOVA, Fisher Criterion, DCT, DWT, Dimensionality Reduction.
Unethical uses of data hiding methods have made Image Steganalysis a very important area of research work in the field of Digital Investigations. Effectiveness of any Image Steganalysis algorithm depends on feature selection and feature reduction. The goal of this paper is to develop a reduced dimensional merged feature set for universal image steganalysis using Fisher Criterion and ANOVA techniques. Statistical features extracted from wavelet subbands and binary similarity patterns extracted from DCT of an image are merged to make combined feature set. Fisher criterion and ANOVA test are applied to evaluate the combined feature vector score and then only those features are selected which are found sensitive in both feature selection methods. These reduced dimensional 15-D feature vector is used to train SVM classifier with RBF kernel. The proposed algorithm is tested against steganography methods like F5, Outguess and LSB based method. Stego images are generated using widely available stego tools for two standard image databases: CorelDraw and BSDS500. Results are further validated using 10 fold cross validation process. The proposed algorithm achieves overall 97% detection accuracy against various steganography methods.
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Mrs. Madhavi Bharatbhai Desai
Uka Tarsadia University - India
Dr. S. V. Patel
Sarvajanik College of Engineering and Technology - India
Miss Bhumi Prajapati
Sarvajanik College of Engineering and Technology - India