Visual Realism Assessment for Deepfake VideosDragar, Luka (Avtor) Emeršič, Žiga (Mentor) Batagelj, Borut (Komentor) deepfakevisual realismdeep learingIn this thesis, we tackle the issues of artificial intelligence and DeepFake technology, which in the era of rapid digitalization, pose significant security and privacy concerns. We focus on the assessment of quality and visual realism of DeepFakes, a key factor for the impact of a forged video. We introduce an effective approach for quantifying the visual realism of DeepFake videos, using an ensemble of ConvNext, a Convolutional Neural Network (CNN), and Eva, a vanilla Vision Transformer (ViT). These models were trained on a subset of the DeepFake Game Competition 2022 (DFGC 2022) dataset to regress to Mean Opinion Scores (MOS) from DeepFake videos. Our work yielded successful results, securing third place in the DeepFake Game Competition on Visual Realism Assessment (DFGC-VRA 2023). The thesis provides a detailed presentation of the employed models, data preprocessing procedures, and training, as well as a comparison of our results with other competitors.20232023-09-13 19:30:09Diplomsko delo/naloga150088VisID: 36871COBISS_ID: 168011011sl