Attacks and design of image recognition CAPTCHAs

Zhu, Bin B. and Yan, Jeff and Li, Qiujie and Yang, Chao and Liu, Jia and Xu, Ning and Yi, Meng and Cai, Kaiwei (2010) Attacks and design of image recognition CAPTCHAs. In: CCS '10 Proceedings of the 17th ACM conference on Computer and communications security. ACM, New York, pp. 187-200. ISBN 9781450302449

Abstract

We systematically study the design of image recognition CAPTCHAs (IRCs) in this paper. We first review and examine all existing IRCs schemes and evaluate each scheme against the practical requirements in CAPTCHA applications, particularly in large-scale real-life applications such as Gmail and Hotmail. Then we present a security analysis of the representative schemes we have identified. For the schemes that remain unbroken, we present our novel attacks. For the schemes for which known attacks are available, we propose a theoretical explanation why those schemes have failed. Next, we provide a simple but novel framework for guiding the design of robust IRCs. Then we propose an innovative IRC called Cortcha that is scalable to meet the requirements of large-scale applications. It relies on recognizing objects by exploiting the surrounding context, a task that humans can perform well but computers cannot. An infinite number of types of objects can be used to generate challenges, which can effectively disable the learning process in machine learning attacks. Cortcha does not require the images in its image database to be labeled. Image collection and CAPTCHA generation can be fully automated. Our usability studies indicate that, compared with Google's text CAPTCHA, Cortcha allows a slightly higher human accuracy rate but on average takes more time to solve a challenge.