In contemporary mobile user authentication systems, verifying user legitimacy has become paramount due to the widespread use of smartphones. Although fingerprint and facial recognition are widely used for mobile authentication, PIN-based authentication is still employed as a fallback option if biometric authentication fails after multiple attempts. Consequently, the system remains susceptible to attacks targeting the PIN when biometric methods are unsuccessful. In response to these concerns, two-factor authentication has been proposed, albeit with the caveat of increased user effort. To address these challenges, this paper proposes a passive authentication system that utilizes keystroke data, a byproduct of primary authentication methods, for background user authentication. Additionally, we introduce a novel image encoding technique to capture the temporal dynamics of keystroke data, overcoming the performance limitations of deep learning models. Furthermore, we present a methodology for selecting suitable behavioral biometric features for image representation. The resulting images, depicting the user's PIN input patterns, enhance the model's ability to uniquely identify users through the secondary channel with high accuracy. Experimental results demonstrate that the proposed imaging approach surpasses existing methods in terms of information capacity. In self-collected dataset experiments, incorporating features from prior research, our method achieved an Equal Error Rate (EER) of 6.7\%, outperforming the existing method's 47.7\%. Moreover, our imaging technique attained a True Acceptance Rate (TAR) of 94.4\% and a False Acceptance Rate (FAR) of 8\% for 17 users.

翻译：在当代移动用户身份验证系统中，由于智能手机的广泛使用，验证用户合法性已变得至关重要。尽管指纹识别和面部识别被广泛用于移动身份验证，但当生物识别多次失败后，基于PIN码的身份验证仍作为备用方案被采用。因此，当生物识别方法失败时，系统仍容易受到针对PIN码的攻击。针对上述问题，研究者提出了双因素身份验证，但这种方式会增加用户操作负担。为应对这些挑战，本文提出了一种被动身份验证系统，该系统利用主要身份验证方法产生的副产品——击键数据，进行后台用户身份验证。此外，我们引入了一种新颖的图像编码技术来捕获击键数据的时间动态特性，从而克服深度学习模型的性能限制。我们还提出了一套为图像表示选择合适行为生物特征的方法论。生成的图像描绘了用户输入PIN码的模式，通过辅助通道显著提升了模型唯一识别用户的能力。实验结果表明，所提出的成像方法在信息容量方面超越了现有方法。在自收集数据集实验中，结合先前研究的特征，我们的方法实现了6.7%的等错误率（EER），优于现有方法的47.7%。此外，针对17名用户，我们的成像技术达到了94.4%的真接受率（TAR）和8%的假接受率（FAR）。