NGCaptcha: A CAPTCHA Bridging the Past and the Future

TL;DR

NGCAPTCHA introduces a hybrid CAPTCHA system combining proof of work and AI-resistant visual challenges to enhance security against advanced AI-based attacks while maintaining user convenience.

Contribution

The paper presents NGCAPTCHA, a novel CAPTCHA framework that integrates lightweight client-side proof of work with AI-resistant visual recognition tasks, addressing current security vulnerabilities.

Findings

Throttles automated attacks through computational cost.

Increases difficulty for AI-based solvers while remaining user-friendly.

Significantly improves CAPTCHA robustness against modern AI techniques.

Abstract

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.