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Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach

Author

Listed:
  • Deeraj Nagothu

    (Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY 13902, USA)

  • Ronghua Xu

    (Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY 13902, USA)

  • Yu Chen

    (Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY 13902, USA)

  • Erik Blasch

    (The U.S. Air Force Research Laboratory, Rome, NY 13441, USA)

  • Alexander Aved

    (The U.S. Air Force Research Laboratory, Rome, NY 13441, USA)

Abstract

With the fast development of Fifth-/Sixth-Generation (5G/6G) communications and the Internet of Video Things (IoVT), a broad range of mega-scale data applications emerge (e.g., all-weather all-time video). These network-based applications highly depend on reliable, secure, and real-time audio and/or video streams (AVSs), which consequently become a target for attackers. While modern Artificial Intelligence (AI) technology is integrated with many multimedia applications to help enhance its applications, the development of General Adversarial Networks (GANs) also leads to deepfake attacks that enable manipulation of audio or video streams to mimic any targeted person. Deepfake attacks are highly disturbing and can mislead the public, raising further challenges in policy, technology, social, and legal aspects. Instead of engaging in an endless AI arms race “fighting fire with fire”, where new Deep Learning (DL) algorithms keep making fake AVS more realistic, this paper proposes a novel approach that tackles the challenging problem of detecting deepfaked AVS data leveraging Electrical Network Frequency (ENF) signals embedded in the AVS data as a fingerprint. Under low Signal-to-Noise Ratio (SNR) conditions, Short-Time Fourier Transform (STFT) and Multiple Signal Classification (MUSIC) spectrum estimation techniques are investigated to detect the Instantaneous Frequency (IF) of interest. For reliable authentication, we enhanced the ENF signal embedded through an artificial power source in a noisy environment using the spectral combination technique and a Robust Filtering Algorithm (RFA). The proposed signal estimation workflow was deployed on a continuous audio/video input for resilience against frame manipulation attacks. A Singular Spectrum Analysis (SSA) approach was selected to minimize the false positive rate of signal correlations. Extensive experimental analysis for a reliable ENF edge-based estimation in deepfaked multimedia recordings is provided to facilitate the need for distinguishing artificially altered media content.

Suggested Citation

  • Deeraj Nagothu & Ronghua Xu & Yu Chen & Erik Blasch & Alexander Aved, 2022. "Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach," Future Internet, MDPI, vol. 14(5), pages 1-20, April.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:5:p:125-:d:798538
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    References listed on IDEAS

    as
    1. Ronghua Xu & Deeraj Nagothu & Yu Chen, 2021. "EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks," Future Internet, MDPI, vol. 13(10), pages 1-24, September.
    2. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
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