Fake Face Detection by Deep Learning Technique – Project Code and Documentation

Fake Face Detection by Deep Learning Technique – Project Code and Documentation

Project Domain / Category

AI/Deep Learning/Machine Learning

Abstract / Introduction

With the growing era of social media, it is difficult to identify the real from fake whether it is any news or face/video of any celebrity, politician, etc. Also, the fake or manipulated faces and videos are being generated enormously which are harder to detect by traditional means of software or methods. Therefore, Deep Learning which is a subset of Machine Learning can be employed to identify real or fake images/faces/videos efficiently.

We are going to detect the faces as real or fake from the given dataset. As there are already multiple images with faces that are generated by various softwares in the dataset. In this project, we will use Convolutional Neural Network (CNN) to detect the fake faces from an online database (https://www.kaggle.com/xhlulu/140k-real-and-fake-faces), we can also discuss about the various datasets and their usage. The images are fed into the detector, and it will then detect whether the image that is fed into it is real or fake.

This is indeed a very interesting project but requires in depth study of deep learning, neural networks.

The following links may help you in better understanding:

Functional Requirements:

The following are the functional requirements of the project:

  1. The tool based application/software must download the given Dataset that contains the database of real and fake images.
  2. The system must be any CNN model that contains hidden layers for fake face detection.
  3. Whenever, any face is given as an input into the detection system, it identifies as real or fake as output.
  4. The system must be able to detect the fake faces generated by any of the android apps like FaceApp, FaceSwap, Wombo etc.

Tools:

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

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Class diagram, activity diagram, data flow diagram, sequence diagram, use case diagram, testing test cases, SRS document and others are need to draw for this project.

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