Dimensionality reduction for classification of blind steganalysis

One of the critical problems in Steganalysis is the reduction of dimension of high dimensional features. It can improve the distinction between the cover and stego, achieve higher classification accuracy. A number of approaches have been elaborated to solve the issues of dimensionality reduction. Moreover, some dimensional methods aren't often considered in blind steganalysis. It is the opportunity to addresses the issue of using those low-dimensional mapping provided by different dimensionality reduction method to improve classification accuracy of blind steganalysis The suggested approach has been subsequently tested through a series of experiments aimed to evaluate the impact of different DR methods, such as PCA, LDA, Isomap, S-Isomap, LLE and SLLE. Experiments on real data sets demonstrated that some dimensionality reduction methods (such as LLE, LDA) can have more discriminative power than other dimensionality reduction methods (such as PCA, Isomap). When Isomap and LLE are compared with S-Isomap and SLLE, the results reveal that the supervised methods performance is better than the unsupervised dimensionality reduction methods in blind steganalysis. © 2015 Academic Journals Inc.