Steganalysis based on distribution characters of stego-images in reduced dimension space

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 stego-images are clustered in a plane while all other information of cover images are scattered more evenly in the whole 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 propose approach.

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