A subspace learning-based method for JPEG mismatched steganalysis

Abstract

The prevailing steganalysis detector trained by a source is used to recognize images from another different source, the detection accuracy typically drops owing to the mismatch between the two sources. In contrast to previous mismatched steganalysis methods, in this paper, we develop an unsupervised subspace learning-based method which has some differences from the ones common used in mismatched steganalysis. The proposed method employs low-rank and sparse constraints on the reconstruction coefficient matrix to maintain the global and local structures of the data. In this way, we can obtain new feature representations so that the feature distributions of the training and test data are close. We further promote the performance of the proposed method by employing the l2,1-norm on the error matrix. Comprehensive experiments on the JPEG mismatched steganalysis are conducted, and the experimental results show that the proposed method can improve the detection accuracy.