Compact image steganalysis for LSB-matching steganography

In this paper, we propose a compact image steganalysis method for the LSB-matching steganography, in which a feature vector composed by only 12 elements is extracted from the image. We analyze the statistical artifact occurred in images when a secret data is embedded in it by the LSB-matching steganography. We selected 12 most relevant features based on the probability density function (PDF) of difference of adjacent pixels and the co-occurrence matrix of the image, which can distinguish stegoimages from the natural images. The Support Vector Machine (SVM) is employed as classifier using the training vectors with 12 elements. The experimental results show that the proposed scheme provides a better discriminate performance than previously proposed methods that require a larger amount of feature elements, such as 27, 35 and 225 feature elements, respectively, for their discriminations.