Fusion of Camera Model and Source Device Specific Forensic Methods for Improved Tamper Detection

Poyraz, Ahmet Gökhan; Dirik, Ahmet Emir; Karaküçük, Ahmet; Memon, Nasir

doi:10.48550/arxiv.2002.10123

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2020

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“…Many methods have proposed alternative representations [27]- [30] of the PRNU fingerprint. A recent method attempts to use a fused CNN-based approach that combines camera model features and PRNU to improve small-scale tampering detection [31].…”

Section: Background and Related Workmentioning

confidence: 99%

Empirical Evaluation of PRNU Fingerprint Variation for Mismatched Imaging Pipelines

Joshi¹,

Korus²,

Khan³

et al. 2020

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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.

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Section: Background and Related Workmentioning

confidence: 99%