A study on JPEG steganalytic features: Co-occurrence matrix vs. Markov transition probability matrix

Abstract

Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features.