Noiseprint: a CNN-based camera model fingerprint
This addresses the need for reliable camera model identification in multimedia forensics, offering an incremental improvement over existing methods like PRNU patterns.
The paper tackled the problem of extracting camera model fingerprints from digital images for forensic analysis, proposing a CNN-based method called noiseprint that suppresses scene content and enhances model-related artifacts, achieving state-of-the-art performance in image forgery localization across multiple datasets.
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 noiseprints can be used for a large variety of forensic tasks, here 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.