## 6 Experimental Results and Discussion
### Temporal Recovery Detection Results Using the Proposed Model based on Multifractal Features
In general, in decoding processes, there may be slight differences in trace sequences due to testing environment. Here, by repeating the process and testing different types of content, the model can be considered as robust. For content creation, action cameras like GoPro can be used. Also, for editing like audio removal, ffmpeg can be applied. Moreover, precision editing can be one of the possible challenges which need to be further analyzed. For content creation, it is possible to set prior acquisition time. Nevertheless, nonadequate editing such as trimming for modern standards like HEVC may produce corrupted video as an unwanted effect, which is well known in the forensic field. The proposed model was not developed for any broadcasting. So far, parsing was performed for sequences of particular length in offline mode, meaning that a significant amount of data are available for obtaining the trace sequences and applying the model. However, the tested duration can be treated as a minimal requirement for the method compared with the commonly used long movie trace sequences [58]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Classifier Type** & **True Positive Rate (TPR)** & **Positive Predictive Value (PPV)** & **Accuracy (Acc)** \\ \hline
1 & WkNN with Euclidean metric (13) & 66.7 & 80 & 75 \\
2 & WkNN with Cityblock metric (14) & 70 & 80.8 & 76.7 \\
3 & WkNN with Mahalanobis metric (15) & 96.7 & 100 & 98.3 \\  & (proposed approach) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of WkNN classifier with different metrics. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Classifier Type** & **True Positive Rate (TPR)** & **Positive Predictive Value (PPV)** & **Accuracy (Acc)** \\ \hline
1 & kNN & 70 & 77.8 & 75 \\
2 & Decision tree & 60 & 56.3 & 56.7 \\
3 & Linear SVM & 46.7 & 56 & 55 \\
4 & Cubic SVM & 93.3 & 90.3 & 91.7 \\
5 & Quadratic SVM & 89.3 & 83.3 & 86.7 \\
6 & WkNN with Mahalanobis metric & 96.7 & 100 & 98.3 \\  & (proposed approach) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of different classifiers in temporal resolution detection. One of the advantages of the proposed approach is found in the selection of a small number of features that do not rely on a reference. In opposition to this, it is expected that including reference or pseudoreference calculations may additionally improve results, which is conducted in recent HFR works for HEVC content of different crf values for perceptual and objective quality assessment [32; 67]. Also, multifractal and fractal approaches have been applied on previous compression standards like LFR/SFR MPEG-4 content using frame size video traces [56; 57; 58; 59]. TRR detection related to HEVC HFR has not been performed so far to the author's knowledge. The effects have not yet been thoroughly investigated in the research community due to difficulties found when dealing with HFR and possible corresponding distortions [10]. HFR changes in temporal resolution affect video content, which is presented here from the LRD and multifractal standpoint. Temporal recovery detection results using the proposed model based on WkNN and specific multifractal features show satisfying performance. The TRR detection approach can be considered useful for HEVC HFR compressed data of different compression quality.