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CSAFE’s Latest Resources

Change cannot happen without community. CSAFE is passionate about achieving its mission to apply a statistically sound and scientifically solid foundation to the interpretation of forensic evidence. To that end, we invite fellow scientists and researchers, along with those in the forensic and legal communities, to use and share these resources as we work together to elevate forensic science and technology.

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The Impact of Multi-Camera Smart Phones on Source Camera Identification

Type: Research Area(s):

Published: 2024 | By: Stephanie Reinders

An investigator has a questioned image from an unknown source and wants to determine whether it came from a camera on a person of interest’s smartphone. This scenario is referred to as source camera identification. Researchers discovered that slight imperfections…

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Demonstrative Evidence and the Use of Algorithms in Jury Trials

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Published: 2024 | By: Rachel Rogers

We investigate how the use of bullet comparison algorithms and demonstrative evidence may affect juror perceptions of reliability, credibility, and understanding of expert witnesses and presented evidence. The use of statistical methods in forensic science is motivated by a lack…

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ShoeCase: A data set of mock crime scene footwear impressions

Type: Research Area(s):

Published: 2023 | By: Abigail Tibben

This project's main objective is to create an open-source database containing a sizeable number of high-quality images of shoe impressions. The Center for Statistics and Applications in Forensic Evidence (CSAFE) team collected images that represented those found at crime scenes…

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Combining reproducibility and repeatability studies with applications in forensic science

Type: Research Area(s):

Published: 2023 | By: Hina Arora

Studying the repeatability and reproducibility of decisions made during forensic examinations is important in order to better understand variation in decisions and establish confidence in procedures. For disciplines that rely on comparisons made by trained examiners such as for latent…

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Reliability of ordinal outcomes in forensic black-box studies

Type: Research Area(s):

Published: 2024 | By: Hina M. Arora

Forensic science disciplines such as latent print examination, bullet and cartridge case comparisons, and shoeprint analysis, involve subjective decisions by forensic experts throughout the examination process. Most of the decisions involve ordinal categories. Examples include a three-category outcome for latent…

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Density-based matching rule: Optimality, estimation, and application in forensic problems

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Published: 2024 | By: Hana Lee

We consider matching problems where the goal is to determine whether two observations randomly drawn from a population with multiple (sub)groups are from the same (sub)group. This is a key question in forensic science, where items with unidentified origins from…

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A finely tuned deep transfer learning algorithm to compare outsole images

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Published: 2023 | By: Moonsoo Jang

In forensic practice, evaluating shoeprint evidence is challenging because the differences between images of two different outsoles can be subtle. In this paper, we propose a deep transfer learning-based matching algorithm called the Shoe-MS algorithm that quantifies the similarity between…

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An automated alignment algorithm for identification of the source of footwear impressions with common class characteristics

Type: Research Area(s):

Published: 2024 | By: Hana Lee

We introduce an algorithmic approach designed to compare similar shoeprint images, with automated alignment. Our method employs the Iterative Closest Points (ICP) algorithm to attain optimal alignment, further enhancing precision through phase-only correlation. Utilizing diverse metrics to quantify similarity, we…

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A deep learning approach for the comparison of handwritten documents using latent feature vectors

Type: Research Area(s): ,

Published: 2024 | By: Juhyeon Kim

Forensic questioned document examiners still largely rely on visual assessments and expert judgment to determine the provenance of a handwritten document. Here, we propose a novel approach to objectively compare two handwritten documents using a deep learning algorithm. First, we…

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Likelihood ratios for changepoints in categorical event data with applications in digital forensics

Type: Research Area(s): ,

Published: 2024 | By: Rachel Longjohn

We investigate likelihood ratio models motivated by digital forensics problems involving time-stamped user-generated event data from a device or account. Of specific interest are scenarios where the data may have been generated by a single individual (the device/account owner) or…

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