PFA-based feature selection for image steganalysis

Abstract

This paper presents a universal image steganalysis method based on feature selection by principal feature analysis (PFA). The goal of this paper is to increase the performance of existing image steganalysis approaches using PFA-based feature selection method and reduce the high dimensionality of the features used in state-of-the-art steganalysis methods. Principal component analysis (PCA) is widely used in pattern recognition applications. However, PCA has disadvantage that, all the generated features are transformed features. While, PFA selects the subset of preliminary features which contains necessary information. PFA is applied on spatial domain subtractive pixel adjacency matrix features and in case of transform domain, CHEN features (intrablock and interblock Markov-based features) and CC-PEV features (PEV features enhanced by Cartesian calibration). The experimental results show that PFA is effective and efficient in eliminating redundant features. Experimental results prove that the use of PFA method in steganalysis is superior in terms of dimensionality reduction of features and increases the classification performance.