Frequency-based Deep-Fake Video Detection using Deep Learning Methods

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ID: 313221
2023
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Abstract
Deep Learning (DL) is an advanced and effective technology widely used in diverse industries, including medical imaging (MI). Data Mining (DM), Image Processing (IP), and Machine Vision (DM). Deep-fake uses DL technology to alter videos to render them indistinguishable from the original humans. The effectiveness of deep-fake has recently obtained significant attention from researchers, and numerous DL-based techniques have been developed to identify deep-fake videos. In this paper, a novel deep-fake video detection method is proposed. The Deep Fake Detection Challenge (DFDC) and Face Forensic datasets were used in the research. In addition, frequency-based frame extraction was conducted on each video during the preprocessing stage. Convolutional Neural Networks (CNN) Long Short-Term Memory (LSTM) - CNN  techniques were used to identify fake videos. The LSTM-CNN approach achieved an accuracy of 82%. To identify fake videos using DL techniques, this work will be helpful to researchers.
Reference Key
imported_1777059864_69ebc8187a903 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Anam Anwar
Journal Journal of Computing & Biomedical Informatics
Year 2023
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/122
Keywords Keywords not found

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