# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 6 Discussion  
Fake media generation using deepfake technology has raised significant concerns, and we have witnessed multiple \"attacks\". Although \"nice\" deepfaked AVS may bring benefits in multiple fields, preventive measures to distinguish fake media from authentic counterparts are necessary to prevent negative impacts. This paper introduces an ENF-fingerprint-based approach to analyze the deepfake-generated media recordings and detect manipulations. While the ENF is verified in indoor audio and video recordings due to the presence of devices connected to the power grid, there are certain limitations. For example, recordings that are generated in outdoor settings or in scenarios where the presence of the ENF is negligible, the proposed ENF authentication is not applicable. Meanwhile, in applications such as conferencing calls that occur in indoor settings under artificial light, the ENF-based detection scheme is an effective countermeasure [63].  
The number of samples collected for the ENF is also a key factor for reliable estimation of the embedded signal. Video cameras based on CCD imaging sensors use a global shutter mechanism, where the ENF-carrying samples are restricted to the number of frames  
Figure 9: (**a**) The latency of executing SSA with different platforms; (**b**) the CPU usage of executing SSA with different platforms.  
collected. Due to the low sampling rate and the higher nominal frequency, the Nyquist criterion is not satisfied [33]. However, the aliasing frequency can be used to identify the signal fluctuations at the cost of reduced accuracy. With the proposed integration of the RFA and a weighted combination of spectral harmonics for ENF enhancement, the algorithms add additional complexity for time-sensitive applications such as surveillance systems. The RFA approach is used to enhance the SNR of the signal prior to the ENF estimation algorithm and thereby is suggested to be applied in scenarios where the external noise interference disrupts the ENF signal noise level down to \(-20\) dB. However, with the increase in signal duration, the time taken for the RFA also exponentially increases compared to the spectral combination method [58]. Figure 10 presents the difference in the execution time for each enhancement algorithm, where the input signal is incremented for \(12\) s for each round. In order to integrate the RFA with the STFT-based ENF estimation, we used a sliding window approach with a window size of 45-60 s and a shift size of five to ten seconds. For an online detection system, manipulations made to the live feed are detected in less than ten seconds of occurrence provided the ENF enhancement and SSA algorithms are integrated.  
ENF authentication is not restricted to specific media types, unlike other trained models that depend on input compatibility. It is applicable to audio and video authentication and results in a generalized solution against media manipulation attacks. In our presented work, the media manipulations were detected using an external reference ENF signal, also referred to as the ground truth signal. Deploying external circuity for this purpose could be redundant, and a central reference ENF database would not be effective since the ENF is different for each power grid. Instead, a distributed authentication scheme could be adopted where the ENF estimated from each device can be used to generate a ground truth signal without relying on an external reference signal [64]. Our previous work proposed a consensus mechanism for edge-based devices to estimate the ENF for continuous media input. The broadcast ENF was used to create a mutually agreed ground truth signal, allowing for detecting any faulty nodes. We recommend our prior work on the ENF-based consensus algorithm to detect forgery attacks for further discussion [56; 63; 64].  
Figure 10: Execution time of the STFT algorithm in combination with ENF enhancement techniques.