Empirical evaluation of PRNU fingerprint variation for mismatched imaging pipelines

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dc.contributor.author Joshi, Sharad
dc.contributor.author Korus, Pawel
dc.contributor.author Khanna, Nitin
dc.contributor.author Memon, Nasir
dc.date.accessioned 2020-04-20T07:26:30Z
dc.date.available 2020-04-20T07:26:30Z
dc.date.issued 2020-04
dc.identifier.citation Joshi, Sharad; Korus, Pawel; Khanna, Nitin and Memon, Nasir, "Empirical evaluation of PRNU fingerprint variation for mismatched imaging pipelines", arXiv, Cornell University Library, DOI: arXiv:2004.01929, Apr. 2020. en_US
dc.identifier.uri http://arxiv.org/abs/2004.01929
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/5329
dc.description.abstract We assess the variability of PRNU-based camera fingerprints with mismatched imaging pipelines (e.g., different camera ISP or digital darkroom software). We show that camera fingerprints exhibit non-negligible variations in this setup, which may lead to unexpected degradation of detection statistics in real-world use-cases. We tested 13 different pipelines, including standard digital darkroom software and recent neural-networks. We observed that correlation between fingerprints from mismatched pipelines drops on average to 0.38 and the PCE detection statistic drops by over 40%. The degradation in error rates is the strongest for small patches commonly used in photo manipulation detection, and when neural networks are used for photo development. At a fixed 0.5% FPR setting, the TPR drops by 17 ppt (percentage points) for 128 px and 256 px patches.
dc.description.statementofresponsibility by Sharad Joshi, Pawel Korus, Nitin Khanna and Nasir Memon
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.title Empirical evaluation of PRNU fingerprint variation for mismatched imaging pipelines en_US
dc.type Pre-Print en_US
dc.relation.journal arXiv


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