# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
## 4 Results and Discussion  
When a video is analyzed using the true \(T_{ro}\) value of the camera used to capture it together with the true ENF nominal value where it is captured, the extracted ENF will be a near-to-perfect match with the reference signal, which shows that the video originated from the camera. Figure 5 shows the analysis performed using a \(T_{ro}\) value of 19.8 ms, which is the \(T_{ro}\) value of the iPhone 6s used to record the video. The extracted ENF provides a better match to the reference signal compared to the analysis results in Figures 6 and 7, where the \(T_{ro}\) values of 13.4 ms (Sony Cybershot DSC-RX 100 II) and 30.9 ms (iPhone 6) were used, respectively. The performance of the estimated ENF signals against the reference signal at different \(T_{ro}\) values were also evaluated in terms of NCC, RMSE, and MAE, as shown in Table 2. The results further show that the \(T_{ro}\) value corresponding to the camera used to capture the video under analysis will lead to the extraction of an ENF signal with the highest correlation and the lowest error rate relative to the reference signal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Model** & \(T_{ro}\) **(ms)** & **NCC** & **RMSE** & **MAE** \\ \hline iPhone 6s back camera & 19.8 & 0.970 & 0.0016 & 0.0012 \\ Sony Cybershot DSC-RX 100 II & 13.4 & 0.940 & 0.0023 & 0.0018 \\ iPhone 5 front camera & 22.9 & 0.961 & 0.0017 & 0.0013 \\ iPhone 5 back camera & 27.4 & 0.934 & 0.0023 & 0.0018 \\ Sony Handycam HDR-TG1 & 14.6 & 0.952 & 0.0020 & 0.0015 \\ Canon SZ30-HS & 18.2 & 0.921 & 0.0018 & 0.0014 \\ iPhone 6 & 30.9 & 0.944 & 0.0022 & 0.0016 \\ \hline \hline \end{tabular}
\end{table}
Table 2: (Dis)similarity between the extracted ENF and the reference ENF.  
Figure 5: Sample of the ENF signal extracted from a video file (red) using a \(T_{ro}\) value of 19.8 ms (iPhone 6s) and matched against the reference signal (black). The bottom right corner shows the measure of similarity and dissimilarity between the extracted signal and the reference signal.  
Figure 8 shows a plot of the correlations of the ENF signals extracted using different \(T_{ro}\) values. It can be observed that the \(T_{ro}\) of the iPhone 6s (19.8 ms) which was used to capture the video used in the analysis produced an ENF signal with the highest correlation.  
Figure 6: Sample of the ENF signal extracted from a video file (red) using a \(T_{ro}\) value of 13.4 ms (Sony Cybershot DSC-RX 100 II) and matched against the reference signal (black). The bottom right corner shows the measure of similarity and dissimilarity between the extracted signal and the reference signal.  
Figure 7: Sample of the ENF signal extracted from a video file (red) using a \(T_{ro}\) value of 30.9 ms (iPhone 6) and matched against the reference signal (black). The bottom right corner shows the measure of similarity and dissimilarity between the extracted signal and the reference signal.  
Our results show that if a \(T_{ro}\) database of all cameras and the database of ENF reference signals are obtained, an ENF-containing video of unknown source can be analyzed using our approach to trace the source camera.