## 4 Experiment
### Threshold Estimation and Scenario-Based Experimental Results
#### 4.2.3 Scenario 2
For the original video BNM_O and the manipulated videos BNM_E1 and BNM_E2, the comparison was processed from the middle frame of among all extracted frames in each of the videos in accord with the pseudocode shown in Figure 3. Within an extremely short time, it was concluded that they were similar, and thus, the search process stopped. Figure 10b illustrates the results from the intersection similarity comparison between Query Video_1 and the videos in the suspect's mobile device under scenario 2. The intersection algorithm also searched for a similar video on the basis of the BNM threshold in Table 5. For two videos irrelevant to the Query video_1 (DNM_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 higher, which is the threshold of the intersection algorithm in the BNM environment. Therefore, once all frames were searched, the comparison process moved to the next video. For the original video BNM_O and the manipulated videos BNM_E1 and BNM_E2, the comparison was processed from the middle frame from among all extracted frames in each of the videos in accord with the pseudocode shown in Figure 3. Within an extremely short time, it was concluded that they were similar, and thus the search process stopped. Figure 10c illustrates the result from the Bhattacharyya similarity comparison between Query Video_1 and the videos in the suspect's mobile device under scenario 2. The Bhattacharyya algorithm also searched for a similar video based on the BNM threshold, as shown in Table 5. For two videos irrelevant to Query video_1 (DNM_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 a BNM environment. Therefore, once all frames were searched, the comparison process moved to the next video. For the original video BNM_O and the manipulated videos BNM_E1 and BNM_E2, a comparison was carried out from the middle of all extracted frames for each of the videos in accord with the pseudocode shown in Figure 3. Within an extremely short time, it was concluded that they were similar, and thus the search process stopped. For readability, the graphs in Figure 10 have no parts without data after adjusting the \(y\)-axis. Figure 9: Comparison in scenario 2. The results of the experiment showed that BNM_O, BNM_E1, and BNM_E2 were similar. Except for similar videos, DNM_D was compared with BNH_D on the basis of the query video (2). Figure 10(a) illustrates the correlation comparison between two videos (DNM_D and BNH_D) on the basis of Query Video_2. The optimization search began from the middle of the video in line with the pseudocode shown in Figure 3.0 As a result, no frame had a similarity of 0.47 or more, which is the threshold of the correlation algorithm in the BNM environment, similar to Query Video_1. Figure 10(b) illustrates the intersection comparison between the two videos (DNM_D and BNH_D) based on Query Video_2. The optimization search began from the middle of the video in line with the pseudocode shown in Figure 3.1 As a result, no frame had a similarity of 0.48 or more, which is the threshold of the intersection algorithm in the BNM environment, similar to Query Video_1. Figure 10(c) illustrates the Bhattacharyya comparison between the two videos (DNM_D and BNH_D) on the basis of Query Video_2. The optimization search began from the middle of the video in line with the pseudocode shown in Figure 3.2 As a result, no frame had a similarity of \(\geq\)0.52, which is the threshold of the Bhattacharyya algorithm in the BNM environment, similar to Query Video_1. In terms of Query Video_2, the results from the comparison between DNM_D and BNH_D were not within the range of the thresholds. Therefore, there were no similar videos. Figure 3.3 Algorithm for similarity comparison between Query video_1 and other videos (scenario 2).