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.

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.

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

scholarly journals Coherence of PRNU weighted estimations for improved source camera identification

2021 ◽  
Author(s):  
Vittoria Bruni ◽  
Michela Tartaglione ◽  
Domenico Vitulano
Keyword(s):  
State Of The Art ◽  
Main Idea ◽  
Similarity Metric ◽  
Opposite Case ◽  
Classification Feature ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Pattern Noise ◽  
Flat Field ◽  
Photo Response

AbstractThis paper presents a method for Photo Response Non Uniformity (PRNU) pattern noise based camera identification. It takes advantage of the coherence between different PRNU estimations restricted to specific image regions. The main idea is based on the following observations: different methods can be used for estimating PRNU contribution in a given image; the estimation has not the same accuracy in the whole image as a more faithful estimation is expected from flat regions. Hence, two different estimations of the reference PRNU have been considered in the classification procedure, and the coherence of the similarity metric between them, when evaluated in three different image regions, is used as classification feature. More coherence is expected in case of matching, i.e. the image has been acquired by the analysed device, than in the opposite case, where similarity metric is almost noisy and then unpredictable. Presented results show that the proposed approach provides comparable and often better classification results of some state of the art methods, showing to be robust to lack of flat field (FF) images availability, devices of the same brand or model, uploading/downloading from social networks.

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