Scholarlink

CAPTCHAs are essential tools in computer security to distinguish between humans and automated programs. Although widely used in web applications to prevent unauthorized access and spam, advances in artificial intelligence have increased attacks against these systems. This study focuses on improving the security of CAPTCHAs using adversarial techniques such as FGSM and PGD, exploring their effectiveness against a deep learning model. Furthermore, a generative adversarial network is employed to strengthen resistance to these attacks. The research also includes human validation to evaluate the robustness of different types of CAPTCHAs against simulated attacks. Our findings demonstrate that while adversarial modifications enhance security, they require careful calibration to avoid excessive usability degradation.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improvement of Text CAPTCHA Codes by Comparing Adversarial Techniques Against Deep Learning Model Attacks

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Luciana Vasquez Montenegro,
  • Giancarlo Lopez Garcia,
  • Edwin Jonathan Escobedo Cardenas

摘要

CAPTCHAs are essential tools in computer security to distinguish between humans and automated programs. Although widely used in web applications to prevent unauthorized access and spam, advances in artificial intelligence have increased attacks against these systems. This study focuses on improving the security of CAPTCHAs using adversarial techniques such as FGSM and PGD, exploring their effectiveness against a deep learning model. Furthermore, a generative adversarial network is employed to strengthen resistance to these attacks. The research also includes human validation to evaluate the robustness of different types of CAPTCHAs against simulated attacks. Our findings demonstrate that while adversarial modifications enhance security, they require careful calibration to avoid excessive usability degradation.