Computationally efficient demosaicing filter estimation for forensic camera model identification

Abstract

Determining the make and model of an image's source camera is an important forensic problem. While significant research has been conducted towards developing new camera model identification algorithms, little research has focused on controlling the computational cost of these algorithms. This becomes an important issue if forensic algorithms are to be used in “big data” scenarios. In this paper, we propose a new approach for controlling the computational cost associated with the algorithm proposed by Swaminathan et al. that identifies an image's source camera using least squares estimates of its demosaicing filter. Through a set of experiments, we show that our algorithm is able to achieve a higher classification accuracy at a fixed computational cost than the existing method. Similarly, our algorithm is able to reach a target classification accuracy at a lower computational cost.