@inproceedings{30a3f2c71f854675bae82e08ad27e0aa,
title = "CAPTCHA Identification Based on Convolution Neural Network",
abstract = "The CAPTCHA is an effective method commonly used in live interactive proofs on the Internet. The widely used CAPTCHAs are text-based schemes. In this paper, we document how we have broken such text-based scheme used by a website CAPTCHA. We use the sliding window to segment 1001 pieces of CAPTCHA to get 5900 images with single-character useful information, a total of 25 categories. In order to make the convolution neural network learn more image features, we augmented the data set to get 129924 pictures. The data set is trained and tested in AlexNet and GoogLeNet to get the accuracy of 87.45% and 98.92%, respectively. The experiment shows that the optimized network parameters can make the accuracy rate up to 92.7% in AlexNet and 98.96% in GoogLeNet.",
keywords = "AlexNet, CAPTCHA, GoogLeNet, segment",
author = "Mengyuan Wang and Yuliang Yang and Mengyu Zhu and Jiaming Liu",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018 ; Conference date: 25-05-2018 Through 27-05-2018",
year = "2018",
month = sep,
day = "20",
doi = "10.1109/IMCEC.2018.8469705",
language = "English",
series = "Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "364--368",
editor = "Bing Xu",
booktitle = "Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018",
address = "United States",
}