FS-SDS: Feature selection for JPEG steganalysis using stochastic diffusion search

Feature extraction and classification based on feature sets are two major components of steganalysis process. The high dimension of feature sets used for steganalysis makes classification a complex and time-consuming process. This paper proposes a novel feature selection algorithm (FS-SDS) for steganalysis. FS-SDS is a wrapper-type feature selection algorithm which selects reduced feature set using Stochastic Diffusion Search. The Stochastic Diffusion Search is a generic population-based search method, which has been adopted successfully in this work for steganalytic feature selection. The experiments are conducted with steganograms of the common JPEG steganography techniques. To show the usefulness and effectiveness of FS-SDS, experiments were conducted on two different feature sets used for steganalysis. The experimental results show that the proposed feature selection not only effectively reduces the dimensionality of the features, but also improves the detection accuracy of the steganalysis process.