[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.21219/jitam.2015.22.3.055

Evaluation of CAPTCHA Efficiency

Youthasoontorn, Passzarkorn (Department of Computer Engineering, Chulalongkorn University)
Phaibulpanich, Akarin (Department of Statistics, Chulalongkorn University)
Piromsopa, Krerk (Department of Computer Engineering, Chulalongkorn University)

Publication Information

Journal of Information Technology Applications and Management / v.22, no.3, 2015 , pp. 55-64 More about this Journal

Abstract

We propose statistical methods for evaluating the efficiency of CAPTCHA. Most people unfairly assumed that machines are not capable at reading precisely. This fact leads to the invention of CAPTCHA, a distorted word or short phase, which is designed to thwart computers and separate human from machines. However, advances in image recognition technologies mean that machines are constantly getting better at recognizing CAPTCHA. This forces CAPTCHA designers to design even more difficult CAPTCHAs to prevent their systems from being gamed by malicious bots. However, this arm race has an unintended side effect on the common users. Many CAPTCHAs are now so hard that many people are unable to read them. This obviously conflicts with the original purpose that CAPTCHA was invented in the first place. Our analysis shows that some CAPTCHAs are more users friendly. In particular, Yahoo-style CAPTCHA is the most friendliness. This suggests that a good CAPTCHA could be a simple text with some distortion that prevents machines from correctly segmenting characters.

Keywords

CAPTCHA; Challenge-response; Evaluation; Efficiency; CAPTCHA Friendliness; Botnet;

Citations & Related Records

Reference

1	Chandavale, A. and Sapkal, A., "An Improved Adaptive Noise Reduction for Secured CAPTCHA", in 2011 4th International Conference on Emerging Trends in Engineering and Technology (ICETET), 2011, pp. 12-17.
2	Chellapilla, K. and Simard, P. Y., "Using Machine Learning to Break Visual Human Interaction Proofs (HIPs)", in Neural Information Processing Systems, 2004.
3	Chellapilla, K., Larson, K., Simard, P., and Czerwinski, M., "Computers beat humans at single character recognition in reading based human interaction proofs (HIPs", in In 2nd Conference on Email and Anti-Spam, 2005.
4	Chellapilla, K., Larson, K., Simard, P., and Czerwinski, M., "Designing human friendly human interaction proofs (HIPs)", 2005, p. 711.
5	El Ahmad, A. S., Yan, J., and Ng, W.-Y., "CAPTCHA Design: Color, Usability, and Security", IEEE Internet Comput., Vol. 16, No. 2, 2012, pp. 44-51. DOI
6	Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., and Shet, V., "Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks", ArXiv-13126082 Cs, 2013.
7	Hsieh, C.-C. and Wu, Z.-Y., "Anti-SIFT Images Based CAPTCHA Using Versatile Characters", in 2013 International Conference on Information Science and Applications (ICISA), 2013, pp. 1-4.
8	Li, S., Shah, S. A. H., Khan, M. A. U., Khayam, S. A., Sadeghi, A.-R., and Schmitz, R., "Breaking e-Banking CAPTCHAs", in Proceedings of the 26th Annual Computer Security Applications Conference, New York, NY, USA, 2010, pp. 171-180.
9	Lv, G., "Recognition of Multi-Fontstyle Characters Based on Convolutional Neural Network", in 2011 Fourth International Symposium on Computational Intelligence and Design (ISCID), Vol. 2, 2011, pp. 223-225.
10	Nanglae, N. and Bhattarakosol, P., "A Study of Human Bio-detection Function under Text-Based CAPTCHA System", in 2012 IEEE/ACIS 11th International Conference on Computer and Information Science (ICIS), 2012, pp. 139-144.
11	Shirali-Shahreza, S. and Shirali-Shahreza, M. H., "Bibliography of works done on CAPTCHA", in 3rd International Conference on Intelligent System and Knowledge Engineering, ISKE 2008, Vol. 1, 2008, pp. 205-210.
12	Shu-Guang, H., Liang, Z., Peng-Po, W., and Hong-Wei, H., "A CAPTCHA Recognition Algorithm Based on Holistic Verification", in 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control, 2011, pp. 525-528.
13	Tamang, T. and Bhattarakosol, P., "Uncover impact factors of text-based CAPTCHA identification", in 2012 7th International Conference on Computing and Convertgence Technology (ICCCT), 2012, pp. 556- 560.
14	Truong, H. D., Turner, C. F., and Zou, C. C., "iCAPTCHA: The Next Generation of CAPTCHA Designed to Defend against 3rd Party Human Attacks", in 2011 IEEE International Conference on Communications (ICC), 2011, pp. 1-6.
15	Yamane, T., Statistics; an introductory analysis. New York: Harper and Row, 1967.
16	Yan, J. and El Ahmad, A. S., "A Low-cost Attack on a Microsoft Captcha", in Proceedings of the 15th ACM Conference on Computer and Communications Security, New York, NY, USA, 2008, pp. 543-554.
17	Yan, J. and El Ahmad, A. S., "CAPTCHA Security: A Case Study", IEEE Secur. Priv., Vol. 7, No. 4, 2009, pp. 22-28.
18	Bursztein, E., Martin, M., and Mitchell, J. C., "Text-based CAPTCHA Strengths and Weaknesses", Comput. Commun. Secur., 2011.
19	Ahn, L. von, Blum, M., and Langford, J., "Telling humans and computers apart automatically", Commun. ACM, Vol. 47, No. 2, 2004, pp. 56-60. DOI
20	Bursztein, E., Bethard, S., Fabry, C., Mitchell, J. C., and Jurafsky, D., "How Good Are Humans at Solving CAPTCHAs? A Large Scale Evaluation", in Proceedings of the 2010 IEEE Symposium on Security and Privacy, Washington, DC, USA, 2010, pp. 399-413.
21	Chandavale, A. and Sapkal, A., "Algorithm for Secured Online Authentication Using CAPTCHA", in 2010 3rd International Conference on Emerging Trends in Engineering and Technology (ICETET), 2010, pp. 292-297.