Detecting Deepfakes

Detection with SVM you own this product

This project is part of the liveProject series Detecting Deepfakes Using Visual Inconsistencies
intermediate Python • beginner scikit-learn and scikit-image • basics of OpenCV
skills learned
Training an SVM classifier resulting in deepfake detection system • Evaluating a system on the test set
Pavel Korshunov
1 week · 8-10 hours per week · INTERMEDIATE

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Look inside
In this liveProject, you’ll develop a machine learning solution that can identify the difference between faces in deepfake videos and real faces. You’ll use a support-vector machine (SVM) classifier approach to determine which videos have the artifacts associated with deepfakes, and combine face detection, feature extraction, and your SVM classifier together in one pipeline to create your detection system.
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.


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:

  • Basics of Jupyter Notebook
  • Basics of OpenCV
  • Basics of scikit-learn
  • Basics of Matplotlib
  • Basics of scikit-image
  • Basic signal and image processing
  • Basic image feature and metrics computation
  • Basic understanding of how classification systems are evaluated
  • Basic understanding of classification with linear support vector machine

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 the industry.

  • Understanding the differences between deepfake faces and real faces
  • Using popular image features from SSIM, PSRN, MSE, DCT coefficients, and histograms
  • Training an SVM classifier resulting in deepfake detection system
  • Evaluating the system on the test set and analyze the results


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
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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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