Visual Data Encryption for Privacy Enhancement in Surveillance Systems

  • Janusz Cichowski
  • Andrzej Czyżewski
  • Bożena Kostek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)


In this paper a methodology for employing reversible visual encryption of data is proposed. The developed algorithms are focused on privacy enhancement in distributed surveillance architectures. First, motivation of the study performed and a short review of preexisting methods of privacy enhancement are presented. The algorithmic background, system architecture along with a solution for anonymization of sensitive regions of interest are described. An analysis of efficiency of the developed encryption approach with respect to visual stream resolution and the number of protected objects is performed. Experimental procedures related to stream processing on a single core, single node and multiple nodes of the supercomputer platform are also provided. The obtained results are presented and discussed. Moreover, possible future improvements of the methodology are suggested.


privacy protection data security information security cryptography multicore processing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kim, K., Davis, L.S.: Object detection and tracking for intelligent video surveillance. In: Lin, W., Tao, D., Kacprzyk, J., Li, Z., Izquierdo, E., Wang, H. (eds.) Multimedia Analysis, Processing and Communications. SCI, vol. 346, pp. 265–288. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Czyżewski, A., Dalka, P.: Moving Object Detection and Tracking for the Purpose of Multimodal Surveillance System in Urban Areas. In: Tsihrintzis, G.A., Virvou, M., Howlett, R.J., Jain, L.C. (eds.) New Direct. in Intel. Interac. Multimedia, SCI, vol. 142, pp. 75–84. Springer, Heidelberg (2008)Google Scholar
  3. 3.
    Ellwart, D., Czyżewski, A.: Viewpoint independent shape-based object classification for video surveillance. In: International Workshop on Image Analysis for Multimedia Interactive Services, Delft, Netherlands (2011)Google Scholar
  4. 4.
    Viola, P., Jones, M.: Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  5. 5.
    Sheng, H., Wen, C., Li, Q., Xiong, Z.: Real-Time Anti-Interference Location of Vehicle License Plates Using High-Definition Video. IEEE Intelligent Transportation Systems Society 1(4), 17–23 (2009)CrossRefGoogle Scholar
  6. 6.
    Szczodrak, M., Kotus, J., Kopaczewski, K., Opatka, K., Czyżewski, A., Krawczyk, H.: Behavior Analysis and Dynamic Crowd Management in Video Surveillance System. In: International Workshop on Database and Expert Systems Applications, pp. 371–375 (2011)Google Scholar
  7. 7.
    Szwoch, G., Dalka, P., Czyżewski, A.: Objects classification based on their physical sizes for detection of events in camera images. In: Signal Processing: Algorithms, Architectures, Arrangements, and Applications. New Trends in Audio and Video, pp. 15–20 (2008)Google Scholar
  8. 8.
    Andrade, E.L., Blunsden, S., Fisher, R.B.: Hidden Markov models for optical flow analysis in crowds. In: International Conference on Pattern Recognition, pp. 460–463 (2006)Google Scholar
  9. 9.
    Krawczyk, H., Knopa, R., Proficz, J.: Basic management strategies on KASKADA platform. In: International Conference on Computer as a Tool, pp. 1–4 (2011)Google Scholar
  10. 10.
    Newton, E., Sweeney, L., Malin, B.: Preserving Privacy by De-identifying Facial Images. IEEE Transactions on Knowledge and Data Engineering 17(2), 232–243 (2005)CrossRefGoogle Scholar
  11. 11.
    Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P.N., Nayar, S.K.: Face Swapping: Automatically Replacing Faces in Photographs. ACM Transactions on Graphics, Proceedings of SIGGRAPH (2008)Google Scholar
  12. 12.
    Rodrigues, J.M., Puech, W., Bors, A.G.: Selective Encryption of Human Skin in JPEG Images. In: IEEE International Conference on Image Processing, pp. 1981–1984 (October 2006)Google Scholar
  13. 13.
    Korus, P., Szmuc, W., Dziech, A.: A scheme for censorship of sensitive image content with high-quality reconstruction ability. In: IEEE International Conference on Multimedia and Expo, pp. 1073–1078 (July 2010)Google Scholar
  14. 14.
    Bloom, J.A., Cox, I.J., Fridrich, J., Kalker, T., Miller, M.L.: Digital Watermarking and Steganography, Boston (2008)Google Scholar
  15. 15.
    Chattopadhyay, A., Boult, T.: PrivacyCam: A Privacy Preserving Camera Using uCLinux on the Blackfin DSP. In: IEEE Workshop on Embedded Vision Systems (2007)Google Scholar
  16. 16.
    Carrillo, P., Kalva, H., Magliveras, S.: Compression Independent Reversible Encryption in Video Surveillance. Journal on Information Security (December 2009)Google Scholar
  17. 17.
    Cichowski, J., Czyżewski, A.: Reversible Video Stream Anonymization for Video Surveillance Systems Based on Pixels Relocation and Watermarking. IEEE International Conference on Computer Vision, Workshop on Visual Surveillance, 1971–1977 (November 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Janusz Cichowski
    • 1
  • Andrzej Czyżewski
    • 1
  • Bożena Kostek
    • 2
  1. 1.Multimedia Systems DepartmentGdansk University of TechnologyPoland
  2. 2.Audio Acoustics LaboratoryGdansk University of TechnologyGdanskPoland

Personalised recommendations