Camera fingerprint, namely PRNU, is a multiplicative noise source contained in each image captured by a given sensor. Source camera identification derives from similarity assessment between camera fingerprint and a candidate image; it requires the extraction of image PRNU and the estimation of camera fingerprint. To this aim, a denoising procedure is commonly applied in both cases and correlation is used for assessing similarity. However, correlation measure strongly depends on the accuracy of camera fingerprint estimation and PRNU image extraction. This paper presents a method for making more robust correlation-based source camera identification. It consists of more than one estimation of camera fingerprint; then, identification consists of the quantification of the amount of concurrence between correlation measures. It is expected higher correspondence between measures whenever the candidate image has been captured by a given device (match case); while lack of correspondence is expected whenever the image does not come from the considered device (no match case). Preliminary experimental results show that the proposed joint correlation measurements contribute to improve the precision of correlation-based source camera identification methods, especially in terms of a reduced number of false positives.

Joint correlation measurements for PRNU-based source identification / Bruni, V.; Salvi, A.; Vitulano, D.. - 11679(2019), pp. 245-256. ((Intervento presentato al convegno 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 tenutosi a Salerno, Italia [10.1007/978-3-030-29891-3_22].

Joint correlation measurements for PRNU-based source identification

Bruni V.;Vitulano D.
2019

Abstract

Camera fingerprint, namely PRNU, is a multiplicative noise source contained in each image captured by a given sensor. Source camera identification derives from similarity assessment between camera fingerprint and a candidate image; it requires the extraction of image PRNU and the estimation of camera fingerprint. To this aim, a denoising procedure is commonly applied in both cases and correlation is used for assessing similarity. However, correlation measure strongly depends on the accuracy of camera fingerprint estimation and PRNU image extraction. This paper presents a method for making more robust correlation-based source camera identification. It consists of more than one estimation of camera fingerprint; then, identification consists of the quantification of the amount of concurrence between correlation measures. It is expected higher correspondence between measures whenever the candidate image has been captured by a given device (match case); while lack of correspondence is expected whenever the image does not come from the considered device (no match case). Preliminary experimental results show that the proposed joint correlation measurements contribute to improve the precision of correlation-based source camera identification methods, especially in terms of a reduced number of false positives.
978-3-030-29890-6
978-3-030-29891-3
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1339877
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