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首页 > 过刊浏览>2018年第27卷第11期 >142-148. DOI:10.15888/j.cnki.csa.006648
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基于卷积神经网络的图像验证码识别
作者:

Image CAPTCHA Recognition Based on Convolutional Neural Network
Author:
  • QIN Bo

    QIN Bo

    Laboratory of Network Computing and High Efficient Algorithm, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;
    Anhui Provincial Key Laboratory of Computing and Communication Software, Hefei 230027, China;
    Institute of Advanced Technology, University of Science and Technology of China, Hefei 230027, China
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  • GU Nai-Jie

    GU Nai-Jie

    Laboratory of Network Computing and High Efficient Algorithm, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;
    Anhui Provincial Key Laboratory of Computing and Communication Software, Hefei 230027, China;
    Institute of Advanced Technology, University of Science and Technology of China, Hefei 230027, China
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  • ZHANG Xiao-Ci

    ZHANG Xiao-Ci

    Laboratory of Network Computing and High Efficient Algorithm, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;
    Anhui Provincial Key Laboratory of Computing and Communication Software, Hefei 230027, China;
    Institute of Advanced Technology, University of Science and Technology of China, Hefei 230027, China
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  • LIN Chuan-Wen

    LIN Chuan-Wen

    Laboratory of Network Computing and High Efficient Algorithm, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;
    Anhui Provincial Key Laboratory of Computing and Communication Software, Hefei 230027, China;
    Institute of Advanced Technology, University of Science and Technology of China, Hefei 230027, China
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  • 摘要
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  • 参考文献 [19]
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    摘要:

    验证码作为一种安全手段，被广泛应用于互联网领域.本文提出了一种基于卷积神经网络的图像验证码识别方法，通过卷积层级联、残差学习、全局池化、分组卷积等技术手段，在保证识别准确率不受影响的前提下，大大降低了网络的参数量.本文以铁路购票网站验证码和正方教务系统验证码为例来测试模型性能.对于铁路购票网站验证码，实验结果显示本文提出的识别方法参数量最少，对图形和中文词组验证码的识别准确率分别达到98.76%和99.14%；对于正方教务系统验证码，本文方法参数量最少且识别准确率为87.30%.

    Abstract:

    As a security measure, CAPTCHA is widely used in Internet. This study proposes a CAPTCHA identification method based on convolutional neural network. Through convolutional layer concatenation, residual learning, global pool, and other technical means, under the premise of ensuring the recognition accuracy rate is not affected, it greatly reduces the amount of network parameters. This study uses the CAPTCHA in the railway ticket website and the CAPTCHA in the educational system as examples to test the performance of the model. For the CAPTCHA in railway ticket website, the experimental results show that this method has the least amount of parameters, and the recognition accuracy of this method is 98.76% for image and the recognition accuracy of the Chinese phrases is 99.14%. For the CAPTCHA in educational system, it has the least amount of parameters and the accuracy is 87.30%.

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秦波,顾乃杰,张孝慈,林传文.基于卷积神经网络的图像验证码识别.计算机系统应用,2018,27(11):142-148

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历史
  • 收稿日期:2018-04-02
  • 最后修改日期:2018-05-11
  • 在线发布日期: 2018-10-24
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