@ericyocam6Shipped: Secure Neural Network Watermarking — AI Model Protection
An open-source AI security proof of concept demonstrating how cryptographic watermarks can be embedded directly into deep neural network weights to support model ownership verification, intellectual property protection, and tamper detection.
The framework combines PyTorch, ResNet-18, GPU-accelerated deep learning, and HMAC-SHA256 to generate deterministic, key-based watermark patterns. By applying small modifications to selected neural network parameters, the system embeds hidden signatures while preserving the model's predictive performance.
The demonstration reports 94.67% baseline model accuracy versus 94.23% after watermarking, representing a 0.44-percentage-point reduction, with negligible model file-size increase. Verification experiments test watermark detection, incorrect keys, original models, and modified models.
Designed for AI researchers, machine learning engineers, and organizations developing proprietary AI systems, this experimental framework explores practical approaches to AI model provenance, ownership verification, and software supply chain security. It remains a research proof of concept rather than a production-validated security solution.
Comments2
Giving up less than half a point of performance to prove ownership is a trade most companies licensing AI would take.
Thanks, Ryan. I agree. A 0.44-percentage-point reduction in accuracy is a relatively small tradeoff if it helps establish model ownership and protect intellectual property. The bigger challenge is ensuring the watermark remains detectable after fine-tuning, pruning, or other model modifications. That's an important area for further research, particularly as organizations increasingly commercialize and license proprietary AI models. Appreciate the feedback!
2 comments
Giving up less than half a point of performance to prove ownership is a trade most companies licensing AI would take.
Thanks, Ryan. I agree. A 0.44-percentage-point reduction in accuracy is a relatively small tradeoff if it helps establish model ownership and protect intellectual property. The bigger challenge is ensuring the watermark remains detectable after fine-tuning, pruning, or other model modifications. That's an important area for further research, particularly as organizations increasingly commercialize and license proprietary AI models. Appreciate the feedback!