Source Camera Model Identification Using Features from contaminated Sensor Noise

Amel Tuama 1 Frédéric Comby 1 Marc Chaumont 1
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : This paper presents a new approach of camera model identification. It is based on using the noise residual extracted from an image by applying a wavelet-based denoising filter in a machine learning framework. We refer to this noise residual as the polluted noise (POL-PRNU), because it contains a PRNU signal contaminated with other types of noise such as the image content. Our proposition consists of extracting high order statistics from POL-PRNU by computing co-occurrences matrix. Additionally, we enrich the set of features with those related to CFA demosaicing artifacts. These two sets of features feed a classifier to perform a camera model identification. The experimental results illustrate the fact that machine learning techniques with discriminant features are efficient for camera model identification purposes.
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Digital-Forensics and Watermarking, LNCS (9569), Springer, pp.83-93, 2016, 14th International Workshop, IWDW 2015, Tokyo, Japan, October 7-10, 2015, Revised Selected Papers, 978-3-319-31959-9 (Print) 978-3-319-31960-5 (Online). 〈10.1007/978-3-319-31960-5_8〉. 〈http://iwdw2015.tokyo/index.php〉
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Soumis le : jeudi 26 novembre 2015 - 12:16:36
Dernière modification le : jeudi 24 mai 2018 - 15:59:23
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Amel Tuama, Frédéric Comby, Marc Chaumont. Source Camera Model Identification Using Features from contaminated Sensor Noise. Digital-Forensics and Watermarking, LNCS (9569), Springer, pp.83-93, 2016, 14th International Workshop, IWDW 2015, Tokyo, Japan, October 7-10, 2015, Revised Selected Papers, 978-3-319-31959-9 (Print) 978-3-319-31960-5 (Online). 〈10.1007/978-3-319-31960-5_8〉. 〈http://iwdw2015.tokyo/index.php〉. 〈lirmm-01234164〉

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