Multimedia Systems

, Volume 22, Issue 5, pp 617–639 | Cite as

Building a post-compression region-of-interest encryption framework for existing video surveillance systems

Challenges, obstacles and practical concerns
  • Andreas Unterweger
  • Kevin Van Ryckegem
  • Dominik Engel
  • Andreas Uhl
Regular Paper


We propose an encryption framework design and implementation which add region-of-interest encryption functionality to existing video surveillance systems with minimal integration and deployment effort. Apart from region-of-interest detection, all operations take place at bit-stream level and require no re-compression whatsoever. This allows for very fast encryption and decryption speed at negligible space overhead. Furthermore, we provide both objective and subjective security evaluations of our proposed encryption framework. Furthermore, we address design- and implementation-related challenges and practical concerns. These include modularity, parallelization and, most notably, the performance of state-of-the-art face detectors. We find that their performance, despite their frequent use in surveillance systems, is not insufficient for practical purposes, both in terms of speed and detection accuracy.


Face detection Encryption JPEG Region of interest Parallelization Subjective evaluation 

Mathematics Subject Classification

68W10 68W27 68W40 94A08 94A60 94A62 



The authors would like to thank Stefan Auer and Alexander Bliem for their initial involvement in the region-of-interest encryption implementation and their ideas for DC correction. In addition, the authors would like to thank Heinz Hofbauer for his valuable suggestions regarding the face detection performance assessment and the image metrics used for security evaluation. Furthermore, the authors thank all the volunteering participants of their face encryption survey. Moreover, the authors thank KeyLemon for providing higher data limits per time unit for batch face detection. Portions of the research in this paper use the FERET database of facial images collected under the FERET program, sponsored by the DOD Counterdrug Technology Development Program Office. This work is supported by FFG Bridge project 832082.


  1. 1.
    Auer, S., Bliem, A., Engel, D., Uhl, A., Unterweger, A.: Bitstream-based JPEG encryption in real-time. Int. J. Digit Crime Forensics 5(3), 1–14 (2013)CrossRefGoogle Scholar
  2. 2.
    Bergeron, C., Lamy-Bergor, C.: Compliant selective encryption for H.264/AVC video streams. In: Proceedings of the IEEE workshop on multimedia signal processing, MMSP’05, pp. 1–4 (2005). doi: 10.1109/MMSP.2005.248641
  3. 3.
    Boult, T.E.: PICO: privacy through invertible cryptographic obscuration. In: IEEE/NFS Workshop on computer vision for interactive and intelligent environments, pp. 27–38. Lexington, KY, USA (2005)Google Scholar
  4. 4.
    Carrillo, P., Kalva, H., Magliveras, S.: Compression independent reversible encryption for privacy in video surveillance. EURASIP J. Inf. Secur. 2009, 1–13 (2009)CrossRefGoogle Scholar
  5. 5.
    Chattopadhyay, A., Boult, T.: PrivacyCam: a privacy preserving camera using uclinux on the blackfin DSP. In: IEEE conference on computer vision and pattern recognition 2007 (CVPR’07), pp. 1–8. Minneapolis, MN, USA (2007)Google Scholar
  6. 6.
    Cheung, S.S., Paruchuri, J.K., Nguyen, T.P.: Managing privacy data in pervasive camera networks. In: IEEE International conference on image processing 2008 (ICIP’08), pp. 1676–1679. San Diego, CA, USA (2008)Google Scholar
  7. 7.
    Choi, S., Han, J.W., Cho, H.: Privacy-preserving H.264 video encryption scheme. ETRI J. 33(6), 935–944 (2011)CrossRefGoogle Scholar
  8. 8.
    Dufaux, F., Ebrahimi, T.: Scrambling for Anonymous visual communications. In: Proceedings of SPIE, applications of digital image processing XXVIII, vol. 5909. SPIE (2005)Google Scholar
  9. 9.
    Dufaux, F., Ebrahimi, T.: Scrambling for privacy protection in video surveillance systems. IEEE Trans. Circuits Syst. Video Technol. 18(8), 1168–1174 (2008). doi: 10.1109/TCSVT.2008.928225 CrossRefGoogle Scholar
  10. 10.
    Dufaux, F., Ebrahimi, T.: A framework for the validation of privacy protection solutions in video surveillance. In: Proceedings of the IEEE international conference on multimedia and Expo. ICME ’10, pp. 66–71. IEEE, Singapore (2010)Google Scholar
  11. 11.
    Dufaux, F., Ouaret, M., Abdeljaoued, Y., Navarro, A., Vergnenegre, F., Ebrahimi, T.: Privacy enabling technology for video surveillance. In: SPIE mobile multimedia/image processing for military and security applications, Lecture Notes in Computer Science. IEEE (2006)Google Scholar
  12. 12.
    Engel, D., Uhl, A., Unterweger, A.: Region of Interest signalling for encrypted JPEG images. In: IH&MMSec’13: Proceedings of the 1st ACM Workshop on information hiding and multimedia security, pp. 165–174. ACM (2013)Google Scholar
  13. 13.
    Hofbauer, H., Uhl, A.: An effective and efficient visual quality index based on local edge gradients. In: IEEE 3rd European workshop on visual information processing, p. 6, Paris, France (2011)Google Scholar
  14. 14.
    Iqbal, R., Shahabuddin, S., Shirmohammadi, S.: Compressed-domain spatial adaptation resilient perceptual encryption of live H.264 video. In: 2010 10th international conference on information sciences signal processing and their applications (ISSPA), pp. 472–475 (2010)Google Scholar
  15. 15.
    Itu-T, H. 264: Advanced video coding for generic audiovisual services (2007).
  16. 16.
    Jain, V., Learned-Miller, E.: Fddb: a benchmark for face detection in unconstrained settings. Tech. Rep. UM-CS-2010-009, University of Massachusetts, Amherst (2010).
  17. 17.
    Kerr, D.A.: Chrominance subsampling in digital images (2012).
  18. 18.
    Khan, M., Jeoti, V., Malik, A.: Perceptual encryption of JPEG compressed images using DCT coefficients and splitting of DC coefficients into bitplanes. In: 2010 International conference on intelligent and advanced systems (ICIAS), pp. 1–6 (2010)Google Scholar
  19. 19.
    Kim, Y., Yin, S., Bae, T., Ro, Y.: A selective video encryption for the region of interest in scalable video coding. In: Proceedings of the TENCON 2007-IEEE region 10 conference, pp. 1–4. Taipei, Taiwan (2007)Google Scholar
  20. 20.
    Kwon, S.G., Choi, W.I., Jeon, B.: Digital video scrambling using motion vector and slice relocation. In: Proceedings of second international conference of image analysis and recognition. ICIAR’05, Lecture Notes in Computer Science, vol. 3656, pp. 207–214. Springer, Toronto, Canada (2005)Google Scholar
  21. 21.
    Lian, S., Sun, J., Wang, Z.: A novel image encryption scheme based-on jpeg encoding. In: Proceedings of the eighth international conference on information visualisation 2004 (IV 2004), pp. 217–220 (2004)Google Scholar
  22. 22.
    Lian, S., Sun, J., Zhang, D., Wang, Z.: A selective image encryption scheme based on JPEG2000 codec. In: Aizawa, Y.N.K., Satoh, S. (eds.) Proceedings of the 5th Pacific Rim conference on multimedia. Lecture Notes in Computer Science, vol. 3332, pp. 65–72. Springer, Berlin (2004)Google Scholar
  23. 23.
    Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: 2002 international conference on image processing vol. 1, pp. I-900–I-903 (2002)Google Scholar
  24. 24.
    Martinez-Ponte, I., Desurmont, X., Meessen, J., Delaigle, J.F.: Robust human face hiding ensuring privacy. In: Proceedings of the 6th international workshop on image analysis for multimedia interactive services (WIAMIS’05) (2005)Google Scholar
  25. 25.
    Melle, A., Dugelay, J.L.: Scrambling faces for privacy protection using background self-similarities. In: 21st IEEE international conference on image processing (ICIP 2014). IEEE, Paris, France (2014)Google Scholar
  26. 26.
    Newton, E., Sweeney, L., Malin, B.: Preserving privacy by de-identifying face images. IEEE Trans. Knowl. Data Eng. 17(2), 232–243 (2005)CrossRefGoogle Scholar
  27. 27.
    Niu, X., Zhou, C., Ding, J., Yang, B.: JPEG encryption with file size preservation. In: International conference on intelligent information hiding and multimedia signal processing 2008 (IIHMSP ’08), pp. 308–311 (2008)Google Scholar
  28. 28.
    Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  29. 29.
    Phillips, P., Wechsler, H., Huang, J., Rauss, P.J.: The FERET database and evaluation procedure for face-recognition algorithms. ImageVis. Comput. 16(5), 295–306 (1998)Google Scholar
  30. 30.
    Puech, W., Rodrigues, J.M.: Crypto-compression of medical images by selective encryption of DCT. In: European signal processing conference 2005 (EUSIPCO’05) (2005)Google Scholar
  31. 31.
    Puech, W., Rodrigues, J.M.: Analysis and cryptanalysis of a selective encryption method for JPEG images. In: WIAMIS ’07: Proceedings of the eight international workshop on image analysis for multimedia interactive services. IEEE Computer Society, Washington, DC, USA (2007). doi: 10.1109/WIAMIS.2007.21
  32. 32.
    Puech, W., Bors, A., Rodrigues, J.: Protection of colour images by selective encryption. In: Fernandez-Maloigne, C. (ed.) Advanced Color Image Processing and Analysis, pp. 397–421. Springer, New York (2013)CrossRefGoogle Scholar
  33. 33.
    Rahman, S.M.M., Hossain, M.A., Mouftah, H., Saddik, A.E., Okamoto, E.: A real-time privacy-sensitive data hiding approach based on chaos cryptography. In: Proceedings of IEEE international conference on multimedia and Expo, pp. 72–77. Suntec City, Singapore (2010)Google Scholar
  34. 34.
    Rajpoot, Q.M., Jensen, C.D.: Security and privacy in video surveillance: requirements and challenges. In: Cuppens-Boulahia, N., Cuppens, F., Jajodia, S., Abou El Kalam, A., Sans, T. (ed.) ICT Systems security and privacy protection, IFIP Advances in information and communication technology, vol. 428, pp. 169–184. Springer, Berlin Heidelberg (2014)Google Scholar
  35. 35.
    Santana, M.C., Déniz-Suárez, O., Hernández-Sosa, D., Lorenzo, J.: A comparison of face and facial feature detectors based on the viola-jones general object detection framework. Mach. Vis. Appl. 22(3), 481–494 (2011)Google Scholar
  36. 36.
    Senior, A., Pankanti, S., Hampapur, A., Brown, L., Tian, Y.L., Ekin, A., Connell, J., Shu, C.F., Lu, M.: Enabling video privacy through computer vision. IEEE Secur. Priv. 3(3), 50–57 (2005)CrossRefGoogle Scholar
  37. 37.
    Seshadrinathan, K., Soundararajan, R., Bovik, A., Cormack, L.: Study of subjective and objective quality assessment of video. IEEE Trans. Image Proces. 19(6), 1427–1441 (2010)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Sohn, H., Anzaku, E., Neve, W.D., Ro, Y.M., Plataniotis, K.: 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. Genova, Italy (2009)Google Scholar
  39. 39.
    Sohn, H., Lee, D., De Neve, W., Plataniotis, K.N., Ro, Y.M.: Objective and subjective evaluation of content-based privacy protection of face images in video surveillance systems using JPEG XR. In: Flammini, F., Setola, R., Franceschetti, G. (eds.) Effective Surveillance for Homeland Security: Balancing Technology and Social Issues, pp. 111–140. CRC Press, Boca Raton (2013)Google Scholar
  40. 40.
    Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Proceedings of the 2013 IEEE conference on computer vision and pattern recognition. CVPR ’13, pp. 3476–3483. IEEE Computer Society, Washington, DC, USA (2013)Google Scholar
  41. 41.
    Tang, L.: Methods for encrypting and decrypting MPEG video data efficiently. In: Proceedings of the ACM multimedia 1996, pp. 219–229. Boston, USA (1996)Google Scholar
  42. 42.
    Tong, L., Dai, F., Zhang, Y., Li, J.: Restricted H.264/AVC video coding for privacy region scrambling. In: 2010 17th IEEE international conference on image processing (ICIP), pp. 2089–2092 (2010)Google Scholar
  43. 43.
    Unterweger, A., Uhl, A.: Length-preserving bit-stream-based JPEG Encryption. In: MM&Sec’12: Proceedings of the 14th ACM multimedia and security workshop, pp. 85–89. ACM (2012)Google Scholar
  44. 44.
    Unterweger, A., Uhl, A.: Slice groups for post-compression region of interest encryption in H.264/AVC and its scalable extension. Signal processing: image communication (2014). AcceptedGoogle Scholar
  45. 45.
    Viola, P., Jones, M.: Robust real-time object detection. Int. J. Comput. Vis. 57, 137–154 (2001)CrossRefGoogle Scholar
  46. 46.
    Wen, J., Severa, M., Zeng, W., Luttrell, M., Jin, W.: A format-compliant configurable encryption framework for access control of video. IEEE Trans. Circuits Syst. Video Technol. 12(6), 545–557 (2002)CrossRefGoogle Scholar
  47. 47.
    Wu, C.P., Kuo, C.C.J.: Fast encryption methods for audiovisual data confidentiality. SPIE Photonics East—symposium on voice. Video, and data communications, vol. 4209, pp. 284–295. MA, USA, Boston (2000)Google Scholar
  48. 48.
    Yang, B., Zhou, C.Q., Busch, C., Niu, X.M.: Transparent and perceptually enhanced JPEG image encryption. In: 16th international conference on digital signal processing, pp. 1–6 (2009)Google Scholar
  49. 49.
    Ye, Y., Zhengquan, X., Wei, L.: A Compressed video encryption approach based on spatial shuffling. In: 8th International conference on signal processing, vol. 4, pp. 16–20 (2006)Google Scholar
  50. 50.
    Zeng, W., Lei, S.: Efficient frequency domain selective scrambling of digital video. IEEE Trans. Multimed. 5(1), 118–129 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Andreas Unterweger
    • 1
  • Kevin Van Ryckegem
    • 2
  • Dominik Engel
    • 2
  • Andreas Uhl
    • 1
  1. 1.Department of Computer SciencesUniversity of SalzburgSalzburgAustria
  2. 2.Josef Ressel Center for User-Centric Smart Grid Privacy, Security, and ControlSalzburg University of Applied SciencesSalzburgAustria

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