公开大规模打字行为数据集，推动身份验证技术进步
KVC-onGoing: Keystroke Verification Challenge
- 构建统一评测平台，使用超18万用户的真实打字数据
- 桌面端最低错误率3.33%，移动端达3.61%，显著优于以往方法
- 支持公平性分析，揭示年龄性别对识别效果的影响
本文提出Keystroke Verification Challenge - onGoing（KVC-onGoing），为研究者提供基于Aalto大学打字数据库的标准化评测平台。数据涵盖超过18.5万名用户的打字序列，来自桌面和移动键盘，模拟真实使用场景。在评估集上，桌面场景下等错误率（EER）低至3.33%，1%假匹配率（FMR）时伪拒绝率（FNMR）为11.96%；移动端对应指标分别为3.61%和17.44%，均显著优于此前最优结果。此外，分析显示年龄与性别对识别性能有一定影响，部分情况下不可忽视。整个框架运行于CodaLab平台。
原文摘要 · Abstract (English)
This article presents the Keystroke Verification Challenge - onGoing (KVC-onGoing), on which researchers can easily benchmark their systems in a common platform using large-scale public databases, the Aalto University Keystroke databases, and a standard experimental protocol. The keystroke data consist of tweet-long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards simulating real-life conditions. The results on the evaluation set of KVC-onGoing have proved the high discriminative power of keystroke dynamics, reaching values as low as 3.33% of Equal Error Rate (EER) and 11.96% of False Non-Match Rate (FNMR) @1% False Match Rate (FMR) in the desktop scenario, and 3.61% of EER and 17.44% of FNMR @1% at FMR in the mobile scenario, significantly improving previous state-of-the-art results. Concerning demographic fairness, the analyzed scores reflect the subjects' age and gender to various extents, not negligible in a few cases. The framework runs on CodaLab.
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