Abstract
Recently, storage and processing large-scale visual media data are being outsourced to Cloud Data Centres (CDCs). However, the CDCs are always third party entities. Thus the privacy of the users’ visual media data may be leaked to the public or unauthorized parties. In this paper we propose a method of privacy preserving foreground extraction of video surveillance through chaotic mapping based encryption in the cloud. The client captures surveillance videos, which are then encrypted by our proposed chaotic mapping based encryption method. The encrypted surveillance videos are transmitted to the cloud server, in which the foreground extraction algorithm is running on the encrypted videos. The results are transmitted back to the client, in which the extraction results are decrypted to get the extraction results in plain videos. The extraction correctness in the encryption videos is similar as that in the plain videos. The proposed method has several advantages: (1) The server only learns the obfuscated extraction results and can not recognize anything from the results. (2) Based on our encryption method, the original extraction method in the plain videos need not be changed. (3) The chaotic mapping ensure high level security and the ability to resistant several attacks.
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References
Upmanyu, M., Namboodiri, A.M., Srinathan, K., Jawahar, C.V.: Efficient privacy preserving video surveillance. In: IEEE 12th International Conference on Computer Vision (ICCV), pp. 1639–1646 (2009)
Chu, K.Y., Kuo, Y.H., Hsu, W.H.: Real-time privacy-preserving moving object detection in the cloud. In: Proceedings of the 21st ACM International Conference on Multimedia (MM 2013), pp. 597–600. ACM, New York (2013). http://dx.doi.org/10.1145/2502081.2502157
Jin, X., Liu, Y., Li, X.D., Zhao, G., Chen, Y.Y., Guo, K.: Privacy preserving face identification through sparse representation. To Appear in the 10th Chinese Conference on Biometric Recognition (CCBR), Tianjin, China, 13–15 November 2015
Huang, C., Nien, H.: Multi chaotic systems based pixel shuffle for image encryption. Opt. Commun. 282, 2123–2127 (2009)
Lian, S., Sun, J., Wang, Z.: A block cipher based on a suitable use of the chaotic standard map. Chaos Soliton Fract. 26(1), 117–129 (2005)
Claude, S.: Communication theory of secrecy systems. Bell Syst. Tech. J. 28(4), 656–715 (1949)
Zhen, P., Zhao, G., Min, L.Q., Jin, X.: Chaos-based image encryption scheme combining dna coding and entropy. Multimedia Tools and Applications (MTA), Published Online: 10 April 2015
Jin, X., Chen, Y., Ge, S., Zhang, K., Li, X., Li, Y., Liu, Y., Guo, K., Tian, Y., Zhao, G., Zhang, X., Wang, Z.: Color image encryption in CIE L*a*b* space. In: Niu, W., Li, G., Liu, J., Tan, J., Guo, L., Han, Z., Batten, L. (eds.) ATIS 2015. CCIS, vol. 557, pp. 74–85. Springer, Heidelberg (2015). doi:10.1007/978-3-662-48683-2_8
Jin, X., Tian, Y.L., Song C.G., Wei, G.Z., Li, X.D., et al.: An invertible and anti-chosen plaintext attack image encryption method based on DNA encoding and chaotic mapping. To Appear in the Chinese Automation Congress, Wuhan, 27–29 November 2015
Li, Y.Z., Li, X.D., Jin, X., Zhao, G., Ge, S.M., et al.: An image encryption algorithm based on zigzag transformation and 3-dimension chaotic logistic map. In: Niu, W., Li, G., Liu, J., Tan, J., Guo, L., Han, Z., Batten, L. (eds.) ATIS 2015. CCIS, vol. 557, pp. 3–13. Springer, Heidelberg (2015). doi:10.1007/978-3-662-48683-2_1
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: International Conference on Computer Vision and Pattern Recognition, vol. 2, p. 2246 (1999)
Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: CDnet 2014: an expanded change detection benchmark dataset. In: Proceedings IEEE Workshop on Change Detection (CDW-2014) at CVPR-2014, pp. 387–394 (2014)
Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (No. 61402021, No. 61402023, No. 61170037), the Fundamental Research Funds for the Central Universities (No. 2014XSYJ01, No. 2015XSYJ25), the Science and Technology Project of the State Archives Administrator. (No. 2015-B-10), and the Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (No. CAAC-ITRB-201403).
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Jin, X. et al. (2016). Private Video Foreground Extraction Through Chaotic Mapping Based Encryption in the Cloud. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_47
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DOI: https://doi.org/10.1007/978-3-319-27671-7_47
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