Human vs. Machine: Analyzing the Robustness of Advertisement Based Captchas

  • Prabaharan Poornachandran
  • P. Hrudya
  • Jothis Reghunadh
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 264)

Abstract

A program that can ensure whether the response is generated by a human, not from a machine, is called CAPTCHA. They have become a ubiquitous defense to protect web services from bot programs. At present most of the visual CAPTCHAs are cracked by programs; as a result they become more complex which makes both human and machine finds it difficult to crack. NLPCAPTCHAs, an acronym for Natural Language Processing CAPTCHA, are introduced to provide both security and revenue to websites owners through advertisements. They are highly intelligent CAPTCHAs that are difficult for a machine to understand whereas it’s a better user experience when compared to traditional CAPTCHA. In this paper we have introduced a method that is able to analyze and recognize the challenges from three different types of NLPCAPTCHAs.

Keywords

NLPCAPTCHA CAPTCHA Tesseract OCR 

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References

  1. 1.
    Cui, J.S., Wang, L.J., Mei, J., Zhang, D., Wang, X., Peng, Y., Zhang, W.Z.: CAPTCHA design based on moving object recognition problem. In: 2010 3rd International Conference on Information Sciences and Interaction Sciences, ICIS (2010)Google Scholar
  2. 2.
    Chandavale, A.A., Sapkal, A.M., Jalnekar, R.M.: Algorithm To Break Visual CAPTCHA. In: Second International Conference on Emerging Trends in Engineering and Technology, ICETET 2009 (2009)Google Scholar
  3. 3.
    Hrudya, P., Gopika, N.G., Poornachandran, P.: Forepart based captcha segmentation and recognition using dftGoogle Scholar
  4. 4.
    Liao, H.-W., Lo, J.-C., Chen, C.-Y.: NTU CSIE CMLAB, NTU CSIE CMLAB, T NTU CSIE CMLAB “GPU catch breaker” Google Scholar
  5. 5.
    Official website of nlpcaptcha, http://www.nlpcaptcha.in/en/index.html
  6. 6.
    Patel, C., Patel, A., Patel, D., Patel, C.M.: Optical Character Recognition by Open Source OCR Tool Tesseract: A Case Study Institute of Computer Applications (CMPICA) Charotar University of Science and Technology(CHARUSAT). International Journal of Computer Applications 55(10), 975–8887 (2012)CrossRefGoogle Scholar
  7. 7.
    Ma, J., Badaoui, B., Chamoun, E.: A Generalized Method to Solve Text-Based CAPTCHAsGoogle Scholar
  8. 8.
    Song, J., Li, Z., Lyu, M.R., Cai, S.: Recognition of merged characters based on forepart prediction, necessity-sufficency matching and character adaptive masking. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 35(1) (February 2005)Google Scholar
  9. 9.
    Thombare, M.B., Derie, P.M., Wandre, A.K.: Integrating-Random-Character-Generation-to-Enhance-CAPTCHA-using-Artificial-Intelligence. International Journal of Electronics, Communication and Soft. Computing Science and Engineering 2(1) (April 2012)Google Scholar
  10. 10.
    El Ahmad, A.S., Yan, J., Tayara, M.: The Robustness of Google CAPTCHAs. School of Computer Science Newcastle 4University, UKGoogle Scholar
  11. 11.
    Fortune, R., Luu, G., Mc Mahon, P.: Cracking CaptchaGoogle Scholar
  12. 12.
    Golle, P.: Machine Learning Attacks against the Asirra CAPTCHA. In: Proceeding, C.C.S. (ed.) 2008 Proceedings of the 15th ACM Conference on Computer and Communications Security, pp. 535–542. ACM, New York (2008)Google Scholar
  13. 13.
    Bursztein, E., Bethard, S., Fabry, C., Mitchell, J.C., Jurafsky, D.: How Good are Humans at Solving CAPTCHAs? A Large Scale EvaluationGoogle Scholar
  14. 14.
    Tam, J., Hyde, S., Simsa, J. Von, L.: AhnComputer Science Department Carnegie Mellon University,“Breaking Audio CAPTCHAs”Google Scholar
  15. 15.
    Yan, J., El Ahmad, A.S.: Breaking Visual CAPTCHAs with Naïve Pattern Recognition Algorithms. In: Twenty-Third Annual Computer Security Applications Conference, ACSAC 2007, School of Computing Science, Newcastle University, UK, December 10-14 (2007)Google Scholar
  16. 16.
    Yan, J., El Ahamad, A.S.: A low-cost attack on a Microsoft Captcha. In: Proceedings of the 15th ACM Conference on Computer and Communications Security, CCS 2008. ACM, New York (2008)Google Scholar
  17. 17.
    Deng, G., Cahill, L.W.: An adaptive Gaussian filter for noise reduction and edge detection. In: Nuclear Science Symposium and Medical Imaging Conference, IEEE Conference Dept. of Electron. Eng., La Trobe Univ., Bundoora, Vic., Australia (1993)Google Scholar
  18. 18.
    Aggarwal, S.: Animated CAPTCHAs and games for advertising. In: Proceeding WWW 2013 Companion Proceedings of the 22nd International Conference on World Wide Web, International World Wide Web Conferences Steering Committee Republic and Canton of Geneva, Switzerland, pp. 1167–1174 (2013)Google Scholar
  19. 19.
    Duan, D., Xie, M., Mo, Q., Han, Z., Wan, Y.: An Improved Hough Transform for Line Detection. In: 2010 International Conference on Computer Application and System Modeling (ICCASM), vol. 2 (October 2010)Google Scholar
  20. 20.
    Takimoto, R.Y., Chalella das Neves, A., Mafalda, R., Sato, A.K., Tavares, R.S., Stevo, N.A., de Sales Guerra Tsuzuki, M.: Detecting function patterns with interval Hough transform. In: 2010 9th IEEE/IAS International Conference Industry Applications, INDUSCON (November 2010)Google Scholar
  21. 21.
    Wang, B., Fan, S.: An Improved CANNY Edge Detection Algorithm. In: Second International Workshop on Computer Science and Engineering, WCSE 2009, Department of Electrical and Information Changsha Univ. of Sci. & Technol., vol. 1 (October 2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Prabaharan Poornachandran
    • 1
  • P. Hrudya
    • 1
  • Jothis Reghunadh
    • 1
  1. 1.Amrita Center for Cyber SecurityAmrita Vishwa VidyapeethamKollamIndia

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