Image-based CAPTCHAs based on neural style transfer

Over the last few years, completely automated public turing test to tell computers and humans apart (CAPTCHA) has been used as an effective method to prevent websites from malicious attacks, however, CAPTCHA designers failed to reach a balance between good usability and high security. In this study, the authors apply neural style transfer to enhance the security for CAPTCHA design. Two image-based CAPTCHAs, Grid-CAPTCHA and Font-CAPTCHA, based on neural style transfer are proposed. Grid-CAPTCHA offers nine stylized images to users and requires users to select all corresponding images according to a short description, and Font-CAPTCHA asks users to click Chinese characters presented in the image in sequence according to the description. To evaluate the effectiveness of this techniques on enhancing CAPTCHA security, they conducted a comprehensive field study and compared them to similar mechanisms. The comparison results demonstrated that the neural style transfer decreased the success rate of automated attacks. Human beings have achieved a successful solving rate of 75.04 and 84.49% on the Grid-CAPTCHA and Font-CAPTCHA schemes, respectively, indicating good usability. The results prove deep learning can have a positive effect on enhancing CAPTCHA security and provides a promising direction for future CAPTCHA study.