CNN for breaking text-based CAPTCHA with noise

Kaixuan Liu; Rong Zhang; Ke Qing

doi:10.1117/12.2281743

21 July 2017 CNN for breaking text-based CAPTCHA with noise

Kaixuan Liu, Rong Zhang, Ke Qing

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104202V (2017) https://doi.org/10.1117/12.2281743
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

Abstract

A CAPTCHA (“Completely Automated Public Turing test to tell Computers and Human Apart”) system is a program that most humans can pass but current computer programs could hardly pass. As the most common type of CAPTCHAs , text-based CAPTCHA has been widely used in different websites to defense network bots. In order to breaking textbased CAPTCHA, in this paper, two trained CNN models are connected for the segmentation and classification of CAPTCHA images. Then base on these two models, we apply sliding window segmentation and voting classification methods realize an end-to-end CAPTCHA breaking system with high success rate. The experiment results show that our method is robust and effective in breaking text-based CAPTCHA with noise.

Citation Download Citation

Kaixuan Liu, Rong Zhang, and Ke Qing "CNN for breaking text-based CAPTCHA with noise", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202V (21 July 2017); https://doi.org/10.1117/12.2281743

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