Abstract

Many areas of forensics are moving away from the notion of classifying evidence simply as a match or non-match. Instead, some use score-based likelihood ratios (SLR) to quantify the similarity between two pieces of evidence, such as a fingerprint obtained from a crime scene and a fingerprint obtained from a suspect. We apply trace-anchored score-based likelihood ratios to the camera device identification problem. We use photo-response non-uniformity (PRNU) as a camera fingerprint and one minus the normalized correlation as a similarity score. We calculate trace-anchored SLRs for 10,000 images from seven camera devices from the BOSSbase image dataset. We include a comparison between our results the universal detector method.

Highlights

  • "Implementation of the likelihood ratio framework for camera identification based on sensor noise patterns." Law, Probability and Risk 10, no. 2 (2011): 149-159

  • The Score-based likelihood ratios (SLR) measures the relative probability of obtaining the similarity score δδ = Δ eeuu, eess if the image eeuu and camera fingerprint eess originate from the same specific camera device

  • If using Peak-to-Correlation Energy (PCE), the decision threshold does not need to be adjusted if a periodic component such as linear pattern is present

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Summary

The camera device identification problem

Unknown source evidence - Digital image eeuu from an unknown camera device (source) is recovered as evidence in a crime. Specific known source evidence - A camera fingerprint eess is estimated from a suspect’s camera device (source) CCss. The prosecution wants to determine whether image eeuu and camera fingerprint eess originate from the same specific camera device CCss (the suspect’s camera device). The prosecution and defense hypotheses: HHpp: image eeuu and camera fingerprint eess originate from the same specific camera device CCss HHdd: image eeuu and camera fingerprint eess do not originate from the same specific camera device CCSS. Match – an image and a camera fingerprint originate from the same camera device Non-match – an image and a camera fingerprint no not originate from the same camera device

Quantify the weight of forensic evidence
Directly model features
PP PP
Prior work with SLRs in device identification
Our research
Most widely used approach to camera device identification
In the universal detector approach
We use the score
Generate two sets of scores
Experiments
Findings
Experiment settings

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