{"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/becaptcha-mouse-synthetic-mouse-trajectories","title":"BeCAPTCHA-Mouse: Synthetic Mouse Trajectories and Improved Bot Detection","arxiv_id":"2005.00890","date":"2020-05-02","proceeding":null,"authors":["Alejandro Acien","Aythami Morales","Julian Fierrez","Ruben Vera-Rodriguez"],"abstract":"We first study the suitability of behavioral biometrics to distinguish between computers and humans, commonly named as bot detection. We then present BeCAPTCHA-Mouse, a bot detector based on: i) a neuromotor model of mouse dynamics to obtain a novel feature set for the classification of human and bot samples; and ii) a learning framework involving real and synthetically generated mouse trajectories. We propose two new mouse trajectory synthesis methods for generating realistic data: a) a function-based method based on heuristic functions, and b) a data-driven method based on Generative Adversarial Networks (GANs) in which a Generator synthesizes human-like trajectories from a Gaussian noise input. Experiments are conducted on a new testbed also introduced here and available in GitHub: BeCAPTCHA-Mouse Benchmark; useful for research in bot detection and other mouse-based HCI applications. Our benchmark data consists of 15,000 mouse trajectories including real data from 58 users and bot data with various levels of realism. Our experiments show that BeCAPTCHA-Mouse is able to detect bot trajectories of high realism with 93% of accuracy in average using only one mouse trajectory. When our approach is fused with state-of-the-art mouse dynamic features, the bot detection accuracy increases relatively by more than 36%, proving that mouse-based bot detection is a fast, easy, and reliable tool to complement traditional CAPTCHA systems.","url_abs":"https://arxiv.org/abs/2005.00890v2","url_pdf":"https://arxiv.org/pdf/2005.00890v2.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":"becaptcha-mouse-synthetic-mouse-trajectories","repo_url":"https://github.com/BiDAlab/BeCAPTCHA-Mouse","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"becaptcha-mouse-synthetic-mouse-trajectories","repo_url":"https://github.com/BiDAlab/TypeNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}