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Advancing child sexual abuse investigations using biometrics and social network analysis

Other authors
Stephen Patterson, David Bright, Arun Ross, Katie Logos, Dana Michalski
Digital media Biometrics Social networks Child abuse Child sexual abuse Machine learning

This paper demonstrates how biometric features can be extracted from people in child sexual abuse material (CSAM) and examined using social network analysis to reveal important patterns across seized media files.

Using an automated software system previously developed by the research team (the Biometric Analyser and Network Extractor), the authors extract, match and plot multiple biometric attributes (face and voice) from a database of CSAM videos compiled by law enforcement. They apply a series of network measures to illustrate how the biometric match data can be used to rapidly pinpoint key media files associated with an investigation, without the need for an investigator to manually review and catalogue all files. Future directions for this research are also discussed.

Publication Details
License type:
All Rights Reserved
Access Rights Type:
Trends and issues in crime and criminal justice, no.668