Streamlining attacks on CAPTCHAs with a computer game
Streamlining attacks on CAPTCHAs with a computer game
CAPTCHA has been widely deployed by commercial web sites as a security technology for purposes such as anti-spam. A common approach to evaluating the robustness of CAPTCHA is the use of machine learning techniques. Critical to this approach is the acquisition of an adequate set of labeled samples, on which the learning techniques are trained. However, such a sample labeling task is difficult for computers, since the strength of CAPTCHAs stems exactly from the difficulty computers have in recognizing either distorted texts or image contents. Therefore, until now, researchers have to manually label their samples, which is tedious and expensive. In this paper, we present Magic Bullet, a computer game that for the first time turns such sample labeling into a fun experience, and that achieves a labeling accuracy of as high as 98% for free. The game leverages human computation to address a task that cannot be easily automated, and it effectively streamlines the evaluation of CAPTCHAs. The game can also be used for other constructive purposes such as 1) developing better machine learning algorithms for handwriting recognition, and 2) training people's typing skills.
2095-2100
International Joint Conferences on Artificial Intelligence
Yan, Jeff
a2c03187-3722-46c8-b73b-439eb9d1a10e
Yu, Su Yang
bc8813ad-ba1e-47ec-b9a3-18f0aba19b78
2009
Yan, Jeff
a2c03187-3722-46c8-b73b-439eb9d1a10e
Yu, Su Yang
bc8813ad-ba1e-47ec-b9a3-18f0aba19b78
Yan, Jeff and Yu, Su Yang
(2009)
Streamlining attacks on CAPTCHAs with a computer game.
In IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence.
International Joint Conferences on Artificial Intelligence.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
CAPTCHA has been widely deployed by commercial web sites as a security technology for purposes such as anti-spam. A common approach to evaluating the robustness of CAPTCHA is the use of machine learning techniques. Critical to this approach is the acquisition of an adequate set of labeled samples, on which the learning techniques are trained. However, such a sample labeling task is difficult for computers, since the strength of CAPTCHAs stems exactly from the difficulty computers have in recognizing either distorted texts or image contents. Therefore, until now, researchers have to manually label their samples, which is tedious and expensive. In this paper, we present Magic Bullet, a computer game that for the first time turns such sample labeling into a fun experience, and that achieves a labeling accuracy of as high as 98% for free. The game leverages human computation to address a task that cannot be easily automated, and it effectively streamlines the evaluation of CAPTCHAs. The game can also be used for other constructive purposes such as 1) developing better machine learning algorithms for handwriting recognition, and 2) training people's typing skills.
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Published date: 2009
Venue - Dates:
21st International Joint Conference on Artificial Intelligence, IJCAI 2009, , Pasadena, United States, 2009-07-11 - 2009-07-16
Identifiers
Local EPrints ID: 500832
URI: http://eprints.soton.ac.uk/id/eprint/500832
ISSN: 1045-0823
PURE UUID: 82d9b3cf-ae1f-488e-8039-5255aba6c20d
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Date deposited: 13 May 2025 17:24
Last modified: 13 May 2025 17:24
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Contributors
Author:
Jeff Yan
Author:
Su Yang Yu
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