Image Pre-classification to improve accuracy of universal steganalysis

Most existing image universal steganalysis methods did not consider the impact of the differences in statistical properties among different images. This paper presents a method to improve the accuracy of JPEG universal steganalysis using image pre-classification. In the training phase, the training images are categorized into k clusters using K-means clustering according to the clustering features which are deduced from the horizontal and vertical intra-block co-occurrence matrices of the absolute value of discrete cosine transform (DCT) coefficients. After that, steganalytic features are extracted from each cluster and the training process is specialized for each cluster separately. Given a test image, the clustering features are extracted and the distances from the test image to each cluster center obtained in the training phase are calculated, and then the test image is pre-classified as the cluster with the minimum distance. After that, the steganalytic features of the test image are extracted and sent to the corresponding classifier, and then the final result (cover or stego) is outputted. Experimental results on typical JPEG steganographic algorithms show that the proposed method can significantly enhance the detection performance of existing steganalytic features. The steganalytic performance of the proposed method is better than previous proposed methods.