# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
## 2 Related Works and Concepts
### Overview of ENF Extraction from Video  
ENF extraction from audio recordings has been extensively researched. Various conventional frequency estimation techniques based on the short-time Fourier transform (STFT) [34] and subspace analysis [35; 36] have been implemented to extract the ENF signals from media recordings. STFT is frequently utilized to analyze time-varying signals, including speech signals and ENF signals. With the STFT-based periodogram approach, the dominant instantaneous frequency can be extracted by finding the peak positions and increasing its accuracy using quadratic interpolation [37] and the weighted energy method [5]. In contrast, approaches based on subspace analysis, including multiple signal classification (MUSIC) [35] and estimation of signal parameters using rotational invariance techniques (ESPRIT) [36], leverage the signal subspace's orthogonal relationship to the noise subspace. ESPRIT implementations require lower computation and storage costs, which gives them an advantage over MUSIC [36]. Researchers have also developed dedicated approaches [38; 39] and approaches that combine STFT and subspace analysis to accurately estimate the ENF signal [5]. However, some preprocessing is necessary prior to the use of the frequency estimation or tracking techniques mentioned above to accurately extract ENF signals from multimedia audio recordings. In the case of video recordings, the temporal fluctuation in light intensity included in the video frames may be leveraged to extract the ENF signal. The variations in ENF in the grid network influences the intensity of illumination from any light source connected tothe power mains. As a result of the light source flickering at both the positive and negative cycles of AC current, the frequency of the light becomes twice the frequency of the mains power. The illumination signal may thus be seen as the absolute representation of the cosine function in Equation (1) [24]. For instance, for any video recording made under indoor illumination powered by 50 Hz power mains, since the polarity of the current changes at double the frequency of the mains power, the light flickers at 100 Hz. In addition, decaying energy's higher harmonics frequently exist at integer multiples of 100 Hz when the mains power signal mildly deviates from a perfect sinusoid. In addition, the higher harmonics bandwidth is larger than the main component since the actual ENF signal of interest is a narrowband signal rather than a completely stable sinusoid. Consequently, the \(n\)th harmonic component bandwidth will be \(n\) times the ENF component bandwidth at 100 Hz. Hence, the Nyquist theorem states that a sampling rate of at least 200 Hz is required to reliably extract illumination frequency from recorded data [24]. Despite the fact that the majority of consumer cameras cannot offer such high frame sampling rates, the illumination frequency can still be estimated from an aliased frequency. Assuming that \(f_{s}\) is the sampling frequency of the camera and \(f_{\mathcal{L}}\) is the light-source illumination frequency, then the aliased illumination frequency \(f_{a}\) is expressed as [30]:  
\[f_{a}=|f_{\mathcal{L}}-j.f_{s}|<\frac{f_{s}}{2},\ \exists j\in N \tag{4}\]  
Therefore, when a 100 Hz illumination signal from a light source is sampled using a camera with a frame rate of 29.97 Hz, the aliased base frequency of the ENF will be obtained as 10.09 Hz, while the aliased second harmonic will be obtained as 9.79 Hz. The aliased effect as a result of an insufficient camera sampling rate is mainly associated with cameras with the global shutter mechanism. The first work on ENF extraction from a video recording [5] calculated the average intensity of each frame of a white wall video to produce an intensity signal. This signal over time was then passed through a temporal bandpass filter with a passband matching the desired frequency to extract the ENF.