通过打字时间模式识别作者真实创作，无需侵入式监控。
Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verification
- 利用打字时序数据捕捉认知行为特征
- 95%准确率区分真实写作与机械复制
- 适合隐私敏感场景的作者身份验证
AI生成文本泛滥加剧了可靠作者验证的需求，而现有基于输出的方法日益不可靠。我们发现普通打字界面能捕获丰富的认知签名——即反映构思、翻译和修改阶段的键入时间模式。基于包含超过13600万次事件的大规模键入数据集，我们定义了认知负荷相关性（CLC），并证明其可有效区分真实创作与机械转录。提出一种非侵入式验证框架，仅收集时间元数据，不破坏隐私。分析评估显示，在给定假设下，判别准确率达85%至95%，并通过证据量化限制生物特征泄露。分析对抗鲁棒性表明，认知签名能抵御时间伪造攻击，因认知通道与语义内容耦合。结论：将作者验证重构为人机交互问题，提供了比侵入式监控更隐私保护的替代方案。
原文摘要 · Abstract (English)
The proliferation of AI-generated text has intensified the need for reliable authorship verification, yet current output-based methods are increasingly unreliable. We observe that the ordinary typing interface captures rich cognitive signatures, measurable patterns in keystroke timing that reflect the planning, translating, and revising stages of genuine composition. Drawing on large-scale keystroke datasets comprising over 136 million events, we define the Cognitive Load Correlation (CLC) and show it distinguishes genuine composition from mechanical transcription. We present a non-intrusive verification framework that operates within existing writing interfaces, collecting only timing metadata to preserve privacy. Our analytical evaluation estimates 85 to 95 percent discrimination accuracy under stated assumptions, while limiting biometric leakage via evidence quantization. We analyze the adversarial robustness of cognitive signatures, showing they resist timing-forgery attacks that defeat motor-level authentication because the cognitive channel is entangled with semantic content. We conclude that reframing authorship verification as a human-computer interaction problem provides a privacy-preserving alternative to invasive surveillance.
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