Universal Steganographic Detection Algorithmin in JPEG Image Using the Data-Dependent Kernel

Current SVM-based image steganographic detection algorithmins haven´t considered the impact of specific data, and the choice of the parameter greatly affects the classification performance, it´s necessary to constructing the kernel function from the perspective of specific data. This paper proposes a steganographic detection method for JPEG image that base on the data-dependent concept, first obtain the initial classifier by SVM training, then the kernel function is modified with conformal transformation by using the information of Support Vectors, retrain with the new kernel to enlarge the spacing around classfication boundary, iterate until getting the best result. Experimental results illustrate our method dose effectively improve the classification accuracy of image universal steganalysis, futhermore, a high classification accuracy under the default parameters makes the algorithmin more practical.