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.