Securing IP in edge AI: neural network watermarking for multimodal models

In the realm of edge AI systems where deep learning is paramount, protecting the intellectual property (IP) of multimodal neural network models is crucial. Current watermarking solutions often bypass the intricacies of multimodal models and the unique constraints of edge environments. Addressing this, a novel watermarking scheme specifically devised for multimodal neural networks is introduced, marking a significant stride in securing these models against IP theft and unauthorized use. A discrete watermark is ingeniously embedded within each modality of a multimodal model, synthesizing a comprehensive watermark that spans the entire model. This method ensures IP protection across varied data types without hampering the model's performance or imposing undue computational demands, making it ideal for resource-limited edge devices. By leveraging the redundancies inherent in multimodal data, watermarks are embedded efficiently, maintaining model integrity and operational effectiveness. A robust verification mechanism is implemented, accurately identifying watermark presence across modalities with minimal computational overhead. Empirical validation on a benchmark dataset demonstrates the method's efficacy in embedding watermarks discreetly while preserving the model's original task performance, showing a 1 to 4% increase in watermark detection rates and a 6 to 10% reduction in false positives compared to existing approaches. This positions the scheme as an effective strategy for IP protection in multimodal neural network models, especially suited for the computational economy required in edge AI systems. The work advances neural network watermarking and addresses the urgent need for scalable IP protection solutions in the evolving AI landscape.