Measuring and mitigating targeted biometric impersonation
Measuring and mitigating targeted biometric impersonation
This study is concerned with the reliability of biometric verification systems when used in forensic applications. In particular, when such systems are subjected to targeted impersonation attacks. The authors expand on the existing work in targeted impersonation, focusing on how best to measure the reliability of verification systems in forensic contexts. It identifies two scenarios in which targeted impersonation effects may occur: (i) the forensic investigation of criminal activity involving identity theft; and (ii) implicit targeting as a result of the forensic investigation process. Also, the first partial countermeasure to such attacks is presented. The countermeasure uses client-specific Z-score normalisation to provide a more consistent false acceptance rate across all enrolled subjects. This reduces the effectiveness of targeted impersonation without impairing the systems accuracy under random zero-effort attacks.
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Bustard, John
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Carter, John N.
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Nixon, Mark S.
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Hadid, Abdenour
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Bustard, John
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Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark S.
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Hadid, Abdenour
aae1595f-98bd-457b-9e1c-fad340301ffc
Bustard, John, Carter, John N., Nixon, Mark S. and Hadid, Abdenour
(2014)
Measuring and mitigating targeted biometric impersonation.
IET Biometrics, .
(doi:10.1049/iet-bmt.2013.0054).
Abstract
This study is concerned with the reliability of biometric verification systems when used in forensic applications. In particular, when such systems are subjected to targeted impersonation attacks. The authors expand on the existing work in targeted impersonation, focusing on how best to measure the reliability of verification systems in forensic contexts. It identifies two scenarios in which targeted impersonation effects may occur: (i) the forensic investigation of criminal activity involving identity theft; and (ii) implicit targeting as a result of the forensic investigation process. Also, the first partial countermeasure to such attacks is presented. The countermeasure uses client-specific Z-score normalisation to provide a more consistent false acceptance rate across all enrolled subjects. This reduces the effectiveness of targeted impersonation without impairing the systems accuracy under random zero-effort attacks.
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bustard iet.pdf
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e-pub ahead of print date: 17 February 2014
Organisations:
Vision, Learning and Control
Identifiers
Local EPrints ID: 363289
URI: http://eprints.soton.ac.uk/id/eprint/363289
ISSN: 2047-4938
PURE UUID: ca94aed9-a61d-4053-bd1b-b1626d53c34f
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Date deposited: 20 Mar 2014 12:21
Last modified: 15 Mar 2024 02:35
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Author:
John Bustard
Author:
John N. Carter
Author:
Abdenour Hadid
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