An Image Steganalysis Method Based on Characteristic Function Moments of Wavelet Subbands

In this paper a universal steganalysis scheme is proposed for images. The scheme is based on the characteristic function moments of three-level wavelet subbands including the further decomposition coefficients of the first scale diagonal subband. The first three order statistical moments of each band are selected to form a feature vector for steganalysis. The Euclidean distance is used as the separability criterion to analysis the effectiveness of feature vectors for classification and the BP neural network is adopted as the classifier. Simulation results show the efficacy of our steganalyzer on several kinds of typical steganography algorithms. Compared to other well-known methods, the proposed scheme performs the best in attacking Jsteg, OutGuess, F5, JHide and S-Tools.