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Performance Evaluation of Source Camera Attribution by Using Likelihood Ratio Methods.

Pasquale Ferrara1, Rudolf Haraksim1, Laurent Beslay1

  • 1Joint Research Centre, European Commission, 21027 Ispra, Italy.

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|July 31, 2024
PubMed
Summary
This summary is machine-generated.

This study transitions camera attribution from difficult similarity scores to interpretable likelihood ratios for digital forensics. This enables more informed decisions in legal cases using source camera attribution.

Keywords:
forensic evidence evaluationlikelihood ratioperformancevideo source attribution

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Area of Science:

  • Digital Forensics
  • Image Analysis
  • Computer Vision

Background:

  • Current source camera attribution methods rely on similarity scores lacking probabilistic interpretation.
  • Standard evaluation metrics like ROC curves offer limited usability in forensic investigations.
  • This hinders informed decision-making for triers of fact.

Purpose of the Study:

  • To demonstrate the transition from similarity scores to likelihood ratios for source camera attribution.
  • To enhance the probabilistic interpretation and forensic usability of digital evidence evaluation.
  • To integrate findings into existing forensic casework.

Main Methods:

  • Likelihood ratios were calculated from Photo Response Non-Uniformity (PRNU) source attribution similarity scores.
  • Experiments compared different strategies for digital images and videos, accounting for their unique characteristics.
  • Results were formatted according to guidelines for validating forensic likelihood ratio methods.

Main Results:

  • Successfully transitioned from similarity scores to probabilistic likelihood ratios.
  • Demonstrated the utility of likelihood ratios in source camera attribution for digital evidence.
  • Provided a framework compatible with forensic validation standards.

Conclusions:

  • Likelihood ratios offer a more interpretable and probabilistically sound approach to source camera attribution.
  • This transition significantly enhances the value of digital evidence in forensic investigations.
  • The proposed method supports more informed decision-making in legal contexts.