# iCTGAN--An Attack Mitigation Technique for Random-vector Attack on Accelerometer-based Gait Authentication Systems

arXiv: https://arxiv.org/abs/2210.00615 · HTML: https://mlsanity.com/paper/2210.00615/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 2 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [PacGAN: The power of two samples in generative adversarial networks](https://mlsanity.com/paper/1712.04086/) (2017)
- [Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning](https://mlsanity.com/paper/1708.09754/) (2017)
- [Continuous Authentication Using One-class Classifiers and their Fusion](https://mlsanity.com/paper/1710.11075/) (2017)
- [On the Resilience of Biometric Authentication Systems against Random Inputs](https://mlsanity.com/paper/2001.04056/) (2020)
- [Authenticating users through their arm movement patterns](https://mlsanity.com/paper/1603.02211/) (2016)

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

- [Dictionary Attack on IMU-based Gait Authentication](https://mlsanity.com/paper/2309.11766/) (2023)
- [Deep Generative Attacks and Countermeasures for Data-Driven Offline Signature Verification](https://mlsanity.com/paper/2312.00987/) (2023)

## Abstract (arXiv)

A recent study showed that commonly (vanilla) studied implementations of accelerometer-based gait authentication systems (vABGait) are susceptible to random-vector attack. The same study proposed a beta noise-assisted implementation (βABGait) to mitigate the attack. In this paper, we assess the effectiveness of the random-vector attack on both vABGait and βABGait using three accelerometer-based gait datasets. In addition, we propose iABGait, an alternative implementation of ABGait, which uses a Conditional Tabular Generative Adversarial Network. Then we evaluate iABGait's resilience against the traditional zero-effort and random-vector attacks. The results show that iABGait mitigates the impact of the random-vector attack to a reasonable extent and outperforms βABGait in most experimental settings.