Behavioral biometric-based smartphone user authentication schemes based on touch/swipe have shown to provide the desired usability. However, their accuracy is not yet considered up to the mark. This is primarily due to the lack of a sufficient number of training samples, e.g., swiping gestures1: users are reluctant to provide many. Consequently, the application of such authentication techniques in the real world is still limited. To overcome the shortage of training samples and make behavioral biometric-based schemes more accurate, we propose the usage of Generative Adversarial Networks (GAN) for generating synthetic samples, in our case, or swiping gestures. GAN is an unsupervised approach for synthetic data generation and has already been used in a wide range of applications, such as image and video generation. However, their use in behavioral biometric-based user authentication schemes has not been explored yet. In this paper, we propose SWIPEGAN - to generate swiping samples to be used for smartphone user authentication. Extensive experimentation and evaluation show the quality of the generated synthetic swiping samples and their efficacy in increasing the accuracy of the authentication scheme.

SWIPEGAN: Swiping Data Augmentation Using Generative Adversarial Networks for Smartphone User Authentication / Buriro, A.; Ricci, F.; Crispo, B.. - (2021), pp. 85-90. (Intervento presentato al convegno 3rd ACM Workshop on Wireless Security and Machine Learning, WiseML 2021 tenutosi a Abu Dhabi nel 2021) [10.1145/3468218.3469039].

SWIPEGAN: Swiping Data Augmentation Using Generative Adversarial Networks for Smartphone User Authentication

Crispo B.
2021-01-01

Abstract

Behavioral biometric-based smartphone user authentication schemes based on touch/swipe have shown to provide the desired usability. However, their accuracy is not yet considered up to the mark. This is primarily due to the lack of a sufficient number of training samples, e.g., swiping gestures1: users are reluctant to provide many. Consequently, the application of such authentication techniques in the real world is still limited. To overcome the shortage of training samples and make behavioral biometric-based schemes more accurate, we propose the usage of Generative Adversarial Networks (GAN) for generating synthetic samples, in our case, or swiping gestures. GAN is an unsupervised approach for synthetic data generation and has already been used in a wide range of applications, such as image and video generation. However, their use in behavioral biometric-based user authentication schemes has not been explored yet. In this paper, we propose SWIPEGAN - to generate swiping samples to be used for smartphone user authentication. Extensive experimentation and evaluation show the quality of the generated synthetic swiping samples and their efficacy in increasing the accuracy of the authentication scheme.
2021
WiseML 2021 - Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning
new york
Association for Computing Machinery, Inc
9781450385619
Buriro, A.; Ricci, F.; Crispo, B.
SWIPEGAN: Swiping Data Augmentation Using Generative Adversarial Networks for Smartphone User Authentication / Buriro, A.; Ricci, F.; Crispo, B.. - (2021), pp. 85-90. (Intervento presentato al convegno 3rd ACM Workshop on Wireless Security and Machine Learning, WiseML 2021 tenutosi a Abu Dhabi nel 2021) [10.1145/3468218.3469039].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/331534
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