# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 1 Introduction
In this paper, we propose to tackle the challenging deepfake attack detection problem leveraging the Electrical Network Frequency (ENF) signal, which is embedded in the recorded AVS data as a fingerprint that is determined by the environmental factors of the recording region. The effectiveness of a fingerprint technique against the deepfake generation model depends on its uniqueness and randomness to avoid forgery and predictions. The ENF is the instantaneous frequency in the electrical power grid with a nominal value of 50/60 Hz, depending on the geographical location [27; 28]. For the rest of this paper, we consider the nominal frequency value as 60 Hz for our testbed in the United States. The Instantaneous Frequency (IF) varies over time due to the varying load balance mechanism and power supply demands, resulting in the fluctuations from the nominal frequency resulting in the ENF signal [29]. The variation in fluctuations is small, and the fluctuations are similar throughout the power grid interconnect. Among the four major power grid interconnects in the USA, the experimental data were collected in the Eastern power grid where the variation of the ENF is in the range of [\(-\)0.02, 0.02] Hz from the nominal frequency [30]. While the ENF signal functions as the main power supply, it also gets embedded in the digital multimedia through background hum [31; 32] or illumination frequency in audio and video recordings [27; 33; 34]. Due to the presence of the ENF in audio-video channels, the manipulation in the ENF signal with respect to time is treated as the manipulation or modification of the multimedia recordings [35; 36; 37]. The ENF signal is also used for forensic analysis of digital evidence, time of recording estimation [38], media synchronization among multiple channels [39], and geographical tagging of the recording [40]. Although the ENF signal is present in the audio and video channels, some challenges exist when using the ENF as a fingerprint mechanism. Due to the lower frequency range, the Signal-to-Noise Ratio (SNR) for reliable ENF estimation is vital to address. In typical deepfake videos, instantaneous frequency estimation is required for estimation, which depends on the spectral estimation techniques used. In order to adopt the ENF as a fingerprint technique, a solution is also needed to address the redundant ENF reference database for comparing the estimated ENF. This paper analyzes the ENF estimation techniques against deepfake audio and video recordings using different spectral estimation techniques and robust and reliable estimation in low SNR recordings. Our contributions in this paper are as follows:  
* Designing of an effective spectral estimation technique using both parametric and non-parametric methods for IF detection. * Utilizing a Robust Filtering Algorithm (RFA) over a weighted SNR to identify the harmonic ENF embedded in media recordings to enhance the ENF signal estimation in the identified ENF. * Implementing an effective detection technique against deepfake attacks and an integrated Singular Spectrum Analysis (SSA) based on the correlation coefficient values to reduce the number of false positives in a real-time video broadcasting scenario. * Demonstrating experimental analysis on the video and audio deepfake attacks' detection using the RFA technique and comparing its effectiveness against traditional spectral estimation techniques. The rest of the paper is organized as follows. Section 2 discusses the background and related work in deepfake detection technologies and ENF fingerprint applications. Section 3 thoroughly discusses the spectral estimation techniques used in this work for the comparison analysis and the ENF enhancement techniques in low SNR recordings followed by the Singular Spectral Analysis (SSA) approach to minimize the false positive rate caused by correlation outliers in Section 4. Section 5 reports the experimental evaluation of the spectral techniques discussed in Section 3 and the performance evaluation of SSA in edge-based devices.