Captcha+breaker !!better!! -

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.

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

The term CAPTCHA was first introduced in 2000 by Luis von Ahn, Manuel Blum, Nicholas J. Hopper, and John Langford [1]. The primary motivation behind CAPTCHA was to create a challenge-response test that could distinguish humans from computers. The test was designed to be easy for humans to solve but difficult for computers to pass. CAPTCHAs have been widely adopted in various applications, including online registration, voting systems, and online transactions. CAPTCHA (Completely Automated Public Turing test to tell

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. This paper provides a comprehensive overview of CAPTCHA,

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

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

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