Shipped: 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.