Abstract
A recent study showed that commonly (vanilla) studied implementations of accelerometer-based gait authentication systems ($v$ABGait) are susceptible to random-vector attack. The same study proposed a beta noise-assisted implementation ($\beta$ABGait) to mitigate the attack. In this paper, we assess the effectiveness of the random-vector attack on both $v$ABGait and $\beta$ABGait using three accelerometer-based gait datasets. In addition, we propose $i$ABGait, an alternative implementation of ABGait, which uses a Conditional Tabular Generative Adversarial Network. Then we evaluate $i$ABGait's resilience against the traditional zero-effort and random-vector attacks. The results show that $i$ABGait mitigates the impact of the random-vector attack to a reasonable extent and outperforms $\beta$ABGait in most experimental settings.
Abstract (translated)
URL
https://arxiv.org/abs/2210.00615
