Forensic analyses of digital images rely heavily on the traces of in-camera and out-camera processes left on the acquired images. Such traces represent a sort of camera fingerprint. If one is able to recover them, by suppressing the high-level scene content and other disturbances, a number of forensic tasks can be easily accomplished. A notable example is the PRNU pattern, which can be regarded as a device fingerprint, and has received great attention in multimedia forensics. In this paper, we propose a method to extract a camera model fingerprint, called noiseprint, where the scene content is largely suppressed and model-related artifacts are enhanced. This is obtained by means of a Siamese network, which is trained with pairs of image patches coming from the same (label +1) or different (label −1) cameras. Although the noiseprints can be used for a large variety of forensic tasks, in this paper we focus on image forgery localization. Experiments on several datasets widespread in the forensic community show noiseprint-based methods to provide state-of-the-art performance. Index Terms-Digital image forensics, noise residual, siamese networks, deep learning.
Cozzolino and Verdoliva REF propose a method based on deep metric learning to extract a camera model fingerprint, where the scene content is largely suppressed and model-related artifacts are enhanced.
Noiseprint: A CNN-Based Camera Model Fingerprint
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security
Computer Science