A fast and accurate steganalysis using Ensemble classifiers

Nowadays the steganographic methods use the more sophisticated image models to increase security; consequently, steganalysis algorithm should build the more accurate models of images to detect them. So, the number of extracted feature is increasing. Most modern steganalysis algorithms train a supervised classifier on the feature vectors. The most popular and accurate one is SVM, but the high training time of SVM inhibits the development of steganalysis. To solve this problem, in this paper we propose a fast and accurate steganalysis methods based on Ensemble classifier and Stacking. In this method, the relation between basic learners decisions and true decision is learned by another classifier. To do this, basic learners decisions are mapped to space of uncorrelated dimensions. The complexity of this method is much lower than that of SVM, while our method improves detection accuracy. Proposed method is a fast and accurate classifier that can be used as a part of any steganalysis algorithm. Performance of this method is demonstrated on two steganographic methods, namely nsF5 and Model Based Steganography. The performance of proposed method is compared to that of Ensemble classifier. Experimental results show that the classification error and training time are lowered by 46% and 88%, respectively.