Person Authentication using Visual Representations of Keyboard Typing Dynamics

Ladislav Peška, Patrik Veselý, T. Skopal, Krisztián Búza
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Abstract

In this paper, we focus on the problem of user's authentication through typing dynamics patterns. We specifically focus on small-sized problems, where it is difficult to fully train corresponding machine (deep) learning algorithms from scratch. Instead, we propose a different approach based on the visualization of the typing patterns and subsequent usage of pre-trained feature extractors from the computer vision domain. We evaluated the approach on a publicly-available dataset and results indicate that this is a viable solution capable to improve over several baselines. Moreover, the proposed visual representation of the data contributes to the explainability of AI.
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使用键盘输入动态的可视化表示的人员认证
本文主要研究了通过输入动态模式实现用户身份认证的问题。我们特别关注小型问题,在这些问题上很难从头开始完全训练相应的机器(深度)学习算法。相反,我们提出了一种不同的方法,基于输入模式的可视化和随后使用来自计算机视觉领域的预训练特征提取器。我们在一个公开可用的数据集上评估了该方法,结果表明这是一个可行的解决方案,能够在几个基线上进行改进。此外,提出的数据可视化表示有助于人工智能的可解释性。
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