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. 2022 Feb 22:8:e881.
doi: 10.7717/peerj-cs.881. eCollection 2022.

Deep learning model for deep fake face recognition and detection

Suganthi St  1 , Mohamed Uvaze Ahamed Ayoobkhan  2 , Krishna Kumar V  3 , Nebojsa Bacanin  4 , Venkatachalam K  5 , Hubálovský Štěpán  5 , Trojovský Pavel  6
Affiliations

Deep learning model for deep fake face recognition and detection

Suganthi St et al. PeerJ Comput Sci. .

Abstract

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.

Keywords: DBN; Deep fake; Deep learning; Fisherface; LBPH; RBM.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Architecture of proposed work.
Figure 2
Figure 2. Pre-processing.
Figure 3
Figure 3. DBN with RBM.
Figure 4
Figure 4. Error rate in accuracy.
Figure 5
Figure 5. Computation time.
Figure 6
Figure 6. Training & validation accuracy and loss in proposed work.
See this image and copyright information in PMC

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