  Detecting the camera model used to shoot a picture enables to solve a wide
series of forensic problems, from copyright infringement to ownership
attribution. For this reason, the forensic community has developed a set of
camera model identification algorithms that exploit characteristic traces left
on acquired images by the processing pipelines specific of each camera model.
In this paper, we investigate a novel approach to solve camera model
identification problem. Specifically, we propose a data-driven algorithm based
on convolutional neural networks, which learns features characterizing each
camera model directly from the acquired pictures. Results on a well-known
dataset of 18 camera models show that: (i) the proposed method outperforms
up-to-date state-of-the-art algorithms on classification of 64x64 color image
patches; (ii) features learned by the proposed network generalize to camera
models never used for training.
