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SecureCAPTCHA: Smarter CAPTCHAs with Patch-Based Defense

  • Unique Paper ID: 175494
  • Volume: 11
  • Issue: 11
  • PageNo: 4075-4080
  • Abstract:
  • As automated attacks against CAPTCHA systems become increasingly sophisticated, traditional text-based and image recognition challenges are becoming vulnerable to machine learning-based attacks. This paper proposes Secure CAPTCHA, a novel CAPTCHA system that incorporates strategically generated adversarial patches into images to create challenges that are easily solvable by humans but resistant to automated attacks. By introducing carefully crafted patch-based perturbations that exploit the fundamental vulnerabilities of neural networks, our system creates a robust defense mechanism against bot attacks while maintaining human usability. We train our system over 100 epochs and evaluate its performance against state-of-the-art deep learning-based CAPTCHA solvers, demonstrating significant improvements in security metrics compared to conventional approaches. Our experimental results show that Secure CAPTCHA reduces successful automated attacks by 91% while maintaining a human success rate of 94%. The system also demonstrates resilience against various adaptive attack strategies, including feature squeezing, adversarial training, and ensemble methods.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 4075-4080

SecureCAPTCHA: Smarter CAPTCHAs with Patch-Based Defense

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