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正日益不可靠。我们观察到，普通打字界面能够捕获丰富的认知特征，即击键时间间隔中可测量的模式，这些模式反映了真实写作过程中的规划、翻译与修订阶段。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指数，并证明其能区分真实写作与机械转录。我们提出了一种非侵入式验证框架，该框架可在现有写作界面内运行，仅收集时间元数据以保护隐私。分析评估表明，在所述假设条件下，该方法的判别准确率达85%至95%，同时通过证据量化技术限制生物特征泄露。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间伪造攻击，因为认知通道与语义内容相互纠缠。结论指出，将作者身份验证重构为人机交互问题，可为入侵式监控提供隐私保护型替代方案。