Work

First Steps Toward Camera Model Identification With Convolutional Neural Networks

Year: 2016

Type: article

Abstract: 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 letter, 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: 1) the proposed method outperforms up-to-date state-of-the-art algorithms on classification of 64 × 64 color image patches; 2) features learned by the proposed network generalize to camera models never used for training.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2016.2641006

Authors: Luca Bondi, Luca Baroffio, David Guera, Paolo Bestagini, Edward J. Delp, Stefano Tubaro

Institutions: Politecnico di Milano, Purdue University West Lafayette

Language: English

Topic: Digital Media Forensic Detection

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Defense Advanced Research Projects Agency, Air Force Research Laboratory

Awards: G2155072849, G4713059963, G931422944