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Detecting Deepfakes

Analysis and Feature Extraction you own this product

This project is part of the liveProject series Detecting Deepfakes Using Visual Inconsistencies
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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liveProject This project is part of the liveProject series Detecting Deepfakes Using Visual Inconsistencies liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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

you will learn

In this liveProject, you’ll learn techniques and tools for image processing and face detection. These skills are easily transferable to common computer vision challenges you’ll face in industry.

  • Understand the differences between deepfake faces and real faces
  • Compare images and their histograms

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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