Three-Project Series
Detecting Deepfakes Using Visual Inconsistencies you own this product
- prerequisites
- intermediate Python • beginner scikit-learn and scikit-image • basics of OpenCV
- skills learned
- face detection • SVM classification • image processing • building a binary classification system
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team
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In this series of liveProjects, you’ll step into the role of a forensics consultant. You’re investigating a ring of cyber criminals who are blackmailing prominent social media personalities with scandalous “deepfake” videos. These videos use a deep learning model to splice a victim’s face onto an actor, creating highly realistic content that can be indistinguishable from the real thing. Your boss wants you to develop a method to efficiently detect these deepfakes from a huge data set of online videos. The method needs to be fast, and also run without needing GPU resources, which are in short supply. Each project in this series covers a different aspect of developing this deepfake detecting solution, with essential computer vision and machine learning skills you can easily transfer to other tasks.
These projects are designed for learning purposes and are not complete, production-ready applications or solutions.
here's what's included
Project 1 Detect Faces in Videos
Face detection has numerous applications across software and is an essential part of the pipeline for detecting deepfake videos. In this liveProject, you’ll implement a component that detects faces in videos, and normalizes them to the same size and visual appearance. This component is a vital foundation for a deepfake detection system.
Project 2 Analysis and Feature Extraction
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.
Project 3 Detection with SVM
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.
book resources
When you start each of the projects in this series, you'll get full access to the following book for 90 days.
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Detecting Deepfakes Using Visual Inconsistencies project for free
Detecting Deepfakes Using Visual Inconsistencies project for free
team
monthly
annual
$49.99
$499.99
only $41.67 per month
- five seats for your team
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free product every time you renew
- choose twelve free products per year
- exclusive 50% discount on all purchases
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Detecting Deepfakes Using Visual Inconsistencies project for free
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 the following:
TOOLS
- 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
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.
