Objective: This research investigates the usability of image-based CAPTCHAs using performance, eye-gaze and subjective measures. Background: Several forms of image-based CAPTCHAs have recently been developed. Few studies have been conducted to examine their usability. Method: Twenty participants received training using four different CAPTCHAs: Asirra, ESP-PIX, SQ-PIX and IMAGINATION. The participants were then asked to complete the CAPTCHA challenge using five CAPTCHAs of each type. The participants were subsequently asked to complete the computer system usability questionnaire (CSUQ) and the NASA Task Load Index test. Upon completing all the tasks, the participants completed a final post-test subjective questionnaire ranking the CAPTCHAs based on preference. Results: Statistically significant differences among the CAPTCHAs were found for all dependent variables other than task completion time. Conclusion: Participants preferred Asirra and ESP-PIX to the other two CAPTCHAs.
Get full access to this article
View all access options for this article.
References
1.
ChellapillaK.LarsonK.SimardP.CzerwinskiM. (2005). Designing human friendly human interaction proofs (HIPs). Paper presented at the CHI ′05: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Portland, Oregon, USA. 711–720. Retrieved from http://doi.acm.org/10.1145/1054972.1055070.
2.
ChewM.TygarJ. D. (2004). Image recognition CAPTCHAs. Proc. Information Security Conference, Palo Alto, CA (pp. 268–279).
3.
DattaR.LiJ.WangJ. (2005). IMAGINATION: A robust image-based CAPTCHA generation system. Proceedings of the 13th Annual ACM International Conference on Multimedia, 2005.
4.
ElsonJ.DouceurJ. R.HowellJ.SaulJ. (2007). Asirra: A CAPTCHA that exploits interest-aligned manual image categorization. In Proceedings of 14th ACM Conference on Computer and Communications Security (CCS), Association for Computing Machinery, Inc., Alexandria, Virginia (pp. 366–374).
5.
GolleP. (2008). Machine learning attacks against the Asirra CAPTCHA. Paper presented at the CCS ′08: Proceedings of the 15th ACM Conference on Computer and Communications Security, Alexandria, Virginia (pp.535–542). Retrieved from http://doi.acm.org/10.1145/1455770.1455838.
6.
HartS. G.StavelandL. (1988). Development of NASA-TLX: Results of empirical and theoretical research. in Human Mental Workload, HancockP.MeshkatiN., Eds., (pp. 239–250), North-Holland Press, Amsterdam, The Netherlands.
7.
JakobR.KarnK. (2000). Eye tracking in human computer interaction and usability research: Ready to deliver promises. Retrieved January 2010, from http://www.cs.tufts.edu/∼jacob/papers/ecem.pdf.
8.
LewisJ. R. (1995). IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use. International Journal of Human Computer Interacion, 7(1), 57–78. Retrieved from http://dx.doi.org/10.1080/10447319509526110.
9.
LillibridgeM.D.AbadiM.BharatK.BroderA.Z. (2001). Method for selectively restricting access to computer systems (U.S. Patent 6,195,698 ed.).
Von AhnL.BlumM.HopperN. J.LangfordJ. (2009). The CAPTCHA web page. http://www.captcha.net.
12.
Von AhnL.BlumM.LangfordJ. (2004a). Telling humans and computers apart automatically. Communications of ACM, 47(2), 56–60. Retrieved from http://doi.acm.org/10.1145/966389.966390.
13.
Von AhnL.MaurerB.McMillenC.AbrahamD.BlumM. (2008). ReCAPTCHA: Human-based character recognition via web security measures. Science, 321(5895), 1465–1468. Retrieved from 10.1126/science.1160379; http://www.sciencemag.org/cgi/content/abstract/321/5895/1465.
