English

Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks

Cryptography and Security 2021-06-16 v1 Computer Vision and Pattern Recognition Human-Computer Interaction

Abstract

Previous studies have demonstrated that commonly studied (vanilla) touch-based continuous authentication systems (V-TCAS) are susceptible to population attack. This paper proposes a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework, which showed more resilience to the population attack. G-TCAS framework was tested on a dataset of 117 users who interacted with a smartphone and tablet pair. On average, the increase in the false accept rates (FARs) for V-TCAS was much higher (22%) than G-TCAS (13%) for the smartphone. Likewise, the increase in the FARs for V-TCAS was 25% compared to G-TCAS (6%) for the tablet.

Cite

@article{arxiv.2106.07867,
  title  = {Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks},
  author = {Mohit Agrawal and Pragyan Mehrotra and Rajesh Kumar and Rajiv Ratn Shah},
  journal= {arXiv preprint arXiv:2106.07867},
  year   = {2021}
}

Comments

2021 IEEE International Joint Conference on Biometrics (IJCB), 8 pages

R2 v1 2026-06-24T03:12:18.827Z