# A New Approach for Deepfake Detection with the Choquet Fuzzy Integral
## 1 Introduction  
With the rapid advancement of deep learning technology in recent years, its use in multimedia is also becoming widespread. Work that could previously take a long time to be performed with image/video processing tools that required expertise has become very short in duration and practical with artificial intelligence technology (AI). With the development of this technology, AI tools have begun to achieve very successful results. The images/videos obtained through deep learning-based methods such as Autoencoders and GAN methods are called deepfakes [1]. Deepfake technology is used in the creative arts, advertising, the film industry, and video games [2]. Another example of the integration of the deepfake with today's technologies is its use in the field of virtual reality [3]. In addition to all these advantages, deepfake technology also has malicious uses. Successful deepfake images/videos, which are easily produced, have become available to anyone, which has led to the spread of deepfake content in the media. Examples of misuse of this technologyhave found wide coverage in the media and have been the subject of films. Deepfake has become a daily concern these days. Deepfake videos are often created by changing head positions on human images, switching between faces, and using methods such as lip syncing to manipulate speech. Although these methods are quite realistic and convincing, many famous companies, such as Kaggle and Facebook, organize competitions to combat them [4]. The number of studies on deepfake detection methods has increased, focusing on the disadvantages of data created with deepfake methods, such as body position, head position, number of blinks according to gender and age, color differences between images, time and space incompatibilities, and voice and noise inconsistencies [5]. Deepfake detection methods are also a subject of recent work. This field has been developed over the years and has been associated with deepfake creation methods. With this development, it has recently begun to be widely discussed in the field of security. Many different datasets have emerged with the remarkable work of users in this field. [PERSON] et al. worked on deepfake detection by using a new activation function and developed MesoNet [6]. [PERSON] et al. analyzed methods used in deepfake detection and presented the first publicly available deepfake video dataset [7]. [PERSON] et al. developed a new methodology using XGBoost, a top-level CNN network identifier, for deepfake detection [8]. [PERSON] et al. employed a detection technique that identifies visual artifacts in images on social media to detect forgery [9]. [PERSON] et al. proposed a deepfake algorithm called DeepfakeNet, which consists of 20 network layers, and studied its performance using the FF+, Kaggle, and TIMIT datasets [10]. [PERSON] extensively reviewed the work on deepfake detection and compared existing studies in the field [11]. [PERSON] provided information about current deepfake creation and detection techniques and compared various datasets [12]. [PERSON] et al. conducted a comparative analysis of deepfake detection methods, explaining their methodologies and presenting performance results in tables [13]. Factors affecting detection methods include training data, image quality and resolution, preprocessing, artificial neural architecture, and classification methods. In another study, its authors presented a more efficient method by combining the EfficientNet and Xception methods [14]. [PERSON] et al. proposed a method for detecting COVID-19 disease using X-ray images of patients. In their method, they used the Choquet fuzzy integral, which combines the InceptionV3, DenseNet121, and VGG19 models, achieving an accuracy of 99% in classification processes [15]. They also proposed a new collection of fuzzy integral-based multi-classifiers to improve the accuracy of Android malware classification. To increase the accuracy of fuzzy integral-based classification applications, a multi-classifier ensemble was used in another study.