A Universal JPEG Image Steganalysis Method Based on Collaborative Representation

In recent years, plenty of advanced approaches for universal JPEG image steganalysis have been proposed due to the need of commercial and national security. Recently, a novel sparse-representation-based method was proposed, which applied sparse coding to image steganalysis [4]. Despite satisfying experimental results, the method emphasized too much on the role of l(1)-norm sparsity, while the effort of collaborative representation was totally ignored. In this paper, we focus on the least square problem in a binary classification model and present a similar yet much more efficient JPEG image steganalysis method based on collaborative representation. We still represent a testing sample collaboratively over the training samples from both classes (cover and stego), while the regularization term is changed from l(1)-norm to l(2)-norm and each class-specific representation residual owns an extra divisor. Experimental results show that our proposed steganalysis method performs better than the recently presented sparse-representation-based method as well as the traditional SVM-based method. Extensive experiments clearly show that our method has very competitive steganalysis performance, while it has significantly less complexity.