An Upcycling Tokenization Method for Credit Card Numbers

Nugier, Cyrius; Leblanc-Albarel, Diane; Blaise, Agathe; Masson, Simon; Huynh, Paul; Piugie, Yris Brice Wandji

doi:10.5220/0010508600150025

Cited by 1 publication

(1 citation statement)

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“…With the increasing use of smartphones to store personal and sensitive information, such as bank account details, personal IDs, passwords, and credit card information, people remain constantly connected, and their mobile devices are at risk of security and privacy breaches by malicious actors [1–3]. Traditional forms of protection such as passcodes, PINs, patterns, facial recognition, and fingerprint scans are all vulnerable to various forms of attack, including smudge attacks, side‐channel attacks, and shoulder‐surfing attacks [1, 3, 4].…”

Section: Introductionmentioning

confidence: 99%

Deep features fusion for user authentication based on human activity

Piugie

Charrier

Manno³

et al. 2023

IET Biometrics

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The exponential growth in the use of smartphones means that users must constantly be concerned about the security and privacy of mobile data because the loss of a mobile device could compromise personal information. To address this issue, continuous authentication systems have been proposed, in which users are monitored transparently after initial access to the smartphone. In this study, the authors address the problem of user authentication by considering human activities as behavioural biometric information. The authors convert the behavioural biometric data (considered as time series) into a 2D colour image. This transformation process keeps all the characteristics of the behavioural signal. Time series does not receive any filtering operation with this transformation, and the method is reversible. This signal-to-image transformation allows us to use the 2D convolutional networks to build efficient deep feature vectors. This allows them to compare these feature vectors to the reference template vectors to compute the performance metric. The authors evaluate the performance of the authentication system in terms of Equal Error Rate on a benchmark University of Californy, Irvine Human Activity Recognition dataset, and they show the efficiency of the approach.

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Section: Introductionmentioning

confidence: 99%