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Segmentation of CAPTCHAs Based on Complex Networks

  • Kun Fang
  • Zhan Bu
  • Zheng You Xia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7530)

Abstract

CAPTCHA is a simple test that is designed to be easily generated by computers and easily recognized by humams, but difficult for computers to solve. It is now almost a standard security technology. The most widely deployed CAPTCHAs are text-based schemes, but to CAPTCHAs, segmenting the connected and distored characters is still an unsolving problem. In this paper, we proposed a Community Divided Model algorithm which based on complex networks to segment these CAPTCHAs. To evaluate the effectiveness of the proposed segmentation algorithm, we conducted several experiments on database which collected some CAPTCHAs from the Internet randomly. The results showed that the proposed algorithm is effective to segment two or more connected and distored characters.

Keywords

CAPTCHA complex networks segmentation connected characters 

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References

  1. 1.
    von Ahn, L., Blum, M., Hopper, N.J., Langford, J.: CAPTCHA: Using Hard AI Problems for Security. In: Biham, E. (ed.) EUROCRYPT 2003. LNCS, vol. 2656, pp. 294–311. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Yan, J., El Ahmad, A.S.: A low-cost attack on a Microsoft CAPTCHA. In: 15th ACM Conference on Computer and Communications Security (2008) Google Scholar
  3. 3.
    Chellapilla, K., Larson, K., Simard, P., Czerwinski, M.: Computers beat humans at single character recognition in reading based Human Interaction Proofs (HIPs). In: 2nd Conference on Email and Anti-Spam (2005)Google Scholar
  4. 4.
    Simard, P.Y., Steinkraus, D., Platt, J.C.: Best Practice for Convolutional Neural Networks Applied to Visual Document Analysis. In: 7th International Conference on Document Analysis and Recognition, pp. 958–962. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  5. 5.
    El Ahmad, A.S., Yan, J., Tayara, M.: The Robustness of Google CAPTCHAs. Bericht, Newcastle University (2011)Google Scholar
  6. 6.
    Mori, G., Malik, J.: Recognising objects in adversarial clutter: breaking a visual CAPTCHA. In: IEEE Conference on Computer Vision & Pattern Recognition (2003)Google Scholar
  7. 7.
    Chellapilla, K., Larson, K., Simard, P.Y., Czerwinski, M.: Building Segmentation Based Human-Friendly Human Interaction Proofs (HIPs). In: Baird, H.S., Lopresti, D.P. (eds.) HIP 2005. LNCS, vol. 3517, pp. 1–26. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Huang, S., Lee, Y., Bell, G., Ou, Z.: A projection-based segmentation algorithm for breaking MSN and YAHOO CAPTCHAs. In: Proceedings of the 2008 International Conference of Signal and Image Engineering, London, UK (2008)Google Scholar
  9. 9.
    Bursztein, E., Martin, M., Mitchell, C.: Text-based CAPTCHA Strengths and Weaknessses. In: 18th ACM Conference on Computer and Communications Security (2011)Google Scholar
  10. 10.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.-U.: Complex networks: Structure and dynamics. Phys. Rep. 424(4-5), 175–308 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)CrossRefGoogle Scholar
  12. 12.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99, 7821–7826 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Bursztein, E., Bethard, S., Fabry, C., Mitchell, J., Jurafsky, D.: How good are humans at solving CAPTCHAs? a large scale evaluation. In: 2010 IEEE Symposium on Security and Privacy (SP), pp. 399–413 (2010)Google Scholar
  14. 14.
  15. 15.
    360buy CAPTCHAs, https://passport.360buy.com/new/registpersonal.aspx (accessed April 2012)
  16. 16.
    Tianya CAPTCHAs, http://passport.tianya.cn/register (accessed March 2012)
  17. 17.
    Windows Live CAPTCHAs, https://signup.msn.cn/register (accessed April 2012)
  18. 18.
    Taobao CAPTCHAs, http://member1.taobao.com/member/new_register.jhtml (accessed April 2012)
  19. 19.
    Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Communications of the ACM 27(3), 236–239 (1984)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kun Fang
    • 1
  • Zhan Bu
    • 1
  • Zheng You Xia
    • 1
  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsChina

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