A JPEG image blind steganography detection method using KCCA feature fusion

Abstract

Feature fusion can effectively improve the steganographic detection capability, but the previous researches of feature fusion in JPEG image steganography detection rarely considered the nonlinear correlation of features. This paper analyzes the correlation of JPEG image steganographic features and fuses features with lowest correlation to obtain better detection capability based on KCCA (Kernel canonical correlation analysis), which has a good ability of nonlinear correlation analysis and can eliminate the redundancy of information between features. Firstly, analyze the "DCT extended feature" and the "markov reduced feature" which are classic features, and the newly proposed "DCT adaptive feature" in 2011. Secondly, select two features with lowest correlation among them for KCCA feature fusion. Finally, carry out experimental contrasts with other related methods. The experimental results show that the proposed method is reasonable and effective.