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Deep Fake Video Detection Using Transfer Learning Approach
The usage of the internet as a fast medium for spreading fake news reinforces the requirement of computational utensils in order to fight for it. Fake videos also called deep fakes that create great intimidation in society in an assortment of social and political behaviour. It can also be utilized f...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552129/ https://www.ncbi.nlm.nih.gov/pubmed/36248771 http://dx.doi.org/10.1007/s13369-022-07321-3 |
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author | Suratkar, Shraddha Kazi, Faruk |
author_facet | Suratkar, Shraddha Kazi, Faruk |
author_sort | Suratkar, Shraddha |
collection | PubMed |
description | The usage of the internet as a fast medium for spreading fake news reinforces the requirement of computational utensils in order to fight for it. Fake videos also called deep fakes that create great intimidation in society in an assortment of social and political behaviour. It can also be utilized for malevolent intentions. Owing to the availability of deep fake generation algorithms at cheap computation power in cloud platforms, realistic fake videos or images are created. However, it is more critical to detect fake content because of the increased complexity of leveraging various approaches to smudge the tampering. Therefore, this work proposes a novel framework to detect fake videos through the utilization of transfer learning in autoencoders and a hybrid model of convolutional neural networks (CNN) and Recurrent neural networks (RNN). Unseen test input data are investigated to check the generalizability of the model. Also, the effect of residual image input on accuracy of the model is analyzed. Results are presented for both, with and without transfer learning to validate the effectiveness of transfer learning. |
format | Online Article Text |
id | pubmed-9552129 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-95521292022-10-11 Deep Fake Video Detection Using Transfer Learning Approach Suratkar, Shraddha Kazi, Faruk Arab J Sci Eng Research Article-Computer Engineering and Computer Science The usage of the internet as a fast medium for spreading fake news reinforces the requirement of computational utensils in order to fight for it. Fake videos also called deep fakes that create great intimidation in society in an assortment of social and political behaviour. It can also be utilized for malevolent intentions. Owing to the availability of deep fake generation algorithms at cheap computation power in cloud platforms, realistic fake videos or images are created. However, it is more critical to detect fake content because of the increased complexity of leveraging various approaches to smudge the tampering. Therefore, this work proposes a novel framework to detect fake videos through the utilization of transfer learning in autoencoders and a hybrid model of convolutional neural networks (CNN) and Recurrent neural networks (RNN). Unseen test input data are investigated to check the generalizability of the model. Also, the effect of residual image input on accuracy of the model is analyzed. Results are presented for both, with and without transfer learning to validate the effectiveness of transfer learning. Springer Berlin Heidelberg 2022-10-11 /pmc/articles/PMC9552129/ /pubmed/36248771 http://dx.doi.org/10.1007/s13369-022-07321-3 Text en © King Fahd University of Petroleum & Minerals 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Article-Computer Engineering and Computer Science Suratkar, Shraddha Kazi, Faruk Deep Fake Video Detection Using Transfer Learning Approach |
title | Deep Fake Video Detection Using Transfer Learning Approach |
title_full | Deep Fake Video Detection Using Transfer Learning Approach |
title_fullStr | Deep Fake Video Detection Using Transfer Learning Approach |
title_full_unstemmed | Deep Fake Video Detection Using Transfer Learning Approach |
title_short | Deep Fake Video Detection Using Transfer Learning Approach |
title_sort | deep fake video detection using transfer learning approach |
topic | Research Article-Computer Engineering and Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552129/ https://www.ncbi.nlm.nih.gov/pubmed/36248771 http://dx.doi.org/10.1007/s13369-022-07321-3 |
work_keys_str_mv | AT suratkarshraddha deepfakevideodetectionusingtransferlearningapproach AT kazifaruk deepfakevideodetectionusingtransferlearningapproach |