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Deepfake Detection using Supervised Temporal Feature Extraction model and LSTM
Chunghwan Lee(이정환),Jaihoon Kim(김재훈),Kijung Yoon(윤기중) 한국방송·미디어공학회 2021 한국방송공학회 학술발표대회 논문집 Vol.2021 No.11
As deep learning technologies becoming developed, realistic fake videos synthesized by deep learning models called “Deepfake” videos became even more difficult to distinguish from original videos. As fake news or Deepfake blackmailing are causing confusion and serious problems, this paper suggests a novel model detecting Deepfake videos. We chose Residual Convolutional Neural Network (Resnet50) as an extraction model and Long Short-Term Memory (LSTM) which is a form of Recurrent Neural Network (RNN) as a classification model. We adopted cosine similarity with hinge loss to train our extraction model in embedding the features of Deepfake and original video. The result in this paper demonstrates that temporal features in the videos are essential for detecting Deepfake videos.