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This study explores advanced machine learning solutions for CAPTCHA recognition, a critical aspect of web security. We have developed a new model, FlowCap-Net, which leverages the strengths of Inception V4 and Liquid Neural Networks to effectively address the challenges of CAPTCHA recognition. FlowCap-Net stands out in our comprehensive evaluation, achieving an exceptional accuracy rate of 98.95%. This model demonstrates remarkable efficiency and precision in deciphering complex CAPTCHA designs, establishing itself as a potent tool against the evolving threats in digital security environments. Our findings highlight FlowCap-Net as a groundbreaking advancement in the field, offering substantial improvements over traditional CAPTCHA recognition technologies.

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Advanced Machine Learning for CAPTCHA Recognition: Evaluating the Efficacy of FlowCap-Net

  • 会议论文

  • 发表日期：

    2026年

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  • Kumar Akuthota,
  • Saritha Anchuri,
  • Yellaturu Deekshitha,
  • R. Praveen Kumar Naidu,
  • Bonthala Balaji,
  • A. Basi Reddy

摘要

This study explores advanced machine learning solutions for CAPTCHA recognition, a critical aspect of web security. We have developed a new model, FlowCap-Net, which leverages the strengths of Inception V4 and Liquid Neural Networks to effectively address the challenges of CAPTCHA recognition. FlowCap-Net stands out in our comprehensive evaluation, achieving an exceptional accuracy rate of 98.95%. This model demonstrates remarkable efficiency and precision in deciphering complex CAPTCHA designs, establishing itself as a potent tool against the evolving threats in digital security environments. Our findings highlight FlowCap-Net as a groundbreaking advancement in the field, offering substantial improvements over traditional CAPTCHA recognition technologies.