Image Steganalysis via Random Subspace Fisher Linear Discriminant Vector Functional Link Network and Feature Mapping

Abstract

As the number of methods for implementing steganography (the hiding of data within a cover) increases, steganalysis, which can detect the presence of such hidden data, is also accompanied by an increase. To improve the accuracy of detection, we propose a new algorithm for processing feature that makes two optimizations into a random vector functional link (RVFL) network. The first optimization locates the processing phase of RVFL, where we model the eigenspectrum by the eigenvalue distribution of the scatter matrix. This eigenspectrum is used to generate the transpose matrix and obtain final features after feature reduction. The second optimization is the use of the random subspace Fisher linear discriminant (FLD) instead of random weights in RVFL. The weights between the input and enhancement nodes more accurately represent the relative importance of the features. The experiments compare the performance of other classifiers with the proposed method using five high-dimensional features. It is shown that the proposed method outperforms other classifiers in these steganalysis methods.