  Source identification is an important topic in image forensics, since it
allows to trace back the origin of an image. This represents a precious
information to claim intellectual property but also to reveal the authors of
illicit materials. In this paper we address the problem of device
identification based on sensor noise and propose a fast and accurate solution
using convolutional neural networks (CNNs). Specifically, we propose a
2-channel-based CNN that learns a way of comparing camera fingerprint and image
noise at patch level. The proposed solution turns out to be much faster than
the conventional approach and to ensure an increased accuracy. This makes the
approach particularly suitable in scenarios where large databases of images are
analyzed, like over social networks. In this vein, since images uploaded on
social media usually undergo at least two compression stages, we include
investigations on double JPEG compressed images, always reporting higher
accuracy than standard approaches.
