docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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Antigravity Agent
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---
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id: wiki-2026-0508-semantic-grounding-provenance
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title: "Semantic Grounding & Provenance"
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Grounding, Provenance, Citation, C2PA, Watermark]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.88
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verification_status: applied
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tags: [grounding, provenance, citation, c2pa, watermark, rag, trust]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: anthropic
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---
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# Semantic Grounding & Provenance
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## 매 한 줄
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> **"매 claim 매 traceable to source — model output ↔ evidence ↔ origin"**. 매 LLM grounding (RAG citation, attribution) + 매 media provenance (C2PA, SynthID watermark). 매 2026 trust stack: 매 Claude/GPT-5 매 inline citations, 매 Adobe/Microsoft/OpenAI 매 C2PA Content Credentials.
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## 매 핵심
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### 매 Two domains
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- **LLM grounding**: 매 generated text → source documents.
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- **Media provenance**: 매 image/video/audio → creation chain (C2PA).
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### 매 LLM grounding tactics
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- 매 RAG with citation tokens (Claude `<cite>`, GPT structured output).
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- 매 self-citation: 매 model emits `[doc_id]` markers.
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- 매 attribution training: 매 supervised on annotated traces.
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- 매 verification post-hoc: 매 entailment classifier 매 NLI score.
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### 매 C2PA standard (2024-2026)
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- **Content Credentials**: 매 cryptographically signed manifest.
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- 매 manifest contains: 매 creator, edits, AI-generation flag, hashes.
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- 매 supported: Adobe (Photoshop/Firefly), OpenAI (DALL-E/Sora), Microsoft (Bing Image Creator), Leica/Sony cameras.
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- 매 verify: contentcredentials.org/verify.
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### 매 Watermarking
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- **SynthID** (Google DeepMind): 매 imperceptible image+audio+text watermark.
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- **Stable Signature**: 매 model-fingerprint embedded in latent.
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- **Tree-Ring**: 매 diffusion latent watermark.
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- 매 robust to crop, compression, paraphrase (text).
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### 매 응용
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1. News verification (Truepic, AP).
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2. RAG-based research assistants with citations.
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3. Court evidence chain-of-custody.
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4. Anti-misinfo (deepfake detection).
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## 💻 패턴
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### Anthropic citations API
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```python
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from anthropic import Anthropic
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client = Anthropic()
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doc = {"type": "document", "source": {"type": "text", "media_type": "text/plain",
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"data": "The Eiffel Tower is 330m tall, completed 1889..."},
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"title": "Eiffel Tower", "citations": {"enabled": True}}
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r = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=1024,
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messages=[{"role": "user", "content": [doc, {"type": "text",
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"text": "How tall is the Eiffel Tower?"}]}],
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)
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for block in r.content:
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if block.type == "text":
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print(block.text)
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for cite in block.citations or []:
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print(f" -> {cite.cited_text} [{cite.document_title}]")
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```
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### Inline citation prompt (model-agnostic)
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```python
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SYSTEM = """Answer using ONLY the provided documents.
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After each claim, cite as [doc_id]. If not in docs, say "not found in sources"."""
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def grounded_answer(question, docs):
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doc_str = "\n".join(f"[{i}] {d}" for i, d in enumerate(docs))
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prompt = f"{doc_str}\n\nQuestion: {question}"
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return llm.generate(SYSTEM, prompt)
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```
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### NLI-based attribution check
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```python
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from transformers import pipeline
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nli = pipeline("text-classification", model="microsoft/deberta-v2-xxlarge-mnli")
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def check_attribution(claim, evidence, threshold=0.7):
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r = nli({"text": evidence, "text_pair": claim})
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entail_score = next(s["score"] for s in r if s["label"] == "ENTAILMENT")
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return entail_score >= threshold, entail_score
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```
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### C2PA manifest read (c2pa-python)
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```python
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from c2pa import Reader
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with open("photo.jpg", "rb") as f:
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reader = Reader.from_stream("image/jpeg", f)
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manifest = reader.json()
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print(manifest)
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# {"manifests": {"...": {"claim_generator": "Adobe Photoshop 25.0",
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# "assertions": [{"label": "c2pa.actions", "data": {"actions": [{"action": "c2pa.created"}]}},
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# {"label": "c2pa.training-mining", "data": {...}}]}}}
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```
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### C2PA manifest write
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```python
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from c2pa import Builder, ManifestDefinition
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manifest_def = {
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"claim_generator": "MyApp/1.0",
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"assertions": [
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{"label": "c2pa.actions", "data": {"actions": [{"action": "c2pa.created"}]}},
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{"label": "c2pa.ai_generative_training", "data": {"use": "notAllowed"}},
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],
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}
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builder = Builder(ManifestDefinition.from_json(manifest_def))
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with open("signing_cert.pem") as cert, open("signing_key.pem") as key:
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builder.sign(cert.read(), key.read(), "sha256",
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source_path="in.jpg", dest_path="out_signed.jpg")
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```
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### SynthID-style text watermark detection
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```python
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def detect_synthid_text(text, model, key):
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# 매 conceptual: 매 measure log-prob bias on hashed-token green list
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tokens = tokenizer(text).input_ids
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score = 0.0
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for i in range(1, len(tokens)):
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green_list = hash_to_greenlist(tokens[i-1], key, vocab_size=50000)
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if tokens[i] in green_list:
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score += 1
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z = (score - 0.5 * len(tokens)) / np.sqrt(0.25 * len(tokens))
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return z > 4 # 매 z>4 → strongly watermarked
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```
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### RAG with span-level grounding
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```python
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def span_grounded_rag(query, retriever, llm):
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chunks = retriever.search(query, k=5)
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answer = llm.generate(prompt=build_prompt(query, chunks))
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# 매 post-hoc: 매 for each sentence 매 find best supporting chunk
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grounding = []
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for sent in split_sentences(answer):
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scores = [embed_sim(sent, c) for c in chunks]
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best = int(np.argmax(scores))
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grounding.append({"sentence": sent, "source": chunks[best],
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"score": float(scores[best])})
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return answer, grounding
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Research assistant | 매 Claude citations API + NLI verify |
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| News content | 매 C2PA Content Credentials |
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| AI-generated image disclosure | 매 C2PA + SynthID watermark |
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| LLM-generated text disclosure | 매 SynthID-Text 또는 disclosed metadata |
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| Court evidence | 매 C2PA + hardware-attested camera |
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**기본값**: 매 LLM 출력 → inline citations + NLI verify; 매 media → C2PA manifest.
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## 🔗 Graph
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- 변형: [[C2PA]]
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- Adjacent: [[Deepfake Detection]] · [[Hallucination]]
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## 🤖 LLM 활용
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**언제**: 매 trust-critical answer (medical, legal), 매 newsroom workflow, 매 AI-content disclosure regulation (EU AI Act).
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**언제 X**: 매 casual chat (overhead), 매 creative writing (citation 매 disruptive).
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## ❌ 안티패턴
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- **Trust without verify**: 매 model-claimed citation 매 hallucinated → 매 NLI 검증 필수.
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- **Fake C2PA**: 매 unsigned manifest 매 ignore — 매 always check signing cert chain.
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- **Watermark-only defense**: 매 strippable in many cases — 매 layer with C2PA + detection.
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- **No span granularity**: 매 doc-level citation 매 too coarse for long docs.
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## 🧪 검증 / 중복
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- Verified (Anthropic Citations API docs, C2PA spec v2.1, Google SynthID papers 2023-2024).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — RAG citation, C2PA, SynthID, NLI verification |
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