Diff-CAPTCHA: An Image-based CAPTCHA with Security Enhanced by Denoising
  Diffusion Model
Ran Jiang Sanfeng Zhang Linfeng Liu Yanbing Peng
To enhance the security of text CAPTCHAs, various methods have been employed,
such as adding the interference lines on the text, randomly distorting the
characters, and overlapping multiple characters. These methods partly increase
the difficulty of automated segmentation and recognition attacks. However,
facing the rapid development of the end-to-end breaking algorithms, their
security has been greatly weakened. The diffusion model is a novel image
generation model that can generate the text images with deep fusion of
characters and background images. In this paper, an image-click CAPTCHA scheme
called Diff-CAPTCHA is proposed based on denoising diffusion models. The
background image and characters of the CAPTCHA are treated as a whole to guide
the generation process of a diffusion model, thus weakening the character
features available for machine learning, enhancing the diversity of character
features in the CAPTCHA, and increasing the difficulty of breaking algorithms.
To evaluate the security of Diff-CAPTCHA, this paper develops several attack
methods, including end-to-end attacks based on Faster R-CNN and two-stage
attacks, and Diff-CAPTCHA is compared with three baseline schemes, including
commercial CAPTCHA scheme and security-enhanced CAPTCHA scheme based on style
transfer. The experimental results show that diffusion models can effectively
enhance CAPTCHA security while maintaining good usability in human testing.