Abstract

Recently, ensemble learning algorithms are proposed to address the challenges of high dimensional classification for steganalysis caused by the curse of dimensionality and obtain superior performance. In this paper, we extend the state-of-the-art steganalysis tool developed by Kodovsky and Fridrich: the Kodovsky’s ensemble classifier and propose a novel method, called CSRS for high-dimensional steganalysis. Different from the Kodovsky’s ensemble classifier which selects features in a completely random way, the proposed CS-RS modifies the generation method of feature subspaces. Firstly, our method employs the chi-square statistic (CS) to measure the weight of each feature in the original feature space and sorts features according to weights. Then the sorted original feature space is partitioned into two parts according to a given dividing point: high correlation part and low correlation part. Finally, the feature subset is formed by selecting features randomly in each part according to the given sampling rate. Experiments with the steganographic algorithms HUGO demonstrate that the proposed CS-RS using the FLD classifier offers training complexity comparable to the Kodovsky’s classifier and significantly increases the performance of the Kodovsky’s classifier in less than 1000-dimensional feature subspaces, gaining 1.2% on the optimal result. In addition, the proposed algorithm outperforms Bagging and AdaBoost and can offer accuracy comparable to L-SVM.