# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 5 Experimental Study and Performance Analysis
### Effects of Spectral Estimation Techniques against Deepfakes  
Spectral estimation techniques have different parameters to control for a reliable estimation. In this work, we used MUSIC- and STFT-based spectral methods. The spectrum is computed from both techniques along with a bandpass filter along the nominal frequency of interest. In the STFT method, the spectral harmonic bands are collected from the spectrogram, whereas the MUSIC method looks for \(K\) complex exponential components in the signal. For the ENF signal, the value of \(K\) is two. Once the spectrum is computed for both techniques, the maximum frequency bin is identified with the help of quadratic interpolation, and the required frequency fluctuations are estimated. Although each spectrum estimation method has its own advantages, for deepfake videos, it is important that the method be more consistent and fast. MUSIC performs better with a lower signal length, whereas STFT relies on the Fourier transform, which needs more data for its computation. In Figure 3, the performance of each method is measured with respect to the input signal. The ENF was estimated from a static background recording under artificial light along with the ground truth reference signal with a sampling rate of 1000 Hz. The input signal was incremented by six seconds for each round, and the correlation coefficient was measured for similarity with the reference signal. Figure 3 clearly shows that MUSIC performed better with lower-duration recordings, but STFT outperformed the MUSIC method with sufficient input data given.  
Figure 2: System architecture of deterring deepfake attacks at the edge network.  
With the performance analysis based on the duration of the input signal, next, the STFT and MUSIC algorithms were tested with a fixed-length input signal on its ENF estimation on video recordings. The video recording used includes a talking head subject with movements recorded under artificial light. Figure 4 represents the correlation coefficient for STFT and the MUSIC algorithm. Here, it is clear that STFT performed better in video-based ENF estimation compared to the MUSIC algorithm. For a reliable deepfake manipulation detection, it is vital that the ENF from unmodified recordings be estimated more reliably, so that any significant changes in the ENF can be marked as a potential manipulation. For some audio and video analysis, the harmonic frequencies in the recordings are targeted with external noise interfering with the embedded ENF. For this purpose, we used reliable estimation techniques such as the weighted combination of multiple harmonics [54] along with the robust filtering algorithm proposed in [58]. Table 1 represents the average SNR of the ENF fingerprints in the media recordings. Using the SNR matrix, the targeted frequency range was identified and the RFA algorithm was used to increase the SNR significantly. From Table 1, for power and audio recordings, the ENF signal is stronger in odd harmonics, and in video recordings, it is stronger in even harmonics since the nominal frequency in videos is 120 Hz.  
Figure 4: Spectrum estimation techniques for ENF estimation in a video recording with a moving subject under artificial light.  
Figure 3: Performance of STFT and MUSIC spectrum estimation methods on ENF estimation based on the length of input signal.