Source Camera Model Identification Using Features from Contaminated Sensor Noise

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9569)


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.


Camera model identification POL-PRNU CFA Co-occurrences matrix Feature extraction Rich model 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Nîmes UniversityNîmes Cedex 1France
  2. 2.Montpellier University, UMR 5506-LIRMMMontpellierFrance
  3. 3.CNRS, UMR 5506-LIRMMMontpellier Cedex 5France

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