Deep learning model for deep fake face recognition and detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019186" target="_blank" >RIV/62690094:18470/22:50019186 - isvavai.cz</a>
Result on the web
<a href="https://peerj.com/articles/cs-881/#" target="_blank" >https://peerj.com/articles/cs-881/#</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7717/peerj-cs.881" target="_blank" >10.7717/peerj-cs.881</a>
Alternative languages
Result language
angličtina
Original language name
Deep learning model for deep fake face recognition and detection
Original language 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PeerJ Computer Science
ISSN
2376-5992
e-ISSN
2376-5992
Volume of the periodical
8
Issue of the periodical within the volume
February
Country of publishing house
GB - UNITED KINGDOM
Number of pages
20
Pages from-to
"Article Number: e881"
UT code for WoS article
000763763300002
EID of the result in the Scopus database
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