# GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer
## 1 Introduction  
Deepfakes are hyper-realistic manipulated media generated using various advanced deep learning (DL) techniques. Deepfake videos are produced by superimposing one person's facial features onto another person's face through techniques such as face replacement, facial reenactment, face editing, and complete face synthesis [1, 2], among others. Recently, deepfakes have become a public concern due to their potential for misuse and the spread of false information [3, 4, 5]. Various deepfake creation methods are readily available for use, and anyone interested can use them to easily modify existing media, presenting a false representation of reality where individuals appear to be speaking or performing actions that never actually took place. Additionally, the manipulation of these videos is carried out on a frame-by-frame basis, making deepfakes seem more realistic and believable. Consequently, deepfake creation techniques have been used to manipulate images andvideos, causing celebrities and politicians to be depicted as saying or doing things that are untrue. The applications of deepfake videos can range from creative uses such as creating realistic special effects or replacing an actor in the film and entertainment industry to potentially harmful attacks, such as using deepfakes to create misleading videos for criminal purposes [6,7]. In the realm of politics, deepfakes have been utilized to create fake political videos, war propaganda, and videos intended to influence elections. This has led to widespread concern about the potential for malicious actors to use deepfakes to spread false information, erode the credibility of political candidates, or even pose a threat to national security. The dissemination of misleading and false information through deepfakes can have real-life consequences, making it imperative to address the need for accurate and reliable deepfake video detection techniques. Deepfake video detection attempts to detect whether a given video has been tampered with or manipulated using deepfake techniques. The challenge of detecting deepfakes has inspired researchers and technology companies to develop various deep learning methods to identify tampered videos [1,3,8,9,10,11]. Currently, deepfake detection methods often rely heavily on visual features [12, 13, 14, 15]. Visual features-based detection can be on a frame-by-frame basis [16] or use the temporal relationship in the differences between frames over time [17,18]. However, these methods mostly encounter challenges in detecting deepfakes that differ from their training data, thus failing to detect deepfakes sufficiently. In recent years, the rapid progress made in advanced generative models like Generative Adversarial Networks (GANs) [19, 20, 21], Variational Autoencoders (VAEs) [22,23], and Diffusion Models (DMs) [24] has made it even more challenging to detect deepfakes based on visual artifacts alone, as deepfakes leave little to no trace of visual clues that help to distinguish them from images of the real world. Several authors have proposed alternative detection methods, such as utilizing biological signals [25], geometric features [26], frequency information [27], spatial features with temporal information [28,29] and generative adversarial network fingerprints [30], as potential solutions to the difficulties in detecting deepfakes. Another approach to spotting deepfake videos involves examining the consistency of pixels or groups of similar pixels [31]. Current deepfake video detection methods generate competitive results when it comes to identifying manipulated videos, but they often fail to generalize to more diverse videos, particularly in environments with different facial poses, light angles, and movements [32]. In an effort to provide more generalization, we propose a novel architecture called the Generative Convolutional Vision Transformer (GenConViT), aimed at detecting a diverse set of fake videos.