Skip to main content
SpringerLink
Log in

Solving Avatar Captchas Automatically

  • Conference paper
Advanced Machine Learning Technologies and Applications (AMLTA 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 322))

Abstract

Captchas are challenge-response tests used in many online systems to prevent attacks by automated bots. Avatar Captchas are a recently-proposed variant in which users are asked to classify between human faces and computer-generated avatar faces, and have been shown to be secure if bots employ random guessing. We test a variety of modern object recognition and machine learning approaches on the problem of avatar versus human face classification. Our results show that using these techniques, a bot can successfully solve Avatar Captchas as often as humans can. These experiments suggest that this high performance is caused more by biases in the facial datasets used by Avatar Captchas and not by a fundamental flaw in the concept itself, but nevertheless our results highlight the difficulty in creating Captcha tasks that are immune to automatic solution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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)

    Chapter  Google Scholar 

  2. Almazyad, A., Ahmad, Y., Kouchay, S.: Multi-modal captcha: A user verification scheme. In: Proceedings of International Conference on Information Science and Applications (ICISA), pp. 1–7. IEEE (2011)

    Google Scholar 

  3. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Chandavale, A., Sapkal, A., Jalnekar, R.: A framework to analyze the security of text based captcha. International Journal of Computer Applications 27(1), 127–132 (2010)

    Article  Google Scholar 

  5. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, pp. 886–893 (2005)

    Google Scholar 

  6. D’Souza, D., Polina, P., Yampolskiy, R.: Avatar captcha: Telling computers and humans apart via face classification. In: Proceedings of IEEE International Conference on Electro/Information Technology (EIT). IEEE (2012)

    Google Scholar 

  7. Elson, J., Douceur, J., Howell, J., Saul, J.: Asirra: a CAPTCHA that exploits interest-aligned manual image categorization. In: In Proceedings of the 14th ACM Conference on Computer and Communications Security, CCS 2007, pp. 366–374 (2007)

    Google Scholar 

  8. Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: Liblinear: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)

    MATH  Google Scholar 

  9. Gao, H., Yao, D., Liu, H., Liu, X., Wang, L.: A novel image based CAPTCHA using jigsaw puzzle. In: Computational Science and Engineering (CSE), pp. 351–356 (2010)

    Google Scholar 

  10. Hall, M.: Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato (1999)

    Google Scholar 

  11. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)

    Article  Google Scholar 

  12. John, G., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Uncertainty in Artificial Intelligence, pp. 338–345 (1995)

    Google Scholar 

  13. Korayem, M., Mohamed, A., Crandall, D., Yampolskiy, R.: Learning visual features for the avatar captcha recognition challenge (2012)

    Google Scholar 

  14. Lowe, D.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, pp. 1150–1157 (1999)

    Google Scholar 

  15. Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)

    Article  MATH  Google Scholar 

  16. Von Ahn, L., Blum, M., Langford, J.: Telling humans and computers apart automatically. Communications of the ACM 47(2), 56–60 (2004)

    Article  Google Scholar 

  17. Wang, L., Chang, X., Ren, Z., Gao, H., Liu, X., Aickelin, U.: Against spyware using CAPTCHA in graphical password scheme. In: Proceedings of IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 760–767. IEEE (2010)

    Google Scholar 

  18. Wolf, L., Hassner, T., Taigman, Y.: Descriptor based methods in the wild. In: ECCV Workshop on Real-Life Images (2008)

    Google Scholar 

  19. Yampolskiy, R.: ICMLA Face Recognition Challenge, http://www.icmla-conference.org/icmla12/

Download references

Author information

Authors and Affiliations

  1. School of Informatics and Computing, Indiana University Bloomington, Indiana, USA

    Mohammed Korayem & David Crandall

  2. Computer Engineering & Computer Science,Speed School of Engineering, University of Louisville, Louisville, Kentucky, USA

    Abdallah A. Mohamed & Roman V. Yampolskiy

  3. Department of Computer Science, Fayoum University, Fayoum, Egypt

    Mohammed Korayem

  4. Department of Mathematics, Menoufia University, Shebin El-Koom, Menoufia, Egypt

    Abdallah A. Mohamed

Authors
  1. Mohammed Korayem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdallah A. Mohamed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Crandall
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Roman V. Yampolskiy
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Cairo University, Egypt

    Aboul Ella Hassanien  & Rabie Ramadan  & 

  2. Ain Shams University, Cairo, Egypt

    Abdel-Badeeh M. Salem

  3. University of Tasmania, TAS, Australia

    Tai-hoon Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Korayem, M., Mohamed, A.A., Crandall, D., Yampolskiy, R.V. (2012). Solving Avatar Captchas Automatically. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35326-0_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35325-3

  • Online ISBN: 978-3-642-35326-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation