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Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA (2003)
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BibTeX
@INPROCEEDINGS{Mori03recognizingobjects,
author = {Greg Mori and Jitendra Malik},
title = {Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA},
booktitle = {},
year = {2003},
pages = {134--141}
}
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Abstract
In this paper we explore object recognition in clutter. We test our object recognition techniques on Gimpy and EZGimpy, examples of visual CAPTCHAs. A CAPTCHA ("Completely Automated Public Turing test to Tell Computers and Humans Apart") is a program that can generate and grade tests that most humans can pass, yet current computer programs can't pass. EZ-Gimpy (see Fig. 1, 5), currently used by Yahoo, and Gimpy (Fig. 2,9) are CAPTCHAs based on word recognition in the presence of clutter. These CAPTCHAs provide excellent test sets since the clutter they contain is adversarial; it is designed to confuse computer programs. We have developed efficient methods based on shape context matching that can identify the word in an EZGimpy image with a success rate of 92%, and the requisite 3 words in a Gimpy image 33% of the time. The problem of identifying words in such severe clutter provides valuable insight into the more general problem of object recognition in scenes. The methods that we present are instances of a framework designed to tackle this general problem.
Keyphrases
visual captcha adversarial clutter object recognition general problem word recognition human apart severe clutter tell computer object recognition technique shape context public turing test ezgimpy image excellent test set valuable insight current computer program computer program efficient method gimpy image visual captchas success rate
