Score-Based Likelihood Ratios for Camera Device Identification Using Cameras of the Same Brand for the Alternative Device Population

Thumbnail Image
Date
2022-02-24
Authors
Reinders, Stephanie
Ommen, Danica
Martin, Abby
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Copyright 2022, The Authors
Authors
Person
Carriquiry, Alicia
Distinguished Professor
Research Projects
Organizational Units
Journal Issue
Is Version Of
Versions
Series
Department
Center for Statistics and Applications in Forensic Evidence
Abstract
Score-based likelihood ratios are a statistical method for quantifying the weight of evidence and have been used in many areas of forensics, including camera device identification1,2,3. Small sensor imperfections caused during manufacturing, called photo response non-uniformity4, leave identifying features, called a camera fingerprint, in the images that a camera takes. The sample correlation measures the similarity (or dissimilarity) between the camera fingerprint from the person of interest’s camera and the camera fingerprint in the questioned image. On its own, it is difficult to know how to interpret this score. Is a score of 0.25 evidence that the questioned image originated from the person of interest’s camera? What about a score of 0.5? To make sense of the score, it is compared with two different reference sets of scores: matching and non-matching. Matching scores are sample correlations between two fingerprints known to come from the person of interest’s camera. Non-matching scores are sample correlations between two fingerprints known to come from two different cameras. An alternative set of cameras that does not include the person of interest’s camera is used to build the set of non-matching scores. It turns out, that researchers have not agreed upon a best method for constructing the alternative population for score-based likelihood ratios5,6. Recently, researchers calculated score-based likelihood ratios for camera device identification using 48 cameras representing 26 models7. This present research explores whether the rates of misleading evidence can be decreased by restricting the alternative device population to cameras of the same brand as the person of interest’s camera.
Comments
The following was presented at the 74th Annual Scientific Conference of the American Academy of Forensic Sciences (AAFS), Seattle, Washington, February 21-25, 2022. Posted with permission of CSAFE.
Description
Keywords
Citation
DOI
Source
Copyright