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