Stanford Security Lunch
Winter 2012

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March 14 Chris Paskov

Title: The Future of CAPTCHAs

Abstract: CAPTCHAs, automated tests that distinguish humans from computers, have become the norm for reducing bot-based abuse in online services. Their popularity has inevitably led to a number of attacks, some of the most effective of which are based on machine-learning. I will survey several recent papers and demonstrate that attacks are generally based on a two-stage segment and classify paradigm that is tailored to the particular distortions used in the CAPTCHA. The status quo is therefore a game of cat and mouse in which designers are constantly trying to come up with new distortions that crackers have not adapted to. What will be the ultimate result of this game? Can we gain a more general understanding of the differences between humans and machines that can answer this question? I will discuss several machine learning algorithms that I am developing to help answer these questions.