Image tampering detection based on stationary distribution of Markov chain

Wei Wang, Jing Dong, Tieniu Tan

Abstract: In this paper, we propose a passive image tampering detection method based on modeling edge information. We model the edge image of image chroma component as a finite-state Markov chain and extract low dimensional feature vector from its stationary distribution for tampering detection. The support vector machine (SVM) is utilized as classifier to evaluate the effectiveness of the proposed algorithm. The experimental results in a large scale of evaluation database illustrates that our proposed method is promising.