Deep Dive: How It Works
The Attribution Database
Catalog Ingestion
All training data is vectorized and stored in a dedicated attribution database.
Catalog Ingestion
Each image is decomposed into concept-level representations across multiple dimensions.
Real-Time Analysis
When a visual is generated or modified, the attribution engine activates automatically. The system searches for similarity patterns between the output and the training set across multiple dimensions:
Composition-Spatial arrangement, framing, visual hierarchy
Style -Artistic treatment, rendering approach, aesthetic qualities Objects -Subject matter, elements, recognizable items
Objects -Subject matter, elements, recognizable items
Texture -Surface qualities, material representation, detail patterns
Background -Environmental context, scene setting, depth
Foreground -Primary subjects, focal elements, layering
This multidimensional analysis captures how different training images influence different aspects of the generation-one image might contribute primarily to composition, another to color palette, a third to object rendering.
The Attribution Vector
Cost-efficient scalable influence measurement platform
For every generation, the system creates an irreversible attribution vector. This vector is a one-way mathematical transformation that:
Maps which training content influenced the output
Calculates the relative weight of each contribution
Captures concept-level influence, not pixel-level copying
Cannot be reversed to reconstruct source images
The technical elegance lies in its efficiency
Attribution extraction is computationally lightweight, with fewer than 50ms added to generation time.
A system scalable to production environments serves thousands of generations simultaneously.
What Makes It Work
Fair
Pay creators based on actual usage, not theoretical value
Consistent
Identical outputs produce identical attribution, zero randomness
Transparent
Results can be visually demonstrated and audited
Modality stable
Attribution from output, not prompt-eliminates disputes
Regulation ready
EU AI Act aligned out of the box
What Makes It Work
Fair
Pay creators based on actual usage, not theoretical value
Consistent
Identical outputs produce identical attribution, zero randomness
Transparent
Results can be visually demonstrated and audited
Modality stable
Attribution from output, not prompt-eliminates disputes
Regulation ready
EU AI Act aligned out of the box
Attribution Agent
For open source and on-premise deployments
For deployments outside Bria's cloud, Attribution Agents run alongside self-hosted models.
The log contains sufficient information to calculate fair payment but cannot be reverse-engineered to reconstruct generated outputs. Client confidentiality protected. Creator compensation enabled.
Deployment options:
Containerized -Kubernetes or Docker alongside your model.
Sidecar -Lightweight daemon for bare-metal.
Batch - Periodic encrypted uploads for air-gapped environments.