Developing automated methods to detect and match face and voice biometrics in child sexual abuse videos

Simple item page
dc.contributor.authorWestlake, B.
dc.contributor.authorBrewer, R.
dc.contributor.authorSwearingen, T.
dc.contributor.authorRoss, A.
dc.contributor.authorPatterson, S.
dc.contributor.authorMichalski, D.
dc.contributor.authorHole, M.
dc.contributor.authorLogos, K.
dc.contributor.authorFrank, R.
dc.contributor.authorBright, D.
dc.contributor.authorAfana, E.
dc.date.issued2022
dc.description.abstractThe proliferation of child sexual abuse material (CSAM) is outpacing law enforcement’s ability to address the problem. In response, investigators are increasingly integrating automated software tools into their investigations. These tools can detect or locate files containing CSAM, and extract information contained within these files to identify both victims and offenders. Software tools using biometric systems have shown promise in this area but are limited in their utility due to a reliance on a single biometric cue (namely, the face). This research seeks to improve current investigative practices by developing a software prototype that uses both faces and voices to match victims and offenders across CSAM videos. This paper describes the development of this prototype and the results of a performance test conducted on a database of CSAM. Future directions for this research are also discussed.
dc.description.statementofresponsibilityBryce Westlake, Russell Brewer, Thomas Swearingen, Arun Ross, Stephen Patterson, Dana Michalski, Martyn Hole, Katie Logos, Richard Frank, David Bright and Erin Afana
dc.identifier.citationTrends and Issues in Crime and Criminal Justice, 2022; 648(648):1-15
dc.identifier.doi10.52922/ti78566
dc.identifier.isbn9781922478566
dc.identifier.issn0817-8542
dc.identifier.issn1836-2206
dc.identifier.orcidMichalski, D. [0000-0002-6585-1856]
dc.identifier.orcidLogos, K. [0000-0001-8811-1810]
dc.identifier.urihttps://hdl.handle.net/2440/135357
dc.language.isoen
dc.publisherAustralian Institute of Criminology
dc.rights© Australian Institute of Criminology 2022. The Australian Institute of Criminology encourages the use of information published on this website. The Commonwealth of Australia owns the copyright in all material produced by the Australian Institute of Criminology. All the material on this website is provided under the latest Creation Commons Attribution licence
dc.source.urihttps://www.aic.gov.au/publications/tandi/tandi648
dc.titleDeveloping automated methods to detect and match face and voice biometrics in child sexual abuse videos
dc.typeJournal article
pubs.publication-statusPublished

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
hdl_135357.pdf
Size:
676.87 KB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

Gender Studies and Social Analysis publications