Passive Steganalysis Using Image Quality Metrics and Multi-class Support Vector Machine

In this paper we propose a scheme using image quality metrics and multi-class support vector machine to identify steganographic domains. Firstly, we classify stegnographic domains into four, i.e. spatial domain, DCT domain, DWT domain and ICA domain. Then we analyze total 26 image quality measures summarized by Ismail Avcibas and choose eight sensitive features based on the analysis of variance technique as feature set to distinguish between cover-images and stego-images which are marked in the four domains respectively. The features' scores are computed between the original images and their Gaussian filtered versions. The classifier between cover and stego-images is built using multi-class support vector machine on the selected feature set. The presented method can not only detect the presence of hidden message but also identify the hiding domains. The experiment results show the proposed scheme achieves good classification results and improves its performance with larger embedded message length.