# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
## 2 Related Works and Concepts
### Overview of ENF Extraction from Video
Some other extraction methods take advantage of the higher ENF sampling frequency of the rolling shutter mechanism, which is based on the number of rows multiplied by the video frame rate. However, the rolling shutter method introduces the problem of idle time between consecutive frames where no sampling is performed. Therefore, some light samples are lost during the idle time period which occurs at the end of every frame. The authors of [25] first leveraged the rolling shutter mechanism to address the inadequate sampling rate for ENF extraction from video recordings. They formulated an L-branch filter-bank model of the mechanism for their analysis and showed how the dominant ENF harmonic is shifted to different frequencies as a result of the idle period in videos recorded with the rolling shutter mechanism. To extract the ENF, the method ignores the idle time and utilizes the average of each row's pixel values as temporal samples and concatenates them to form a row signal when the foreground has a uniform color or is eliminated using motion compensation. The row signal is then passed through a spectrogram to generate the ENF traces. The ENF is then extracted by finding the dominant instantaneous frequency within a narrow band around the desired frequency. The multi-rate signal analysis employed in this method to evaluate the concatenated signal reveals that neglecting the frame's idle time during direct concatenation might result in mild distortion to the estimated ENF traces [46]. To avert such distortion, the authors of [29] proposed a periodic zero-padding approach to deal with the idle time problem. The authors argued that instead of ignoring the idle period, an equally uniform sampling along time should be conducted by returning zeros to the end of each row signal until they reached the length of time that corresponded to the idle period before the concatenation. This zero-padding strategy is capable of producing ENF traces that are free of distortion, but it does need prior knowledge of the length of the idle period. The idle period can be determined using the camera read-out time, which is model-specific [21]. Experimental results demonstrated that the approach enhanced the SNR of the estimated ENF signal. The authors of [28] introduced a MUSIC combining spectrum method that exploits the rolling shutter mechanism without knowing the idle time. Figure 3 shows the overall framework of the proposed method. Given a test video, the method computes row-by-row the average pixel intensity and concatenates these values in a singular time series, s[n], using a sampling rate of frame rate multiplied by the number of rows. The time-series signal, s[n], is then passed through the preprocessing stages, where the local average is subtracted to enable a more precise result, particularly when a video features movement. The signal, s[n], is further down-sampled to 1 kHz after applying an anti-aliasing filter. Using the resulting signal, s[n], MUSIC and Fourier domain analyses are conducted in parallel, and the respective spectra are combined to extract the ENF. The proposed method is applied to time-of-recording verification, and the experimental results showed its effectiveness even in the more difficult scenario of 1 min recordings of both static and non-static videos. The authors of [47] designed a phase-based method to extract the ENF signal leveraging the rolling shutter mechanism. The method attempted to address the idle time problem by employing row-by-row samples individually at the frame level to avoid any discontinuity caused by the idle time. The proposed method is modeled as:  
\[X_{j}^{\theta}[\epsilon]=A_{j}.\sin\left(2\pi\frac{f_{j}}{f_{s}}\epsilon+ \varnothing_{j}\right)+d_{j} \tag{5}\]  
With a parameter set \(\theta=(A_{j},f_{j},\varnothing_{j},d_{j})\), where \(A_{j}\) is the amplitude, \(f_{j}\) is the light intensity instantaneous frequency, \(\varnothing_{j}\) is the initial phase of the ENF signal when the initial row of the \(j\)th frame was obtained, and \(d_{j}\) is the DC value.