Taxonomy and Modeling of Impersonation in e-Border Authentication

Yanushkevich, Svetlana; Samoil, Steven; Shmerko, Vlad; Manderson, Travis; Drahanský, Martin

doi:10.1109/est.2015.18

Cited by 7 publications

(17 citation statements)

References 11 publications

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“…3) Risk and trust propagation. For example, if a person is on a watchlist, the risk of mis-identification or impersonation [61] is propagated through other states such as rik of misdetection of a concealed item. The risk propagation problem was studied, in particular, as a multi-echelon supply chain problem in [40].…”

Section: Fundamental Operations On Risk and Trustmentioning

confidence: 99%

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Cognitive Identity Management: Risks, Trust and Decisions using Heterogeneous Sources

Yanushkevich

Howells

Crockett

et al. 2019

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)

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This work advocates for cognitive biometric-enabled systems that integrate identity management, risk assessment and trust assessment. The cognitive identity management process is viewed as a multi-state dynamical system, and probabilistic reasoning is used for modeling of this process. This paper describes an approach to design a platform for risk and trust modeling and evaluation in the cognitive identity management built upon processing heterogeneous data including biometrics, other sensory data and digital ID. The core of an approach is the perception-action cycle of each system state. Inference engine is a causal network that uses various uncertainty metrics and reasoning mechanisms including Dempster-Shafer and Dezert-Smarandache beliefs.

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Section: Fundamental Operations On Risk and Trustmentioning

confidence: 99%

“…Computing the risk and trust under identity uncertainty has only been so far, partially studies. For example, effects of impersonation were studied in [61]. The risk countermeasures were studied in [6].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Identity Management: Risks, Trust and Decisions using Heterogeneous Sources

Yanushkevich

Howells

Crockett

et al. 2019

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)

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“…The phenomenon of impersonation (passive or zero-effort attack) has been experimentally detected in speech recognition by Doddington et al [36] and in other forms of biometric modalities [37][38][39]. Unfortunately, not only are passive attacks an inherent property of watchlist technology, impersonation from other sources, such as social networks, can also influence the reliability of the watchlist [40,41]. One possible mitigation method for impersonation is the use of multiple biometrics [26].…”

Section: Literature Reviewmentioning

confidence: 99%

“…1. Sources of personal information, such as national and international databases [2] and virtual sources (media and social networks) [37,41,70].…”

Section: Definitionmentioning

confidence: 99%

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Biometric‐enabled watchlists technology

et al. 2017

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For Entry-Exit technologies, such as US VISIT and Smart Borders (e-borders), a watchlist normally contains highquality biometric traits and is checked only against visitors. The situation can change drastically if low-quality images are added into the watchlist. Motivated by this fact, we introduce a systematic approach to assessing the risk of travellers using a biometric-enabled watchlist where some latency of the biometric traits is allowed. The main results presented herein include: (1) a taxonomical view of the watchlist technology, and (2) a novel risk assessment technique. For modelling the watchlist landscape, we propose a risk categorisation using the Doddington metric. We evaluate via experimental study on large-scale facial and fingerprint databases, the risks of impersonation and mis-identification in various screening scenarios. Other contributions include a study of approaches to designing a biometric-enabled watchlist for e-borders: a) risk control and b) improving performance of the e-border via integrating the interview supporting machines.

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