Telling humans and computers apart automatically
Communications of the ACM - Information cities
Reappraising cognitive styles in adaptive web applications
Proceedings of the 15th international conference on World Wide Web
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization
Proceedings of the 14th ACM conference on Computer and communications security
User-Centric Profiling on the Basis of Cognitive and Emotional Characteristics: An Empirical Study
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Machine learning attacks against the Asirra CAPTCHA
Proceedings of the 15th ACM conference on Computer and communications security
What's up CAPTCHA?: a CAPTCHA based on image orientation
Proceedings of the 18th international conference on World wide web
Balancing usability and security in a video CAPTCHA
Proceedings of the 5th Symposium on Usable Privacy and Security
The adaptive web: methods and strategies of web personalization
The adaptive web: methods and strategies of web personalization
On the necessity of user-friendly CAPTCHA
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An empirical study on efficiency and effectiveness of localized vs. Latin-based CAPTCHA challenges
Proceedings of the 17th Panhellenic Conference on Informatics
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A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is nowadays a widely used security mechanism for constructing a high-confidence proof that the entity interacting with a remote service is actually a human being. Aiming to investigate the relation among users' cognitive styles, and CAPTCHA challenges in terms of preference and performance, a study is in progress which entails a psychometric-based survey for extracting users' cognitive styles, combined with a real usage scenario with two variations of CAPTCHA mechanisms. A total of 131 participants of age between 19 and 25 participated in the reported study providing interesting insights with respect to users' cognitive styles and CAPTCHA preference and performance issues.