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Blind background extraction from videos in the cloud

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Abstract

Nowadays, more and more people choose to upload their data and information to cloud. The cloud saves users’ data and information and starts providing computing services and artificial intelligence analysis. However, the privacy of users’ information and data will be completely exposed to the cloud without any protection. In this paper, we propose two methods of the blind (privacy-preserving) background extraction from video surveillance for both scenarios with the single-party cloud server (SCS) and multi-party cloud servers (MCS). We combine 1D logistics chaotic encryption with background subtraction based on a mixed Gaussian model, and propose a blind background subtraction method for a single server (SCS) . We combine the Chinese Remainder Theorem (CRT) and the ViBE background subtraction method and propose a multi-server blind background subtraction method (MCS). The test set for the experiment is CDW-2014, the experimental results show that our method has satisfactory results in recognition accuracy, recognition speed, and security analysis.

The proposed methods have several advantages: (1) Based on our encryption method, the background extraction method in the original video does not need to be changed; (2) The server does not recognize any valid information for the calculation results; (3) Single cloud server (SCS) uses the chaotic mapping can ensure high-level security and resistance to several attacks; (4) Multiple cloud servers (MCS) can improve data security and improve processing efficiency. This method can accurately extract the background like the original ViBE algorithm while protecting the privacy of client video data.

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Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (grant numbers 61701008, 61772047), the Open Project Program of State Key Laboratory of Cryptology (grant number MMKFKT201804), the Beijing Natural Science Foundation (grant number 19L2040), the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (grant number VRLAB2019C03) and the Fundamental Research Funds for the Central Universities (grant number 328201907).

Parts of this paper have previously appeared in our previous work [5, 6]. This is the extended journal version of the conference papers. The main differences between this journal version and the conference version are: We have expanded our previous work in more details and experimental results. We performed complete efficiency tests on single-party cloud server (SCS) method and multi-party cloud servers (MCS) method, including two methods for testing the processing speed of single-frame images at different resolutions, two methods for testing the accuracy of the complete segment video, and more comprehensive and reliable security analysis.

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Authors and Affiliations

  1. Department of Cyber Security, Beijing Electronic Science and Technology Institute, Beijing, 100070, China

    Xin Jin, Hongyu Zhang, Xiaodong Li & Hongbo Sun

  2. State Key Laboratory of Cryptology, P.O. Box 5159, Beijing, 100878, China

    Xin Jin

  3. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Haoyang Yu

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  1. Xin Jin
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  2. Haoyang Yu
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  3. Hongyu Zhang
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  4. Xiaodong Li
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  5. Hongbo Sun
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Correspondence to Xiaodong Li.

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Jin, X., Yu, H., Zhang, H. et al. Blind background extraction from videos in the cloud. Multimed Tools Appl 79, 28755–28771 (2020). https://doi.org/10.1007/s11042-020-09386-4

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  • DOI: https://doi.org/10.1007/s11042-020-09386-4

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