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Breaking Text CAPTCHA by Repeated Information

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Information Security Applications (WISA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10763))

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Abstract

CAPTCHA is a simple challenge-response tool to determine whether the user is a bot or human. The user must answer required text, calculate questions, or choose some images from the provider’s choice. D portal site, which is one of the most famous web portal site in Korea, asks text response in CAPTCHA image when joining a cafe group, but this CAPTCHA is structured in a very regular format which can be read very simply if used repeatedly. We can read the text characters by bot with very high accuracy through some easy steps, among 2,000 sample CAPTHCAs.

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References

  1. Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1950)

    Article  MathSciNet  Google Scholar 

  2. 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). https://doi.org/10.1007/3-540-39200-9_18

    Chapter  Google Scholar 

  3. Chellapilla, K., Larson, K., Simard, P., Czerwinski, M.: Designing human friendly human interaction proofs (HIPs). In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 711–720. ACM (2005)

    Google Scholar 

  4. Yan, J., El Ahmad, A.S.: Usability of CAPTCHAs or usability issues in CAPTCHA design. In: Proceedings of the 4th Symposium on Usable Privacy and Security, pp. 44–52. ACM (2008)

    Google Scholar 

  5. Datta, R., Li, J., Wang, J.Z.: IMAGINATION: a robust image-based CAPTCHA generation system. In: Proceedings of the 13th Annual ACM International Conference on Multimedia, pp. 331–334. ACM (2005)

    Google Scholar 

  6. Elson, J., Douceur, J.R., Howell, J., Saul, J.: Asirra: a CAPTCHA that exploits interest-aligned manual image categorization. In: ACM Conference on Computer and Communications Security, vol. 7, pp. 366–374 (2007)

    Google Scholar 

  7. Gossweiler, R., Kamvar, M., Baluja, S.: What’s up CAPTCHA?: a CAPTCHA based on image orientation. In: Proceedings of the 18th International Conference on World Wide Web, pp. 841–850. ACM (2009)

    Google Scholar 

  8. Zhu, B.B., Yan, J., Bao, G., Yang, M., Xu, N.: CAPTCHA as graphical passwords—a new security primitive based on hard AI problems. IEEE Trans. Inf. Forensics Secur. 9(6), 891–904 (2014)

    Article  Google Scholar 

  9. Thangavelu, S., Purusothaman, T., Gowrison, G.: Emoji CAPTCHA: a secured picture character approach against OCR Attacks. Prevent 7(1) (2017). https://ijcsits.org/papers/vol7no12017/1vol7no1.pdf

  10. Gao, H., Liu, H., Yao, D., Liu, X., Aickelin, U.: An audio CAPTCHA to distinguish humans from computers. In: 2010 Third International Symposium on Electronic Commerce and Security (ISECS), pp. 265–269. IEEE (2010)

    Google Scholar 

  11. Singh, V.P., Pal, P.: Survey of different types of CAPTCHA. Int. J. Comput. Sci. Inf. Technol. 5(2), 2242–2245 (2014)

    Google Scholar 

  12. Bhalani, S.D., Mishra, S.: A survey on CAPTCHA technique based on drag and drop mouse action. Int. J. Tech. Res. Appl. 3(2), 188–189 (2015)

    Google Scholar 

  13. Abdalla, K., Kaya, M.: An evaluation of different types of CAPTCHA: effectiveness, user-friendliness, and limitations. Int. J. Sci. Res. Inf. Syst. Eng. (IJSRISE) 2(3) (2017). http://ijsrise.com/index.php/IJSRISE/article/view/51

  14. Mori, G., Malik, J.: Recognizing objects in adversarial clutter: breaking a visual CAPTCHA. In: Proceedings of the 2003 IEEE Computer Society Conference on IEEE Computer Vision and Pattern Recognition, vol. 1, p. I (2003)

    Google Scholar 

  15. Yan, J., El Ahmad, A.S.: A low-cost attack on a Microsoft CAPTCHA. In: Proceedings of the 15th ACM Conference on Computer and Communications Security, pp. 543–554. ACM (2008)

    Google Scholar 

  16. Bursztein, E., Martin, M., Mitchell, J.: Text-based CAPTCHA strengths and weaknesses. In: Proceedings of the 18th ACM Conference on Computer and Communications Security, pp. 125–138. ACM (2011)

    Google Scholar 

  17. Starostenko, O., Cruz-Perez, C., Uceda-Ponga, F., larcon-Aquino, V.: Breaking text-based CAPTCHAs with variable word and character orientation. Pattern Recogn. 48(4), 1101–1112 (2015)

    Article  Google Scholar 

  18. Kim, J., Kim, S., Kim, H.J.: Breaking character and natural image based CAPTCHA using feature classification. J. Korea Inst. Inf. Secur. Cryptol. 25(5), 1011–1019 (2015)

    Article  Google Scholar 

  19. Chew, M., Tygar, J.D.: Image recognition CAPTCHAs. In: Zhang, K., Zheng, Y. (eds.) ISC 2004. LNCS, vol. 3225, pp. 268–279. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30144-8_23

    Chapter  Google Scholar 

  20. Tam, J., Simsa, J., Hyde, S., Ahn, L.V.: Breaking audio CAPTCHAs. In: Advances in Neural Information Processing Systems, pp. 1625–1632 (2009)

    Google Scholar 

  21. Chellapilla, K., Simard, P.Y.: Using machine learning to break visual human interaction proofs (HIPs). In: Advances in Neural Information Processing Systems, pp. 265–272 (2005)

    Google Scholar 

  22. Golle, P.: Machine learning attacks against the Asirra CAPTCHA. In: Proceedings of the 15th ACM Conference on Computer and Communications Security, pp. 535–542. ACM (2008)

    Google Scholar 

  23. Wang, Y., Huang, Y., Zheng, W., Zhou, Z., Liu, D., Lu, M.: Combining convolutional neural network and self-adaptive algorithm to defeat synthetic multi-digit text-based CAPTCHA. In: 2017 IEEE International Conference on IEEE Industrial Technology (ICIT), pp. 980–985 (2017)

    Google Scholar 

  24. CAPTCHA. http://www.captcha.net/

  25. MNIST. http://yann.lecun.com/exdb/mnist/

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Acknowledgments

This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD060048AD).

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Correspondence to Kyungho Lee .

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Woo, J.H., Park, M., Lee, K. (2018). Breaking Text CAPTCHA by Repeated Information. In: Kang, B., Kim, T. (eds) Information Security Applications. WISA 2017. Lecture Notes in Computer Science(), vol 10763. Springer, Cham. https://doi.org/10.1007/978-3-319-93563-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-93563-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93562-1

  • Online ISBN: 978-3-319-93563-8

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