Steganalysis of Multi-Class JPEG Images Based on Expanded Markov Features and Polynomial Fitting

In this article, based on the Markov approach proposed by shi et al [1], we expand it to the inter-blocks of the DCT domain, calculate the difference of the expanded Markov features between the testing image and the calibrated version, and combine these difference features and the polynomial fitting features on the histogram of the DCT coefficients as detectors. We reasonably improve the detection performance in multi-class JPEG images. We also compare the steganalysis performance among the feature reduction/selection methods based on principal component analysis, singular value decomposition, and Fisher's linear discriminant.