Detecting deepfake videos by visual-audio synchronism: work-in-progress

Different to traditional works on frame-level features and temporal characteristics, we propose a deepfake video detection method based on visual-audio synchronism, which compares the audio stream and the visual stream by an improved siamese neural network. We combine the audio stream and visual stream as a 2-channel input and design a 2-branches network to achieve the visual-audio synchronism detection. Preliminary experiments demonstrate the efficiency of the proposed method, which can achieve the highest accuracy compared with other existing methods.