Feature selection for blind image steganalysis using neighborhood rough sets

Abstract

Feature extraction for blind image steganalysis produces much features or high dimensional data, which bring about time consuming and even a low detection percentage. As being one of the most important phases of preprocessing, feature selection can reduce these extracted features, and improve the p erformance of steganalysis. Firstly, we introduce the Neighborhood Rough Sets (NRS) to the field of blind image steganalysis. Then, some concepts of feature significance and feature reduct are presented based on NRS. Furthermore, we propose a Feature Selection approach by NRS for blind image steganalysis (FSNRS). The FSNRS has the ability to delete redundant features, meanwhile maintaining the classification accuracy of a steganalysis system. The FSNRS is a filter feature selection technique for blind image steganalysis, which filtrates extracted features depending on a positive region preserving in NRS. The compact feature subset with a shortest feature dimension for blind image steganalysis is selected. Moreover, some experiments for blind steganalysis using SVM and KNN classifiers on selected feature subset are carried out. The experimental results show that our proposed approach can obtain compact features for blind image steganalysis and the performances of classifiers on those selected features are improved. Since the FSNRS is used with an adjustable neighborhood parameter, as a result, the classification performance of selected features is better than that of original whole features in most cases.