SEIMCHA: a new semantic image CAPTCHA using geometric transformations




As protection of web applications are getting more and more important every day, CAPTCHAs are facing booming attention both by users and designers. Nowadays, it is well accepted that using visual concepts enhance security and usability of CAPTCHAs. There exist few major different ideas for designing image CAPTCHAs. Some methods apply a set of modifications such as rotations to the original image saved in the data base, to make the CAPTCHA more secure.
In this paper, two different approaches for designing image based CAPTCHAs are introduced. The first one _ which is called Tagging image CAPTCHA _ is based on pre-tagged images, using geometric transformations to increase security, and the second approach tries to enhance the first one by eliminating the use of tags and relying on semantic visual concepts. In fact, recognition of upright orientation is used as a visual cue. The usability of the proposed approaches is verified using human subjects. An estimation of security is also obtained by different kinds of attacks. Further studies are done on the proposed transformations and also on the properness of each original image for each approach. Results suggest a practical Semantic Image CAPTCHA which is usable and secure compared to its peers.


[1] K.A. Kluever and R. Zanibbi. Balancing usability and security in a video CAPTCHA. In Proceedings of the 5th Symposium on Usable Privacy and Security (SOUPS), page 14. ACM, 2009.

[2] L.V. Ahn, M. Blum, N.J. Hopper, and J. Langford. Captcha: Using hard ai problems for security. In Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques, pages 294-311. Springer-Verlag, 2003.

[3] S.A. Ross, J.A. Halderman, and A. Finkelstein. Sketcha: a captcha based on line drawings of 3d models. In Proceedings of the 19th international conference on World Wide Web, pages 821-830. ACM, 2010.

[4] AA Chandavale, AM Sapkal, and RM Jalnekar. A Framework to analyze the security of Text based CAPTCHA. International Journal of Computer Applications IJCA, 1(27):127-132, 2010.

[5] R. Gossweiler, M. Kamvar, and S. Baluja. What's up captcha?: a captcha based on image orientation. In Proceedings of the 18th international conference on World Wide Web, pages 841-850. ACM, 2009.

[6] M.H. Shirali-Shahreza and M. Shirali-Shahreza. Persian/arabic baffletext captcha. Journal of universal computer science, 12(12):1783-1796, 2006.

[7] J. Elson, J.R. Douceur, J. Howell, and J. Saul. Asirra: a captcha that exploits interest-aligned manual image categorization. CCS, 7:366-374, 2007.

[8] R. Soni and D. Tiwari. Improved captcha method. International Journal of Computer Applications IJCA, 1(25):107-109, 2010.

[9] M.E. Hoque, D.J. Russomanno, and M. Yeasin. 2d captchas from 3d models. In SoutheastCon, 2006. Proceedings of the IEEE, pages 165-170. IEEE, 2005.

[10] The CAPTCHA Project.

[11] T. Pavlidis. Why meaningful automatic tagging of images is very hard. In Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on, pages 1432-1435. IEEE, 2009.

[12] L. Zhang, M. Li, and H.J. Zhang. Boosting image orientation detection with indoor vs. outdoor classification. In Applications of Computer Vision, 2002. (WACV 2002). Proceedings. Sixth IEEE Workshop on, pages 95-99. IEEE, 2002.

[13] S. Lyu. Automatic image orientation determination with natural image statistics. In Proceedings of the 13th annual ACM international conference on Multimedia, pages 491-494. ACM, 2005.

[14] M. Shirali-Shahreza and S. Shirali-Shahreza. Advanced collage captcha. In Information Technology: New Generations, 2008. ITNG 2008. Fifth International Conference on, pages 1234-1235. IEEE, 2008.

[15] Vlad Atanasiu. ROIRotate Function; A Function to Fill Corners of Rotated Image. Available from, Updated2008.

[16] M. Mehrnejad, A. Ghaemi, A. Harati, and E. Toreini. A new image based CAPTCHA based on geometric transformations. In 8th International ISC Conference on Information Security and Cryptology, FUM, Iran, 2011.

[17] D.G. Lowe. Object recognition from local scale invariant features. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, volume 2, pages 1150-1157. Ieee,1999.

[18] S. Belongie and J. Malik. Matching with shape contexts. In Content-based Access of Image and Video Libraries, 2000. Proceedings. IEEE Workshop on, pages 20-26. IEEE, 2000.

[19] TinEye.