CAPTCHA Image Generation using Style Transfer Learning in Deep Neural Network

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CAPTCHA is widely used as a security solution to prevent automated attack tools on websites. However, CAPTCHA is difficult to recognize human perception when it gives a lot of distortion to have resistance against the automated attack. In this paper, we propose a method to deceive the machine while maintaining the human perception rate by applying the style transfer method. This method creates a style-plugged-CAPTCHA image by combining the styles of different images while maintaining the content of the original CAPTCHA sample. We used 6 datasets in the actual site and used Tensorflow as the machine learning library. Experimental results show that the proposed method reduces the recognition rate of the DeCAPTCHA system to 3.5% while maintaining human perception.
Publisher
Korea Institute of Information Security & Cryptology
Issue Date
2019-08-22
Language
English
Citation

The 20th World Conference on Information Security Applications (WISA 2019), pp.234 - 246

ISSN
0302-9743
DOI
10.1007/978-3-030-39303-8_18
URI
http://hdl.handle.net/10203/263965
Appears in Collection
CS-Conference Papers(학술회의논문)
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