# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 5 Experimental Study and Performance Analysis
### ENF Enhancement Using the RFA  
The efficiency of the RFA was tested on real-world audio recordings with ENF embedded though background hum. We used the STFT algorithm to estimate the harmonic frequency with the highest SNR and estimate the ENF signal. However, due to external noise, the ENF estimated from a single harmonic frequency had a significantly lower correlation coefficient when compared with its reference ENF. Using the RFA to enhance the ENF in the frequency of interest, the noise was suppressed, and then, the ENF was estimated from the new spectral bins. Figure 5 represents the correlation coefficient histogram of the audio ENF before and after applying the RFA to the recordings. Although it was unclear if the recordings carried any ENF signature before the RFA, it can be clearly seen that the RFA enhanced the embedded ENF and was more reliable for better ENF estimation from recordings with a lower SNR. ENF estimation from video recordings depends on the presence of artificial light in the recording. With lower-intensity light in the background, the ENF is not reliable due to the interference of other in-camera noise such as ISO sensor noise and other subject-movement-related pixel disturbances. We tested the performance of enhancing the harmonics of the ENF in a video recordings with lower illumination intensity and noise. In Figure 6, we used the STFT method to estimate the ENF from the video recording with and without the RFA to enhance the ENF harmonics. With the improvement in the correlation coefficient of the RFA-enhanced ENF signal, the ENF can be reliably estimated from video recordings with a lower SNR, as long as it carries the embedded artificial light fluctuations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Media** & **60 Hz** & **120 Hz** & **180 Hz** & **240 Hz** & **300 Hz** & **360 Hz** \\ \hline
**Power** & 39.88 & 2.456 & 38.647 & 0 & 14.48 & 4.534 \\ \hline
**Audio** & 9.761 & 0.888 & 27.94 & 7.106 & 43.717 & 10.585 \\ \hline
**Video** & 0 & 8.396 & 0 & 90.163 & 0 & 1.439 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SNR of the ENF fingerprint in media recordings. Figure 5: Correlation coefficient histogram collected from real-world audio recordings before and after applying the robust filtering algorithm. Attacks on audio and video recordings such as deepfakes alter the original samples of the recordings to create a false perception. Along with the samples, the embedded ENF frequency fluctuations, which are temporal sensitive, are also altered, resulting in interference of the ENF fingerprint. Using a reference signal recorded at the same time instant, the manipulations to the multimedia recordings can be detected and also localized with a reliable ENF estimation method [35; 56]. Figure 7 shows the drop in the correlation coefficient of the audio and video deepfake recordings where the ENF was estimated from the RFA-enhanced harmonics. For the video deepfake, the whole recording was swapped with an alternate trained face model, and this resulted in a drop in the overall correlation for the whole video. For the audio recording, a partial deepfake recording was generated and appended to the original recording. The correlation coefficient can also be used to localize the forgery. In order to deploy the proposed authentication scheme to reliably authenticate a continuous stream of media input such as surveillance system monitoring, the ENF should be reliably estimated for better correlation. However, sometimes, it is susceptible to outliers that occur due to a frame being skipped due to network delay or frame obfuscation. In order to address the outliers and reduce the false alarm rate, we integrated Singular Spectrum Analysis (SSA) to observe the correlation coefficient vector and suppress the outliers. The performance analysis and the computation overhead were studied from the perspective of edge-based computers. Figure 6: Enhancing the embedded ENF signature with the RFA in video recording with a low SNR and external noise.