Authenticating Video Feeds using Electric Network Frequency Estimation at the Edge

Deeraj Nagothu; Yu Chen; Alexander Aved; Erik Blasch

Research Article

Authenticating Video Feeds using Electric Network Frequency Estimation at the Edge

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@ARTICLE{10.4108/eai.4-2-2021.168648,
    author={Deeraj Nagothu and Yu Chen and Alexander Aved and Erik Blasch},
    title={Authenticating Video Feeds using Electric Network Frequency Estimation at the Edge},
    journal={EAI Endorsed Transactions on Security and Safety},
    volume={7},
    number={24},
    publisher={EAI},
    journal_a={SESA},
    year={2021},
    month={2},
    keywords={Video Data Authentication, Electrical Network Frequency (ENF) Estimation, Internet of Video Things (IoVT), Edge Computing, Visual Layer Backdoor Attacks},
    doi={10.4108/eai.4-2-2021.168648}
}

Deeraj Nagothu
Yu Chen
Alexander Aved
Erik Blasch
Year: 2021
Authenticating Video Feeds using Electric Network Frequency Estimation at the Edge
SESA
EAI
DOI: 10.4108/eai.4-2-2021.168648

Deeraj Nagothu¹, Yu Chen¹^,*, Alexander Aved², Erik Blasch²

1: Department of Electrical and Computer Engineering, Binghamton University, USA
2: The U.S. Air Force Research Laboratory, USA

*Contact email: ychen@binghamton.edu

Abstract

Large scale Internet of Video Things (IoVT) supports situation awareness for smart cities; however, the rapid development in artificial intelligence (AI) technologies enables fake video/audio streams and doctored images to fool smart city security operators. Authenticating visual/audio feeds becomes essential for safety and security, from which an Electric Network Frequency (ENF) signal collected from the power grid is a prominent authentication mechanism. This paper proposes an ENF-based Video Authentication method using steady Superpixels (EVAS). Video superpixels group the pixels with uniform intensities and textures to eliminate the impacts from the fluctuations in the ENF estimation. An extensive experimental study validated the effectiveness of the EVAS system. Aiming at the environments with interconnected surveillance camera systems at the edge powered by an electricity grid, the proposed EVAS system achieved the design goal of detecting dissimilarities in the image sequences.

Keywords: Video Data Authentication, Electrical Network Frequency (ENF) Estimation, Internet of Video Things (IoVT), Edge Computing, Visual Layer Backdoor Attacks

Received: 2020-12-14
Accepted: 2021-01-26
Published: 2021-02-04
Publisher: EAI

: http://dx.doi.org/10.4108/eai.4-2-2021.168648

Copyright © 2021 Deeraj Nagothu et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium solong as the original work is properly cited.