Empirical Evaluation of PRNU Fingerprint Variation for Mismatched Imaging Pipelines

Sharad Joshi, Pawel Korus, Nitin Khanna, Nasir Memon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2020 IEEE International Workshop on Information Forensics and Security, WIFS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728199306
DOIs
StatePublished - Dec 6 2020
Event2020 IEEE International Workshop on Information Forensics and Security, WIFS 2020 - New York, United States
Duration: Dec 6 2020Dec 11 2020

Publication series

Name2020 IEEE International Workshop on Information Forensics and Security, WIFS 2020

Conference

Conference2020 IEEE International Workshop on Information Forensics and Security, WIFS 2020
CountryUnited States
CityNew York
Period12/6/2012/11/20

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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