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Moving object detection in the encrypted domain

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The privacy-preserving moving object detection has drawn a lot of interest lately. Nevertheless, current approaches use Paillier’s scheme for encryption that impractical in real-time applications due to high computational complexity. In addition, none of them are fully compatible with popular background modeling methods. In this paper, a fast and secure encryption scheme for a surveillance system has been proposed. The algorithm allows the detection of a moving object to be implemented directly in the encryption domain. The proposed scheme separates every pixel into two parts. The first part of a pixel (most significant bits) is scrambled to encrypt the image, and the second part of the pixel (least significant bits) remains unchanged. This strategy allows the proposed encryption scheme to be compatible with the mixture of Gaussians (GMM) that is one of the most widely used background modeling methods to detect moving objects. The proposed scheme requires low computations and produces almost the same detection result as the GMM when it is applied to unencrypted videos. Security analysis of the proposed method also proves the robustness of the encryption process.

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  1. Barnich O, Van Droogenbroeck M (2011) ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724

    Article  MathSciNet  Google Scholar 

  2. Bestagini P, Milani S, Tagliasacchi M and Tubaro S (2013) “Local tampering detection in video sequences,” Proceedings of IEEE 15th International Workshop on Multimedia Signal Processing (MMSP), Pula, pp. 488–493

  3. Bitouk D, Kumar N, Dhillon S, Belhumeur P, Nayar SK (2008) Face swapping: automatically replacing faces in photographs. ACM Trans Graph 27(3):1–8

    Article  Google Scholar 

  4. Bouwmans T (2011) Recent advanced statistical background modeling for foreground detection-a systematic survey. Rec Pat Comput Scie 4(3):147–176

    Google Scholar 

  5. Bouwmans T (2014) “Traditional and recent approaches in background modeling for foreground detection: An overview,” Computer Science Review, vol. 11, pp. 31–66

  6. Bruckner D, Picus C, Velik R, Herzner W, Zucker G (2012) Hierarchical semantic processing architecture for smart sensors in surveillance networks. IEEE Trans Ind Inf 8(2):291–301

    Article  Google Scholar 

  7. Celik MU, Lemma AN, Katzenbeisser S, van der Veen M (2008) Lookup-table-based secure client-side embedding for spread-spectrum watermarks. IEEE Trans Inf Forensics Secur 3(3):475–487

    Article  Google Scholar 

  8. Chu KY, Kuo YH and Hsu WH (2013) “Real-time privacy-preserving moving object detection in the cloud,” Proceedings of 21st ACM International Conference on Multimedia, New York, USA, pp. 597–600

  9. Dufaux F, Ebrahimi T (2008) Scrambling for privacy protection in video surveillance systems. IEEE Trans Circuits Syst Video Technol 18(8):1168–1174

    Article  Google Scholar 

  10. Erkin Z, Franz M, Guajardo J, Katzenbeisser S, Lagendijk I, Toft T (2009) Privacy-preserving face recognition proceedings of the 9th international symposium on privacy enhancing technologies. Heidelberg, Berlin, pp 235–253

    Google Scholar 

  11. EC Funded (2001) CAVIAR project/IST

  12. Gil MJM, Malenstein J (2007) “Innovative technology for monitoring traffic, vehicles and drivers,” 6th Framework Programme, European Commission, Technical Report

  13. Heidstra J, Goldenbeld C, Mäkinen T, Nilsson G, and Sagberg F (2000) “New concepts in automatic enforcement,” 4th Framework Programme, European Commission, Technical Report

  14. Honghai L, Shengyong C, Kubota N (2013) Intelligent video systems and analytics: asurvey. IEEE Trans Ind Inf 9(3):1222–1233

    Article  Google Scholar 

  15. Jung CR (2009) Efficient background subtraction and shadow removal for monochromatic video sequences. IEEE Trans Multimedia 11(3):571–577

    Article  Google Scholar 

  16. KaewTraKulPong P and Bowden R (2001) “An improved adaptive background mixture model for realtime tracking with shadow detection,” Proceedings of the 2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01, pp. 135–144

  17. Kafai M, Bhanu B (2012) Dynamic Bayesian networks for vehicle classification in video. IEEE Trans Ind Inf 8(1):100–109

    Article  Google Scholar 

  18. Lian F-L, Lin Y-C, Kuo C-T, Jean J-H (2012) Voting-based motion estimation for real-time video transmission in networked mobile camera systems. IEEE Trans Ind Inf 9(1):172–180

    Article  Google Scholar 

  19. Maddalena L, Petrosino A (2008) A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans Image Process 17(7):1168–1177

    Article  MathSciNet  Google Scholar 

  20. Martin K, Plataniotis KN (2008) Privacy protected surveillance using secure visual object coding. IEEE Trans Circuits Syst Video Technol 18(8):1152–1162

    Article  Google Scholar 

  21. Mukherjee D, Wu Q, Nguyen T (2013) Gaussian mixture model with advanced distance measure based on support weights and histogram of gradients for background suppression. IEEE Trans Ind Inf 10(2):1086–1096

    Article  Google Scholar 

  22. Newton EM, Sweeney L, Malin B (2005) Preserving privacy by de-identifying face images. IEEE Trans Knowl Data Eng 17(2):232–243

    Article  Google Scholar 

  23. Osadchy M, Pinkas B, Jarrous A and Moskovich B (2010) “SCiFI - A system for secure face identification,” Proceedings of IEEE Symposium on Security and Privacy, pp. 239–254

  24. Paillier P (1999) “Public-key cryptosystems based on composite degree residuosity classes,” Proceedings of EUROCRYPT on Advances in Cryptology, pp. 223–238

  25. Ribnick E, Atev S, Masoud O, Papanikolopoulos N and Voyles R (2006) “Real-time detection of camera tampering,” Proceedings of IEEE International Conference on Video and Signal Based Surveillance, Sydney, Australia, pp. 10–15

  26. Sadeghi AR, Schneider T and Wehrenberg I (2010) “Efficient privacy-preserving face recognition,” Proceedings of International Conference on Information, Security and Cryptology, pp. 229–244

  27. Sagberg F (2000) “Automatic enforcement technologies and system,” 4th Framework Programme, European Commission, Technical Report

  28. Senior A (2009) Protecting privacy in video surveillance. Springer, New York

    Book  Google Scholar 

  29. Senior A, Pankanti S, Hampapur A, Brown L, Ying-Li T, Ekin A, Connell J, Chiao-Fe S, Max L (2005) Enabling video privacy through computer vision. IEEE Secur Priv 3(3):50–57

    Article  Google Scholar 

  30. Shannon C (1949) Communication theory of secrecy systems. Bell Syst Tech J 28(4):656–715

    Article  MathSciNet  MATH  Google Scholar 

  31. Shengyong C, Jianhua Z, Youfu L, Jianwei Z (2012) A hierarchical model incorporating segmented regions and pixel descriptors for video background subtraction. IEEE Trans Ind Inf 8(1):118–127

    Article  Google Scholar 

  32. Shukla R, Prakash HO, Bhushan RP, Bhushan RP and Varadan G (2013) “Sampurna Suraksha: unconditionally secure and authenticated one time pad cryptosystem,” Proceddings of Machine Intelligence and Research Advancement, Katra, pp. 174–178

  33. Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757

    Article  Google Scholar 

  34. Tao D, Jin L, Wang Y, Li X (2013) Rank preserving discriminant analysis for human behavior recognition on wireless sensor networks. IEEE Trans Ind Inf 10(1):813–823

    Article  Google Scholar 

  35. Tianzhu Z, Si L, Changsheng X, Hanqing L (2013) Mining semantic context information for intelligent video surveillance of traffic scenes. IEEE Trans Ind Inf 9(1):149–160

    Article  Google Scholar 

  36. Tran C, Trivedi MM (2011) 3-D posture and gesture recognition for interactivity in smart spaces. IEEE Trans Ind Inf 8(1):178–187

    Article  Google Scholar 

  37. Zeng YC, Hsu CY, Luo YF, Chou HY and Liao HYM (2010) “Object detection in encryption-based surveillance system,” Proceedings of APSIPA Annual Summit and Conference, pp. 89–94

  38. Zhou X, Li Y, He B, Bai T (2013) GM-PHD-based multi-target visual tracking using entropy distribution and game theory. IEEE Trans Ind Inf 10(2):1064–1076

    Article  Google Scholar 

  39. Zivkovic Z (2004) Improved adaptive gaussian mixture model for background subtraction. Proc 17th Int Conf Patt Recog 2:28–31

    Article  Google Scholar 

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This work was supported by National Science Council, Taiwan, under Grants MOST 103-2221-E-468-007-MY2 and NSC 102-2221-E-110-032-MY3.

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Correspondence to Chia-Hung Yeh.

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Lin, CY., Muchtar, K., Lin, JY. et al. Moving object detection in the encrypted domain. Multimed Tools Appl 76, 9759–9783 (2017).

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  • Moving object detection
  • Video encryption
  • Mixture of Gaussians
  • Background modeling