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AI Research
arXiv CS.AI·Jun 9·5 min read
FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprint
TL;DR
Existing model fingerprinting techniques are vulnerable to false claim attacks, allowing adversaries to falsely assert ownership of third-party models. FIT-Print is a new method designed to enhance model ownership verification by using targeted fingerprinting.
✦ Why It Matters
Engineers can implement FIT-Print to enhance the security of their model ownership claims against fraudulent assertions.
Key Takeaways
- Adopt targeted fingerprinting to enhance model security.
- Utilize FIT-ModelDiff for analyzing output distances.
- Implement FIT-LIME for robust feature attribution.
- Ensure 100% ownership verification against model reuse.
How It Works
FIT-Print employs optimization techniques to create a targeted fingerprint that serves as a unique signature for each model. This targeted approach contrasts with traditional methods that rely on arbitrary sample outputs, making it more resistant to false claims.
By focusing on specific model characteristics, FIT-Print can accurately verify ownership while preventing unauthorized assertions.