Study of an Image-Based CAPTCHA that is Resistant to Attacks Using Image Recognition Systems

In today’s digital age, image-based CAPTCHAs are increasingly vulnerable to attacks using annotation services, which tag images and classify images according to their contents, or reverse image search services. To prevent such attacks, an image-based CAPTCHA was proposed that takes advantage of the fact that humans can correctly recognize images containing many discontinuous points, while existing image recognition systems misrecognize them. However, this CAPTCHA proved susceptible to attacks using noise reduction filters. The objective of the present study is to create a CAPTCHA using images that are resistant to such filters. Images used in the new CAPTCHA were realized by increasing the proportion of lines forming discontinuous surfaces in images. Experimental results demonstrated a human recognition rate of 95.8%, with the image recognition systems successfully identifying only one image overall. Moreover, when a noise reduction filter was applied, the recognition rate was lower than those reported in previous studies.