The Neural Network Watermarking – Traceability (NNW-NNT) standard specifies methods to evaluate the performance of a Neural Network subjected to Active (watermarking) or Passive (Fingerprinting) traceability technology.
Robustness and Imperceptibility
A practical watermark must balance multiple requirements. NNW-NNT provides methods to evaluate:
- Robustness, measuring the ability of a watermark to remain detectable after media processing, compression, editing, or other transformations.
- Imperceptibility, measuring the extent to which embedding affects the quality and user perception of the media.
- Reliability, measuring the accuracy of watermark detection and extraction under different operating conditions.
These evaluation procedures help developers optimise systems according to application requirements.
Open and Flexible Design
MPAI-NNW is based on simple principles: technology-neutral evaluation, objective performance metrics, reproducible testing procedures, and support for innovation. Developers can adopt different neural network architectures, embedding techniques, and detection methods while relying on a common framework to demonstrate performance and conformance.
Benefits
The NNW-NNT standard helps:
- Researchers and Developers to evaluate and compare watermarking technologies using common criteria.
- Technology Providers to demonstrate the robustness, efficiency, and quality of their solutions.
- Content Owners and Distributors to deploy trustworthy technologies for content identification, protection, and management.
- System Integrators to select watermarking solutions based on measurable and standardised performance indicators.
- Users and Regulators to rely on transparent evaluation methods and reproducible results.
Ultimately, NNW-NNT promotes a competitive ecosystem of interoperable neural network watermarking technologies by providing a common framework for assessing robustness, imperceptibility, reliability, and computational efficiency. This enables the deployment of trustworthy watermarking solutions across a broad range of digital media applications.