Deepfake Detection
Learn to identify AI-generated fake videos, images, and audio. Master CNN-based detection, biological signal analysis, frequency domain techniques, and audio deepfake detection to combat the growing threat of synthetic media.
Your Learning Path
Follow these lessons in order to master deepfake detection techniques.
1. Introduction
The deepfake threat landscape, real-world impact, and why detection is critical for trust in digital media.
2. How Deepfakes Work
Face swap, lip sync, GANs, diffusion models, autoencoders, and the generation techniques behind deepfakes.
3. Detection Techniques
CNN-based detection, biological signals, frequency analysis, temporal inconsistencies, and attention-based methods.
4. Tools
FaceForensics++, DeepFaceLab detection, Microsoft Video Authenticator, and open-source detection frameworks.
5. Audio Deepfakes
Voice cloning detection, spectrogram analysis, ASVspoof, FakeAVCeleb dataset, and multi-modal detection.
6. Best Practices
Production deployment, adversarial robustness, generalization across generators, and ethical considerations.
What You'll Learn
By the end of this course, you'll be able to:
Understand Deepfake Tech
Know how deepfakes are created and what artifacts they leave behind.
Detect Visual Deepfakes
Apply CNN-based and biological signal detection to identify fake videos and images.
Detect Audio Deepfakes
Identify synthetic speech and cloned voices using spectrogram and ML analysis.
Deploy Detection Systems
Build and deploy deepfake detection pipelines for real-world applications.