Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
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DOI：
10.3390/fi14050125
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发表时间：
2022-04
期刊：
影响因子：
3.4
通讯作者：
Deeraj Nagothu;Ronghua Xu;Yu Chen;E. Blasch;Alexander J. Aved
中科院分区：
文献类型：
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作者：
Deeraj Nagothu;Ronghua Xu;Yu Chen;E. Blasch;Alexander J. Aved
随着第五代/第六代（5G/6 G）通信和视频物联网（IoVT）的快速发展，出现了广泛的大规模数据应用（例如，全天候全时视频）。这些基于网络的应用程序高度依赖于可靠、安全和实时的音频和/或视频流（AVS），因此成为攻击者的目标。虽然现代人工智能（AI）技术与许多多媒体应用程序集成，以帮助增强其应用程序，但通用对抗网络（GAN）的发展也导致了深度伪造攻击，可以操纵音频或视频流来模仿任何目标人物。Deepfake攻击非常令人不安，可能会误导公众，在政策、技术、社会和法律的方面带来进一步的挑战。本文提出了一种新的方法，该方法利用嵌入在AVS数据中的电气网络频率（ENF）信号作为指纹来解决检测深度伪造的AVS数据的挑战性问题。在低信噪比（SNR）条件下，研究了短时傅里叶变换（STFT）和多信号分类（MUSIC）谱估计技术在瞬时频率（IF）检测中的应用。为了进行可靠的身份验证，我们使用频谱组合技术和鲁棒滤波算法（RFA）增强了噪声环境中通过人工电源嵌入的ENF信号。所提出的信号估计工作流程部署在连续的音频/视频输入上，以抵抗帧操纵攻击。一个奇异谱分析（SSA）的方法被选择，以尽量减少假阳性率的信号相关性。提供了对深度伪造的多媒体记录中的可靠的基于ENF边缘的估计的广泛实验分析，以便于区分人为改变的媒体内容的需要。
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.