# A New Approach for Deepfake Detection with the Choquet Fuzzy Integral
## 5 Discussions  
Studies in the literature that aim to combine the results of different sub-methods are given in Table 7. In study [57], a combination of Google and FFDF datasets was used. A single result was obtained from six different ResNet version results (50, 50V2, 101, 101V2, 152, and 152V2) obtained with ensemble learning. As a result, a 95.5% AUC score was obtained.  
In study [58], the results of seven different networks, namely XceptionNet, InceptionV3, InceptionV2, MobilNet, ResNet101, DenseNet121, and DenseNet169, were given to a CNN classifier. While InceptionV3 had a minimum AUC value of 86.6% and a maximum AUC value of 97.6%, for Xception, an AUC value of 100% was reached with the proposed method. Results with different datasets were not included in the study. There is no information about the working times of the methods. In study [59], the results of different versions of EfficientNet were combined. In the study conducted on the FF-DF and DFDC datasets, the AUC values of 94.4% and 87.8% were obtained, respectively, when the B4, B4 ST, B4 Att, and B4 AttST models were used together. It is noteworthy that a higher AUC value of 88.1% was obtained for the DFDC dataset when only B4, B4 ST, and B4 Att were used, and without using B4 AttST. In study [60], an ensemble method is proposed with Xception, Xception with Attention, and EfficientNet with Attention models. In the study, metrics are given as a result of the methods, which worked separately on the DFDC and CelebDF datasets. The best results were obtained with Xception with Attention, with 97.2% for DFDC and 97.8% for CELEB-DF. The AUC values obtained with the proposed ensemble method were 97.5% for DFDC and 98.4% for CELEB-DF. It is seen that the application of the attention layer is an important contribution to the success of the method. Findings regarding the running time of the method are not included. In study [61], the results of 12 different ensemble methods and six different models (EfficientNet AttB4 ST-AttB4-B4 ST-B4, Xception, and ResNet) were combined. As a result of the training and testing operations performed on the FF-DF dataset, the highest individual performance was obtained with EfficientNet AttB4 and B4 ST, with 95.9%. In the ensemble methods, the lowest performance was obtained with the Max method, with 76.2%. The highest performance was obtained with Multi Layer Perception, with 98.4%. In the runtimes, provided without specifying the hardware features, the high runtime of the SVM-based method is striking. In addition, it is understood that the time elapsed for the ensemble method is given only with the data in relation to microseconds, and an impression cannot be obtained about the loads that the individual methods will bring to the system.  
As a result, in the studies mentioned, the individual method performance levels are generally high and the contributions obtained with the ensemble method are limited. In this respect, it is seen that the performance levels obtained with the help of the Choquet fuzzy integral are more effective in terms of the ensemble. In addition, up to seven different methods have been used in different studies, and although comparisons cannot be made in terms of working times, it is thought that the use of a large number of methods will increase the required resources.