标题： Aura-CAPTCHA：一种强化学习和GAN增强的多模态CAPTCHA系统 显示英文标题

标题： Aura-CAPTCHA: A Reinforcement Learning and GAN-Enhanced Multi-Modal CAPTCHA System

摘要： Aura-CAPTCHA作为一种多模态CAPTCHA系统被开发出来，以解决传统方法在人工智能技术（如光学字符识别OCR和对抗性图像处理）日益突破时出现的漏洞。 设计中集成了生成对抗网络GANs用于生成动态图像挑战，强化学习RL用于自适应难度调整，以及大语言模型LLMs用于创建文本和音频提示。 视觉挑战包括至少包含三个正确图像的3x3网格选择，而音频挑战则将随机数字和单词组合成一个任务。 强化学习根据错误尝试次数、响应时间和可疑用户行为来调整难度。 在真实流量上的评估显示，人类成功率为92%，机器人绕过率为10%，显著优于现有的CAPTCHA系统。 该系统为保护在线应用提供了一种强大且可扩展的方法，同时保持对用户的可访问性，解决了之前研究中提到的空白。 显示英文摘要

摘要： Aura-CAPTCHA was developed as a multi-modal CAPTCHA system to address vulnerabilities in traditional methods that are increasingly bypassed by AI technologies, such as Optical Character Recognition (OCR) and adversarial image processing. The design integrated Generative Adversarial Networks (GANs) for generating dynamic image challenges, Reinforcement Learning (RL) for adaptive difficulty tuning, and Large Language Models (LLMs) for creating text and audio prompts. Visual challenges included 3x3 grid selections with at least three correct images, while audio challenges combined randomized numbers and words into a single task. RL adjusted difficulty based on incorrect attempts, response time, and suspicious user behavior. Evaluations on real-world traffic demonstrated a 92% human success rate and a 10% bot bypass rate, significantly outperforming existing CAPTCHA systems. The system provided a robust and scalable approach for securing online applications while remaining accessible to users, addressing gaps highlighted in previous research.