Image steganalysis using a bee colony based feature selection algorithm

Feature selection is one of the most significant phases of pre-analysis processing, which can influence the performance of steganalysis. In this paper, we have proposed a new feature-based blind steganalysis method for detecting stego images from the cover images in JPEG images using a feature selection technique based on artificial bee colony (IFAB). Most usual techniques for feature selection are wrapper methods and filter methods which IFAB is one of the wrapper based feature selection methods. Artificial bee colony (ABC) algorithm is inspired by honey bees' social behavior in their search for perfect food sources. However, in the suggested algorithm, classifier performance and the dimension of the selected feature vector are dependent on heuristic information for ABC. As a result, we can choose the adaptive feature subset with respect to the shortest feature dimension and the improved performance of the classifier. The experimental results show that the proposed approach is easy to be employed for steganalysis purposes. Moreover, since IFAB is used as one of wrapper methods, as a result, its overall performance is better than several recent and well-known feature selection methods.