Efficient steganography detection by means of compression-based integral classifier

We propose a model of an integral classifier in order to solve the problem of binary steganalysis by means of machine-learning tools more efficiently. The problem of binary steganalysis consists in recognizing whether a given container is empty or contains a certain payload embedded via a certain steganographic algorithm. In steganalysis, such problem is often solved using such machine-learning techniques as the support vector machine and the ensemble classifier. Instead of using a single classifier (as it is done now) which is intended to make an ultimate decision about whether the container is empty or not, the proposed in this paper integral classifier consists of several classifiers and works in such a way that each of them processes only those containers which satisfy a certain condition. Within the proposed model, we develop a compression-based integral classifier which works as follows. The training set of classifiers is splitted into several subsets according to the containers compression rate; then a corresponding number of classifiers are trained, but each classifier is injected only with an ascribed subset. The testing containers are distributed between the classifiers (also according to their compression rate) and the decision about the certain container is made by the chosen classifier. In order to demonstrate the power of the integral classifier, we performed some experiments using the famous de-facto standard images database BOSSbase 1.01 as a source of the containers along with contemporary content-adaptive embedding algorithms HUGO, WOW and S-UNIWARD. Comparison with state-of-the-art results (obtained for the single support vector machine and the ensemble classifier) demonstrated that, depending on the case, the integral classifier allows to decrease the detection error by 0.05–0.16.