This 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.

Coherence of PRNU weighted estimations for improved source camera identification / Bruni, V.; Tartaglione, M.; Vitulano, D.. - In: MULTIMEDIA TOOLS AND APPLICATIONS. - ISSN 1380-7501. - (2021). [10.1007/s11042-020-10477-5]

Coherence of PRNU weighted estimations for improved source camera identification

Bruni V.
;
Tartaglione M.;Vitulano D.
2021

Abstract

This 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.
Image forensics; Normalized correlation coefficient; PRNU source camera identification
01 Pubblicazione su rivista::01a Articolo in rivista
Coherence of PRNU weighted estimations for improved source camera identification / Bruni, V.; Tartaglione, M.; Vitulano, D.. - In: MULTIMEDIA TOOLS AND APPLICATIONS. - ISSN 1380-7501. - (2021). [10.1007/s11042-020-10477-5]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1494429
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