FIT-Print: Toward False-Claim-Resistant Model Ownership Verification via Targeted Fingerprint
Summary
Existing model fingerprinting techniques (methods that create unique digital signatures to prove ownership of AI models) are vulnerable to false claim attacks, where attackers can fraudulently claim they own models they didn't create. This paper introduces FIT-Print, a targeted fingerprinting approach that uses optimization to create verifiable signatures resistant to these false claims, offering two specific methods (bit-wise FIT-ModelDiff and list-wise FIT-LIME) that achieved 100% success in preventing false ownership claims while maintaining accurate ownership verification.
Solution / Mitigation
The paper proposes FIT-Print, a targeted fingerprinting paradigm that 'actively counters false claim attacks' by leveraging 'optimization to transform the fingerprint into a verifiable, targeted signature.' Two specific black-box fingerprinting methods are introduced: 'bit-wise FIT-ModelDiff' which 'utilizes output distances' and 'list-wise FIT-LIME' which utilizes 'feature attributions as robust model signatures.' The framework demonstrated '100% defense success rate' against false claim attacks and '100% ownership verification rate.'
Classification
Related Issues
Original source: http://ieeexplore.ieee.org/document/11557390
First tracked: June 18, 2026 at 08:03 PM
Classified by LLM (prompt v3) · confidence: 85%