Type2Branch: Keystroke Biometrics Based on a Dual-Branch Architecture With Attention Mechanisms and Set2set Loss

In 2021, the pioneering work TypeNet showed that keystroke dynamics verification could scale to hundreds of thousands of users with minimal performance degradation. Recently, the KVC-onGoing competition1 has provided an open and robust experimental protocol for evaluating keystroke dynamics verification systems of such scale. This article describes Type2Branch, the model and techniques that achieved the lowest error rates at the KVC-onGoing, in both desktop and mobile typing scenarios. The novelty aspects of the proposed Type2Branch include: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i>) synthesized timing features emphasizing user behavior deviation from the general population, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i>) a dual-branch architecture combining recurrent and convolutional paths with various attention mechanisms, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii</i>) a new loss function named Set2set that captures the global structure of the embedding space, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iv</i>) a training curriculum of increasing difficulty. Considering five enrollment samples per subject of approximately 50 characters typed, the proposed Type2Branch achieves state-of-the-art performance with mean per-subject Equal Error Rates (EERs) of 0.77% and 1.03% on evaluation sets of respectively 15,000 and 5,000 subjects for desktop and mobile scenarios. With a fixed global threshold for all subjects, the EERs are respectively 3.25% and 3.61% for desktop and mobile scenarios, outperforming previous approaches by a significant margin. The source code for dataset generation, model, and training process is publicly available<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>.

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Publication Details

Journal
IEEE Transactions on Information Forensics and Security
Published
2025-01-01
DOI
https://doi.org/10.1109/tifs.2025.3574992
Citations
2
Primary Topic
User Authentication and Security Systems
Type
article
Field-Weighted Citation Impact
6.49

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article

Type2Branch: Keystroke Biometrics Based on a Dual-Branch Architecture With Attention Mechanisms and Set2set Loss

Nahuel González, Rubén Tolosana, Giuseppe Stragapede, Rubén Vera-Rodríguez
2 citations
IEEE Transactions on Information Forensics and Security
User Authentication and Security Systems
6.49
article

Type2Branch: Keystroke Biometrics Based on a Dual-Branch Architecture With Attention Mechanisms and Set2set Loss

Nahuel González, Rubén Tolosana, Giuseppe Stragapede, Rubén Vera-Rodríguez
article en
2 citations

Abstract

In 2021, the pioneering work TypeNet showed that keystroke dynamics verification could scale to hundreds of thousands of users with minimal performance degradation. Recently, the KVC-onGoing competition1 has provided an open and robust experimental protocol for evaluating keystroke dynamics verification systems of such scale. This article describes Type2Branch, the model and techniques that achieved the lowest error rates at the KVC-onGoing, in both desktop and mobile typing scenarios. The novelty aspects of the proposed Type2Branch include: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i>) synthesized timing features emphasizing user behavior deviation from the general population, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i>) a dual-branch architecture combining recurrent and convolutional paths with various attention mechanisms, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii</i>) a new loss function named Set2set that captures the global structure of the embedding space, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iv</i>) a training curriculum of increasing difficulty. Considering five enrollment samples per subject of approximately 50 characters typed, the proposed Type2Branch achieves state-of-the-art performance with mean per-subject Equal Error Rates (EERs) of 0.77% and 1.03% on evaluation sets of respectively 15,000 and 5,000 subjects for desktop and mobile scenarios. With a fixed global threshold for all subjects, the EERs are respectively 3.25% and 3.61% for desktop and mobile scenarios, outperforming previous approaches by a significant margin. The source code for dataset generation, model, and training process is publicly available<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>.

IEEE Transactions on Information Forensics and SecurityVol. 20
Universidad de Buenos Aires (AR), Universidad Autónoma de Madrid (ES)
Comunidad de Madrid, Universidad Autónoma de Madrid, Ministerio de Ciencia e Innovación
Openalex Percentile: Top 4%
User Authentication and Security Systems
6.49
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