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.

翻译：AI生成文本的激增加剧了对可靠作者身份验证的需求，然而当前基于输出的方法日益不可靠。我们发现，常规键盘界面能够捕获丰富的认知特征——击键时序中可测量的模式反映了真实创作过程中规划、翻译和修订等阶段。基于包含超过1.36亿次事件的击键大数据集，我们定义了认知负荷相关性（Cognitive Load Correlation, CLC）指标，并证明其能区分真实创作与机械转录。我们提出了一种非侵入式验证框架，该框架可嵌入现有写作界面运行，仅收集时序元数据以保护隐私。分析评估表明，在指定假设条件下，该方法的判别准确率可达85%至95%，同时通过证据量化限制生物特征泄露。我们分析了认知特征的对抗鲁棒性，证明其能抵御击败运动级身份验证的时序伪造攻击，因为认知通道与语义内容紧密耦合。我们得出结论：将作者身份验证重新定义为一种人机交互问题，为侵入式监控提供了保护隐私的替代方案。