First Steps Toward Camera Model Identification with Convolutional Neural Networks
This addresses forensic problems like copyright infringement for the digital forensics community, representing an incremental improvement with a data-driven approach.
The paper tackled camera model identification by proposing a convolutional neural network that learns features directly from images, achieving state-of-the-art performance on a dataset of 18 models and demonstrating generalization to unseen models.
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.