Making contactless payments using a smartwatch is increasingly popular, but this payment medium lacks traditional biometric security measures such as facial or fingerprint recognition. In 2022, Sturgess et al. proposed WatchAuth, a system for authenticating smartwatch payments using the physical gesture of reaching towards a payment terminal. While effective, the system requires the user to undergo a burdensome enrolment period to achieve acceptable error levels. In this dissertation, we explore whether applications of deep learning can reduce the number of gestures a user must provide to enrol into an authentication system for smartwatch payment. We firstly construct a deep-learned authentication system that outperforms the current state-of-the-art, including in a scenario where the target user has provided a limited number of gestures. We then develop a regularised autoencoder model for generating synthetic user-specific gestures. We show that using these gestures in training improves classification ability for an authentication system. Through this technique we can reduce the number of gestures required to enrol a user into a WatchAuth-like system without negatively impacting its error rates.

翻译：使用智能手表进行非接触式支付越来越普及，但这一支付媒介缺乏面部或指纹识别等传统生物识别安全措施。2022年，Sturgess等人提出了WatchAuth系统，该系统通过检测用户伸手靠近支付终端的物理动作来认证智能手表支付。尽管效果显著，但该方案要求用户经历繁琐的注册流程才能达到可接受的错误率。本文探讨了深度学习能否减少用户注册智能手表支付认证系统所需提供的动作次数。我们首先构建了一个基于深度学习的认证系统，其在目标用户仅提供有限动作样本的场景下仍优于现有最佳方案。随后开发了正则化自编码器模型，用于生成合成性的用户专属动作数据。实验表明，将这类合成数据用于训练可提升认证系统的分类能力。借助该技术，我们能在不负面影响错误率的前提下，减少类似WatchAuth系统中用户注册所需提供的动作次数。