CAPTCHA image generation systems using generative adversarial networks

Abstract

We propose new CAPTCHA image generation systems by using generative adversarial network (GAN) techniques to strengthen against CAPTCHA solvers. To verify whether a user is human, CAPTCHA images are widely used on the web industry today. We introduce two different systems for generating CAPTCHA images, namely, the distance GAN (D-GAN) and composite GAN (C-GAN). The D-GAN adds distance values to the original CAPTCHA images to generate new ones, and the C-GAN generates a CAPTCHA image by composing multiple source images. To evaluate the performance of the proposed schemes, we used the CAPTCHA breaker software as CAPTCHA solver. Then, we compared the resistance of the original source images and the generated CAPTCHA images against the CAPTCHA solver. The results show that the proposed schemes improve the resistance to the CAPTCHA solver by over 67.1% and 89.8% depending on the system.