Open AccessDeep learning model for deep fake face recognition and detection
Author(s) -
Suganthi ST,
Mohamed Uvaze Ahamed Ayoobkhan,
K. Vinoth Kumar,
Nebojša Bačanin,
K. Venkatachalam,
Štěpán Hubálovský,
Pavel Trojovský
Publication year - 2022
Publication title -
peerj. computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.806
H-Index - 24
ISSN - 2376-5992
DOI - 10.7717/peerj-cs.881
Subject(s) - deep learning , artificial intelligence , computer science , deep belief network , pattern recognition (psychology) , face (sociological concept) , classifier (uml) , convolutional neural network , computer vision , face detection , facial recognition system , histogram , histogram of oriented gradients , deep neural networks , image (mathematics) , social science , sociology
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.