Image Steganalysis by the Distribution Characters of Stego-images in Reduced Dimension Space

Abstract

In this paper we propose an Improved Kernel Linear Discriminant Analysis algorithm to analyze the distribution differences between cover images and stego-images in the reduced dimensional space. We observe that the hidden information, the information hidden in the cover images, of stegoimages are clustered in some plane while all other information of cover-images are scattered more evenly in this space and have no other clusters. Based on this fact, we develop a steganalysis scheme to discriminate stego-images from innocent images. The experiment results show the effectiveness of the proposed approach.