Analysis and Feature Extraction

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prerequisites
intermediate Python • beginner scikit-learn and scikit-image • basics of OpenCV
skills learned
understand the differences between deepfake faces and real faces • compare images and their histograms
Pavel Korshunov
1 week · 8-10 hours per week · INTERMEDIATE
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catalog / Data Science

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Look inside
Feature extraction is an essential part of detecting deepfake videos. In this liveProject, you’ll analyze both deepfaked and real video data in order to determine what features are common in faked videos. You’ll then compute those features for faces detected in the videos to determine which are fake and which are real.
This project is a part of the series Detecting Deepfakes Using Visual Inconsistencies.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

prerequisites

This liveProject is for developers who know Python, the basics of machine learning, and the basics of processing image data. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of Jupyter Notebook
  • Basics of OpenCV
  • Basics of scikit-learn
  • Basics of Matplotlib
  • Basics of scikit-image
TECHNIQUES
  • Basic signal and image processing

features

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