Source camera device identification based on raw images
Abstract
This paper investigates the problem of identifying the source imaging device of the same model for a natural raw image. The approach is based on the Poissonian-Gaussian noise model which can accurately describe the distribution of the given image. This model relies on two parameters considered as unique fingerprint to identify source cameras of the same model. The identification is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test (LRT) is presented and its performance is theoretically established. The statistical performance of LRT serves as an upper bound of the detection power. For a practice use, when the image parameters are unknown and camera parameters are known, a detector based on estimation of those parameters is designed. Numerical results on simulated data and real natural raw images highlight the relevance of our proposed approach.