Image Tracing System for Neural Network Data Provenance
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Solution Overview
Problem
Neural networks face challenges in tracing data provenance and ownership, especially when using real-world data combined with synthetic data, due to complex and ill-structured dataset assembly processes, and the need to maintain privacy and licensing requirements.
Innovation Solution
An image tracing system that tags three-dimensional assets with unique identifiers, generates two-dimensional images with metadata embedding these identifiers, and uses a curation manager to optimize and expand images for training neural networks, ensuring traceability and ownership verification through embedded metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world data is used for training, then training accuracy is improved, but privacy and licensing requirements create complexity in data assembly and traceability
Solution Approach 1:
The patent creates synthetic data copies that replicate the statistical properties and training value of real-world data while eliminating privacy and licensing constraints. The synthetic data generation system produces artificial training datasets that preserve the essential characteristics needed for model training without containing sensitive real-world information, thus resolving the contradiction between training accuracy and data complexity.
Solution Approach 2:
The patent introduces synthetic data as an intermediary between real-world data and training requirements. This intermediary layer maintains the beneficial properties of real data for training accuracy while filtering out the problematic aspects related to privacy and licensing, allowing training to proceed without direct use of constrained real-world datasets.
2Object-affected harmful factors
If synthetic data is generated, then privacy concerns are reduced, but traceability of source materials and assets becomes problematic
Solution Approach 1:
The patent applies preliminary action by embedding traceability metadata and source attribution information into the synthetic data generation process before the data is used for training. This ensures that even though the data is synthetic, the provenance information is preserved from the outset, preventing loss of traceability information while maintaining privacy protection.
Solution Approach 2:
The patent implements nesting by embedding multiple layers of information within the synthetic data structure, including source material attributions, generation parameters, and provenance metadata nested within the synthetic datasets. This nested structure allows traceability information to be contained within the synthetic data itself, maintaining privacy while enabling source verification.
3Adaptability or versatility
If diverse data from many sources is assembled, then training quality is improved, but the process becomes ill-structured and complex
Solution Approach 1:
The patent applies universality by creating a unified synthetic data generation system that can produce diverse training data across multiple domains and applications from a single framework. This multi-functional system replaces the need for separate data assembly processes for different data sources, simplifying the overall process while maintaining the ability to generate diverse, high-quality training data tailored to specific training requirements.
Data Source
AI summary
A method includes tagging, by at least one processor, one or more three-dimensional assets with a unique identifier and storing the one or more three-dimensional assets in a database, creating, by the at least one processor, a three-dimensional model based on the one or more three-dimensional assets and loading the three-dimensional model in a simulator, generating, by the at least one processor, a two-dimensional image that is a representation of the three-dimensional model in the simulator, the two-dimensional image comprising metadata that includes each unique identifier for each three-dimensional asset of the three-dimensional model displayed in the two-dimensional image, and assigning, by the at least one processor, the two-dimensional image with a unique identifier and storing each unique identifier for each three-dimensional asset of the three-dimensional model displayed in the two-dimensional image in metadata for the two-dimensional image.