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A Lightweight Secure IoT Surveillance Framework Based on DCT-DFRT Algorithms

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Machine Learning for Cyber Security (ML4CS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11806))

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Abstract

In this paper, we propose an energy-efficient surveillance framework for real-time video processing. The proposed framework guarantees the confidentiality of important frames and transfers them for a real-time decision. First, we extract the keyframes from the surveillance video using a lightweight summarization technique based on a fast histogram-clustering approach. Then, we employ an enhanced discrete cosine transform (DCT) compression technique to reduce the size of the extracted key frames. Finally, the cryptosystem encrypts these keyframes using a lightweight image encryption scheme based on discrete fractional random transform (DFRT) and Chen chaotic system. The proposed framework is fast and ensures real-time processing. Furthermore, this framework has the ability to reduce the transmission cost, and storage required during transmitting the video surveillance.

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Authors and Affiliations

  1. School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China

    Rafik Hamza, Alzubair Hassan & Akash Suresh Patil

Authors
  1. Rafik Hamza
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  2. Alzubair Hassan
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  3. Akash Suresh Patil
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Corresponding author

Correspondence to Rafik Hamza .

Editor information

Editors and Affiliations

  1. Xidian University, Xi’an, China

    Xiaofeng Chen

  2. Fujian Normal University, Fuzhou, China

    Xinyi Huang

  3. Swinburne University of Technology, Hawthorn, VIC, Australia

    Jun Zhang

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Hamza, R., Hassan, A., Patil, A.S. (2019). A Lightweight Secure IoT Surveillance Framework Based on DCT-DFRT Algorithms. In: Chen, X., Huang, X., Zhang, J. (eds) Machine Learning for Cyber Security. ML4CS 2019. Lecture Notes in Computer Science(), vol 11806. Springer, Cham. https://doi.org/10.1007/978-3-030-30619-9_19

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  • DOI: https://doi.org/10.1007/978-3-030-30619-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30618-2

  • Online ISBN: 978-3-030-30619-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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