Fake News Video Clips Detection by Deep Learning Methods Test phase, srs, design phase and source code final deliverable

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Fake News Video Clips Detection by Deep Learning Methods Test phase, srs, design phase and source code final deliverable

Category

AI/Deep Learning/Machine Learning (web-based)

Abstract / Introduction

Videos of fake news are often disseminated in the media. Because of the excellent quality of modified content, it might be difficult to distinguish between fake and real news. These modified or false contents can take any shape, including false news, faces that have been altered fake audio, and fake video, among others. It requires some effort to distinguish between authentic and fake content because it can falsely degrade any politician or celebrity. By utilizing AI/deep learning techniques on a dataset that will be provided by the supervisor, this project aims to identify these video clips of fake news.

Pre-Requisites:

This is indeed a very interesting project but requires in depth study of deep learning, neural networks. The following links may help you better understand:

Deep Learning Tutorial: https://www.youtube.com/watch?v=VyWAvY2CF9c

Example of Fake News Video Clips: https://www.youtube.com/watch?v=36K33bVetn8

Functional Requirements:

The following are the functional requirements of the project:

• The application/software must download the given Dataset that contains the database of real and fake video clips of the news.
• The system must consist of a neural network model that contains hidden layers for the fake news detection.
• Whenever, any video clip is given as an input into the detection system, it identifies as real or fake as output.
• The system must be able to detect the swapped fake faces and/or swapped audio in manipulated content.

Tools:

• Python (programming language)
• Keras (API)
• Tensorflow (open source software library for machine learning)
• Jupyter Notebook (open source web application) or Google Colab
• Matplotlib (library)
• Numpy (library for the python)

Supervisor:

Name: Sonia Salman