Noiseprint: A CNN-Based Camera Model Fingerprint

Abstract

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.

Hue Modification Localization By Pair Matching

“…We propose to verify the inconsistency of P i and P j by means of a Siamese neural network [9]. Siamese neural networks have been recently exploited for applications in multimedia forensics [10]- [12]. This network architecture consists of two identical sub-networks f θ , followed by a non-linear classifier g γ that outputs an inconsistency score z ij whose standard logistic activation is defined as:…”

Combining PRNU and noiseprint for robust and efficient device source identification

“…In this work, we tackle these problems and propose a novel source identification strategy which improves the performance of PRNU-based methods when only a few, or even just one image is available for estimation, and when only small images may be processed. To this end, we rely on a recent approach for camera model identification [12] and use it to improve the PRNU-based source device identification performance. Camera model identification has received great attention in recent years, with a steady improvement of performance, thanks to the availability of huge datasets on which it is possible to train learning-based detectors, and the introduction of convolutional neural networks (CNN).…”

“…Of course, this makes the problem easier to face, given that in a realistic scenario it is not possible to include in the training phase all the possible camera models. A further step in this direction can be found in [12], where the use of a new fingerprint has been proposed, called noiseprint, related to camera model artifacts and extracted by means of a CNN trained in siamese modality. Noiseprints can be used in PRNU-like scenarios but require much less data to reach a satisfactory performance [19].…”

“…Recently, a camera-model fingerprint has been proposed [12], called noiseprint, extracted by means of a suitably trained convolutional neural network (CNN). A noiseprint is an image-size pattern, like the PRNU, in which the high-level scene content is removed and model-related artifacts are emphasized.…”

“…During training, thanks to a suitable loss function, the network learns to emphasize the similarities among positive pairs, that is, the camera model artifacts. In [12] noiseprints have been shown to enable the accomplishment of numerous forensic tasks, especially image forgery localization.…”