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An Advanced Framework for Critical Infrastructure Protection Using Computer Vision Technologies

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Cyber-Physical Security for Critical Infrastructures Protection (CPS4CIP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12618))

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Abstract

Over the past decade, there has been unprecedented advancements in the field of computer vision by adopting AI-based solutions. In particular, cutting edge computer vision technology based on deep-learning approaches has been deployed with an extraordinary degree of success. The ability to extract semantic concepts from continuous processing of video stream in real-time has led to the investigation of such solutions to enhance the operational security of critical infrastructure against intruders. Despite the success of computer vision technologies validated in a laboratory environment, there still exists several challenges that limit the deployment of these solutions in operational environment. Addressing these challenges, the paper presents a framework that integrates three main computer vision technologies namely (i) person detection; (ii) person re-identification and (iii) face recognition to enhance the operational security of critical infrastructure perimeter. The novelty of the proposed framework relies on the integration of key technical innovations that satisfies the operational requirements of critical infrastructure in using computer vision technologies. One such requirement relates to data privacy and citizen rights, following the implementation of General Data Protection Regulation across Europe for the successful adoption of video surveillance for infrastructure security. The video analytics solution proposed in the paper integrates privacy preserving technologies, high-level rule engine for threat identification and a knowledge model for escalating threat categorises to human operator. The various components of the proposed framework has been validated using commercially available graphical processing units for detecting intruders. The performance o the proposed framework has been evaluated in operational environments of the critical infrastructure. An overall accuracy of 97% is observed in generating alerts against malicious intruders.

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Notes

  1. 1.

    https://defender-project.eu/.

  2. 2.

    https://www.aescrypt.com/.

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Acknowledgement

The research activities leading to this publication has been partly funded by the European Union Horizon 2020 Research and Innovation program under MAGNETO RIA project (grant agreement No. 786629) and DEFENDER IA project (grant agreement No. 740898).

Author information

Authors and Affiliations

  1. Venaka Media Limited, 393, Roman Road, London, E3 5QS, UK

    Krishna Chandramouli

  2. Multimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK

    Ebroul Izquierdo

Authors
  1. Krishna Chandramouli
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  2. Ebroul Izquierdo
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Corresponding author

Correspondence to Krishna Chandramouli .

Editor information

Editors and Affiliations

  1. Norwegian Computing Center, Oslo, Norway

    Habtamu Abie

  2. University of Trento and Fondazione Bruno Kessler, Trento, Italy

    Silvio Ranise

  3. Università degli Studi di Genova, Genoa, Italy

    Luca Verderame

  4. IEIIT Institute, Consiglio Nazionale delle Ricerche (CNR), Genoa, Italy

    Enrico Cambiaso

  5. SINTEF A.S., Oslo, Norway

    Rita Ugarelli

  6. Engineering Ingegneria Informatica S.p.A., Rome, Italy

    Gabriele Giunta

  7. Instituto Superior de Engenharia do Porto, Porto, Portugal

    Isabel Praça

  8. University of Padua, Padua, Italy

    Federica Battisti

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Chandramouli, K., Izquierdo, E. (2021). An Advanced Framework for Critical Infrastructure Protection Using Computer Vision Technologies. In: Abie, H., et al. Cyber-Physical Security for Critical Infrastructures Protection. CPS4CIP 2020. Lecture Notes in Computer Science(), vol 12618. Springer, Cham. https://doi.org/10.1007/978-3-030-69781-5_8

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  • DOI: https://doi.org/10.1007/978-3-030-69781-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69780-8

  • Online ISBN: 978-3-030-69781-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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