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Detecting Compromised Edge Smart Cameras using Lightweight Environmental Fingerprint Consensus

Authors:

Deeraj Nagothu,

Alexander AvedAuthors Info & Claims

SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems

Pages 505 - 510

https://doi.org/10.1145/3485730.3493684

Published: 15 November 2021 Publication History

Abstract

Rapid advances in the Internet of Video Things (IoVT) deployment in modern smart cities has enabled secure infrastructures with minimal human intervention. However, attacks on audio-video inputs affect the reliability of large-scale multimedia surveillance systems as attackers are able to manipulate the perception of live events. For example, Deepfake audio/video attacks and frame duplication attacks can cause significant security breaches. This paper proposes a Lightweight Environmental Fingerprint Consensus based detection of compromised smart cameras in edge surveillance systems (LEFC). LEFC is a partial decentralized authentication mechanism that leverages Electrical Network Frequency (ENF) as an environmental fingerprint and distributed ledger technology (DLT). An ENF signal carries randomly fluctuating spatio-temporal signatures, which enable digital media authentication. With the proposed DLT consensus mechanism named Proof-of-ENF (PoENF) as a backbone, LEFC can estimate and authenticate the media recording and detect byzantine nodes controlled by the perpetrator. The experimental evaluation shows feasibility and effectiveness of proposed LEFC scheme under a distributed byzantine network environment.

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Cited By

Nagothu DPoredi NChen Y(2023)Evolution of Attacks on Intelligent Surveillance Systems and Effective Detection TechniquesIntelligent Video Surveillance - New Perspectives10.5772/intechopen.105958Online publication date: 8-Feb-2023
https://doi.org/10.5772/intechopen.105958
Nagothu DXu RChen YBlasch EAved A(2022)Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints ApproachFuture Internet10.3390/fi1405012514:5(125)Online publication date: 21-Apr-2022
https://doi.org/10.3390/fi14050125
Poredi NNagothu DChen YLi XAved AArdiles-Cruz EBlasch E(2022)Robustness of Electrical Network Frequency Signals as a Fingerprint for Digital Media Authentication2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)10.1109/MMSP55362.2022.9949315(1-6)Online publication date: 26-Sep-2022
https://doi.org/10.1109/MMSP55362.2022.9949315
Show More Cited By

Index Terms

Detecting Compromised Edge Smart Cameras using Lightweight Environmental Fingerprint Consensus
1. Security and privacy
  1. Security services
    1. Authentication

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cover image ACM Conferences

SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems

November 2021

686 pages

ISBN:9781450390972

DOI:10.1145/3485730

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 15 November 2021

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Research-article
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Funding Sources

U.S. Air Force Office of Scientific Research (AFOSR)
U.S. National Science Foundation (NSF)

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SenSys '21

Sponsor:

SenSys '21: The 19th ACM Conference on Embedded Networked Sensor Systems

November 15 - 17, 2021

Coimbra, Portugal

Acceptance Rates

SenSys '21 Paper Acceptance Rate 25 of 139 submissions, 18%;

Overall Acceptance Rate 198 of 990 submissions, 20%

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Cited By

Nagothu DPoredi NChen Y(2023)Evolution of Attacks on Intelligent Surveillance Systems and Effective Detection TechniquesIntelligent Video Surveillance - New Perspectives10.5772/intechopen.105958Online publication date: 8-Feb-2023
https://doi.org/10.5772/intechopen.105958
Nagothu DXu RChen YBlasch EAved A(2022)Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints ApproachFuture Internet10.3390/fi1405012514:5(125)Online publication date: 21-Apr-2022
https://doi.org/10.3390/fi14050125
Poredi NNagothu DChen YLi XAved AArdiles-Cruz EBlasch E(2022)Robustness of Electrical Network Frequency Signals as a Fingerprint for Digital Media Authentication2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)10.1109/MMSP55362.2022.9949315(1-6)Online publication date: 26-Sep-2022
https://doi.org/10.1109/MMSP55362.2022.9949315
Blasch EShen DChen GInsaurralde CNiu R(2022)Artificial Intelligence Fusion of Information for Aerospace (AIFIA) Systems2022 IEEE Aerospace Conference (AERO)10.1109/AERO53065.2022.9843668(1-8)Online publication date: 5-Mar-2022
https://doi.org/10.1109/AERO53065.2022.9843668
Xu RNagothu DChen Y(2022)ECOM: Epoch Randomness-Based Consensus Committee Configuration for IoT BlockchainsPrinciples and Practice of Blockchains10.1007/978-3-031-10507-4_7(135-154)Online publication date: 4-Jul-2022
https://doi.org/10.1007/978-3-031-10507-4_7

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