Image forensics, besides understanding if a digital image has been forged, often aims at determining information about image origin. In particular, it could be worthy to individuate which is the kind of source (digital camera, scanner or computer graphics software) that has generated a certain photo. Such an issue has already been studied in literature, but the problem of doing that in a blind manner has not been faced so far. It is easy to understand that in many application scenarios information at disposal is usually very limited; this is the case when, given a set of L images, the authors want to establish if they belong to K different classes of acquisition sources, without having any previous knowledge about the number of specific types of generation processes. The proposed system is able, in an unsupervised and fast manner, to blindly classify a group of photos without neither any initial information about their membership nor by resorting at a trained classifier. Experimental results have been carried out to verify actual performances of the proposed methodology and a comparative analysis with two SVM-based clustering techniques has been performed too.

Acquisition source identification through a blind image classification / Amerini, Irene; Becarelli, Rudy; Bertini, Bruno; Caldelli, Roberto. - In: IET IMAGE PROCESSING. - ISSN 1751-9659. - 9:4(2015), pp. 329-337. [10.1049/iet-ipr.2014.0316]

Acquisition source identification through a blind image classification

AMERINI, IRENE
;
2015

Abstract

Image forensics, besides understanding if a digital image has been forged, often aims at determining information about image origin. In particular, it could be worthy to individuate which is the kind of source (digital camera, scanner or computer graphics software) that has generated a certain photo. Such an issue has already been studied in literature, but the problem of doing that in a blind manner has not been faced so far. It is easy to understand that in many application scenarios information at disposal is usually very limited; this is the case when, given a set of L images, the authors want to establish if they belong to K different classes of acquisition sources, without having any previous knowledge about the number of specific types of generation processes. The proposed system is able, in an unsupervised and fast manner, to blindly classify a group of photos without neither any initial information about their membership nor by resorting at a trained classifier. Experimental results have been carried out to verify actual performances of the proposed methodology and a comparative analysis with two SVM-based clustering techniques has been performed too.
2015
image forensics; acquisition source identification; SVM-based clustering techniques; generation processes; blind image classification; image origin; digital image; acquisition sources; initial information
01 Pubblicazione su rivista::01a Articolo in rivista
Acquisition source identification through a blind image classification / Amerini, Irene; Becarelli, Rudy; Bertini, Bruno; Caldelli, Roberto. - In: IET IMAGE PROCESSING. - ISSN 1751-9659. - 9:4(2015), pp. 329-337. [10.1049/iet-ipr.2014.0316]
File allegati a questo prodotto
File Dimensione Formato  
Amerini_Acquisition-source-identification_2015.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 391.85 kB
Formato Adobe PDF
391.85 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1325035
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 5
social impact