[1] L. von Ahn, M. Blum, N. J. Hopper, and J. Langford, "CAPTCHA: Using Hard AI Problems for Security," in Proceedings of the 22nd Annual International Cryptology Conference, 2000.
[4] J. K. Lal, P. S. Kumar, and S. K. Sahu, "A Survey on CAPTCHA and CAPTCHA Breaking Techniques," Journal of Intelligent Information Systems, vol. 54, no. 2, pp. 267-286, 2020. captcha+breaker
The results show that the machine learning-based approach can achieve high accuracy on simple text-based CAPTCHAs, but the accuracy decreases as the CAPTCHA becomes more distorted or noisy. Manning and H. Schütze
CAPTCHAs are widely used to prevent automated programs from accessing a system or performing certain actions. However, with the advancement of artificial intelligence and machine learning techniques, CAPTCHAs have become increasingly vulnerable to being broken. This paper provides a comprehensive overview of CAPTCHA, its history, types, and vulnerabilities. Additionally, we discussed various CAPTCHA breaker techniques, including machine learning-based approaches, and analyzed their effectiveness. The experimental results show that the machine learning-based approach can achieve high accuracy on simple text-based CAPTCHAs, but the accuracy decreases as the CAPTCHA becomes more distorted or noisy. "Foundations of Statistical Natural Language Processing
Future work includes exploring more advanced machine learning-based approaches, such as deep learning, to improve the accuracy of CAPTCHA breakers. Additionally, we plan to investigate the use of CAPTCHAs in various applications, such as online registration and voting systems, and evaluate their effectiveness in preventing automated programs from accessing these systems.
[3] Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.
[2] C. D. Manning and H. Schütze, "Foundations of Statistical Natural Language Processing," MIT Press, 1999.