Abstract
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.
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Acknowledgement
This work was supported by the Institute for Information and Communications Technology Promotion (2018-0-00420, 2019-0-00426) and supported by the National Research Foundation of Korea (2017R1C1B2003957, 2017R1A2B4006026).
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Kwon, H., Yoon, H., Park, KW. (2020). CAPTCHA Image Generation Using Style Transfer Learning in Deep Neural Network. In: You, I. (eds) Information Security Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11897. Springer, Cham. https://doi.org/10.1007/978-3-030-39303-8_18
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