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A Low-cost Attack on a Microsoft CAPTCHA

Lookup NU author(s): Dr Jeff Yan, Ahmad Salah El Ahmad



CAPTCHA is now almost a standard security technology. The most widely used CAPTCHAs rely on the sophisticated distortion of text images rendering them unrecognisable to the state of the art of pattern recognition techniques, and these text-based schemes have found widespread applications in commercial websites. The state of the art of CAPTCHA design suggests that such text-based schemes should rely on segmentation resistance to provide security guarantee, as individual character recognition after segmentation can be solved with a high success rate by standard methods such as neural networks. In this paper, we analyse the security of a text-based CAPTCHA designed by Microsoft and deployed for years at many of their online services including Hotmail, MSN and Windows Live. This scheme was designed to be segmentation-resistant, and it has been well studied and tuned by its designers over the years. However, our simple attack has achieved a segmentation success rate of higher than 90% against this scheme. It took ~80 ms for our attack to completely segment a challenge on a desktop computer with a 1.86 GHz Intel Core 2 CPU and 2 GB RAM. As a result, we estimate that this Microsoft scheme can be broken with an overall (segmentation and then recognition) success rate of more than 60%. On the contrary, its design goal was that "automatic scripts should not be more successful than 1 in 10,000" attempts (i.e. a success rate of 0.01%). For the first time, we show that a CAPTCHA that is carefully designed to be segmentation-resistant is vulnerable to novel but simple attacks. Our results show that it is not a trivial task to design a CAPTCHA scheme that is both usable and robust.

Publication metadata

Author(s): Yan J, Salah El Ahmad A

Publication type: Report

Publication status: Published

Series Title: School of Computing Science Technical Report Series

Year: 2008

Pages: 22

Print publication date: 01/04/2008

Source Publication Date: April 2008

Report Number: 1093

Institution: School of Computing Science, University of Newcastle upon Tyne

Place Published: Newcastle upon Tyne