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Deep learning model for deep fake face recognition and detection
Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By usi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044351/ https://www.ncbi.nlm.nih.gov/pubmed/35494811 http://dx.doi.org/10.7717/peerj-cs.881 |
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author | ST, Suganthi Ayoobkhan, Mohamed Uvaze Ahamed V, Krishna Kumar Bacanin, Nebojsa K, Venkatachalam Štěpán, Hubálovský Pavel, Trojovský |
author_facet | ST, Suganthi Ayoobkhan, Mohamed Uvaze Ahamed V, Krishna Kumar Bacanin, Nebojsa K, Venkatachalam Štěpán, Hubálovský Pavel, Trojovský |
author_sort | ST, Suganthi |
collection | PubMed |
description | Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace. |
format | Online Article Text |
id | pubmed-9044351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90443512022-04-28 Deep learning model for deep fake face recognition and detection ST, Suganthi Ayoobkhan, Mohamed Uvaze Ahamed V, Krishna Kumar Bacanin, Nebojsa K, Venkatachalam Štěpán, Hubálovský Pavel, Trojovský PeerJ Comput Sci Algorithms and Analysis of Algorithms Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace. PeerJ Inc. 2022-02-22 /pmc/articles/PMC9044351/ /pubmed/35494811 http://dx.doi.org/10.7717/peerj-cs.881 Text en ©2022 St et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms ST, Suganthi Ayoobkhan, Mohamed Uvaze Ahamed V, Krishna Kumar Bacanin, Nebojsa K, Venkatachalam Štěpán, Hubálovský Pavel, Trojovský Deep learning model for deep fake face recognition and detection |
title | Deep learning model for deep fake face recognition and detection |
title_full | Deep learning model for deep fake face recognition and detection |
title_fullStr | Deep learning model for deep fake face recognition and detection |
title_full_unstemmed | Deep learning model for deep fake face recognition and detection |
title_short | Deep learning model for deep fake face recognition and detection |
title_sort | deep learning model for deep fake face recognition and detection |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044351/ https://www.ncbi.nlm.nih.gov/pubmed/35494811 http://dx.doi.org/10.7717/peerj-cs.881 |
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