Using Multi-step Transition Matrices for Camera Model Identification

Recently, camera model identification becomes one of the most popular research topics in digital forensics field. Since every camera imaging processing left artifacts on its final output image, and some of them can be considered as model-specific ‘traces’ of its source camera, camera model can be classified only with a single image by catching these ‘traces’. This paper presents a camera model identification method based on multi-step transition matrices. We firstly model JPEG image coefficients by Markov process. Then, one-step and two-step transition matrices along different directions are extracted respectively. Finally, 58 statistics calculated from these matrices are used to perform camera model identification as features. In our experiment, we chose images from seven camera models in Dresden Image Database as our experiment samples. Experiments results show that the average detection accuracy of this method can reach to 99.27%. Compared with previous Markov method, our approach can perform better only using 58-D features.