Advertisement

CAPTCHA Recognition Based on Kohonen Maps

  • Yujia SunEmail author
  • Jan Platoš
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1035)

Abstract

CAPTCHA is a security technology commonly used to differentiate between computers and humans. Text-based CAPTCHA is the most widely used method. This paper presents an approach based on the Kohonen maps neural network for recognizing CAPTCHA. This method first preprocesses the given CAPTCHA, segments its characters, extracts character features, and recognizes characters based on the Kohonen maps cluster analysis results. These experimental results show that the proposed method performs well in recognizing CAPTCHA.

References

  1. 1.
    Ahn, L.V., Blum, M., Langford, J.: Telling humans and computers apart automatically. Commun. ACM 47, 56–60 (2004)CrossRefGoogle Scholar
  2. 2.
    Yan, J., Ahmad, A.E.: Usability of CAPTCHAs or usability issues in CAPTCHA design. In: The 4th Symposium on Usable Privacy and Security, USA, pp. 44–52 (2008)Google Scholar
  3. 3.
    Platos, J., Snasel, V., Kromer, P., Abraham, A.: Detecting insider attacks using non-negative matrix factorization. In: The Fifth International Conference on Information Assurance and Security, pp. 693–696 (2009)Google Scholar
  4. 4.
    Chen, J., Luo, X., Guo, Y., Zhang, Y., Gong, D.: A survey on breaking technique of text-based CAPTCHA. Secur. Commun. Netw. 1–15 (2017)Google Scholar
  5. 5.
    Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)CrossRefzbMATHGoogle Scholar
  6. 6.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, New York (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Guthikonda, S.M.: Kohonen Self-Organizing Maps. Wittenberg University (2005). http://www.shy.am/wp-content/uploads/2009/01/kohonen-self-organizing-maps-shyam-guthikonda.pdf
  8. 8.
    Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering applications of the self-organizing map. Proc. IEEE 84, 1358–1384 (1996)CrossRefGoogle Scholar
  9. 9.
    Osadchy, M., HernandeZ-Castro, J., Gibson, S., Dunkelman, O., Perez-Cabo, D.: No bot expects the DeepCAPTCHA! introducing immutable adversarial examples, with applications to CAPTCHA generation. IEEE Trans. Inf. Forensics Secur. 12, 2640–2653 (2017)CrossRefGoogle Scholar
  10. 10.
    Huang, S.-Y., Lee, Y.-K., Bell, G., Ou, Z.-H.: A projection-based segmentation algorithm for breaking MSN and YAHOO CAPTCHAs. In: International Conference of Signal and Image Engineering(ICSIE 2008), London, UK (2008)Google Scholar
  11. 11.
    Vondrak, I.: ANN. Technical report. http://vondrak.vsb.cz/download/ANN.pdf
  12. 12.
    Snasel, V., Platos, J., Kromer, P., Abraham, A.: Matrix factorization approach for feature deduction and design of intrusion detection systems. In: The Fourth International Conference on Information Assurance and Security, pp. 172–179 (2008)Google Scholar
  13. 13.
    Platos, J., Kromer, P., Snasel, V., Abraham, A.: Searching similar images- Vector Quantization with S-tree. In: IEEE CASoN (2012) 304-388Google Scholar
  14. 14.
    Wang, X.A., Weng, J., Ma, J.F., Yang, X.Y.: Cryptanalysis of public authentication protocol for outsourced databases with multi-user modification. Inf. Sci. 488, 13–18 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Technical University of OstravaOstrava, PorubaCzech Republic
  2. 2.Hebei GEO UniversityShijiazhuangChina

Personalised recommendations