BMP steganalysis based on feature fusion and improved RSM ensemble
HE Feng-yin · Fuzhou daxue xuebao. Ziran kexue ban · 2014
At present,most of the method of BMP steganalysis mainly adopted the single feature and a single strong classifier,which prone to the problems of training samples sensitivity and difficult to improve the classification accuracy. In order to solve these problems,the method of BMP steganalysis is proposed based on feature fusion and improved RSM ensemble. The method firstly serial fusion Moulin feature and SPAM feature. Then selected features which have high classification ability using SFS algorithm as fixed features,the remaining features were selected randomly in the remaining feature space,and then the feature subset was build using fixed features and features selected randomly,finally member classifier was trained on the subset of features,and the finial decision was made by the majority voting procedure. Experimental results show that,in various embedding rates,this method provides more accuracy than the traditional method against steganographic methods of BMP( e. g. LSB matching,LSB replacement,SS and QIM).