Universal CAPTCHA Recognition implemented by Deep Neural Network

• Main Authors: Zi-Yi Yuan, 袁子意
• Other Authors: Farn Wang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/9h6sqp

碩士 === 國立臺灣大學 === 電子工程學研究所 === 107 === CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a widely used technology on the internet to check if the person registering or trying to comment is a real human or a computer program attempting to spam the site. They are usually fully automated, requiring little human maintenance, thus having benefits in cost and reliability. Despite the convenience on internet security problems, this technology is not very friendly to software testers. Therefore, in this paper we investigate the various combinations of methods based on deep neural networks to recognize CAPTCHA images. The main method is divided into two steps. The first step is to use YOLO, a real-time object detection model to decide the exact location of CAPTCHA on a web page. The second step is to use an end-to-end neural network aimed at recognizing indivisible CAPTCHA images, rather than the traditional segmentation technology used to recognize divisible CAPTCHA images. We use convolution neural network and recurrent neural network for CAPTCHA recognition. Experimental results confirm that our method used in this paper achieves test accuracy of around 99% in the seven types of CAPTCHA.