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Home/Authors/Rhine Andotra

Rhine Andotra

2 indexed papers

Recent (6 mo)
2
With code
0
Influential cites
0
Benchmarked
0

Publications per year

2
26

Top categories

Crypto×2AI×2Society×2

Frequent co-authors

Shubhashis Sengupta2×
Benjamin McCarty2×
Milind Savagaonkar2×

Research Timeline

2026
Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenance, human verification, and continuous governance to manage trust in the generative AI era.

Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenance, human verification, and continuous governance to manage trust in the generative AI era.

Highlighted terms show continued research focus across papers

Papers

cs.CRcs.AIcs.CYRecentMay 30, 2026

Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era

Shubhashis Sengupta, Benjamin McCarty, Milind Savagaonkar, Rhine Andotra

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

View →
cs.CRcs.AIcs.CYRecentMay 30, 2026

Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era

Shubhashis Sengupta, Benjamin McCarty, Milind Savagaonkar, Rhine Andotra

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

View →