Research on Captcha Recognition of Lightweight Convolutional Neural Network with Gabor

doi:10.3969/j.issn.1671-1122.2020.07.009

Abstract

Abstract:

As a widely used verification method, captcha effectively identifies the logged-in users, which is of great significance to the protection of network security. To solve the problem of large parameters and difficult training cost of convolutional neural network, this paper proposes an captcha recognition method based on the combination of Gabor features and convolutional neural network to realize the recognition and classification. Gabor operator is used to extract the detail features as the input of convolution neural network, and the improved depthwise separable convolutions to obtain the features at different scales and increased the model differentiation. Finally, the experimental results show that the improved convolutional neural network has a practical significance for the average recognition accuracy of the verification code of about 98%.

Key words: separable convolution layer, convolutional neural network, captcha recognition, Gabor

CLC Number:

TP309

LIU Jing, ZHANG Xueqian, LIU Quanming. Research on Captcha Recognition of Lightweight Convolutional Neural Network with Gabor[J]. Netinfo Security, 2020, 20(7): 77-84.

Figures/Tables 10

References 23

[1]	KWON H, KIM Y, YOON H, et al. CAPTCHA Image Generation Systems Using Generative Adversarial Networks[J]. Ieice Transactions on Information & Systems, 2018,12(2):543-546.
[2]	WANG F, JUN G H, FAN G, et al. Chinese Character CAPTCHA Recognition Based on Convolution Neural Network[EB/OL]. https://www.researchgate.net/publication/311251657_Chinese_character_CAPTCHA_recognition_based_on_convolution_neural_network, 2019-10-15.
[3]	KAZUMA A. Server for Implementing Image Processing Functions Requested by A Printing Device[J]. Environmental Pollution, 2018,152(3):543-552. URL pmid: 17703858
[4]	CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Deep Lab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018,40(4):834. doi: 10.1109/TPAMI.2017.2699184 URL pmid: 28463186
[5]	MILTIADIS A, BARR E T, PREMKUMAR D, et al. A Survey of Machine Learning for Big Code and Naturalness[J]. ACM Computing Surveys, 2018,51(4):1-37.
[6]	RAISSI M, KARNIADAKIS G E. Hidden Physics Models: Machine Learning of Nonlinear Partial, Differential Equations[EB/OL]. http://www.researchgate.net/publication/318868462_Hidden_Physics_Models_Machine_Learning_of_Nonlinear_Partial_Differential_Equations, 2019-10-15.
[7]	CHEN X, MAKKI K, YEN K, et al. Sensor Network Security: A Survey[J]. IEEE Communications Surveys & Tutorials, 2009,11(2):52-73.
[8]	SARWAR S S, PANDA P, ROY K. Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks[C]// IEEE. 2017 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), July 24-26, 2017, Taipei, China. NJ: IEEE, 2017: 1-6.
[9]	MEHTA R, EGIAZARIAN K. Dominant Rotated Local Binary Patterns (DRLBP) for Texture Classification[J]. Pattern Recognition Letters, 2016,71(11):16-22.
[10]	YI K M, TRULLS E, LEPETIT V, et al. Lift: Learned Invariant Feature Transform[EB/OL]. http://www.researchgate.net/publication/308277668_LIFT_Learned_Invariant_Feature_Transform, 2019-10-15.
[11]	YAN G, YU M, YU Y, et al. Real-Time Vehicle Detection Using Histograms of Oriented Gradients and AdaBoost Classification[J]. Optik, 2016,127(19):7941-7951.
[12]	WANG Hu, FENG Lin, SUN Yuzhe. Research and Design of Digital Character-Based CAPTCHA Decoder[J]. Computer Engineering and Applications, 2007,43(32):86-88.
	王虎, 冯林, 孙宇哲. 数字验证码识别算法的研究和设计[J]. 计算机工程与应用, 2007,43(32):86-88.
[13]	TIAN Huaichuan. Research and Application on Captcha Recognition and Prevention Based on Neural Network[D]. Harbin: Harbin Institute of Technology, 2010.
	田怀川. 基于神经网络的图形验证码识别及防识别的研究与应用[D]. 哈尔滨:哈尔滨工业大学, 2010.
[14]	GANG Lv, HAO Ping. Research on Recognition of CAPTCHA Based on Neural Network[J]. Journal of Zhejiang University of Technology, 2010,38(4):433-436.
	吕刚, 郝平. 基于神经网络的数字验证码识别研究[J]. 浙江工业大学学报, 2010,38(4):433-436.
[15]	CAO Yanrong, GONG Yanhong, JIA Huizhen. Verification Code Recognition Method Based on Adversarial Network[J]. Computer Engineering and Applications, 2020,56(8):199-204.
	曹廷荣, 龚燕红, 贾惠珍. 基于对抗网络的验证码识别方法[J]. 计算机工程与应用, 2020,56(8):199-204.
[16]	SCHMIDHUBER , JÜRGEN . Deep Learning in Neural Networks: An Overview[J]. Neural Networks, 2015,61(1):85-117.
[17]	LITJENS , GEERT , KOOI , et al. A Survey on Deep Learning in Medical Image Analysis[J]. Medical Image Analysis, 2017,42(9):60-88. doi: 10.1016/j.media.2017.07.005 URL
[18]	SHIN H C, ROTH H R, GAO M, et al. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN, Architectures, Dataset Characteristics and Transfer Learning[J]. IEEE Transactions on Medical Imaging, 2016,35(5):1285-1298. doi: 10.1109/TMI.2016.2528162 URL pmid: 26886976
[19]	ZHANG Q, YANG L T, CHEN Z, et al. A Survey on Deep Learning for Big Data[J]. Information Fusion, 2018,42(10):146-157. doi: 10.1016/j.inffus.2017.10.006 URL
[20]	LEDDY J. Ultrathin Layer Convolution[J]. Journal of Electroanalytical Chemistry, 1991,300(1-2):295-307.
[21]	LUAN S, CHEN C, ZHANG B, et al. Gabor Convolutional Networks[J]. IEEE Transactions on Image Processing, 2018,27(9):1. doi: 10.1109/TIP.2017.2787262 URL
[22]	SZEGEDY C, LIU W, JIA Y, et al. Going Deeper with Convolutions[C]// IEEE. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. NJ: IEEE, 2015: 1-9.
[23]	CHOLLET F. Xception: Deep Learning with Depthwise Separable Convolutions[C]// IEEE. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu. NJ: IEEE, 2017: 1800-1807.

Dropout	准确率/%	时间/ ms
0.1	97.1	112
0.3	98.3	112
0.5	98.0	112

学习率	准确率/%	时间/ms
0.1	91.8	79
0.01	98.3	112
0.001	96.4	127

批量大小	准确率/%	时间/ms
5	97.8	134
10	98.3	112
50	94.8	93

网络模型	准确率/%
VGG16模型	96.1
VGG19模型	96.7.
Gabor算子	93.3
深度可分离卷积模型	97.4
本文方法	98.3