Abstract
The CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) is a common and effective security mechanism applied by many websites and applications. CAPTCHA recognition is an important and practical problem in text recognition research. Compared with traditional methods, DCNN (Deep Convolutional Neural Network) has achieved competitive accuracy in CAPTCHA recognition recently. However, current CAPTCHA recognition researches based on DCNN usually use conventional convolution network, which causes high computation complexity and great computing resource consumption. Aiming at the problems, we propose an Accurate, Light and Efficient network for CAPTCHA recognition (ALEC) based on the encoder-decoder structure. The ALEC can greatly reduce the computation complexity and parameters while ensuring the recognition accuracy. In this paper, standard convolutions are replaced by depthwise separable convolutions to improve computational efficiency. The architecture utilizes group convolution and convolution channels reduction to build a deep narrow network, which reduces the model parameters and improves generalization performance. Additionally, effective and efficient attention modules are applied to suppress the background noise and extract valid foreground context. Experiments demonstrate that ALEC not only has higher speed with fewer parameters but also improves the accuracy of CAPTCHA recognition. In detail, the ALEC achieves about 4 times speed up over the standard ResNet-18 while reducing 97% parameters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265–283 (2016)
El Ahmad, A.S., Yan, J., Tayara, M.: The robustness of Google CAPTCHA’s. Computing Science, Newcastle University (2011)
El Ahmad, A.S., Yan, J., Marshall, L.: The robustness of a new captcha. In: Proceedings of the Third European Workshop on System Security, pp. 36–41. ACM (2010)
Garg, G., Pollett, C.: Neural network captcha crackers. In: 2016 Future Technologies Conference (FTC), pp. 853–861. IEEE (2016)
Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 369–376. ACM (2006)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hou, L., Yao, Q., Kwok, J.T.: Loss-aware binarization of deep networks. arXiv preprint arXiv:1611.01600 (2016)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Huang, S.-Y., Lee, Y.-K., Bell, G., Zhan-he, O.: An efficient segmentation algorithm for captchas with line cluttering and character warping. Multimedia Tools Appl. 48(2), 267–289 (2010)
Kim, Y.-D., Park, E., Yoo, S., Choi, T., Yang, L., Shin, D.: Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint arXiv:1511.06530 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Le, T.A., Baydin, A.G., Zinkov, R., Wood, F.: Using synthetic data to train neural networks is model-based reasoning. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 3514–3521. IEEE (2017)
Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)
Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 122–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_8
Al Nachar, R., Inaty, E., Bonnin, P.J., Alayli, Y.: Breaking down captcha using edge corners and fuzzy logic segmentation/recognition technique. Secur. Commun. Netw. 8(18), 3995–4012 (2015)
Qing, K., Zhang, R.: A multi-label neural network approach to solving connected captchas. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1313–1317. IEEE (2017)
Rui, C., Jing, Y., Hu, R., Huang, S.: A novel LSTM-RNN decoding algorithm in captcha recognition. In: 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 766–771. IEEE (2013)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2015)
Sifre, L., Mallat, S.: Rigid-motion scattering for image classification. Ph. D. dissertation (2014)
Singh, V.P., Pal, P.: Survey of different types of captcha. Int. J. Comput. Sci. Inf. Technol. 5(2), 2242–2245 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Tulloch, A., Jia, Y.: High performance ultra-low-precision convolutions on mobile devices. arXiv preprint arXiv:1712.02427 (2017)
von Ahn, L., Blum, M., Hopper, N.J., Langford, J.: CAPTCHA: using hard AI problems for security. In: Biham, E. (ed.) EUROCRYPT 2003. LNCS, vol. 2656, pp. 294–311. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-39200-9_18
Wang, J., Qin, J.H., Xiang, X.Y., Tan, Y., Pan, N.: Captcha recognition based on deep convolutional neural network. Math. Biosci. Eng 16(5), 5851–5861 (2019)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Yan, J., El Ahmad, A.S.: Breaking visual captchas with Naive pattern recognition algorithms. In: Twenty-Third Annual Computer Security Applications Conference (ACSAC 2007), pp. 279–291. IEEE (2007)
Yan, J., El Ahmad, A.S.: A low-cost attack on a Microsoft captcha. In: Proceedings of the 15th ACM Conference on Computer and Communications Security, pp. 543–554. ACM (2008)
Zhang, L., Zhang, L., Huang, S.G., Shi, Z.X.: A highly reliable captcha recognition algorithm based on rejection. Acta Automatica Sinica 37(7), 891–900 (2011)
Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)
Zhu, M., Gupta, S.: To prune, or not to prune: exploring the efficacy of pruning for model compression. arXiv preprint arXiv:1710.01878 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, N., Jiang, Q., Song, Q., Zhang, R., Wei, X. (2020). ALEC: An Accurate, Light and Efficient Network for CAPTCHA Recognition. In: Bai, X., Karatzas, D., Lopresti, D. (eds) Document Analysis Systems. DAS 2020. Lecture Notes in Computer Science(), vol 12116. Springer, Cham. https://doi.org/10.1007/978-3-030-57058-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-57058-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57057-6
Online ISBN: 978-3-030-57058-3
eBook Packages: Computer ScienceComputer Science (R0)
