Detecting the camera model used to shoot a picture enables to solve a wide series of forensic problems, from copyright infringement to ownership attribution. For this reason, the forensic community has developed a set of camera model identification algorithms that exploit characteristic traces left on acquired images by the processing pipelines specific of each camera model. In this letter, we investigate a novel approach to solve camera model identification problem. Specifically, we propose a data-driven algorithm based on convolutional neural networks, which learns features characterizing each camera model directly from the acquired pictures. Results on a well-known dataset of 18 camera models show that: 1) the proposed method outperforms up-to-date state-of-the-art algorithms on classification of 64 × 64 color image patches; 2) features learned by the proposed network generalize to camera models never used for training.

First Steps Toward Camera Model Identification with Convolutional Neural Networks

Bondi, Luca;Baroffio, Luca;Bestagini, Paolo;Tubaro, Stefano
2017

Abstract

Detecting the camera model used to shoot a picture enables to solve a wide series of forensic problems, from copyright infringement to ownership attribution. For this reason, the forensic community has developed a set of camera model identification algorithms that exploit characteristic traces left on acquired images by the processing pipelines specific of each camera model. In this letter, we investigate a novel approach to solve camera model identification problem. Specifically, we propose a data-driven algorithm based on convolutional neural networks, which learns features characterizing each camera model directly from the acquired pictures. Results on a well-known dataset of 18 camera models show that: 1) the proposed method outperforms up-to-date state-of-the-art algorithms on classification of 64 × 64 color image patches; 2) features learned by the proposed network generalize to camera models never used for training.
Camera model identification; convolutional neural networks (CNN); image forensics; Signal Processing; Electrical and Electronic Engineering; Applied Mathematics
File in questo prodotto:
File Dimensione Formato  
07786852.pdf

Accesso riservato

Descrizione: Articolo
: Publisher’s version
Dimensione 497.67 kB
Formato Adobe PDF
497.67 kB Adobe PDF   Visualizza/Apri
11311-1045402_Tubaro.pdf

accesso aperto

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 852.12 kB
Formato Adobe PDF
852.12 kB Adobe PDF Visualizza/Apri

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/11311/1045402
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 163
  • ???jsp.display-item.citation.isi??? 138
social impact