JPEG Steganalysis Based on Classwise Non-Principal Components Analysis and Multi-Directional Markov Model
Guorong Xuan, Xia Cui, Yun Q. Shi, Wen Chen, Xuefeng Tong, Cong Huang · 2007
This paper presents a new steganalysis scheme to attack JPEG steganography. The 360 dimensional feature vectors sensitive to data embedding process are derived from multidirectional Markov models in the JPEG coefficients domain. The class-wise non-principal components analysis (CNPCA) is proposed to classify steganograpghy in the high-dimensional feature vector space. The experimental results have demonstrated that the proposed scheme outperforms the existing steganalysis techniques in attacking modern JPEG steganographic schemes-F5, Outguess, MB1 and MB2.