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Tutorials

Welcome to the Veridex tutorials! These guides will help you master AI content detection across all modalities.

🎯 Learning Path

Follow these tutorials in order for the best learning experience:

graph TD
    A[Quick Start] --> B{Choose Modality}
    B -->|Text| C[Text Detection Guide]
    B -->|Image| D[Image Detection Guide]
    B -->|Audio| E[Audio Detection Guide]
    C --> F[Ensemble Detection]
    D --> F
    E --> F
    F --> G[Production Deployment]

Available Tutorials

🚀 Getting Started

Quick Start (5 minutes)

Get up and running with Veridex in under 5 minutes. Perfect for first-time users.

You'll learn:

  • How to install Veridex
  • Run your first detection
  • Interpret results
Beginner 5 min

📝 Text Detection

Text Detection Guide

Learn to detect AI-generated text using various signals and techniques.

You'll learn:

  • Choosing the right text detector
  • Understanding perplexity and burstiness
  • Using Binoculars for high accuracy
  • Interpreting linguistic signals
Beginner-Intermediate 15 min

🖼️ Image Detection

Image Detection Guide

Master image deepfake detection with frequency analysis and diffusion techniques.

You'll learn:

  • Image preprocessing best practices
  • Frequency domain analysis
  • Using DIRE for diffusion artifacts
  • Error Level Analysis (ELA)
  • GPU vs CPU considerations
Intermediate 20 min

🎵 Audio Detection

Audio Detection Guide

Detect synthetic voice and deepfake audio using spectral and foundation model approaches.

You'll learn:

  • Audio format requirements
  • Spectral analysis for quick screening
  • AASIST for anti-spoofing
  • Wav2Vec for production-grade detection
  • Silence pattern analysis
Intermediate 20 min

🎯 Advanced Topics

Ensemble Detection

Combine multiple signals for robust, production-ready detection.

You'll learn:

  • Fusion strategies for multiple signals
  • Building custom detection pipelines
  • Confidence calibration
  • Production deployment patterns
Advanced 25 min

💡 Tutorial Tips

Best Practices

  • Start with Quick Start - Get familiar with the basics first
  • Run the code - All examples are tested and working
  • Experiment - Try different parameters and inputs
  • Read the concepts - Understanding the theory helps with practical application

Prerequisites

  • Python 3.9 or higher
  • Basic understanding of Python
  • Optional: Familiarity with machine learning concepts

📚 Additional Resources

After completing the tutorials, check out:

🆘 Need Help?

If you get stuck:

  1. Check the Troubleshooting Guide
  2. Review the FAQ
  3. Search GitHub Issues
  4. Ask in GitHub Discussions

🎯 Ready to Start?

Begin with the Quick Start Tutorial to get hands-on experience!