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.
[3] Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015. captcha+breaker
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. The term CAPTCHA was first introduced in 2000
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. The test was designed to be easy for
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.
[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.
| CAPTCHA Type | Accuracy | | --- | --- | | Simple text-based CAPTCHA | 90% | | Distorted text-based CAPTCHA | 80% | | Noisy text-based CAPTCHA | 70% |