# A New Approach for Deepfake Detection with the Choquet Fuzzy Integral
## 4 Experimental Results  
The characteristics of the datasets used for the test are briefly explained in Table 3. These datasets are the most preferred datasets used in training and test data in other methods. As seen in Table 3, the numbers of videos in the datasets, the durations of the videos, the numbers of frames in the videos, the resolution of the videos, and the file formats of the videos affect the training performance of the application. They also affect the speed, accuracy, and production quality of the application. In addition, the dataset should be suitable for the focused algorithm in the detection method. For example, in an application that detects inconsistencies in head poses, a dataset created for artificial blinks should not be used in the training process. The effect of datasets is also clearly seen in the accuracy performance of applications. Table 3 shows that the test accuracy rate of the dataset used in the training set is different when compared to another dataset. Some applications deal with neural network and feature extraction or temporal inconsistencies between frames, while some applications use both [25; 26; 35]. We randomly selected 100 data elements from the DFDC, CELEB-DF, and FF-DF datasets; this is called the \"custom dataset\" in the rest of the paper. The custom dataset \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Real**} & \multicolumn{2}{c}{**Fake**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Resolution**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Actor**} \\ \cline{2-2} \cline{5-6}  & **Video** & & & & & & & & \\ \hline DeepFake-TIMIT- & \multirow{2}{*}{320} & \multirow{2}{*}{34.0 k} & \multirow{2}{*}{320} & \multirow{2}{*}{34.0 k} & \multirow{2}{*}{2018} & \multirow{2}{*}{64} & faceswap-GAN & VidTIMITDataset & 32 \\ LQ (DF-TIMIT) [7] & & & & & & & & \\ \hline DeepFake-TIMIT- & \multirow{2}{*}{320} & \multirow{2}{*}{34.0 k} & \multirow{2}{*}{320} & \multirow{2}{*}{34.0 k} & \multirow{2}{*}{2018} & \multirow{2}{*}{128} & faceswap-GAN & VidTIMITDataset & 32 \\ HQ [7] & & & & & & & & \\ \hline FaceForensics++ & \multirow{2}{*}{1000} & \multirow{2}{*}{509.9 k} & \multirow{2}{*}{1000} & \multirow{2}{*}{509.9 k} & \multirow{2}{*}{2019} & DeepFakes, & \multirow{2}{*}{YouTube} & \multirow{2}{*}{977} \\ (FF-DF) [41] & & & & & & & & \\ \hline Facebook DeepFake & \multirow{2}{*}{1131} & \multirow{2}{*}{488.4 k} & \multirow{2}{*}{4119} & \multirow{2}{*}{1783.3 k} & \multirow{2}{*}{2019} & \multirow{2}{*}{480, 720,} & \multirow{2}{*}{8 different methods:} & \multirow{2}{*}{Volunteer Actors} & \multirow{2}{*}{960} \\ Challenge & \multirow{2}{*}{1131} & \multirow{2}{*}{488.4 k} & \multirow{2}{*}{4119} & \multirow{2}{*}{1783.3 k} & \multirow{2}{*}{2019} & \multirow{2}{*}{256} & DeepFake synthesis algorithm & YouTube & 59 \\ \hline CELEB-DF [43] & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of datasets. Figure 9: Calculating combinational g values for the classical Choquet fuzzy integral; x = Expert/classifier, g = Choquet fuzzy measure values. The sample values are shown in black [52]. is used for the test algorithm. We ran each algorithm alone on these videos and recorded the detection results, and then we ran all three algorithms on the same video and gave the detection results to the Choquet fuzzy integral as an input value, giving our algorithms importance degrees according to the detection results they gave. We converted the values we received into accuracy rates and recorded them as experimental results. After the individual weight or the fuzzy density for each detection algorithm is obtained, the fuzzy measures which represent the degrees of interdependence for all subsets of the deepfake detection algorithms can be calculated by using (1). Since there are three inputs being considered, there are eight subsets available. Table 4 shows results of the measures for all subsets.