IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)

Stragapede, Giuseppe; Vera-Rodríguez, Rubén; Tolosana, Rubén; Morales, Aythami; Fiérrez, Julián; Ortega‐Garcia, Javier; Rasnayaka, Sanka; Seneviratne, Sachith; Dissanayake, Vipula; Liebers, Jonathan; Islam, Ashhadul; Belhaouari, Samir Brahim; Ahmad, Sumaiya; Jabin, Suraiya

doi:10.1109/ijcb54206.2022.10007985

Cited by 6 publications

(2 citation statements)

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“…Keystroke Dynamics (KD) refers to the typing behavior of human subjects. It is commonly regarded as a behavioral biometric trait, similarly to voice [1], signature [2], [3], gait [4]- [6], touch gestures [7], [8], etc. In comparison with its physiological counterparts such as face or fingerprint, behavioral biometrics represent a more challenging technical problem in terms of recognition performance as they are in general characterized by a higher intra-user variability, and lower inter-user variability.…”

Section: A Keystroke Dynamics For Biometric Recognitionmentioning

confidence: 99%

“…https://userinterfaces.aalto.fi/ 5 https://userinterfaces.aalto.fi/136Mkeystrokes/ 6 https://userinterfaces.aalto.fi/typing37k/7 As the datasets are fixed with a single choice of subjects, the conclusions obtained from the reported results can be impacted. To verify that the score fluctuations are not significant, we randomly split the evaluation score lists (Sec.VOLUME 11, 2023 …”

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confidence: 99%

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Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation

Stragapede,

Vera-Rodriguez,

Tolosana

et al. 2024

IEEE Access

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Analyzing keystroke dynamics (KD) for biometric verification has several advantages: it is among the most discriminative behavioral traits; keyboards are among the most common human-computer interfaces, being the primary means for users to enter textual data; its acquisition does not require additional hardware, and its processing is relatively lightweight; and it allows for transparently recognizing subjects. However, the heterogeneity of experimental protocols and metrics, and the limited size of the databases adopted in the literature impede direct comparisons between different systems, thus representing an obstacle in the advancement of keystroke biometrics. To alleviate this aspect, we present a new experimental framework to benchmark KD-based biometric verification performance and fairness based on tweet-long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards, extracted from the Aalto Keystroke Databases. The framework runs on CodaLab in the form of the Keystroke Verification Challenge (KVC) a . Moreover, we also introduce a novel fairness metric, the Skewed Impostor Ratio (SIR), to capture inter-and intra-demographic group bias patterns in the verification scores. We demonstrate the usefulness of the proposed framework by employing two state-of-the-art keystroke verification systems, TypeNet and TypeFormer, to compare different sets of input features, achieving a less privacy-invasive system, by discarding the analysis of text content (ASCII codes of the keys pressed) in favor of extended features in the time domain. Our experiments show that this approach allows to maintain satisfactory performance.

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Section: A Keystroke Dynamics For Biometric Recognitionmentioning

confidence: 99%

mentioning

confidence: 99%

Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation

Stragapede,

Vera-Rodriguez,

Tolosana

et al. 2024

IEEE Access

Self Cite

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TypeFormer: transformers for mobile keystroke biometrics

Stragapede,

Delgado-Santos,

Tolosana

et al. 2024

Neural Comput & Applic

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The broad usage of mobile devices nowadays, the sensitiveness of the information contained in them, and the shortcomings of current mobile user authentication methods are calling for novel, secure, and unobtrusive solutions to verify the users’ identity. In this article, we propose TypeFormer, a novel transformer architecture to model free-text keystroke dynamics performed on mobile devices for the purpose of user authentication. The proposed model consists in temporal and channel modules enclosing two long short-term memory recurrent layers, Gaussian range encoding, a multi-head self-attention mechanism, and a block-recurrent transformer layer. Experimenting on one of the largest public databases to date, the Aalto mobile keystroke database, TypeFormer outperforms current state-of-the-art systems achieving equal error rate values of 3.25% using only five enrolment sessions of 50 keystrokes each. In such way, we contribute to reducing the traditional performance gap of the challenging mobile free-text scenario with respect to its desktop and fixed-text counterparts. To highlight the design rationale, an analysis of the experimental results of the different modules implemented in the development of TypeFormer is carried out. Additionally, we analyse the behaviour of the model with different experimental configurations such as the length of the keystroke sequences and the amount of enrolment sessions, showing margin for improvement.

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