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Understanding trust in privacy-aware video surveillance systems

  • Hatem A. Rashwan
  • Agusti Solanas
  • Domènec Puig
  • Antoni Martínez-Ballesté
Regular Contribution

Abstract

Recent advances in pervasive video surveillance systems pave the way for a comprehensive surveillance of every aspect of our lives, hence, leading us to a state of dataveillance. Computerized and interconnected systems of cameras could be used to profile, track and monitor individuals for the sake of security. Notwithstanding, these systems clearly interfere with the fundamental right of the individuals to privacy. Most literature on privacy in video surveillance systems concentrates on the goal of detecting faces and other regions of interest and in proposing different methods to protect them. However, the trustworthiness of those systems and, by extension, of the privacy they provide are mostly neglected. In this article, we define the concept of trustworthy privacy-aware video surveillance system. Moreover, we assess the techniques proposed in the literature according to their suitability for such a video surveillance system. Finally, we describe the properties that a deployment of a trustworthy video surveillance system must fulfill.

Keywords

Video surveillance Privacy Trust 

Notes

Acknowledgments

This work was partly funded by the Spanish Government through Project CONSOLIDER INGENIO 2010 CSD2007-0004 “ARES” and Project TIN2011-27076-C03-01 “CO-PRIVACY”, by the Government of Catalonia under Grant 2009 SGR 1135 and by Universitat Rovira i Virgili through Project 2012R2B-01 VIPP.

References

  1. 1.
    Clarke, R.: Introducing pits and pets: technologies affecting privacy. http://www.rogerclarke.com/DV/PITsPETs.html
  2. 2.
    United Nations: The universal declaration of human rights. http://www.un.org/en/documents/udhr/
  3. 3.
    Council of Europe: Convention for the protection of human rights and fundamental freedoms. http://conventions.coe.int/treaty/en/Treaties/Html/005.htm
  4. 4.
    The Trusted Computing Group: http://www.trustedcomputtinggroup.org
  5. 5.
    Senior, A. (ed.): Protecting Privacy in Video Surveillance. Springer, New York (2009)Google Scholar
  6. 6.
    Winkler, T., Rinner, B.: A systematic approach towards user-centric privacy and security for smart camera networks. In: Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras, pp. 133–141. ACM, New York, USA (2010)Google Scholar
  7. 7.
    Dufaux, F., Navarro, A., Ebrahimi, T.: Privacy enabling technology for video surveillance. In: Proceedings of SPIE—The International Society for Optics and Photonics, vol. 6250, 1 May 2006Google Scholar
  8. 8.
    Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20, 23–38 (1998)CrossRefGoogle Scholar
  9. 9.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
  10. 10.
    Hadid, A., Pietikainen, M., Ahonen, T.: A discriminative feature space for detecting and recognizing faces. In: Proceedings of Computer Vision and Pattern Recognition, vol. II, pp. 797–804. IEEE Computer Society (2004)Google Scholar
  11. 11.
  12. 12.
    Bouwmans, T., El Baf, F., Vachon, B.: Background modeling using mixture of Gaussians for foreground detection—a survey. Handbook of Pattern Recognition and Computer Vision 4(2), 181–199 (2010)Google Scholar
  13. 13.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of Computer Vision and Pattern Recognition, vol. II, pp. 246–252 (1999)Google Scholar
  14. 14.
    Elgammal, A.M., Harwood, D., Davis, L.S.: Nonparametric model for background subtraction. In: Proceedings of IEEE European Conference Computer Vision, pp. 751–767 (2000)Google Scholar
  15. 15.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.S.: Background modeling and subtraction by codebook construction. In: International Conference on Image Processing, vol. V, pp. 3061–3064 (2004)Google Scholar
  16. 16.
    Weickert, J., Bruhn, A., Brox, T., Papenberg, N.: A survey on variational optic flow methods for small displacements. Math. Stat. 10(I), 103–136 (2006)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Blacket, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. Int. J. Comput. Vis. 92(1), 1–31 (2011)CrossRefGoogle Scholar
  18. 18.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 1981 DARPA Image Understanding Workshop, pp. 121–130, April 1981Google Scholar
  19. 19.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow: a retrospective. Artif. Intell. 59(1–2), 81–87 (1993)CrossRefGoogle Scholar
  20. 20.
    Farnebäck, G.: Fast and accurate motion estimation using orientation tensors and parametric motion models. In: International Conference on Pattern Recognition, vol. I, pp. 135–139 (2000)Google Scholar
  21. 21.
    Bruhn, A., Weickert, J., Kohlberger, T., Schnorr, C.: A multigrid platform for real-time motion computation with discontinuity-preserving variational methods. Int. J. Comput. Vis. 70(3), 257–277 (2006)CrossRefGoogle Scholar
  22. 22.
    CAVIAR: Context aware vision using image-based active recognition. http://homepages.inf.ed.ac.uk/rbf/CAVIAR/
  23. 23.
    Chabrier, S., Emile, B., Rosenberger, C., Laurent, H.: Unsupervised performance evaluation of image segmentation. EURASIP J. Appl. Signal Process. (2006). http://asp.eurasipjournals.com/content/2006/1/096306
  24. 24.
    Berger, A.M.: Privacy Mode for Acquisition Cameras and Camcorders. Sony Corporation, US patent 6, 067, 399 edition (2000)Google Scholar
  25. 25.
    Newton, E.N., Sweeney, L., Main, B.: Preserving privacy by de-identifying face images. IEEE Trans. Knowl. Data Eng. 17(2), 232–243 (2005)CrossRefGoogle Scholar
  26. 26.
    Wickramasuri, J., Datt, M., Mehrotra, S., Venkatasubramanian, N.: Privacy protecting data collection in media spaces. In: Proceedings of the 12th Annual ACM International Conference on Multimedia, pp. 48–55, October 2004Google Scholar
  27. 27.
    Wactlar, H., Stevens, S., Ng, T.: Enabling personal privacy protection preferences in collaborative video observation. NSF Award. http://www.nsf.gov/awardsearch/showAward.do?awardNumber=0534625
  28. 28.
    Carrillo, P., Kalva, H., Magliveras, S.: Compression independent reversible encryption for privacy in video surveillance. EURASIP J. Inf. Secur.—Special issue on enhancing privacy protection in multimedia systems, no. 5 (2009)Google Scholar
  29. 29.
    Tansuriyavong, S., Hanaki, S.: Privacy protection by concealing persons in circumstantial video image. In: Proceedings of the Workshop on Perceptive User Interfaces, pp. 1–4 (2001)Google Scholar
  30. 30.
    Cavallaro, A.: Privacy in video surveillance. IEEE Signal Process. Mag. 24(2), 168–169 (2007)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Shahid, Z., Chaumnt, M., Puech, W.: Fast protection Of H.264/AVC by selective encryption of CAVLC And CABAC for I and P frames. IEEE Trans. Circuits Syst. Video Technol. 21(5), 565–576 (2011)CrossRefGoogle Scholar
  32. 32.
    Boult, T.E.: PICO: Privacy through invertible cryptographic obscuration. In: IEEE Workshop on Computer Vision for Interactive and Intelligent Environments, pp. 27–38 (2005)Google Scholar
  33. 33.
    Yabuta, K., Kitazawa, H., Tanaka, T.: A new concept of security camera monitoring with privacy protection by masking moving objects. In: Proceedings of International Conference on Pattern Recognition, pp. 404–407 (2005)Google Scholar
  34. 34.
    Martin, K., Plataniotis, K.N.: Privacy protected surveillance using secure visual object coding. IEEE Trans. Circuits Syst. Video Technol. 18(8), 1152–1162 (2008)CrossRefGoogle Scholar
  35. 35.
    Cheung, S.S., Paruchuri, J.K., Nguyen, T.P.: Managing privacy data in pervasive camera networks. In: Proceedings of IEEE International Conference on Image Processing, pp. 1676–1679, October 2008Google Scholar
  36. 36.
    Sohn, H., AnzaKu, E.T., Neve, W.D., Ro, Y.M., Plataniotis, K.N.: Privacy protection in video surveillance systems using scalable video coding. In: Proceedings of the Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 424–429 (2009)Google Scholar
  37. 37.
    Dufaux, F., Ebrahimi, T.: Scrambling for privacy protection in video surveillance systems. IEEE Trans. Circuits Syst. Video Technol. 18(8), 1168–1174 (2008)CrossRefGoogle Scholar
  38. 38.
    Martinez-Ponte, I., Desurmont, X., Meessen, J., Delaigle, J.: Robust human face hiding ensuring privacy. In: Proceedings of International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS). Genova, Italy (2005)Google Scholar
  39. 39.
    Fukuoka, N., et al.: Delivery method for viewer-specific privacy protected video using discrete wavelet transform. In: Proceedings of IEEE International Conference on Image Processing, pp. 2285–2288, October 2012Google Scholar
  40. 40.
    Group, T.C.: TPM main specification, v1.2, rev. 103. http://www.trustedcomputinggroup.org/resources/tpm_main_specification (2007)
  41. 41.
    Secure Real-time Transport Protocol: RFC 3177. https://tools.ietf.org/html/rfc3711
  42. 42.
    Trusted Timestamping: RFC 3161. https://tools.ietf.org/html/rfc3161

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Hatem A. Rashwan
    • 1
  • Agusti Solanas
    • 1
  • Domènec Puig
    • 1
  • Antoni Martínez-Ballesté
    • 1
  1. 1.Dept. Enginyeria Informàtica i MatemàtiquesUniversitat Rovira i VirgiliTarragonaSpain

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