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 sam- ples, e.g., swiping gestures: 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 be- havioral 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 un- supervised 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 swip- ing samples and their efficacy in increasing the accuracy of the authentication scheme.