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

arXiv: https://arxiv.org/abs/2106.07867 · HTML: https://mlsanity.com/paper/2106.07867/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results](https://mlsanity.com/paper/1610.07930/) (2016)
- [Continuous Authentication Using One-class Classifiers and their Fusion](https://mlsanity.com/paper/1710.11075/) (2017)
- [Continuous Authentication of Smartphones Based on Application Usage](https://mlsanity.com/paper/1808.03319/) (2018)
- [On the Resilience of Biometric Authentication Systems against Random Inputs](https://mlsanity.com/paper/2001.04056/) (2020)
- [Insights from BB-MAS -- A Large Dataset for Typing, Gait and Swipes of the Same Person on Desktop, Tablet and Phone](https://mlsanity.com/paper/1912.02736/) (2019)
- [On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment](https://mlsanity.com/paper/2006.09501/) (2020)

## Built on by (most cited first, 3 of 3)

- [GANTouch: An Attack-Resilient Framework for Touch-based Continuous Authentication System](https://mlsanity.com/paper/2210.01594/) (2022)
- [Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication](https://mlsanity.com/paper/2110.14597/) (2021)
- [Dictionary Attack on IMU-based Gait Authentication](https://mlsanity.com/paper/2309.11766/) (2023)

## Abstract (arXiv)

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.