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🔏 Model Watermarking

Protect your AI models from theft and verify ownership

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Privacy-Preserving ML

Introduction to Model Watermarking

🎯 Why Watermark Models?

Training large AI models costs millions of dollars and months of compute time. Model watermarking embeds hidden signatures to prove ownership and detect unauthorized use or theft.

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High Stakes

GPT-4 training cost ~$100M. Model theft is a serious threat to IP.

🚨 Model Theft Scenarios

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Model Extraction

Attackers query API to recreate model functionality

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Insider Threats

Employees leak proprietary models to competitors

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Fine-tuning Attacks

Stolen models are adapted for unauthorized applications

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Model Marketplaces

Unauthorized copies sold on black markets

🔍 Watermarking vs Fingerprinting

Watermarking

Ownership Proof

Embeds a single signature proving the model belongs to you

One signatureCopyright protectionProve authorship

Fingerprinting

Identify Users

Unique signatures for each distributed copy to trace leaks

Unique per userTrace leaksIdentify source

✅ Watermark Requirements

Fidelity

Watermark should not degrade model accuracy

Robustness

Survive fine-tuning, pruning, and model compression

Undetectability

Attackers cannot detect or remove the watermark easily

Efficiency

Fast verification without expensive computations

🎨 Watermarking Approaches

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Backdoor-based

Trigger inputs produce specific outputs

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Parameter-based

Embed signature in model weights

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Output-based

Statistical patterns in predictions