Blind Image Steganalysis Based on Contourlet Transform

This paper presents a new blind approach of image Steganalysis based on contourlet transform and non linear support vector machine. Properties of Contourlet transform are used to extract features of images, and non linear support vector machine is used to classify the stego and cover images. The important aspect of this paper is that, it uses the minimum number of features in the transform domain and gives a better accuracy than many of the existing stegananlysis methods. The efficiency of the proposed method is demonstrated through experimental results. Also its performance is compared with the state of the art wavelet based steganalyzer (WBS), Feature based steganalyzer (FBS) and Contourlet based steganalyzer (WBS). Finally, the results show that the proposed method is very efficient in terms of its detection accuracy and computational cost.