VeriBypasser: An automatic image verification code recognition system based on CNN

Abstract

The rapid development of network technology has brought great convenience to people’s daily life in recent years. The application of network technology in finance, economy, and education is flourishing, dramatically improving people’s quality of life and promoting society’s development. However, the development of network technology brought some new technical problems. Authenticating and controlling access to shared resources has become a significant problem, which limits the development of smart cities. Nowadays, most websites use accounts and passwords for authentication. In order to prevent violent scanning, image-based human–machine inspection solutions are widely used on major platforms. Nonetheless, some human–computer verification schemes can be bypassed or attacked by artificial intelligence. This paper proposes an improved generation method of image verification code and an automatic recognition system based on Cellular Neural Network (CNN), which can achieve an accuracy of 85.20% for a mix of the visual reasoning Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) containing 4-bit case-insensitive letters and digits, the slider CAPTCHA containing one slide, and inference puzzle CAPTCHA containing a 4 × 2 grid. A brief evaluation of system threat assignment with such verification approaches based on four machine learning methods is also proposed.