Steganalysis by Recombining Spatial Rich Model

Abstract

Steganalysis is an important topic in the field of information security. Steganalysis is mainly based on the traditional machine learning method, represented by the spatial rich model, and has high detection performance. Aiming at the problems of high feature dimension, strong redundancy and long feature extraction time in the spatial rich model, this paper conducts a series of feature selection operations and explores four different model combination methods: single quantization factor combination method, single filter kernel type combination method, mixed selecting combination method, Pearson correlation coefficient combination method. In addition, a feature preprocessing means are added. The experiment proves that the combination of mixed selecting combination and Pearson correlation coefficient can be stable with the performance of steganalysis before dimension reduction, but the performance dimension ratio is higher.