Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verification

Computer Science > Cryptography and Security [Submitted on 26 Feb 2026 (v1), last revised 24 Apr 2026 (this version, v2)] Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verification David Condrey 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. Comments: 7 pages Subjects: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) MSC classes: 68T10, 91E45, 68U35 ACM classes: K.6.5; H.5.2; I.5.4 Cite as: arXiv:2603.00177 [cs.CR]   (or arXiv:2603.00177v2 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.00177 Focus to learn more Submission history From: David L. Condrey [view email] [v1] Thu, 26 Feb 2026 20:02:55 UTC (18 KB) [v2] Fri, 24 Apr 2026 06:26:04 UTC (18 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.HC cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)