# A New Approach for Deepfake Detection with the Choquet Fuzzy Integral
## 6 Conclusions  
Images/videos produced with deepfake technology have started to become more realistic in recent years. Today, deepfake technologies are used in sectors such as movies and games, as well as for malicious purposes. Damage can be prevented by detecting deepfakes used for malicious purposes such as blackmail, fraud, and humiliation.  
Algorithms generally used in deepfake detection focus on the shortcomings of deepfake production methods. Some methods are better at detecting real entries, and some methods are better at detecting fake entries; XceptionNet and EfficientNet are corresponding examples. A Choquet fuzzy integral-based method is proposed in this study to increase the overall performance by using the advantages of both algorithms for deepfake detection. In the proposed method, the outputs obtained from running Xception, EfficientNet, and their hybrid are given as input to the Choquet fuzzy integral.  
The FaceForensics++, DFDC, Celeb-DF, and DeepFake-TIMIT datasets were used in the study. Firstly, it has been confirmed by the accuracy and f1-score metrics presented above that the method increases the performance on the compiled dataset, which is a subset of these datasets. Then, in order to make comparisons, all datasets were tested separately, and comparisons were made based on AUC values generally used in the literature. In the comparisons given, the outputs obtained from the proposed method, as well as instances run with DDA1 (XceptionNet based), DDA2 (hybrid use), and DDA3 (EfficientNet based) are given for each dataset. While the best result for the DFDC dataset was obtained from the DDA 3 model, with 87.98% AUC, 95.3% AUC was obtained using the Choquet fuzzy integral with the proposed method. It can be seen that while the maximum CELEB-DF AUC value is 77.45% with the DDA 1 algorithm, it increases to 92.6% with the proposed method. Similarly, FF-DF achieved the best AUC, 95.50%, with the DDA 1 model, and this rate was increased to 99.8% with the proposed method. Finally, DDA 2 gave the highest AUC value for the DF-TIMIT dataset, with 74.1%. With the proposed method, this value was increased to 89.7%.  
The results show that the performance for each dataset is improved compared to using the methods separately. When compared to studies in the literature, it can be seen that the results are superior to those of other studies, with 99.8% success for the FaceForensics++ dataset. As to consideration of the inference working time of the proposed method, it appears to be less than twice the working time of an individual method.  
In our study, it is seen that higher performance levels than the maximum performance can be achieved by combining different algorithms using the Choquet fuzzy integral. It seems that it would be possible to further increase the performance by changing the methods used in the Choquet fuzzy integral method. In future studies, research will be conducted on scenarios increasing the number of models and using generalized Choquet integrals.  
Conceptualization, M.K.; Methodology, M.K.; Software, I.I. and S.A.; Formal analysis, H.Y.; Investigation, H.Y.; Writing--original draft, I.I. and S.A.; Writing--review and editing, H.Y.; Visualization, H.Y.; Project administration, M.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 122E676. The authors thank TUBITAK for their support. This study was supported by FIRAT University Scientific Research Projects Unit (FUBAP) under Grant Number MF.24.44. The authors thank FUBAP for their support.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The work for this article was carried out within the scope of [PERSON]'s doctoral dissertation, named \"Real-Time Detection of Deepfake Videos Using Machine Learning\". The supervisor of the thesis: [PERSON].  
The authors declare no conflicts of interest.