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 / Machine Learning / Computer Vision
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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 designed for learning purposes and is not a complete, production-ready application or solution.

project author

Pavel Korshunov
Pavel Korshunov is a researcher at Idiap Research Institute, Switzerland, working on detection of audio-visual inconsistencies and Deepfakes. Previously, he worked on problems related to high dynamic range imaging, crowdsourcing, and visual privacy. He received PhD from National University of Singapore and MSc from St. Petersburg State University, Russia. He has over 70 research papers with several best paper awards and is a co-editor of JPEG XT standard.

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

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