Sampling for Forensic Practitioners Short Course

This series is scheduled to begin on Friday, April 28, 2023. A registration form can be found below. 

Presenter:

Alicia Carriquiry
Director of CSAFE
Iowa State University

About the Short Course:

Session 1 is the first session in the three-session short course, Sampling for Forensic Practitioners. Dr. Alicia Carriquiry covers populations and sampling frames, and sampling methods including geometric sampling. The emphasis will be on understanding how these methods are used to aid and enhance current forensic science practices. This short course will be held online in three sessions: April 28, May 5, and May 12. Registration is free, and you only need to register once to attend all three sessions. Participants who attend all sessions will receive a certificate of completion.

If you are interested in participating in this course and would like to suggest topics for discussion, please contact us at your earliest convenience and we will try to accommodate your request.

Statistical Thinking for Forensic Practitioners Short Course

This series is scheduled to begin on Friday, March 31, 2023. A registration form can be found below. 

Session 1 Description:

Probability Concepts and their Relevance to Forensic Science is the first session in the four-session short course, Statistical Thinking for Forensic Practitioners

Probability is the mathematical language of uncertainty. Probabilities are used to describe the frequency or likelihood of events or to characterize measurement uncertainty. In this first session, we introduce the laws of probability and their application in forensic settings. Specific topics include:

  • Definition and interpretation of probability
  • Basic laws of probability
  • Conditional probability and independence of events
  • Bayes’ Theorem and Bayesian statistics

Topics are illustrated with examples drawn from forensic science and relevant legal cases.

Presenter:

Hal Stern
Co-Director of CSAFE
Provost, Executive Vice Chancellor, and Chancellor’s Professor – University of California, Irvine

About the Short Course:

Session 1 is the first session in the four-part short course, Statistical Thinking for Forensic Practitioners, Dr. Hal Stern introduces fundamental concepts from probability and statistics –– motivated by forensic issues –– followed by a detailed investigation of how they apply to assess forensic evidence’s probative value. This short course will be held online in four sessions. Each session builds upon the previous one(s) and recordings will be available in the event registrants are unable to attend one or more of the live sessions. Researchers, collaborators, and members of the broader forensics and statistics communities are encouraged to attend. Short course registrants who attend all sessions will receive a certificate of completion.

Insights: Score-Based Likelihood Ratios for Camera Device Identification

INSIGHTS

Score-Based Likelihood Ratios for Camera Device Identification

OVERVIEW

In the developing field of digital image forensics, it is important to be able to identify cameras and other digital devices involved in crimes. However, current camera identification methods fail to quantify the strength of evidence, making it challenging for such evidence to withstand scrutiny in courts. Researchers funded by CSAFE propose using Score-Based Likelihood Ratios to quantify the weight of evidence in digital camera identification.

Lead Researchers

Stephanie Reinders, PhD
Yong Guan, PhD
Danica Ommen, PhD
Jennifer Newman, PhD

Journal

Journal of Forensic Sciences

Publication Date

6 February 2022

Publication Number

IN 126 STAT

Goals

1

Create Score-Based Likelihood Ratios (SLRs) to evaluate the strength of camera identification evidence

2

Compare different SLR models to determine which is the most accurate

The Study

All cameras have small manufacturing imperfections that cause slight variations among pixels in the camera sensor array. These imperfections are known as Photo-Response Non-Uniformities (PRNUs), which create a sort of “camera fingerprint” on images taken with that camera. These PRNUs can be used to identify the device used for a questioned image.

Reinders et al. used a dataset of 4,800 images from a total of 48 known camera devices. They then calculated a similarity score (notated as Δ) between questioned images (Q) and the PRNUs (K) of each camera.

From this, they constructed three different SLRs, each meant to determine the likelihood that a questioned image Q and Person of Interest’s camera’s PRNU K came from the same camera (hypothesis Hp), compared to the likelihood that Q and K came from different cameras (hypothesis Hd).

The three constructed SLR equations

Trace-Anchored SLR: Considers similarity scores between a questioned sample of evidence and samples from the alternative population

Source-Anchored SLR: Considers similarity scores between samples from a specific known source and samples from the alternative population

General Match SLR: Considers similarity scores between samples from randomly selected sources

RESULTS

Focus on the future

 

The data used in this study was a closed set, where all images came from the same known 26 devices, and were RAW, center-cropped, auto-exposure, and landscape orientation. Future studies may include an open set, with a larger variety of devices and image types, which may yield different results.

Several researchers have employed an “Inconclusive Zone” that does not result in a definitive match or non-match. This could be included in future studies, and if used in courts, could put further burden of proof on the prosecution and greater benefit of the doubt for the defense.