Application of Pattern for New CAPTCHA Generation Idea

  • Thawatwong Lawan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 733)


Application of pattern for new CAPTCHA generation idea aims to present concept that applies mathematics theory. In this study, pattern is chosen for CAPTCHAs generating. There are 400 participants who approaching this study on the internet. Three type of pattern CAPTCHAs with two sample were study. There are shape pattern, color pattern and shape-color pattern. Amount of first correct answers, amount of total answers, percentage of success, amount of spent time and five point usability score were collected. The result shows that the most amount of first correct answers is color-shape pattern at 363. It also conforms to Color-shape CAPTCHA which shows the highest percentage of success at 97.06. In the amount of spent time, shape-color pattern CAPTCHAs indicates least time to solve at 5.25 s. The total spent time to find the correct answer of all type CAPTCHAs are 5.25 to 8.87 s. Usability score result shows that shape pattern CAPTCHAs is the highest score in all aspects at 4.34 with non-difference significant at p-value < 0.01. The approach rates over level 4.00 in all, which means the approach feels all type of Pattern CAPTCHAs practical is useful.


Pattern Geometric shape CAPTCHAs 


  1. 1.
    Von Ahn, L., Blum, M., Hopper, N.J., Langford, J.: CAPTCHA: using hard AI problems for security. In: Proceeding of Eurocrypt, pp. 294–311. Springer (2003)Google Scholar
  2. 2.
    Singh, V., Pal, P.: Survey of different types of CAPTCHA. Int. J. Comput. Sci. Inf. Technol. 5(2), 2242–2245 (2014)Google Scholar
  3. 3.
    Abdullah Hasan, W.K.: A survey of current research on CAPTCHA. Int. J. Comput. Sci. Eng. Surv. 7(3), 1–21 (2016)CrossRefGoogle Scholar
  4. 4.
    Elson, J., Douceur, J.R., Howell, J., Saul, J.: Asirra: a CAPTCHA that exploits interest-aligned manual image categorization. In: 14th International Proceedings of ACM CCS 2007, pp. 366–374. ACM, New York (2007)Google Scholar
  5. 5.
    Yan J., Ahmad, A.S.E.: Usability of CAPTCHAs or usability issues in CAPTCHA design. In: Symposium on Usable Privacy and Security (SOUPS 2008), pp. 44–52, Pittsburgh, PA, USA (2008)Google Scholar
  6. 6.
    Von Ahn, L., Maurer, B., McMillen, C., Abraham, D., Blum, M.: RECAPTCHA: human-based character recognition via web security measures. Science 321(1), 1465–1468 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Gossweiler, R., Kamvar, M., Baluja, S.: What’s up CAPTCHA?: A CAPTCHA based on image orientation. In: 18th International Conference on World Wide Web (WWW 2009), pp. 841–850. ACM, Madrid, Spain (2009)Google Scholar
  8. 8.
    Cambridge Advanced Learner’s Dictionary & Thesaurus.“pattern”/. Accessed 09 Mar 2017
  9. 9.
  10. 10.
    The Annenberg Foundation. Accessed 09 Mar 2017
  11. 11.
    Komatsu, H., Ideura, Y.: Relationships between color, shape, and pattern selectivity of neurons in the inferior temporal cortex of the monkey. J. Neurophysiol. 70(2), 677–694 (1993)CrossRefGoogle Scholar
  12. 12.
    Thailand Internet User Survey 2016. Electronic Transactions Development Agency (Public Organization). Accessed 09 Mar 2017
  13. 13.
    Bursztein, E., Bethard S., Fabry, C., Mitchell, J.C., Jurafsky, D.: How good are humans at solving CAPTCHAs? A large scale evaluation. homepage. Accessed 09 Mar 2017
  14. 14.
    Youthasoontorn, P., Phaibulpanich, A., Piromsopa, K.: Evaluation of CAPTCHAs efficiency. J. Inf. Technol. Appl. Manag. 22(3), 55–64 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science, Faculty of InformaticsMahasarakham UniversityKamrieng, KantarawichaiThailand

Personalised recommendations