DeepFakeDG: A Deep Learning Approach for Deep Fake Detection and Generation | ||||
| Journal of Computing and Communication | ||||
| Article 4, Volume 2, Issue 2, July 2023, Page 31-37 PDF (516.72 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jocc.2023.307056 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Diaa s AbdElminaam   1; Natalie Sherif2; zeina Ayman2; Mariam Mohamed2; Mohamed Hazem2
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| 1Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt | ||||
| 2Faculty of computer science ; Misr International University | ||||
| 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. | ||||
| Keywords | ||||
| Neural Machine Translation; Sequence to Sequence Model; Sign Language; Deep learning; Transformer | ||||
| References | ||||
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