Breaking reCAPTCHAv2
Authors: name: Andreas Plesner, name: Tobias Vontobel, name: Roger Wattenhofer

Our work examines the efficacy of employing advanced machine learning methods
to solve captchas from Google's reCAPTCHAv2 system. We evaluate the
effectiveness of automated systems in solving captchas by utilizing advanced
YOLO models for image segmentation and classification. Our main result is that
we can solve 100% of the captchas, while previous work only solved 68-71%.
Furthermore, our findings suggest that there is no significant difference in
the number of challenges humans and bots must solve to pass the captchas in
reCAPTCHAv2. This implies that current AI technologies can exploit advanced
image-based captchas. We also look under the hood of reCAPTCHAv2, and find
evidence that reCAPTCHAv2 is heavily based on cookie and browser history data
when evaluating whether a user is human or not. The code is provided alongside
this paper.