This repository contains the code for the paper Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks authored by Mohit Agrawal*, Pragyan Mehrotra*, Rajesh Kumar and Rajiv Ratn Shah (* indicates equal contribution) accepted at International Joint Conference on Biometrics (IJCB 2021). The code has been tested on Pytorch 1.3.1, Sklearn 0.22.2 and Python 3.6.8.
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.
Please find the code in below link:
https://github.com/midas-research/GANTouch-TBIOM/
All the experiments can be found in the files.
If you face any problem in running this code, you can contact us at {pragyan18168, rajivratn}@iiitd.ac.in, [email protected] and [email protected]
If you find this work useful, please consider citing it as:
@INPROCEEDINGS{Agrawal2021TCAS,
title={Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks},
author={Agrawal, Mohit and Mehrotra, Pragyan and Kumar, Rajesh and Shah, Rajiv Ratn},
booktitle={IJCB},
year={2021},
}
Copyright (c) 2021 Mohit Agrawal, Pragyan Mehrotra, Rajesh Kumar, Rajiv Ratn Shah.
For license information, see LICENSE or http://mit-license.org