Joshi, SharadSharadJoshiKorus, PawelPawelKorusKhanna, NitinNitinKhannaMemon, NasirNasirMemon2025-08-312025-08-312020-12-06[9781728199306]10.1109/WIFS49906.2020.93609112-s2.0-85102515122https://d8.irins.org/handle/IITG2025/25670We 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.falseConference Paperhttps://arxiv.org/pdf/2004.019296 December 202039360911cpConference Proceeding4