Breaking visual CAPTCHAs with naïve pattern recognition algorithms

Yan, Jeff and El Ahmad, Ahmad Salah (2008) Breaking visual CAPTCHAs with naïve pattern recognition algorithms. In 23rd Annual Computer Security Applications Conference, ACSAC 2007. IEEE. pp. 279-291 . (doi:10.1109/ACSAC.2007.47).

Record type: Conference or Workshop Item (Paper)

Abstract

Visual CAPTCHAs have been widely used across the Internet to defend against undesirable or malicious bot programs. In this paper, we document how we have broken most such visual schemes provided at Captchaservice.org, a publicly available web service for CAPTCHA generation. These schemes were effectively resistant to attacks conducted using a high-quality Optical Character Recognition program, but were broken with a near 100% success rate by our novel attacks. In contrast to early work that relied on sophisticated computer vision or machine learning algorithms, we used simple pattern recognition algorithms but exploited fatal design errors that we discovered in each scheme. Surprisingly, our simple attacks can also break many other schemes deployed on the Internet at the time of writing: their design had similar errors. We also discuss defence against our attacks and new insights on the design of visual CAPTCHA schemes.

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