maresen
Junior Member
- Sep 17, 2017
- 147
- 104
Just found this on twitter:
" Solvees 100% of the CAPTCHAs presented by reCAPTCHAv2, surpassing the success rates of previous works, which range from 68% to 71%repo: https://github.com/aplesner/Breaking-reCAPTCHAv2…abs: https://arxiv.org/abs/2409.08831 "
x.com/arankomatsuzaki/status/1835912247266132432
" Solvees 100% of the CAPTCHAs presented by reCAPTCHAv2, surpassing the success rates of previous works, which range from 68% to 71%repo: https://github.com/aplesner/Breaking-reCAPTCHAv2…abs: https://arxiv.org/abs/2409.08831 "
x.com/arankomatsuzaki/status/1835912247266132432
The GitHub repository and the associated research paper on arXiv titled "Breaking reCAPTCHAv2" focus on analyzing and defeating Google's reCAPTCHAv2 system using advanced machine learning techniques. Here are the main points from both sources:
- **Objective**: The project aims to evaluate the effectiveness of reCAPTCHAv2, a popular CAPTCHA system, in distinguishing between human users and bots. It employs deep learning models, particularly YOLO (You Only Look Once), for image segmentation and classification to solve image-based CAPTCHAs.
- **Key Findings**:
- The research achieved a 100% success rate in solving reCAPTCHAv2 CAPTCHAs, significantly surpassing previous success rates of 68% to 71%[1][5].
- There is no significant difference in the number of challenges humans and bots must solve to pass the CAPTCHAs, indicating that current AI technologies can effectively exploit these systems[1][5].
- The study reveals that reCAPTCHAv2 relies heavily on cookie and browser history data to determine if a user is human, suggesting that CAPTCHA systems should consider user-specific data more carefully in their design[1][5].
- **Technical Requirements**:
- Python 3.9
- Firefox with Geckodriver
- Various Python libraries listed in `requirements.txt`
- **Installation and Usage**:
- Clone the repository and install the required libraries.
- Set up Geckodriver by downloading it from the official site and adding it to your system's PATH.
- Use `solve_recaptcha.py` for solving a single CAPTCHA challenge or `test_environment.py` for multiple challenges.
- **Project Structure**:
- Contains directories for assets, models, utility scripts, and more.
- Includes scripts for changing IP addresses (specific to macOS) and tools for data collection and analysis.
- **System Compatibility**: The code was developed on macOS M1, but most features should work on other operating systems. However, certain functionalities like the VPN script may not be compatible outside macOS.
This project provides insights into the vulnerabilities of reCAPTCHAv2 and offers tools for bypassing its security measures using machine learning. The research highlights significant implications for the design of CAPTCHA systems in terms of security against automated attacks.
Citations:
[1] https://arxiv.org/abs/2409.08831
[2] https://github.com/sarperavci/GoogleRecaptchaBypass/activity
[3] https://info.arxiv.org/about/index.html
[4] reddit.com/r/MachineLearning/comments/10cgm8d/p_i_built_arxivsummarycom_a_list_of_gpt3/
[5] https://github.com/aplesner/Breaking-reCAPTCHAv2
[6] jtl-software.de/extension-store/google-recaptcha-v2-jtl-shop-5
[7] https://akifmt.github.io/dotnet/2023-08-13-blazor-implementing-google-recaptcha-v2/
[8] https://twitter.com/emollick
[9] http://www.arxiv.org/abs/2409.08831
- **Objective**: The project aims to evaluate the effectiveness of reCAPTCHAv2, a popular CAPTCHA system, in distinguishing between human users and bots. It employs deep learning models, particularly YOLO (You Only Look Once), for image segmentation and classification to solve image-based CAPTCHAs.
- **Key Findings**:
- The research achieved a 100% success rate in solving reCAPTCHAv2 CAPTCHAs, significantly surpassing previous success rates of 68% to 71%[1][5].
- There is no significant difference in the number of challenges humans and bots must solve to pass the CAPTCHAs, indicating that current AI technologies can effectively exploit these systems[1][5].
- The study reveals that reCAPTCHAv2 relies heavily on cookie and browser history data to determine if a user is human, suggesting that CAPTCHA systems should consider user-specific data more carefully in their design[1][5].
- **Technical Requirements**:
- Python 3.9
- Firefox with Geckodriver
- Various Python libraries listed in `requirements.txt`
- **Installation and Usage**:
- Clone the repository and install the required libraries.
- Set up Geckodriver by downloading it from the official site and adding it to your system's PATH.
- Use `solve_recaptcha.py` for solving a single CAPTCHA challenge or `test_environment.py` for multiple challenges.
- **Project Structure**:
- Contains directories for assets, models, utility scripts, and more.
- Includes scripts for changing IP addresses (specific to macOS) and tools for data collection and analysis.
- **System Compatibility**: The code was developed on macOS M1, but most features should work on other operating systems. However, certain functionalities like the VPN script may not be compatible outside macOS.
This project provides insights into the vulnerabilities of reCAPTCHAv2 and offers tools for bypassing its security measures using machine learning. The research highlights significant implications for the design of CAPTCHA systems in terms of security against automated attacks.
Citations:
[1] https://arxiv.org/abs/2409.08831
[2] https://github.com/sarperavci/GoogleRecaptchaBypass/activity
[3] https://info.arxiv.org/about/index.html
[4] reddit.com/r/MachineLearning/comments/10cgm8d/p_i_built_arxivsummarycom_a_list_of_gpt3/
[5] https://github.com/aplesner/Breaking-reCAPTCHAv2
[6] jtl-software.de/extension-store/google-recaptcha-v2-jtl-shop-5
[7] https://akifmt.github.io/dotnet/2023-08-13-blazor-implementing-google-recaptcha-v2/
[8] https://twitter.com/emollick
[9] http://www.arxiv.org/abs/2409.08831