Universal steganalysis based on statistical models using reorganization of block-based DCT coefficients

The goal of stganography is to hide information into media without disclosing the fact of existing communication. Currently, stganography such as least significant bit (LSB), quantization index modulation (QIM) and spread spectrum (SS), has become increasingly widespread. Steganalysis as a counterpart of stganography is to detect the presence of it. In this paper, we present a new universal steganalysis method based on statistical models of the image's discrete cosine transform (DCT) coefficients. In fact, the block-based DCT by proper reorganization of its coefficients can have similar characteristics to wavelet transforms. The presented universal steganalysis method utilizes these characteristics to build statistical models of the image and its prediction-error image. Features extracted from the re-organization DCT blocks of host images and theirs prediction-error images and features extracted from steg images and theirs prediction-error images are used to train the SVM classifier. In the testing, features from those potential images are inputted the trained-well classifier to determine where the potential images are stego images or not. The experiments have shown that the proposed method outperforms in general prior-arts of steganalysis methods based on wavelet transform domain.