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In the era of rapid digitalization and artificial intelligence advancements,the development of DeepFake technology has posed significant security andprivacy concerns. This paper presents an effective measure to assess the visualrealism of DeepFake videos. We utilize an ensemble of two Convolutional NeuralNetwork (CNN) models: Eva and ConvNext. These models have been trained on theDeepFake Game Competition (DFGC) 2022 dataset and aim to predict Mean OpinionScores (MOS) from DeepFake videos based on features extracted from sequences offrames. Our method secured the third place in the recent DFGC on Visual RealismAssessment held in conjunction with the 2023 International Joint Conference onBiometrics (IJCB 2023). We provide an over\-view of the models, datapreprocessing, and training procedures. We also report the performance of ourmodels against the competition's baseline model and discuss the implications ofour findings.

Beyond Detection: Visual Realism Assessment of Deepfakes