Model Watermarking & IP Protection
Embed and detect watermarks in ML models to prove ownership and detect model theft.
This project focuses on intellectual property protection for machine learning models using watermarking techniques. It implements and evaluates both trigger-based and parameter-perturbation watermarking methods to assert model ownership, ensuring resilience against removal attacks and model extraction.
Tech Stack
- PyTorch · TensorFlow
- Watermark embedding and detection pipelines
- Resilience and tampering evaluation framework
Example (Trigger-set Watermarking)
# Embed watermark by fine-tuning with trigger images
trigger_data, trigger_labels = generate_trigger_set()
model.train()
for x, y in zip(trigger_data, trigger_labels):
out = model(x.unsqueeze(0))
loss = criterion(out, y.unsqueeze(0))
loss.backward()
optimizer.step()
# Later: verify watermark by testing trigger accuracy
verify_score = evaluate_watermark(model, trigger_data, trigger_labels)
print(f"Watermark verification score: {verify_score:.2f}")
Project Highlights
- Trigger-based Watermarking: Embed synthetic patterns that cause known model outputs.
- Parameter Perturbation: Introduce imperceptible weight changes as ownership signatures.
- Robustness Evaluation: Test watermark survival after pruning, fine-tuning, and extraction attacks.
- Detection Pipeline: Statistical verification of ownership using watermark response metrics.