Testing Neural Networks for Critical Systems
A novel requirements-based approach to ensure AI safety
This research introduces a structured methodology for requirements-based testing of deep neural networks (DNNs), addressing a critical gap in ensuring AI system reliability.
- Translates formal system requirements into concrete test cases for neural networks
- Generates test suites that verify compliance with safety specifications
- Enables systematic validation for DNNs in high-stakes environments
- Establishes a foundation for more rigorous certification processes
For security professionals, this approach offers a pathway to more comprehensive risk assessment and compliance verification when deploying neural networks in safety-critical applications.