Breaking Visual CAPTCHAs with Naïve Pattern Recognition Algorithms

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  2. Dr Jeff Yan
  3. Ahmad Salah El Ahmad
Author(s)Yan J, Salah El Ahmad A
Publication type Report
Series TitleSchool of Computing Science Technical Report Series
Legacy DateMay 2007
Report Number1026
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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, 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 success rate of 99% ~ 100% 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.
InstitutionSchool of Computing Science, University of Newcastle upon Tyne
Place PublishedNewcastle upon Tyne
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