基于卷积神经网络的验证码识别

Q. Tian, Qishun Song, Hongbo Wang, Zhihong Hu, Siyu Zhu
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引用次数: 1

摘要

基于卷积神经网络的验证码识别系统。为了加强网络安全防御工作,本文提出了一种基于卷积神经网络的验证码识别系统。该系统结合了互联网技术和大数据技术,结合先进的验证码技术,可以在一定程度上防止黑客的暴力破解行为。此外,系统结合卷积神经网络,使验证码由数字和字母组合而成,提高了验证码的复杂度和用户账号的安全性。在此基础上,系统采用阈值分割法和投影定位法构建了8层卷积神经网络模型,增强了验证码输入环节的安全性。研究结果表明,该系统可以增强验证码的复杂度,提高验证码的识别率,提高用户计费的安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Verification Code Recognition Based on Convolutional Neural Network
Verification code recognition system based on convolutional neural network. In order to strengthen the network security defense work, this paper proposes a novel verification code recognition system based on convolutional neural network. The system combines Internet technology and big data technology, combined with advanced captcha technology, can prevent hackers from brute force cracking behavior to a certain extent. In addition, the system combines convolutional neural network, which makes the verification code combine numbers and letters, which improves the complexity of the verification code and the security of the user account. Based on this, the system uses threshold segmentation method and projection positioning method to construct an 8-layer convolutional neural network model, which enhances the security of the verification code input link. The research results show that the system can enhance the complexity of captcha, improve the recognition rate of captcha, and improve the security of user accounting.
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