# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 7 Conclusions  
Emerging technologies such as deepfakes have become a common source for generating misinformation to affect trust in online media. Different from existing work on deep-learning-based detection models trained to identify deepfakes, we tackled the problem of identifying frame manipulations such as deepfakes using an environmental fingerprint technique. Using the Electrical Network Frequency (ENF) signal embedded in media recordings through artificial power sources, the integrity of the recording can be verified in both the spatial and temporal domains. In this work, we present a comprehensive analysis of effective spectral estimation techniques such as Short-Time Fourier Transform (STFT) and Multiple Signal Classification (MUSIC) against low Signal-to-Noise Ratio (SNR) media recordings. Our experimental results concluded that STFT is more reliable for ENF estimation. However, according to our findings, for media recordings with a short duration, the MUSIC algorithm has better performance for spectral estimation.  
In addition to spectral analysis techniques, we tested signal enhancement algorithms such as the Robust Filtering Algorithm (RFA) and weighted harmonics combinations against deepfake audio and video recordings. From our experiments, the RFA technique significantly improved the SNR of the embedded ENF signal and resulted in reliable verification of signal authenticity. We also integrated the proposed method for online media verification, and based on the experimental results, we adopted STFT with the RFA algorithm considering the execution time complexity in our testbed. Furthermore, to minimize the false positive rate due to outliers, we deployed our ENF-based authentication scheme with the Singular Spectrum Analysis (SSA) method to improve the performance of detecting media manipulations. The results demonstrated a reliable and comprehensive tool against fake media distribution, adaptable to heterogeneous media recordings made under the influence of the power grid.  
Conceptualization, D.N., R.X., Y.C., E.B. and A.A.; Data curation, D.N. and R.X.; Formal analysis, D.N. and R.X.; Funding acquisition, Y.C.; Investigation, A.A.; Methodology, D.N., R.X., Y.C. and E.B.; Project administration, Y.C.; Resources, Y.C., E.B. and A.A.; Software, D.N. and R.X.; Supervision, Y.C. and A.A.; Validation, D.N.; Visualization, R.X.; Writing--original draft, D.N., R.X. and Y.C.; Writing--review & editing, E.B. and A.A. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the U.S. National Science Foundation (NSF) via Grant CNS-2039342 and the U.S. Air Force Office of Scientific Research (AFOSR) Dynamic Data and Information Processing Program (DDIP) via Grant FA9550-21-1-0229. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Air Force.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ AVS & Audio and/or Video Stream \\ CMOS & Complementary Metal-Oxide Semiconductor \\ CCD & Charge-Coupled Device \\ CUSUM & Cumulative Sum of deviations \\ DL & Deep Learning \\ ENF & Electrical Network Frequency \\ FFT & Fast Fourier Transform \\ \end{tabular}