Characterizing Statistical Separability in TP-CRIV for Probabilistic AI Models
Third-party challenge-response identity verification (TP-CRIV) enables an independent verifier to assess whether a claimant possesses a model identical to a remotely deployed model without directly accessing the reference model. However, for probabilistic AI models, repeated executions of the same query may produce different outputs and therefore different verification observations. This raises the question of how such stochastic evidence should be accumulated and how much evidence is required for reliable verification. In this work, we characterize statistical separability in TP-CRIV of probabilistic AI models. Specifically, we relate challenge-wise behavior of matching and non-matching ...
| Authors | Teruki Sano, Minoru Kuribayashi, Masao Sakai et al. |
|---|---|
| Primary category | cs.CR |
| Published | 8 October 2026 |
| Last updated at source | 14 October 2026 |
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