CAPTCHAs are widely employed for distinguishing humans from automated bots online. However, current vision based CAPTCHAs face escalating security risks: traditional attacks continue to bypass many deployed CAPTCHA schemes, and recent breakthroughs in AI, particularly large scale vision models, enable machine solvers to significantly outperform humans on many CAPTCHA tasks, undermining their original design assumptions. To address these issues, we introduce NGCAPTCHA, a Next Generation CAPTCHA framework that integrates a lightweight client side proof of work (PoW) mechanism with an AI resistant visual recognition challenge. In NGCAPTCHA, a browser must first complete a small hash based PoW before any challenge is displayed, throttling large scale automated attempts by increasing their computational cost. Once the PoW is solved, the user is presented with a human friendly yet model resistant image selection task that exploits perceptual cues current vision systems still struggle with. This hybrid design combines computational friction with AI robust visual discrimination, substantially raising the barrier for automated bots while keeping the verification process fast and effortless for legitimate users.

翻译：验证码广泛应用于在线区分人类与自动化机器人。然而，当前基于视觉的验证码面临日益严峻的安全风险：传统攻击手段持续绕过许多已部署的验证码方案，而人工智能领域的最新突破，特别是大规模视觉模型的发展，使得机器求解器在许多验证码任务上的表现显著超越人类，这动摇了其原始设计假设。为解决这些问题，我们提出了NGCAPTCHA——一个融合轻量级客户端工作量证明机制与抗人工智能视觉识别挑战的新一代验证码框架。在NGCAPTCHA中，浏览器必须在显示任何挑战前先完成一个基于哈希的小型工作量证明，通过增加计算成本来抑制大规模自动化尝试。工作量证明解决后，用户将面对一个对人类友好但对模型具有抵抗力的图像选择任务，该任务利用了当前视觉系统仍难以处理的感知线索。这种混合设计将计算阻力与抗人工智能的视觉判别相结合，在保持合法用户验证过程快速轻松的同时，显著提高了自动化机器人的突破门槛。