A local variance based approach to alleviate the scene content interference for source camera identification

Identifying the source camera of images is becoming increasingly important nowadays. A popular approach is to use a type of pattern noise called photo-response non-uniformity (PRNU). The noise of image contains the patterns which can be used as a fingerprint. Despite that, the PRNU-based approach is sensitive towards scene content and image intensity. The identification is poor in areas having low or saturated intensity, or in areas with complicated texture. The reliability of different regions is difficult to model in that it depends on the interaction of scene content and the characteristics of the denoising filter used to extract the noise. In this paper, we showed that the local variance of the noise residual can measure the reliability of the pixel for PRNU-based source camera identification. Hence, we proposed to use local variance to characterize the severeness of the scene content artifacts. The local variance is then incorporated to the general matched filter and peak to correlation energy (PCE) detector to provide an optimal framework for signal detection. The proposed method is tested against several state-of-art methods. The experimental results show that the local variance based approach outperformed other state-of-the-art methods in terms of identification accuracy.