# A New Approach for Deepfake Detection with the Choquet Fuzzy Integral
## 2 Materials and Methods
### Deepfake Video Creation  
\"Deepfake\" is a combination of the words \"deep\" and \"fake\", signifying the integration of deep learning and forgery. The first deepfake video was created by a Reddit user with the username \"deepfake\", resulting in the term \"deepfake\" being used to describe such videos ever since [17]. Deepfake technology generates fake images within videos by training the program on a dataset using a neural network. These fake images can be created through techniques such as face swapping, lip-syncing, and voice swapping. Deepfake creation techniques can be broadly categorized into two main groups: learning-based methods and approaches inspired by computer graphics. Examples of learning-based methods include Deepfakes, Faceswap, and FaceswapGAN [18]. Although there are various neural network architectures, deepfake videos are typically created using structures composed of variations and combinations of convolutional networks and encoder-decoder networks. Commonly used networks include Convolutional Autoencoders, Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). To create a deepfake, two components are needed, an encoder and a decoder. The encoder is utilized in both the creation and the training processes of fake videos. The decoder, on the other hand, is employed to differentiate between real and fake data. As shown in Figure 1, an input image is fed into to the encoder to generate fake videos. The encoder takes a sample face from this image, together with a sample face from another encoder. Subsequently, these samples are transmitted to the decoder. The decoder then merges the two incoming images to generate a fake image. Another method for creating deepfakes is through GANs (Generative Adversarial Networks), which consist of two neural networks working in opposition to each other. While the generator aims to produce realistic images (as illustrated in Figure 2), the discriminator attempts to differentiate between real and fake images. If the generator successfully deceives the discriminator, the discriminator adjusts its decision-making process based on the received feedback. Conversely, if the discriminator identifies the generated image as fake, the generator updates itself to generate more convincing images. Figure 1: General structure of autoencoders used to generate deepfake images [12]. Another technology employed in deepfake generation is the CNN (Convolutional Neural Network). CNNs learn hierarchical patterns from images and can be utilized to detect artifacts within images. Additionally, RNNs (Recurrent Neural Networks) are used in deepfake generation. RNNs are a type of neural network capable of processing sequential and variable-length data, essentially providing a form of memory within the network. This facilitates the exchange of information between previous and current states. Deepfake video generation can be classified into categories such as reenactment, replacement, editing, and synthesis [18]. A list of featured creation methods is provided in Table 1. It is evident from the table that fake images are generated by incorporating multiple features such as mouth, expression, pose, gaze, and body in the methods.