Deep learning model for deep fake face recognition and detection

被引：0

作者：

Suganthi S.T. ^{[1
]}

Ayoobkhan M.U.A. ^{[2
]}

Kumar V.K. ^{[3
]}

Bacanin N. ^{[4
]}

Venkatachalam K. ^{[5
]}

Stepán H. ^{[5
]}

Pavel T. ^{[6
]}

机构：

[1] Department of Computer Engineering, Lebanese French University, Erbil

[2] Computing Department, Westminster International University in Tashkent, Tashkent

[3] Department of Computer Science Engineering, Sri Ramakrishna Engineering College, Coimbatore

[4] Department of Computing, Singidunum University, Belgrade

[5] Department of Applied Cybernetics, Faculty of Science, University of Hradec Kralove, Hradec Kralove

[6] Department of Mathematics, Faculty of Science, University of Hradec Kralove, Hradec Kralove

来源：

PeerJ Computer Science | 2022年 / 8卷

关键词：

Dbn; Deep fake; Deep learning; Fisherface; Lbph; Rbm;

D O I：

10.7717/PEERJ-CS.881

中图分类号：

学科分类号：

摘要：

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. © Copyright 2022 St et al.

引用

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