Enhancing the Security of Large Character Set CAPTCHAs Using Transferable Adversarial Examples

Deep learning attacks have successfully cracked CAPTCHAs (automated tests that distinguish humans from bots) that use large character sets, especially those with alphabets from languages like Chinese. This paper proposes ACG (Adversarial Large Character Set CAPTCHA Generation), a framework that makes CAPTCHAs harder to attack by adding adversarial perturbations (intentional distortions that confuse AI recognition systems) through two modules: one that prevents character recognition and another that adds global visual noise, reducing attack success rates from 51.52% to 2.56%.