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Source-anchored, trace-anchored, and general match score-based likelihood ratios for camera device identification.

Stephanie Reinders^1,2, Yong Guan^2,3, Danica Ommen^1,2

¹Department of Statistics, Iowa State University, Ames, Iowa, USA.

Journal of Forensic Sciences

|February 7, 2022

Summary

This study introduces a framework to compare three score-based likelihood ratio (SLR) methods for forensic camera identification using photo-response non-uniformity (PRNU) fingerprints. Trace-anchored SLRs demonstrated superior performance in identifying the source camera.

Keywords:

digital cameras digital evidence digital images forensic camera identification score-based likelihood ratios and SLR

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Area of Science:

Digital Forensics
Image Analysis
Pattern Recognition

Background:

Forensic camera device identification links crime scene images to specific cameras.
Photo-response non-uniformity (PRNU) from sensor imperfections creates unique camera fingerprints.
Existing methods often yield binary decisions, lacking probabilistic strength quantification.

Purpose of the Study:

To introduce and evaluate a framework for comparing different score-based likelihood ratio (SLR) methods in camera device identification.
To quantify the evidential strength of PRNU-based camera fingerprints using statistical methods.
To compare the performance of source-anchored, trace-anchored, and general match SLRs.

Main Methods:

Utilized photo-response non-uniformity (PRNU) estimates as digital camera fingerprints.
Employed correlation distance as a similarity score between image fingerprints.
Calculated and compared three types of score-based likelihood ratios (SLRs): source-anchored, trace-anchored, and general match.
Tested the methods on 48 camera devices across four diverse image databases (ALASKA, BOSSbase, Dresden, StegoAppDB).

Main Results:

Trace-anchored SLRs exhibited the highest performance in distinguishing between cameras based on PRNU fingerprints.
General match SLRs showed the lowest performance among the evaluated methods.
The framework provided a robust comparison of SLR types for forensic camera identification.

Conclusions:

Trace-anchored score-based likelihood ratios offer a more reliable method for forensic camera device identification compared to source-anchored and general match approaches.
The developed framework effectively quantifies the evidential weight in camera identification, enhancing forensic analysis.
PRNU-based camera fingerprinting combined with appropriate SLR methods is a valuable tool in digital forensics.