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arXiv · 1603.01068

First Steps Toward Camera Model Identification with Convolutional Neural Networks

Luca Bondi·Luca Baroffio·David Güera·Paolo Bestagini·Edward J. Delp·Stefano Tubaro
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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 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.

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Luca Bondi, Luca Baroffio, David Güera, Paolo Bestagini, Edward J. Delp, Stefano Tubaro. 2017-09-26. First Steps Toward Camera Model Identification with Convolutional Neural Networks. https://doi.org/10.1109/lsp.2016.2641006

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