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Indexed by:会议论文

Date of Publication:2014-10-18

Included Journals:EI、CPCI-S、Scopus

Page Number:285-289

Key Words:Steganalysis; binary classification; least square; collaborative representation

Abstract: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.