## 4 Experiment
### Threshold Estimation and Scenario-Based Experimental Results
#### 4.2.2 Scenario 1
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video Name** & **Running Time** & **File Size** &
\begin{tabular}{c} **Frame (Width \(\times\)** \\ **Height)** \\ \end{tabular} & **Frame Speed** \\ \hline DNM\_D & 31 s & 11.9 MB & 1920 \(\times\) 1088 & 24.00 frames/s \\ BNM\_O & 9 min 14 s & 210 MB & 1920 \(\times\) 1088 & 29.97 frames/s \\ BNM\_D & 10 s & 3.9 MB & 1920 \(\times\) 1088 & 29.98 frames/s \\ BNH\_D & 9 min 14 s & 210 MB & 1920 \(\times\) 1088 & 29.97 frames/s \\ BNM\_E & 2 min 10 s & 49.8 MB & 1920 \(\times\) 1088 & 29.97 frames/s \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Videos in suspect’s smart phone (scenario 1). Figure 8b illustrates the results of the intersection similarity comparison with the videos in the suspect's mobile device under scenario 1. The intersection algorithm also searched for a similar video on the basis of the BNM threshold shown in Table 5. For three videos irrelevant to the query video (DNM_D, BNM_D, and BNH_D), the optimization search began from the middle of the video in line with the pseudocode shown in Figure 3. As a result, no frame had a similarity of 0.48 or more, which is the threshold of the intersection algorithm within the BNM environment. Therefore, after all frames were searched, the comparison process went to the next video. For the original video BNM_O and the manipulated video BNM_E, a comparison was carried out from the middle of the total extracted frames in each of the two videos in accord with the pseudocode shown in Figure 3. As with the correlation algorithm, the intersection algorithm determined within an extremely short time that they were similar, and thus the search stopped. Figure 8c illustrates the results from the Bhattacharyya similarity comparison on the videos in the suspect's mobile device under scenario 1. A similar video was identified based on the BNM threshold in Table 5. For the three videos irrelevant to the query video (DNM_D, BNM_D, and BNH_D), the optimization search began from the middle of the video in line with the pseudocode shown in Figure 3. As a result, no frame had a similarity of \(\leq\)0.52, which is the threshold of the Bhattacharyya algorithm in the BNM environment. Therefore, once all frames were searched, the comparison process moved to the next video. As for the original video BNM_O and the manipulated video BNM_E, the comparison was carried out from the middle of the second with the middle of the total extracted frames in each of the two videos in accord with the pseudocode shown in Figure 3. Similar to the correlation and intersection algorithms, the Bhattacharyya algorithm determined within an extremely short time that they were similar, and thus the search stopped. For readability, the graphs in Figure 8 do not show the parts without data after adjusting the y-axis. Using a manipulated video, a forensic investigation was applied to the suspect's mobile device. As a result, it was possible to find not only a manipulated video  
Figure 8: Algorithm similarity comparisons between query video and other videos (scenario 1). but also the original video. Only 30% of the total frames of the original and manipulated videos were investigated. Nevertheless, it is possible to identify that they were similar. Once an original video is secured, if victims are shown or certain scenes in the original video are edited separately, it is possible to find all edited videos and reveal other crimes. This experiment is applicable to various real-world cases, such as the gradually increasing amount of revenge pornography, threatening acquaintances over social media, and the SNS distribution of illegally filmed videos after file size reduction.