TP-CRIV: A Framework for Third-Party Challenge-Response Identity Verification of AI Models
Artificial intelligence (AI) models are increasingly deployed through remote services, making model misappropriation a growing concern. Existing approaches, including watermarking, fingerprinting, and model similarity analysis, primarily rely on predefined evidence or direct behavioral comparison and do not explicitly evaluate whether the claimant currently possesses and can utilize model-dependent information relevant to the claimed model identity. In this paper, we propose Third-Party Challenge-Response Identity Verification (TP-CRIV) for AI models. TP-CRIV targets a third-party verification setting in which the verifier has neither white-box nor API access to the claimant's model, can ...
| Authors | Teruki Sano, Minoru Kuribayashi, Masao Sakai et al. |
|---|---|
| Primary category | cs.CR |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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