A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs

Author:

George DileepORCID,Lehrach WolfgangORCID,Kansky KenORCID,Lázaro-Gredilla MiguelORCID,Laan Christopher,Marthi Bhaskara,Lou Xinghua,Meng Zhaoshi,Liu YiORCID,Wang Huayan,Lavin AlexORCID,Phoenix D. Scott1

Affiliation:

1. Vicarious AI, 2 Union Square, Union City, CA 94587, USA.

Abstract

Computer or human? Proving that we are human is now part of many tasks that we do on the internet, such as creating an email account, voting in an online poll, or even downloading a scientific paper. One of the most popular tests is text-based CAPTCHA, where would-be users are asked to decipher letters that may be distorted, partially obscured, or shown against a busy background. This test is used because computers find it tricky, but (most) humans do not. George et al. developed a hierarchical model for computer vision that was able to solve CAPTCHAs with a high accuracy rate using comparatively little training data. The results suggest that moving away from text-based CAPTCHAs, as some online services have done, may be a good idea. Science , this issue p. eaag2612

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference107 articles.

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3. E. Bursztein M. Martin J. C. Mitchell “Text-based CAPTCHA strengths and weaknesses ” in Proceedings of the 18th ACM Conference on Computer and Communications Security (ACM 2011) pp. 125–138.

4. G. Mori J. Malik “Recognizing objects in adversarial clutter: Breaking a visual CAPTCHA ” in 2003 IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society 2003) pp. I-134–I-141.

5. V. Mansinghka T. D. Kulkarni Y. N. Perov J. Tenenbaum “Approximate Bayesian image interpretation using generative probabilistic graphics programs ” in Advances in Neural Information Processing Systems 26 (2013) pp. 1520–1528.

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