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An Image-based CAPTCHA System with Correction of Sub-images  

Chung, Woo-Keun (부산대학교 컴퓨터공학과)
Ji, Seung-Hyun (부산대학교 컴퓨터공학과)
Cho, Hwan-Gue (부산대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.16, no.8, 2010 , pp. 873-877 More about this Journal
Abstract
CAPTCHA is a security tool that prevents the automatic sign-up by a spam or a robot. This CAPTCHA usually depends on the smart readability of humans. However, the common and plain CAPTCHA with text-based system is not difficult to be solved by intelligent web-bot and machine learning tools. In this paper, we propose a new sub-image based CAPTCHA system totally different from the text based system. Our system offers a set of cropped sub-image from a whole digital picture and asks user to identify the correct orientation. Though there are some nice machine learning tools for this job, but they are useless for a cropped sub-images, which was clearly revealed by our experiment. Experiment showed that our sub-image based CAPTCHA is easy to human solver, but very hard to all kinds of machine learning or AI tools. Also our CAPTCHA is easy to be generated automatical without any human intervention.
Keywords
Sub-Image; CAPTCHA;
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1 Vailaya, A., Zhang, H., Yang, C., Liu, F., Jain, A., "Automatic Image Orientation Detection," IEEE Transactions on Image Processing, vol.11, no.7, 2007.
2 Luo, J. & Boutell, M., "A probabilistic approach to image orientation detection via confidence-based integration of low level and semantic cues," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.5, pp.715-726, 2005.   DOI
3 Baluja, S. "Automated image-orientation detection: a scalable boosting approach," Pattern Analysis & Applications, vol.10, no.3, 2007.
4 Wang, Y., & Zhang, H., "Detecting Image Orientation based on low level visual content," Computer Vision and Image Understanding, pp.328-346, 2004.
5 Huang, S.Y., Lee, Y.K., Bell, G. Ou, Z.h., "A Projection-based Segmentation Algorithm for Breaking MSN and YAHOO CAPTCHAs," Proc. of Signal and Image Engineering, 2008.
6 Gossweiler. Rich and Kamvar et al, "What's Up CAPTCHA? A CAPTCHA Based on Image Orientation," Proc. Of WWW 09, pp.841-850, 2009.
7 Elson, J., Douceur, J. Howell, J., Saul, J., "Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization," Proc. of the 14th ACM conference on Computer and communications security, pp.366-374, 2007.
8 Golle, P., "Machine Learning Attacks against the Asirra CAPTCHA," Proc of the 15th ACM conference on Computer and communications security, pp.535-542, 2008.
9 Mori, G., Malik, J., "Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA," Proc. of Computer Vision and Pattern Recognition, 2003.
10 Chellapilla, K., Larson, K., Simard, P., Czerwinski, M., "Designing Human Friendly Human Interaction Proofs(HIPs)," pp.711-720, 2005.