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

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.