GANTouch: An Attack-Resilient Framework for Touch-based Continuous Authentication System
This work addresses security vulnerabilities in authentication systems for users of touch-based devices, but it is incremental as it builds on existing GAN methods for a specific domain.
The study tackled the vulnerability of touch-based continuous authentication systems to active adversarial attacks by proposing a GAN-assisted framework (G-TCAS) and comparing it to vanilla implementations (V-TCAS) under various attack scenarios. The results showed that G-TCAS had lower increases in false accept rates (14% and 12.5% vs. 27.5% and 21.5% for V-TCAS) under more damaging attacks, and the system was found to be fair across genders.
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.