Abstract
In this work, we analyze the vulnerability of the dynamic textual CAPTCHA (http://fexteam.gz01.bdysite.com/blog/2014/07/captcha-gif/.) and propose a new method to automatically identify the CAPTCHA, which is based on Basic Vector Space Search Engine (BVSSE) and Convolutional Neural Network (CNN). Specifically, by exploiting the specific “Symmetric Frame Vulnerability”, we can remove most of the noise, therefore greatly reducing the difficulty of cracking. In the process of cracking, we first use the BVSSE to identify the CAPTCHA . The method is simple and fast, but there are problems such as a low recognition rate. Then we choose the CNN to identify the CAPTCHA, and finally get a recognition rate of 99.98% with the average speed of 0.092 s/gif. To have a deeper understanding of the internal recognition process, we visualize the intermediate output of the CNN model. In general, by comparing the two identification methods and visualizing the model, the entire recognition process becomes easier to understand. Based on the above experimental results and analyses, we finally summarize a new and general CAPTCHA attack method and discuss the security of the dynamic textual CAPTCHA .
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References
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
Nguyen, V.D., Chow, Y.-W., Susilo, W.: Breaking an animated CAPTCHA scheme. In: Bao, F., Samarati, P., Zhou, J. (eds.) ACNS 2012. LNCS, vol. 7341, pp. 12–29. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31284-7_2
Karthik, C.P., Recasens, R.A.: Breaking Microsoft’s CAPTCHA. Technical report (2015)
Fang, W., Zhou, X.: The research on image extraction an segmentation algorithm in license plate recognition. In: ITOEC (2015)
Chellapilla, K., Larson, K., Simard, P.: Security enhancement in captcha recognition using animated GIF images. Int. J. Comput. Appl. (2017)
Zhao, B., Weng, H., Ji, S.: Towards evaluating the security of real-world deployed image. In: AISec (2018)
Chollet, F.: Deep Learning with Python, pp. 164–205. Manning Publications Company, Shelter Island (2017)
Acknowledgment
This work was partly supported by NSFC under No. 61772466 and U1836202, the Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars under No. LR19F020003, the Provincial Key Research and Development Program of Zhejiang, China under No. 2017C01055, and the Alibaba-ZJU Joint Research Institute of Frontier Technologies.
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A Appendix
A Appendix
1.1 A.1 Grid Optimization Noise Removal Algorithm
Basic implementation idea: by counting the number of other black pixels in the nine squares around the black pixel, we can determine whether the current black pixel is an isolated noise point. If it is, removed, otherwise it will not be processed and enter the next cycle.
Nine-square grid around a black dot
In the specific implementation process, you need to consider the following details: as shown in Fig. 16, the pixels in the image can be divided into three categories:
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vertex A
For the class A point, calculate the three neighboring points (as shown by the red box).
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non-vertex boundary point B
For the class B point, calculate the surrounding five points (as shown by the red box).
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internal point C
For the class C point, calculate eight points around (as shown by the red box).
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Chen, Y., Liu, Q., Du, T., Chen, Y., Ji, S. (2020). Symmetric Frame Cracking: A Powerful Dynamic Textual CAPTCHAs Cracking Policy. In: Liu, Z., Yung, M. (eds) Information Security and Cryptology. Inscrypt 2019. Lecture Notes in Computer Science(), vol 12020. Springer, Cham. https://doi.org/10.1007/978-3-030-42921-8_9
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DOI: https://doi.org/10.1007/978-3-030-42921-8_9
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