Abstract
Over the past decade, several public web services made an attempt to prevent automated scripts and exploitation by bots by interrogating a user to solve a Turing-test challenge (commonly known as a CAPTCHA) before using the service. A CAPTCHA is a cryptographic protocol whose underlying hardness assumption is based on an artificial intelligence problem. CAPTCHAs challenges rely on the problem of distinguishing images of living or non-living objects (a task that is easy for humans). User studies proves, it can be solved by humans 99.7% of the time in under 30 s while this task is difficult for machines. The security of image based CAPTCHAs challenge is based on the presumed difficulty of classifying CAPTCHAs database images automatically.
In this paper, we proposed a classification model which is 95.2% accurate in telling apart the images used in the CAPTCHA database. Our method utilizes layered features optimal tuning with an improved VGG16 architecture of Convolutional Neural Networks. Experimental simulation is performed using Caffe deep learning framework. Later, we compared our experimental results with significant state-of-the-art approaches in this domain.
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
BotBarrier.com. On the web. http://www.botbarrier.com/
Chellapilla, K., Larson, K., Simard, P., Czerwinski, M.: Designing human friendly human interaction proofs (HIPs). In: Proceedings of ACM CHI 2005 Conference on Human Factors in Computing Systems. Email and Security, pp. 711–720 (2005)
Chew, M., Tygar, J.D.: Image recognition CAPTCHAs. In: Zhang, K., Zheng, Y. (eds.) ISC 2004. LNCS, vol. 3225, pp. 268–279. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30144-8_23
Naor, M.: Verification of a human in the loop or Identification via the Turing Test. Unpublished Manuscript (1997). Electronically: www.wisdom.weizmann.ac.il/~naor/PAPERS/human.ps
Lillibridge, M.D., Adabi, M., Bharat, K., Broder, A.: Method for selectively restricting access to computer systems. Technical report, US Patent 6,195,698, Applied April 1998 and Approved February 2001
Coates, A.L., Baird, H.S., Fateman, R.J.: Pessimal print: a reverse turing test. In: Proceedings of the International Conference on Document Analysis and Recognition (ICDAR 2001), Seattle WA, pp. 1154–1159 (2001)
Xu, J., Lipton, R., Essa, I.: Hello, are you human. Technical Report GIT-CC-00-28, Georgia Institute of Technology, November 2000
von Ahn, L., Blum, M., Hopper, N.J., Langford, J.: The CAPTCHA (2000). http://www.captcha.net
von Ahn, L., Blum, M., Langford, J.: Telling humans and computers apart (Automatically) or how lazy cryptographers do AI. Commun. ACM (2002, to appear)
Simard, P., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: International Conference on Document Analysis and Recognition, pp. 958–962. IEEE Computer Society (2003)
Mori, G., Malik, J.: Recognizing objects in adversarial clutter: breaking a visual CAPTCHA. In: Conference on Computer Vision and Pattern Recognition (CVPR 2003), pp. 134–144. IEEE Computer Society (2003)
Kwon, S., Cha, S.: A paradigm shift for the CAPTCHA race: adding uncertainty to the process. IEEE Softw. 33(6), 80–85 (2016)
Elson, J., Douceur, J., Howell, J., Saul, J.: Asirra: a CAPTCHA that exploits interest-aligned manual image categorization. In: Proceedings of ACM CCS 2007, pp. 366–374 (2007)
Azakami, T., Shibata, C., Uda, R.: Challenge to impede deep learning against CAPTCHA with ergonomic design. In: IEEE 41st Annual Computer Software and Applications Conference, Italy (2017)
Golle, P., Wagner, D.: Cryptanalysis of a cognitive authentication scheme. In: Proceedings of the 2007 IEEE Symposium on Security and Privacy, pp. 66–70. IEEE Computer Society (2007)
Mori, G., Malik, J.: Recognizing objects in adversarial clutter: breaking a visual CAPTCHA. In: Proceedings of the 2003 Conference on Computer Vision and Pattern Recognition, pp. 134–144. IEEE Computer Society (2003)
SlashDot. Yahoo CAPTCHA Hacked. http://it.slashdot.org/it/08/01/30/0037254.shtml. Accessed 29 Jan 2008
Websense Blog: Google’s CAPTCHA busted in recent spammer tactics, 22 February 2008. http://securitylabs.websense.com/content/Blogs/2919.aspx
Yan, J., El Ahmad, A.: A low-cost attack on a Microsoft CAPTCHA. In: Proceedings of ACM CCS (2008, to appear)
Chow, R., Golle, P., Jakobsson, M., Wang, X., Wang, L.: Making CAPTCHAs clickable. In: Proceedings of HotMobile (2008)
Goodfellow, I.J., Bulatov, Y., Ibarz, J., Arnoud, S., Shet, V.: Multi-digit number recognition from street view imagery using deep convolutional neural networks. In: Proceedings of ICLR, April 2014
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. arXiv:1411.4555 [cs.CV], 20 April 2015
Golle, P.: Machine learning attacks against the Asirra CAPTCHA. In: CCS 2008, Virginia, USA, 27–31 October 2008
Althamary, I.A., El-Alfy, E.M.: A more secure scheme for CAPTCHA-based authentication in cloud environment. In: 8th International Conference on Information Technology (ICIT), Jordan, May 2017
Tang, M., Gao, H., Zhang, Y.: Research on deep learning techniques in breaking text-based CAPTCHAs and designing image-based CAPTCHA. IEEE Trans. Inf. Forensics Secur. 13(10), 2522–2537 (2018)
Acknowledgments
We would like to thank P.V. Ananda Mohan, Ashutosh Saxena for their valuable suggestions, insights and observations. We would also like to thank the anonymous reviewers whose comments helped improve this paper.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Singh, A., Tiwari, V., Tentu, A.N. (2018). A Machine Vision Attack Model on Image Based CAPTCHAs Challenge: Large Scale Evaluation. In: Chattopadhyay, A., Rebeiro, C., Yarom, Y. (eds) Security, Privacy, and Applied Cryptography Engineering. SPACE 2018. Lecture Notes in Computer Science(), vol 11348. Springer, Cham. https://doi.org/10.1007/978-3-030-05072-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-05072-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05071-9
Online ISBN: 978-3-030-05072-6
eBook Packages: Computer ScienceComputer Science (R0)