With the growing popularity of smartphones, continuous and implicit authentication of such devices via behavioral biometrics such as touch dynamics becomes an attractive option. Specially, when the physical biometrics are challenging to utilize, and their frequent and continuous usage annoys the user. This paper presents a touchstroke authentication model based on several classification algorithms and compare their performances in authenticating legitimate smartphone users. The evaluation results suggest that it is possible to achieve comparable authentication accuracies with an average accuracy of 91% considering the best performing model. This research is supervised by Dr. Debzani Deb (debd@wssu.edu), Department of Computer Science at Winston-Salem State University, NC.
Use of Auxiliary Classifier Generative Adversarial Network in Touchstroke Authentication
With the growing popularity of smartphones, continuous and implicit authentication of such devices via behavioral biometrics such as touch dynamics becomes an attractive option, especially when the physical biometrics are challenging to utilize, or their frequent and continuous usage annoys the user. However, touch dynamics is vulnerable to potential security attacks such as shoulder surfing, camera attack, and smudge attack. As a result, it is challenging to rule out genuine imposters while only relying on models that learn from real touchstrokes. In this paper, a touchstroke authentication model based on Auxiliary Classifier Generative Adversarial Network (AC-GAN) is presented. Given a small subset of a legitimate user's touchstrokes data during training, the presented AC-GAN model learns to generate a vast amount of synthetic touchstrokes that closely approximate the real touchstrokes, simulating imposter behavior, and then uses both generated and real touchstrokes in discriminating real user from the imposters. The presented network is trained on the Touchanalytics dataset and the discriminability is evaluated with popular performance metrics and loss functions. The evaluation results suggest that it is possible to achieve comparable authentication accuracies with Equal Error Rate ranging from 2% to 11% even when the generative model is challenged with a vast more »
- Award ID(s):
- 1900087
- Publication Date:
- NSF-PAR ID:
- 10296831
- Journal Name:
- 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
- Page Range or eLocation-ID:
- 252 to 257
- Sponsoring Org:
- National Science Foundation
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- https://doi.org/10.1109/icmla51294.2020.00049
