# A New Approach for Deepfake Detection with the Choquet Fuzzy Integral
## 2 Materials and Methods
### Deepfake Video Detection  
The detection of deepfake videos employs various methods to identify fraudulent content. Numerous techniques have been developed for deepfake detection, leading to significant advancements in this field. The rapid development of deepfake technology in recent years has raised significant concerns, particularly regarding the lag in deepfake detection capabilities. In response to these concerns, major technology companies such as Facebook and Microsoft have initiated studies and provided support for research into deepfake detection. Additionally, competitions have been organized in this field, with the Deepfake Detection Challenge standing out as a prominent initiative. This challenge is a collaborative effort involving DARPA (the United States Defense Advanced Research Projects Agency), Facebook, Microsoft, and The Partnership on AI [13; 22]. Deepfake detection operates in a manner similar to the creation of deepfakes themselves. Neural networks, such as CNNs, RNNs, and DNNs, are commonly employed for this purpose. These networks are trained on datasets to recognize facial features and patterns indicative of manipulation. Detection methods encompass a range of techniques, including analyzing body language, detecting blinking patterns, assessing head position, and scrutinizing mimic movements. In deepfake detection, various factors are considered, such as biological signals, spatial and temporal feature analysis, head position, body language, and color inconsistencies. For instance, one approach involves the detection of forgery by analyzing blinking patterns. [PERSON] et al., in their forgery detection studies, successfully identified manipulation by analyzing eye blinks [23]. This method compares the number of blinks in a video subject to those of a normal individual, detecting discrepancies as indicative of forgery. Through blink analysis, the detection process discerns irregularities in blinking behavior, aiding in the identification of deepfake content. Many deepfake detection methods rely on the analysis of individual video frames [24]. When deepfake videos are created, the focus is often on a single frame, causing inconsistencies between frames. We can detect forgery by detecting the temporal and spatial differences between the frames by performing the forgery detection on many frames in stead of a single frame. In this detection method, a temporal sequence is created, and analysis is performed between them. We compared the deepfake detection approaches used today and those widely used recently in Table 2. According to the table, most of the detection applications have used CNN and LSTM in their models, rely on features in their content, and utilize videos as their application source. The structures used in artificial neural architecture, such as CNN, RNN, LSTM, RCN, LCRN, DNN, Capsule CNN, and AE have specific effective properties, which has led to the formation of many mixed architectures in applications. As a result, the unique features of the applications have come to the fore. When we look at the comparisons, it is seen that some applications are more accurate, some applications are faster, and some applications are better within each general area. The technology creates an architecture according to the solution method it focuses on and shows a more successful performance in that area. This situation is similar to a treatment being given according to the type of disease.