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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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Submitted on : Thursday, November 26, 2015 - 12:16:36 PM
Last modification on : Monday, October 11, 2021 - 1:24:07 PM
Long-term archiving on: : Friday, April 28, 2017 - 11:25:11 PM


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Amel Tuama, Frédéric Comby, Marc Chaumont. Source Camera Model Identification Using Features from contaminated Sensor Noise. IWDW: International Workshop on Digital Watermarking, Oct 2015, Tokyo, Japan. pp.83-93, ⟨10.1007/978-3-319-31960-5_8⟩. ⟨lirmm-01234164⟩



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