FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprint
arXiv:2501.15509v5 Announce Type: replace-cross Abstract: Model fingerprinting has emerged as a crucial mechanism for safeguarding the intellectual property of open-source models, offering a non-intrusive approach that requires no modifications to the protected model. However, our analysis reveals that existing fingerprinting techniques are fundamentally vulnerable to false claim attacks, wherein adversaries can fraudulently assert ownership over independent third-party models. We demonstrate that this vulnerability stems from the untargeted nature of current methods, which evaluate model simi
The proliferation of open-source models necessitates robust IP protection methods, and the growing sophistication of adversarial attacks highlights the urgency for more resilient verification techniques.
Safeguarding intellectual property in AI models is crucial for fostering innovation, preventing misuse, and ensuring trust in the development and deployment of advanced AI systems.
The introduction of targeted fingerprinting offers a defense against false-claim attacks, potentially making model ownership verification more reliable and reducing the risk of IP theft in open-source AI.
- · AI model developers
- · Open-source AI contributors
- · Model verification service providers
- · Legal and IP protection firms
- · Adversaries attempting false claims
- · Entities engaging in IP theft
- · Untargeted fingerprinting methods
Increased confidence in the provenance and ownership of open-source AI models.
Potential for new business models around secure AI model licensing and distribution.
Reduced friction in collaboration and greater investment in open-source AI development due to enhanced IP protection.
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Read at arXiv cs.LG