An Image Steganalysis Method Based on Characteristic Function Moments and PCA

In this paper, a universal steganalysis scheme is proposed for images. The scheme is based on the characteristic function (CF) moments of three-level wavelet subbands including the further decomposition coefficients of the first scale diagonal subband. The first three statistical moments of each wavelet band of test image and prediction-error image are selected to form 102 dimensional features for steganalysis. Principal Components Analysis (PCA) is utilized to reduce the features and the support vector machine (SVM) is adopted as the classifier. The experimental results show the proposed scheme has good performance in attacking JPHide and JSteg.