Source Camera Model Identification Using Features from contaminated Sensor Noise - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2016

Source Camera Model Identification Using Features from contaminated Sensor Noise

Amel Tuama
  • Function : Author
Frédéric Comby
Marc Chaumont

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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Dates and versions

lirmm-01234164 , version 1 (26-11-2015)

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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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