title: Breaking reCAPTCHAv2
summary: 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.
paper_authors._id: 66e98d0914066ff97210db29
paper_authors.name: Andreas Plesner
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-09-17T14:15:43.822Z
paper_authors._id: 662a3289fb04f23fa920f4f0
paper_authors.avatarUrl: /avatars/13c51dc7930845b8f2db9ca5f868e15c.svg
paper_authors.fullname: Andreas Plesner
paper_authors.type: user
paper_authors.user: aplesner-eth
paper_authors._id: 66e98d0914066ff97210db2a
paper_authors.name: Tobias Vontobel
paper_authors._id: 66e98d0914066ff97210db2b
paper_authors.name: Roger Wattenhofer
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-06T12:49:04.333Z
paper_authors._id: 64afd0c109d6573c1d89dbf7
paper_authors.avatarUrl: /avatars/ed6bd7133517590f46847a0930d7a049.svg
paper_authors.fullname: Roger Wattenhofer
paper_authors.type: user
paper_authors.user: Rogrrr
paper_title: Breaking reCAPTCHAv2
paper_summary: 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.
paper_ai_summary: Advanced YOLO models achieve perfect captcha solving rates for reCAPTCHAv2, suggesting potential vulnerabilities in its evaluation methods.
paper_ai_keywords: YOLO models image segmentation classification captchas reCAPTCHAv2 cookie data browser history data