Advertisement

Visual CAPTCHA for Data Understanding and Cognitive Management

  • Natalia Krzyworzeka
  • Lidia OgielaEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 12)

Abstract

In this paper a new algorithms for understanding processes and cognitive management will be presented. We propose the use of visual CAPTCHA protocols for data security and cognitive analysis dedicated to authentication procedures. The idea behind a new CAPTCHA generating method depicted in this article was to create a scheme that is difficult to break, by applying various forms of letters distortion and introducing different types of added noise. Scheme generating algorithm was designed with the knowledge of image processing methods such as detection and recognition techniques, commonly used by CAPTCHA breaking software. Cognitive features of the presented solution could be applied in the field of information understanding, semantic description and authentication procedures. CAPTCHA generating algorithm presented here was implemented in MATLAB.

Keywords

Visual captcha Security algorithms Data understanding Cognitive management Service management 

Notes

Acknowledgments

This work has been supported by the AGH University of Science and Technology research Grant No 11.11.200.307.

References

  1. 1.
    Chellapilla, K., Larson, K., Simard, P., Czerwinski, M.: Building segmentation based human friendly human interaction proofs (HIPs). In: The 2nd International Workshop on Human Interactive Proofs (2005)Google Scholar
  2. 2.
    Gregg, M., Schneier, B.: Security Practitioner and Cryptography Handbook and Study Guide Set. Wiley, Hoboken (2014)Google Scholar
  3. 3.
    Ogiela, L.: Cognitive computational intelligence in medical pattern semantic understanding. In: Guo, M.Z., Zhao, L., Wang, L.P. (eds.) ICNC 2008: Fourth International Conference on Natural Computation, vol. 6, Proceedings. Jian, Peoples R China, 18–20 October 2008, pp. 245–247 (2008)Google Scholar
  4. 4.
    Ogiela, L.: Towards cognitive economy. Soft. Comput. 18(9), 1675–1683 (2014)CrossRefGoogle Scholar
  5. 5.
    Ogiela, L., Ogiela, M.R.: Data mining and semantic inference in cognitive systems. In: Xhafa, F., Barolli, L., Palmieri, F., et al. (eds.) 2014 International Conference on Intelligent Networking and Collaborative Systems (IEEE INCoS 2014), Salerno, Italy, 10–12 September 2014, pp. 257–261 (2014)Google Scholar
  6. 6.
    Ogiela, L., Ogiela, M.R.: Management information systems. Lect. Notes Electr. Eng. 331, 449–456 (2015)CrossRefzbMATHGoogle Scholar
  7. 7.
    Ogiela, M.R., Ogiela, U.: Grammar encoding in DNA-Like secret sharing infrastructure. Lect. Notes Comput. Sci. 6059, 175–182 (2010)CrossRefzbMATHGoogle Scholar
  8. 8.
    Ogiela, M.R., Ogiela, U.: Secure Information Management Using Linguistic Threshold Approach. Springer, London (2014)CrossRefzbMATHGoogle Scholar
  9. 9.
    Ogiela, U., Takizawa, M., Ogiela, L.: Security of selected secret sharing schemes, in innovative mobile and internet services in ubiquitous computing. Advances in Intelligent Systems and Computing, vol. 612. Springer (2018). doi: 10.1007/978-3-319-61542-4_37
  10. 10.
    Roshanbin, N.: Interweaving unicode, color, and human interactions to enhance CAPTCHA security. Software Engineering and Intelligent Systems Department of Electrical and Computer Engineering, University of Alberta Narges Roshanbin (2014)Google Scholar
  11. 11.
    Yan, S.Y.: Computational Number Theory and Modern Cryptography. Wiley, Hoboken (2013)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

Personalised recommendations