Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/157582
Title: | Deep learning-based video forgery detection | Authors: | Cao, Xinyi | Keywords: | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
Issue Date: | 2022 | Publisher: | Nanyang Technological University | Source: | Cao, X. (2022). Deep learning-based video forgery detection. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157582 | Abstract: | 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. | URI: | https://hdl.handle.net/10356/157582 | Schools: | School of Electrical and Electronic Engineering | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Theses |
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File | Description | Size | Format | |
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CAOXINYI_dissertation.pdf Restricted Access | Deep Learning-Based Video Forgery Detection | 2.34 MB | Adobe PDF | View/Open |
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