ML Anthology

Distortion Estimation Techniques in Solving Visual CAPTCHAs

Gabriel Moy, Nathan Jones, Curt Harkless, Randall Potter
CVPR 2004 pp. 23-28
doi:10.1109/CVPR.2004.84 /cvpr/2004/moy2004cvpr-distortion/

Abstract

This paper describes two distortion estimation techniques for object recognition that solve EZ-Gimpy and Gimpy-r, two of the visual CAPTCHAs ("completely automated public turing test to tell computers and humans apart") with high degrees of success. A CAPTCHA is a program that generates and grades tests that most humans can pass but current computer programs cannot pass. We have developed a correlation algorithm that correctly identifies the word in an EZ-Gimpy challenge image 99% of the time and a direct distortion estimation algorithm that correctly identifies the four letters in a Gimpy-r challenge image 78% of the time.

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Cite

Text

Moy et al. "Distortion Estimation Techniques in Solving Visual CAPTCHAs." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004. doi:10.1109/CVPR.2004.84

Markdown

[Moy et al. "Distortion Estimation Techniques in Solving Visual CAPTCHAs." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004.](https://mlanthology.org/cvpr/2004/moy2004cvpr-distortion/) doi:10.1109/CVPR.2004.84

BibTeX

@inproceedings{moy2004cvpr-distortion,
  title     = {{Distortion Estimation Techniques in Solving Visual CAPTCHAs}},
  author    = {Moy, Gabriel and Jones, Nathan and Harkless, Curt and Potter, Randall},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2004},
  pages     = {23-28},
  doi       = {10.1109/CVPR.2004.84},
  url       = {https://mlanthology.org/cvpr/2004/moy2004cvpr-distortion/}
}