# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
## 2 Related Works and Concepts
### Overview of ENF Extraction from Video
Given that the video's content was largely consistent between frames, the existence of a considerable amount/value of energy in the frequency of interest was attributed to the ENF signal. In the second experiment with video recording with movement, directly averaging the pixel values of the whole frame may not have been an appropriate preprocessing step prior to carrying out frequency analysis because of the inconsistency in the content of each frame of the video. The authors, however, averaged the pixel intensities of relatively steady regions in the video where there was not much inconsistency and extracted the ENF signal from the average pixel intensity spectrogram. The extracted ENF was utilized for time-of-recording and tampering detection applications. The authors of [22; 23; 40] also designed ENF extraction methods based on calculating the average intensity of each frame when the foreground is uniform (in the case of a white wall video) or eliminated using motion compensation (in the case of videos with motion) to generate the intensity signal which is passed through Fourier analysis to extract the ENF. The extracted ENF is further used for video synchronization applications. Researchers have also employed image segmentation approaches for analyzing videos for ENF analysis. The authors of [24; 26] proposed ENF estimation methods based on exploiting super-pixels. The method reported in [24] computed the average number of steady super-pixels instead of all steady pixels contained in a frame. The method was applied to a video dataset of 160 videos of different lengths recorded under different conditions using cameras that adopted both CMOS and CCD sensors. The method segmented each video into super-pixel regions and identified the steady points within each region throughout all the frames. The steady pixels of all regions of each video frame were then averaged to generate the intensity signal from which a \"so-called ENF vector\" was computed along each successive video frame of a particular shot. Then, the similarity of the computed ENF vectors was examined to detect the presence or absence of an ENF signal in the test video. The method described in [26] is based on simple linear iterative clustering (SLIC) [41; 42] for image segmentation. The authors employed the SLIC algorithm to generate regions of similar properties termed super-pixels whose average intensity exceeds a certain threshold to generate the mean intensity time series. The authors opined that, in those regions, the embedded ENF is not impeded by interference and noise, including shadows, textures, and brightness, resulting in more precise estimations irrespective of whether the test video is static or non-static. The method was applied to a public dataset of static and non-static CCD videos [43], and the mean intensity time-series signal generated was passed through an ESPRIT or STFT method to estimate the ENF. Another method based on averaging pixels with particular characteristics to extract the ENF from non-static videos was proposed by the author of [27]. The method first employed a background subtraction algorithm named ViBe [44] to mitigate the deviation brought by movement and applied a differentiator filter at pixel level to eliminate the time-dependent mean value at each point before averaging the pixels. Luminance differences beyond a set threshold are suppressed, requiring only pixels classified as valid in both the current and previous frames to be processed. The frame-level signal is passed to a phase-locked loop-based FM demodulator [45] after preprocessing to extract the ENF, considering its autoregressive manner. The method is applied to estimate the time of recording of CCD videos, and the simulation results showed its effective performance. However, if the variations caused by movement affect a significant number of pixels in every frame, this method may be insufficient for extracting the ENF.