A Hierarchical Learning Framework for Steganalysis of JPEG Images

Abstract

JPEG Steganalysis is an important technique for forensic analysis of images on online social networks. This paper proposes a novel hierarchical learning framework for JPEG steganalysis. It is based on the observation that both regions of an image with different textural complexity and regions of different images with similar textural complexity tend to have different embedding probabilities. In the training stage of our framework, images are firstly clustered into a number of categories using Gaussian Mixture Model (GMM). Then, images in each category are decomposed into smaller blocks, and these blocks are also clustered into limited classes. Finally, a classifier is trained for each class of blocks. In the testing stage, an image and its blocks are also classified using trained GMM, and each block is tested on corresponding classifiers to make the final decision by weighed sum of individual results. Extensive experimental results show a better performance of our framework compared with some other previous learning framework.