reCAPTCHAv2 achieves 100% success, challenging its effectiveness as a human verification tool.
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📚 https://arxiv.org/pdf/2409.08831
Solutions in this Paper 🛠️:
• Developed an automated system using YOLO v8 models for image segmentation and classification
• Fine-tuned YOLO v8 on a dataset of 14k image/label pairs for classification tasks
• Utilized pre-trained YOLO v8 for segmentation tasks
• Implemented VPN usage, realistic mouse movements, and browser history/cookies simulation
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Key Insights from this Paper 💡:
• VPN usage crucial to avoid being flagged as suspicious
• Realistic mouse movements improve bot performance
• Browser history and cookies significantly reduce challenge frequency
• Bot performance closely mimics human performance in solving CAPTCHAs
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Results 📊:
• 100% success rate in solving reCAPTCHAv2 challenges (vs 68-71% in previous studies)
• Bot performance statistically similar to human solvers (p-value: 0.11)
🔐 The impact of VPN usage, mouse movements, and browser history/cookies on captcha solvability
Using a VPN was crucial to avoid being flagged as suspicious after multiple attempts.
Implementing realistic mouse movements using Bézier curves improved the bot's performance by making its interactions appear more human-like.
Including browser history and cookies from a real user session drastically reduced the number of challenges presented, indicating that reCAPTCHAv2 heavily relies on this data to assess whether a user is human.
