Steganalysis of JPEG images using enhanced neighbouring joint density features

In this study, a blind steganalysis approach which accurately discloses low rate data hiding schemes is proposed. The absolute values of neighbouring joint density (absNJ) are used for feature extraction. In this way, the intra- and inter-block situations are employed providing a variety of different features. Aside from the absolute values of discrete cosine transform (DCT) coefficients, the differential DCT coefficients are also exploited to extract the features. Moreover, the absNJ features will be extended and used over differential DCT coefficients. It is shown that applying the Pth power of the DCT coefficients instead of their first power gains more discriminative features. Then, using the ensemble classifier, the cover image is discriminated from the stego one. Experimental results indicate the efficiency of the new scheme in comparison with the previously presented schemes.