Source Camera Model Identification Using Features from contaminated Sensor Noise - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Source Camera Model Identification Using Features from contaminated Sensor Noise

Amel Tuama
  • Fonction : Auteur
Frédéric Comby
Marc Chaumont

Résumé

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.
Fichier principal
Vignette du fichier
IWDW2015_TUAMA_COMBY_CHAUMONT_Source_Camera_Model_Identification.pdf (1.12 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

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

Identifiants

Citer

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⟩
192 Consultations
795 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More