# GANTouch: An Attack-Resilient Framework for Touch-based Continuous Authentication System

arXiv: https://arxiv.org/abs/2210.01594 · HTML: https://mlsanity.com/paper/2210.01594/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 2 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models](https://mlsanity.com/paper/1805.06605/) (2018)
- [Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results](https://mlsanity.com/paper/1610.07930/) (2016)
- [Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning](https://mlsanity.com/paper/1708.09754/) (2017)
- [Continuous Authentication for Voice Assistants](https://mlsanity.com/paper/1701.04507/) (2017)
- [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)
- [Forgery-Resistant Touch-based Authentication on Mobile Devices](https://mlsanity.com/paper/1506.02294/) (2015)
- [Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks](https://mlsanity.com/paper/2106.07867/) (2021)
- [Continuous User Authentication via Unlabeled Phone Movement Patterns](https://mlsanity.com/paper/1708.04399/) (2017)
- [Adversary Models for Mobile Device Authentication](https://mlsanity.com/paper/2009.10150/) (2020)

## Built on by (most cited first, 2 of 2)

- [Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization](https://mlsanity.com/paper/2604.09574/) (2026)
- [Dictionary Attack on IMU-based Gait Authentication](https://mlsanity.com/paper/2309.11766/) (2023)

## Abstract (arXiv)

Previous studies have shown that commonly studied (vanilla) implementations of touch-based continuous authentication systems (V-TCAS) are susceptible to active adversarial attempts. This study presents a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework and compares it to the V-TCAS under three active adversarial environments viz. Zero-effort, Population, and Random-vector. The Zero-effort environment was implemented in two variations viz. Zero-effort (same-dataset) and Zero-effort (cross-dataset). The first involved a Zero-effort attack from the same dataset, while the second used three different datasets. G-TCAS showed more resilience than V-TCAS under the Population and Random-vector, the more damaging adversarial scenarios than the Zero-effort. On average, the increase in the false accept rates (FARs) for V-TCAS was much higher (27.5% and 21.5%) than for G-TCAS (14% and 12.5%) for Population and Random-vector attacks, respectively. Moreover, we performed a fairness analysis of TCAS for different genders and found TCAS to be fair across genders. The findings suggest that we should evaluate TCAS under active adversarial environments and affirm the usefulness of GANs in the TCAS pipeline.