PRNU-based Source Camera Identification for Multimedia Forensics

2021 ◽  
Author(s):  
Eitan Flor ◽  
Ramazan Aygun ◽  
Suat Mercan ◽  
Kemal Akkaya
Keyword(s):  
Multimedia Forensics ◽  
Source Camera Identification ◽  
Camera Identification

Estimate of PRNU Noise Based on Different Noise Models for Source Camera Identification

2010 ◽  
Vol 2 (2) ◽  
pp. 21-33 ◽  
Author(s):  
Irene Amerini ◽  
Roberto Caldelli ◽  
Vito Cappellini ◽  
Francesco Picchioni ◽  
Alessandro Piva
Keyword(s):  
Noise Model ◽  
Digital Content ◽  
Wavelet Domain ◽  
Multimedia Forensics ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Novel Method ◽  
Photo Response ◽  
Noise Models ◽  
Open Issues

Identification of the source that has generated a digital content is considered one of the main open issues in multimedia forensics community. The extraction of photo-response non-uniformity (PRNU) noise has been so far indicated as a mean to identify sensor fingerprint. Such a fingerprint can be estimated from multiple images taken by the same camera by means of a de-noising filtering operation. In this paper, the authors propose a novel method for estimating the PRNU noise in source camera identification. In particular, a MMSE digital filter in the un-decimated wavelet domain, based on a signal-dependent noise model, is introduced and compared with others commonly adopted for this purpose. A theoretical framework and experimental results are provided and discussed.

Estimate of PRNU Noise Based on Different Noise Models for Source Camera Identification

2012 ◽  
pp. 9-20
Author(s):  
Irene Amerini ◽  
Roberto Caldelli ◽  
Vito Cappellini ◽  
Francesco Picchioni ◽  
Alessandro Piva
Keyword(s):  
Digital Filter ◽  
Theoretical Framework ◽  
Noise Model ◽  
Multimedia Forensics ◽  
Multiple Images ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Novel Method ◽  
Photo Response ◽  
Noise Models

Identification of the source that has generated a digital content is considered one of the main open issues in multimedia forensics community. The extraction of photo-response non-uniformity (PRNU) noise has been so far indicated as a mean to identify sensor fingerprint. Such a fingerprint can be estimated from multiple images taken by the same camera by means of a de-noising filtering operation. In this paper, the authors propose a novel method for estimating the PRNU noise in source camera identification. In particular, a MMSE digital filter in the un-decimated wavelet domain, based on a signal-dependent noise model, is introduced and compared with others commonly adopted for this purpose. A theoretical framework and experimental results are provided and discussed.

Wavelet energy feature based source camera identification for ear biometric images

2020 ◽  
Vol 130 ◽  
pp. 139-147 ◽  
Author(s):  
Debbrota Paul Chowdhury ◽  
Sambit Bakshi ◽  
Pankaj Kumar Sa ◽  
Banshidhar Majhi
Keyword(s):  
Wavelet Energy ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Feature Based ◽  
Energy Feature

scholarly journals Source Camera Identification Issues

2011 ◽  
Vol 3 (4) ◽  
pp. 1-15
Author(s):  
Yongjian Hu ◽  
Chang-Tsun Li ◽  
Changhui Zhou ◽  
Xufeng Lin
Keyword(s):  
Classification Accuracy ◽  
Selection Criteria ◽  
Current Source ◽  
Image Features ◽  
Forensic Identification ◽  
Forensic Investigation ◽  
Identification Schemes ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Popular Image

Statistical image features play an important role in forensic identification. Current source camera identification schemes select image features mainly based on classification accuracy and computational efficiency. For forensic investigation purposes; however, these selection criteria are not enough. Consider most real-world photos may have undergone common image processing due to various reasons, source camera classifiers must have the capability to deal with those processed photos. In this work, the authors first build a sample camera classifier using a combination of popular image features, and then reveal its deficiency. Based on the experiments, suggestions for the design of robust camera classifiers are given.

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