Google's reCAPTCHA is no match for new AI system that cracks it with 100% success
Key Points
- Researchers at ETH Zurich have developed a method that allows them to completely bypass Google's reCAPTCHAv2 system. They use advanced YOLO models for image segmentation and classification.
- The scientists were able to solve all three types of reCAPTCHAv2 tasks 100 percent of the time, a significant improvement over previous studies that only achieved success rates of 68-71 percent.
- The results raise questions about the future of image-based CAPTCHAs. For future studies, the researchers recommend expanding the dataset for segmentation tasks and investigating the threshold for possible blocking with continuous CAPTCHA solving.
A new study from ETH Zurich researchers shows that Google's reCAPTCHAv2 system can be completely bypassed using advanced image recognition algorithms.
The ETH Zurich team developed a method to bypass reCAPTCHAv2 with 100% success using YOLO models for image segmentation and classification. Their system can automatically solve all three types of reCAPTCHAv2 tasks: classifying images in a 3x3 grid, segmenting a single image, and handling dynamic classification with changing images.
To train their models, the researchers used a dataset of about 14,000 labeled images for classification tasks. For segmentation, they used a pre-trained YOLOv8 model already trained on many classes.
The 100% success rate marks significant progress over previous studies, which only achieved 68-71% success in cracking reCAPTCHAv2. The researchers found that reCAPTCHAv2 relies heavily on cookie and browser data to identify human users. By using a VPN, realistic mouse movements, and browser data, their automated system went undetected.
The ETH Zurich team has published their source code to enable further research. They recommend expanding the dataset for segmentation tasks and investigating at what point continuous CAPTCHA solving triggers blocking.
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