Deep learning-based video forgery detection
The harm of deepfake is becoming more and more serious in today’s new media era, especially in video deepfake. Therefore, we conduct experiments on two public video datasets Celeb-DF-v2, DFDC and a relabelled TMC media dataset, using an end-to-end structure of video input and video classification ou...
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Nanyang Technological University
2022
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sg-ntu-dr.10356-1575822022-05-12T11:49:30Z Deep learning-based video forgery detection Cao, Xinyi Alex Chichung Kot School of Electrical and Electronic Engineering EACKOT@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering::Electronic systems::Signal processing The harm of deepfake is becoming more and more serious in today’s new media era, especially in video deepfake. Therefore, we conduct experiments on two public video datasets Celeb-DF-v2, DFDC and a relabelled TMC media dataset, using an end-to-end structure of video input and video classification output, combining the state-of-the-art Convolutional Neural Network (CNN) models with the Vision Transformer architecture and the Long Short-Term Memory (LSTM) architecture. It is found that the longer the frame length of the video, the more accurate the detection. In the case of video length of 30 frames, we obtain competitive AUC scores of 0.932 on the DFDC dataset, 0.980 on the Celeb-DF-V2 dataset and 0.953 on the TMC dataset. Master of Science (Signal Processing) 2022-05-12T11:49:30Z 2022-05-12T11:49:30Z 2022 Thesis-Master by Coursework Cao, X. (2022). Deep learning-based video forgery detection. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157582 https://hdl.handle.net/10356/157582 en application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Cao, Xinyi Deep learning-based video forgery detection |
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The harm of deepfake is becoming more and more serious in today’s new media era, especially in video deepfake. Therefore, we conduct experiments on two public video datasets Celeb-DF-v2, DFDC and a relabelled TMC media dataset, using an end-to-end structure of video input and video classification output, combining the state-of-the-art Convolutional Neural Network (CNN) models with the Vision Transformer architecture and the Long Short-Term Memory (LSTM) architecture. It is found that the longer the frame length of the video, the more accurate the detection. In the case of video length of 30 frames, we obtain competitive AUC scores of 0.932 on the DFDC dataset, 0.980 on the Celeb-DF-V2 dataset and 0.953 on the TMC dataset. |
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Alex Chichung Kot |
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Alex Chichung Kot Cao, Xinyi |
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Thesis-Master by Coursework |
author |
Cao, Xinyi |
author_sort |
Cao, Xinyi |
title |
Deep learning-based video forgery detection |
title_short |
Deep learning-based video forgery detection |
title_full |
Deep learning-based video forgery detection |
title_fullStr |
Deep learning-based video forgery detection |
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Deep learning-based video forgery detection |
title_sort |
deep learning-based video forgery detection |
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Nanyang Technological University |
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2022 |
url |
https://hdl.handle.net/10356/157582 |
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1734310307953639424 |