Captcha+breaker Link

[3] Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.

We conducted experiments on a dataset of text-based CAPTCHAs to evaluate the effectiveness of the machine learning-based approach. The results are shown in Table 1. captcha+breaker

[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. Hinton, "Deep Learning," Nature, vol

[2] C. D. Manning and H. Schütze, "Foundations of Statistical Natural Language Processing," MIT Press, 1999. 436-444, 2015

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a widely used challenge-response test designed to determine whether the user is human or a computer. The primary goal of CAPTCHA is to prevent automated programs, also known as bots, 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 will discuss various CAPTCHA breaker techniques, including machine learning-based approaches, and analyze their effectiveness.

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.