arXiv:2407.10887v4 Announce Type: replace-cross Abstract: Growing concerns over the theft and misuse of Large Language Models (LLMs) underscore the need for effective fingerprinting to link a model to its original version and detect misuse. We define five essential properties for a successful fingerprint: Transparency, Efficiency, Persistence, Robustness, and Unforgeability. We present a novel fingerprinting framework that provides verifiable proof of ownership while preserving fingerprint integrity. Our approach makes two main contributions. First, a chain and hash technique that cryptographi
The rapid proliferation and increasing value of LLMs make intellectual property protection and misuse detection critical concerns right now.
This development offers a technical solution to verify LLM ownership and detect unauthorized use, which is crucial for fostering innovation and trust in AI.
The ability to persistently fingerprint LLMs fundamentally changes how intellectual property can be managed and enforced in the AI domain.
- · LLM Developers
- · AI IP Lawyers
- · Cloud Providers
- · Model Thieves
- · Unattributed AI Model Users
Increased trust and investment in proprietary large language models.
New business models emerging around verifiable model licensing and usage analytics.
Potential for an 'AI copyright' industry distinct from traditional software IP, impacting regulatory frameworks globally.
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Read at arXiv cs.AI