CNN-based fast source device identification
This incremental improvement addresses image forensics for applications like intellectual property protection and illicit material tracing, particularly in scenarios with large image databases such as social networks.
The paper tackled the problem of identifying the source device of an image using sensor noise, proposing a CNN-based method that is faster and more accurate than conventional approaches, with higher accuracy reported for double JPEG compressed images.
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.