期刊
IEEE SIGNAL PROCESSING LETTERS
卷 24, 期 3, 页码 259-263出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2016.2641006
关键词
Camera model identification; convolutional neural networks (CNN); image forensics.
资金
- DARPA
- Air Force Research Laboratory (AFRL) [FA875016-2-0173]
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 64x64 color image patches; 2) features learned by the proposed network generalize to camera models never used for training.
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