Color noise correlation-based splicing detection for image forensics

Itier, Vincent; Strauss, Olivier; Morel, Laurent; Puech, William

Type de document :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1007/s11042-020-10326-5

Titre :

Color noise correlation-based splicing detection for image forensics

Auteur(s) :

Itier, Vincent [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Strauss, Olivier [Auteur]
Image & Interaction [ICAR]
Morel, Laurent [Auteur]
NETHEOS
Puech, William [Auteur]
Image & Interaction [ICAR]

Titre de la revue :

Multimedia Tools and Applications

Pagination :

13215-13233

Éditeur :

Springer Verlag

Date de publication :

2021-04

ISSN :

1380-7501

Discipline(s) HAL :

Informatique [cs]/Traitement du signal et de l'image [eess.SP]

Résumé en anglais : [en]

Today, it has become very easy to manipulate digital images using image processing tools and software such as Adobe Photoshop (https://www.adobe.com/products/photoshop.html). Tampering with images by splicing is an operation ...
Lire la suite >Today, it has become very easy to manipulate digital images using image processing tools and software such as Adobe Photoshop (https://www.adobe.com/products/photoshop.html). Tampering with images by splicing is an operation that consists of cutting-and-pasting an area of an image into another host image. In this paper, we propose to detect and localize such manipulations by analyzing the correlation of the image noise across the three color channels RGB, which is an intrinsic feature of the digital photography acquisition process. More precisely, we propose to detect the border between the background (host image) and the spliced area. Using a sliding window, we detect the blocks that span across the two areas which are characterized by two different color noise correlations. To do this, we propose specific features that are able to highlight these blocks. After the feature extraction, we introduce a learning phase using a Random Forest Classifier. Experimental results, specifically on the Columbia database, show very good results in comparison to other current state of the art methods.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Projet ANR :

statistiques rObustEs pour l'apprentIssage Léger

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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