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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • 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