Captcha+breaker Page

| CAPTCHA Type | Accuracy | | --- | --- | | Simple text-based CAPTCHA | 90% | | Distorted text-based CAPTCHA | 80% | | Noisy text-based CAPTCHA | 70% |

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. captcha+breaker

[2] C. D. Manning and H. Schütze, "Foundations of Statistical Natural Language Processing," MIT Press, 1999. | CAPTCHA Type | Accuracy | | ---

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

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.

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.