ABSTRACT
Although image-based CAPTCHAs have been introduced to overcome the security level limitation of the previous text-based CAPTCHAs, image-based CAPTCHAs still have problems such as user-unfriendliness in answer inference and wasted transmission cost. To cope with these issues, we propose a novel image-text fusion CAPTCHA model which uses a single image augmented with text hints that helps the users to guess the answers of the CAPTCHA problems more conveniently. According to the experiment results, the proposed CAPTCHA scheme has a higher correct answer rate than that of the previous scheme since the proposed scheme is able to help the users to infer the correct answer of the given CAPTCHA image more easily using the available text hints.
- Ahn, L., Blum, M. and Hopper, N. 2000. CAPTCHA: Telling Humans and Computers Apart Automatically. Retrieved 2011 from http://www.captcha.net/. Google ScholarDigital Library
- Yan, J. and El-Ahmad, A. S. 2008. A Low-cost Attack on a Microsoft CAPTCHA. Research Paper. School of Computing Science, Newcastle University, UK.Google Scholar
- Chellapilla, K. and Simard, P. Y. 2004. Using Machine Learning to Break Visual Human Interaction Proofs (HIPs). Advances in Neural Information Processing Systems, Vol. 17. MIT Press, 265--272.Google Scholar
- Chew, M. and Tygar, J. D. 2004. Image Recognition CAPTCHAs. In Proceedings of the 7th International Information Security Conference. 268--279.Google Scholar
- Elson, J., Douceur, J. R. and Howell, J. 2007. Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization. In Proceedings of the 14th ACM Conference on Computer and Communications Security. 535--542. Google ScholarDigital Library
- Warner, O. THEPCSpy: KittenAuth. Retrieved 2011 from http://www.thepcspy.com/.Google Scholar
- Datta, R., Li, J. and Wang, J. Z. 2005. IMAGINATION: A Robust Image-based CAPTCHA Generation System. In Proceedings of the 13th ACM International Conference on Multimedia. 331--334. Google ScholarDigital Library
- Zhu, B. B., Yan, J., Li, Q. et al. 2011. Attacks and Design of Image Recognition CAPTCHAs. In Proceedings of the 17th ACM Conference on Computer and Communications Security. 187--220. Google ScholarDigital Library
- Rui, Y. and Liu, Z. 2004. ARTiFACIAL: Automated reverse Tuning test using FACIAL features. In Proceedings of the 11th ACM International Conference on Multimedia. 295--298. Google ScholarDigital Library
- Google Mail Service. https://mail.google.com/.Google Scholar
- Golle, P. 2008. Machine Learning Attacks against the Asirra CAPTCHA. In Proceedings of the 15th ACM Conference on Computer and Communications Security. 535--542. Google ScholarDigital Library
- Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D. and Jordan, M. 2002. Matching words and pictures. Machine Learning Research Journal, Vol. 3, 1107--1135. Google ScholarDigital Library
Index Terms
- A User-friendly Image-Text Fusion CAPTCHA for Secure Web Services
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