{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/kvc-ongoing-keystroke-verification-challenge","title":"KVC-onGoing: Keystroke Verification Challenge","arxiv_id":"2412.20530","date":"2024-12-29","proceeding":null,"authors":["Giuseppe Stragapede","Ruben Vera-Rodriguez","Ruben Tolosana","Aythami Morales","Ivan DeAndres-Tame","Naser Damer","Julian Fierrez","Javier Ortega-Garcia","Alejandro Acien","Nahuel Gonzalez","Andrei Shadrikov","Dmitrii Gordin","Leon Schmitt","Daniel Wimmer","Christoph Großmann","Joerdis Krieger","Florian Heinz","Ron Krestel","Christoffer Mayer","Simon Haberl","Helena Gschrey","Yosuke Yamagishi","Sanjay Saha","Sanka Rasnayaka","Sandareka Wickramanayake","Terence Sim","Weronika Gutfeter","Adam Baran","Mateusz Krzysztoń","Przemysław Jaskóła"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2412.20530v1","url_pdf":"https://arxiv.org/pdf/2412.20530v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"kvc-ongoing-keystroke-verification-challenge","repo_url":"https://github.com/yamagishi0824/kvc-dualnet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"fairness","task_name":"Fairness"}],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}