Universal Steganographic Detection in JPEG Image Using the Graph Laplacian Semi-supervised Kernel

Current image steganographic detection algorithms are unable to make full use of the geometry of unlabeled image examples, detection performance is subject to a few labeled examples, which is utilized for training. In this paper, we propose an effective steganographic detection method for JPEG image that rely on the overall dataset. The method is combined with semi-supervised kernel in the presence of unlabeled examples. Semi-supervised kernel method constructs data adjacency graph to obtain Gram matrix, then we obtain the proposed method by incorporating graph Laplacian into kernel-based algorithms, which is effective integration of the cluster assumption and manifold assumption. Our method utilizes the geometry of all examples with manifold regularization to produce smooth decision functions and thus improving the performance universal steganographic detection. Experimental results show the effectiveness of our proposed method.