A novel steganalysis framework of heterogeneous images based on GMM clustering

The current steganalysis frameworks involve a large number of techniques for feature extraction and classification. However, one of their common defects is treating all images as equal, thus ignoring the variability of statistical properties of different images, which motivates us to propose a novel steganalysis framework based on Gaussian mixture model (GMM) clustering in the study, targeting at heterogeneous images with different texture complexity. There are two main improvements compared to the current steganalysis frameworks. First, in the training stage, the GMM clustering algorithm is exploited to classify the training samples into limited categories automatically, and then design corresponding steganalyzers for each category; second, in the testing stage, the posterior probability of testing samples belonging to each category is calculated, and the samples are submitted to the steganalyzers corresponding to the maximum posterior probability for test. Extensive experimental results aiming at least significant bit matching (LSBM) steganography and two adaptive steganography algorithms show that the proposed framework outperforms the steganalysis system that is directly trained on a mixed dataset, and also indicate that our framework exhibits better detection performance compared to the representative framework for using image contents in most circumstances and similar detection performance in few cases.