Reward Simulator

How it works

1. Select or upload an image that represents AI-generated content
2. The system finds similar images that might have influenced the generation in a database of 10M images (OpenImages)
3. Based on your parameters, it calculates potential rewards for:
   • Original image authors
   • Rights owners (e.g., stock photo companies, galleries)

Key assumptions

• Attribution scores indicate the level of influence of training images
• Rewards are distributed based on subscription revenue
• Calculations use simplified models and are for demonstration purposes

Use cases

• Explore fair compensation models for AI training data
• Simulate different revenue sharing scenarios
• Understand the relationship between model training and attribution

Technical details

Image similarity search powered by DINOv2 and FAISS. DINOv2 is used to extract image features of all the images in the database (10M images from OpenImages). FAISS is used to perform efficient similarity search to find the most similar images to the input image, and output the attribution scores.

• DINOv2 - Vision transformer for image feature extraction
• FAISS - Efficient similarity search library, more specifically:
  • HNSW (Hierarchical Navigable Small World) for approximate nearest neighbor search
  • PQ (Product Quantization) for memory-efficient vector compression