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307056

DeepFakeDG: A Deep Learning Approach for Deep Fake Detection and Generation

Article

Last updated: 24 Dec 2024

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Tags

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Abstract

The main idea of this project is to develop a web application that can help to detect whether the input data we provide of people, whether celebrities or people in general, are real or fake and generate deepfakes themselves. Recently, with the evolution of technology and advanced image editing tools, people can easily get manipulated, as deepfake algorithms can easily create fake videos and images that people can't distinguish from authentic ones, an emerging problem threatening the trustworthiness of online information. Deepfakes mainly affect public figures, celebrities, and politicians. Forged videos are videos that contain fake images over real ones. In this research, there are methods used with Machine and deep learning approaches that will be used with the dataset composed of deep fake videos and authentic ones to detect these manipulations and protect the government from criminals. There will be various techniques used to distinguish real from fake using face swapping, or is there something off regarding its behavior, or if a voice of a person is used with another person's voice, etc. The deep fake detector can be used in courts and police stations to reduce the likelihood of crimes and frauds that may happen and detect them. This project aims to make a website to detect whether videos are fake or not. More and above, the proposed model will also provide a deepfake generation efficiency.

DOI

10.21608/jocc.2023.307056

Keywords

neural machine translation, Sequence to Sequence Model, Sign Language, Deep learning, Transformer

Authors

First Name

Diaa

Last Name

AbdElminaam

MiddleName

s

Affiliation

Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt

Email

diaa.salama@miuegypt.edu.eg

City

-

Orcid

0000-0002-1544-9906

First Name

Natalie

Last Name

Sherif

MiddleName

-

Affiliation

Faculty of computer science ; Misr International University

Email

natalie1901991@miuegypt.edu.eg

City

cairo

Orcid

-

First Name

zeina

Last Name

Ayman

MiddleName

-

Affiliation

Faculty of computer science ; Misr International University

Email

zeina1900600@miuegypt.edu.eg

City

cairo

Orcid

-

First Name

Mariam

Last Name

Mohamed

MiddleName

-

Affiliation

Faculty of computer science ; Misr International University

Email

mariam1901474@miuegypt.edu.eg

City

cairo

Orcid

-

First Name

Mohamed

Last Name

Hazem

MiddleName

-

Affiliation

Faculty of computer science ; Misr International University

Email

mohamed02155@miuegypt.edu.eg

City

cairo

Orcid

-

Volume

2

Article Issue

2

Related Issue

42348

Issue Date

2023-07-01

Receive Date

2023-05-27

Publish Date

2023-07-01

Page Start

31

Page End

37

Online ISSN

2636-3577

Link

https://jocc.journals.ekb.eg/article_307056.html

Detail API

https://jocc.journals.ekb.eg/service?article_code=307056

Order

4

Type

Original Article

Type Code

731

Publication Type

Journal

Publication Title

Journal of Computing and Communication

Publication Link

https://jocc.journals.ekb.eg/

MainTitle

DeepFakeDG: A Deep Learning Approach for Deep Fake Detection and Generation

Details

Type

Article

Created At

24 Dec 2024