Image Texture Energy-Entropy-Based Blind Steganalysis

A novel approach of blind steganalysis is proposed, which is based on image texture energy-entropy features. Image complexity describes the difference of image's content and texture. Steg-image's texture is ordinarily more complicated than that of cover image. For analyzing image complexity, using image texture features to measure the statistical differences between cover image and steg-image. In the paper, we analyze image complexity based on image texture segmentation technique, and use Laws' image texture energy-entropy features to measure the statistical differences between cover image and steg-image. Applying these texture features, blind steganalysis is implemented. Support Vector Machine (SVM) is used as classifier to distinguish whether a given image is embedded into the convert message. Experiment results show that the proposed approach is greatly valuable and our blind steganalysis method attains a good testing accurate rate.