Beyond Detection: Visual Realism Assessment of Deepfakes

Understand

In the era of rapid digitalization and artificial intelligence advancements, the development of DeepFake technology has posed significant security and privacy concerns.

• This paper presents an effective measure to assess the visual realism of DeepFake videos.
• We utilize an ensemble of two Convolutional Neural Network (CNN) models: Eva and ConvNext.
• These models have been trained on the DeepFake Game Competition (DFGC) 2022 dataset and aim to predict Mean Opinion Scores (MOS) from DeepFake videos based on features extracted from sequences of frames.