Beyond Semantics: Uncovering the Physics of Fakes via Universal Physical Descriptors for Cross-Modal Synthetic Detection explores A novel approach to detect AI-generated images by extracting universal physical features, achieving near-perfect accuracy across diverse datasets and generative models.. Commercial viability score: 7/10 in Synthetic Media Detection.
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This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/beyond-semantics-uncovering-the-physics-of-fakes-via-universal-physical-descriptors-for-cross-modal-synthetic-detection
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
Canonical ID beyond-semantics-uncovering-the-physics-of-fakes-via-universal-physical-descriptors-for-cross-modal-synthetic-detection | Route /paper/beyond-semantics-uncovering-the-physics-of-fakes-via-universal-physical-descriptors-for-cross-modal-synthetic-detection
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/beyond-semantics-uncovering-the-physics-of-fakes-via-universal-physical-descriptors-for-cross-modal-synthetic-detectionMCP example
{
"tool": "get_paper",
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"arxiv_id": "2604.04608"
}
}source_context
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}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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Dimensions overall score 7.0
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