Source camera identification via low dimensional PRNU features

Identifying the source of digital images is the key task in the community of image forensics. Sensor pattern noise dominantly serves as an intrinsic fingerprint or feature for dealing with the problem of source camera identification. However, how to decrease the dimensionality of the pattern noise while guaranteeing the detection power remains a hot topic. The goal of this paper is to investigate the problem of source camera identification for natural images in JPEG format. By considering the image texture, we propose to design a new classifier with adopting a weight function, leading to the remarkable reduction of the feature dimensionality. In the extensive experiments, it is verified that our proposed algorithm performs comparably with the prior art. Besides, the robustness of the proposed classifier is also evaluated when the query images are attacked by post-processing techniques such as JPEG compression, noise adding, noise removing and image cropping.