A Multimode Texture Spatial Steganalysis Based on CleanImage

A multimode texture spatial domain steganalysis based on cleanimage is proposed. Firstly, the image is divided into different sets according to the gradient energy. And then, the images in these sets are restored by using wavelet QMF filter, and attacked by secondLSB steganography, respectively. Finally, a 3D feature vector is extracted, which is further trained by oneclass SVM such that a hypersphere SVM is obtained. The advantage of this proposed method is that the stego images are not required during the training, and have excellent result in spatial domain. When the embedding rate is 100%, 50%, and 20%, the smooth set has the classification rate of 100%, 99.73%, and 66.22%, and the rough set has the classification rate of 97.6%, 72.6%, and 29.4% , respectively.