arXiv (All)
open access
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 an
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Watermarking large language models in Europe: interpreting the AI act in light of technology#977385
- The reach of a verification tool decides its value: A controlled study of verification surface, artifact quality, and cost in AI coding agents#622630
- Process Matters more than Output for Distinguishing Humans from Machines#1130545
- Improve Trust and Adversarial Robustness of AI models in Remote Sensing#307622
- Multi-Jurisdictional Legal Identity Assurance for Capability Gating: A Design-Science Proposal for Tiered, Reusable Identity Assurance of Natural, Juridical, and Machine Entities#1154910
Record · ID 1077193
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive