Empirical Evaluation of PRNU Fingerprint Variation for Mismatched Imaging Pipelines
This addresses a practical issue for forensic analysts in real-world camera identification, but it is incremental as it evaluates existing methods under new conditions.
The paper tackled the problem of PRNU-based camera fingerprint variability under mismatched imaging pipelines, showing that fingerprints exhibit significant variations leading to degraded detection statistics, with correlation dropping to 0.38 and true positive rate decreasing by 17 percentage points at a fixed false positive rate.
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.