A Comprehensive Analysis of Cognitive CAPTCHAs Through Eye Tracking

Autor: Nghia Dinh, Lidia Dominika Ogiela, Kiet Tran-Trung, Tuan Le-Viet, Vinh Truong Hoang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IEEE Access, Vol 12, Pp 47190-47209 (2024)
Druh dokumentu: article
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2024.3373542
Popis: CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) has long been employed to combat automated bots. It accomplishes this by utilizing distortion techniques and cognitive characteristics. When it comes to countering security attacks, cognitive CAPTCHA methods have proven to be more effective than other approaches. The advancement of eye-tracking technology has greatly improved human-computer interaction (HCI), enabling users to engage with computers without physical contact. This technology is widely used for studying attention, cognitive processes, and performance. In this specific research, we conducted eye-tracking experiments on participants to investigate how their visual behavior changes as the complexity of cognitive CAPTCHAs varies. By analyzing the distribution of eye gaze on each level of CAPTCHA, we can assess users’ visual behavior based on eye movement performance and process metrics. The data collected is then employed in Machine Learning (ML) algorithms to categorize and examine the relative importance of these factors in predicting performance. This study highlights the potential to enhance any cognitive CAPTCHA model by gaining insights into the underlying cognitive processes.
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