# Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion
## 4 Experimental Results and Analyses
### Objective Performance Evaluation  
Table 1 lists the average PMR of the amplitude spectrum on NS and PS for different detection methods. On videos in any format in NS, the average PMR of the noise-level detection is higher than those of residual detection and similarity detection. On videos in QCIF, CIF and 1080P format in PS, the average PMR of the noise-level detection is also the highest among all methods. Considering all the instances in NS and PS, the average PMR on NS is 8.63 and 8.79 for residual detection and similarity detection, respectively; however, the average PMR on NS is only 2.62 for noise-level detection; the average PMR on PS of noise-level detection has 6.87 and 3.62 PMR gains when compared with those of residual detection and similarity detection. Table 1 also lists the relative difference \(\Delta\) between the average PMR values on NS and PS. A large \(\Delta\) value means the amplitude spectrum has a significant spike. For videos in any format, the \(\Delta\) value of the noise-level detection is higher than those of other detection methods. Considering all the instances in NS and PS, the \(\Delta\) value of the noise-level detection is 0.91, but the \(\Delta\) values of residual detection and similarity detection are 0.61 and 0.65, respectively. The results indicate that the noise-level detection obtains a large spike when compared with residual detection and similarity detection. The average PMR on NS of noise-level detection ranges in [2.42, 2.92] while the average PMR on PS of noise-level detection ranges in [27.32, 30.17]. There is obviously a big gap between the above two ranges, so the automatic detection can be realized by a hard threshold decision. According to the cross-validation proposed by [6], the threshold _Thr_ is set to be 5, and the FNR, FPR and DA of different detection methods are calculated as shown in Table 2. For residual detection and similarity detection, some mistakes occur on NS and, especially for videos in 1080 P format, the FNR value is up to 1.00, i.e., all original videos are misjudged as forged. For the noise-level detection, the FNR values are 0.00 for videos in QCIF, CIF and 720P format, and the FNR value is only 0.25 for videos in 1080P format. These results indicate that noise-level detection can accurately identify the original video. For videos in any format in PS, FPR values of noise-level detection are 0.00, which indicates that noise-level detection can accurately identify the forged video. Considering all the video sequences, the FNR and FPR values of noise-level detection are 0.04 and 0.00, respectively, and they are much lower than those of residual detection and similarity detection. The DA of the noise-level detection is 0.98 which is, respectively, 0.39 and 0.36 higher than those of residual detection and similarity detection. To summarize the above, the proposed noise-level detection guarantees a better detection performance when compared with residual detection and similarity detection. Figure 5: Illustrations of Fourier spectrums for both original and forged video sequences: (**a**) _Soccer_ sequence in QCIF format, (**b**) _Mobile_ sequence in CIF format, (**c**) _Mobcal_ sequence in 720P format, (**d**) _Tractor_ sequence in 1080P format.