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.

10_Wiki/Topic_Programming/DevOps_and_Security/Adversarial Code Stylometry.md

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+---
+id: wiki-2026-0508-adversarial-code-stylometry
+title: Adversarial Code Stylometry
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [P-Reinforce-AUTO-36585B, Code Authorship Obfuscation]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [security, ml, privacy, deanonymization]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: scikit-learn
+---
+
+# Adversarial Code Stylometry
+
+## 매 한 줄
+> **"매 source code 의 author 를 statistical fingerprint 로 식별 — 그리고 attacker 는 이를 회피한다."**. Code stylometry 는 AST features + n-grams + lexical patterns 로 author 를 95% accuracy 로 deanonymize 가능; adversarial stylometry 는 transformation/obfuscation 으로 이를 무력화한다.
+
+## 매 핵심
+
+### 매 Feature Family
+- **Lexical**: identifier length, naming convention, comment density.
+- **Syntactic (AST)**: subtree frequency, depth distribution, control-flow patterns.
+- **Layout**: indentation, brace style, line length.
+- **Semantic**: API choice, idiom preference (list comp vs loop).
+
+### 매 Attack Surface
+- **Open-source contributors** — GitHub commits 의 deanonymization.
+- **Malware authorship** — APT attribution.
+- **Plagiarism detection** — academic/hiring context.
+- **Bug bounty / leak** — anonymous reporter identification.
+
+### 매 Defense
+1. Code transformation (Caliskan 2018 — paraphrase preserving semantics).
+2. LLM-mediated rewrite (rewrite via Claude/GPT to neutralize style).
+3. Style transfer to another author (mimicry).
+4. Mechanical normalization (autoformatter + identifier randomization).
+
+## 💻 패턴
+
+### AST Feature Extractor
+```python
+import ast
+from collections import Counter
+
+def ast_node_freq(source: str) -> Counter:
+    tree = ast.parse(source)
+    return Counter(type(n).__name__ for n in ast.walk(tree))
+```
+
+### Author Classifier (sklearn)
+```python
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.pipeline import Pipeline
+
+pipe = Pipeline([
+    ("tfidf", TfidfVectorizer(analyzer="char_wb", ngram_range=(2, 5))),
+    ("rf", RandomForestClassifier(n_estimators=300, n_jobs=-1)),
+])
+pipe.fit(train_sources, train_authors)
+```
+
+### Style Obfuscation via Rewrite
+```python
+import anthropic
+
+client = anthropic.Anthropic()
+
+def neutralize_style(code: str) -> str:
+    msg = client.messages.create(
+        model="claude-opus-4-7",
+        max_tokens=4096,
+        messages=[{"role": "user", "content": f"""Rewrite this code to neutralize authorial style.
+Preserve semantics exactly. Use generic identifiers, standard idioms, mechanical formatting.
+
+```python
+{code}
+```"""}],
+    )
+    return msg.content[0].text
+```
+
+### Mimicry Attack (target style)
+```python
+def mimic(code: str, target_samples: list[str]) -> str:
+    """Rewrite `code` to look like `target_samples` author."""
+    target_blob = "\n---\n".join(target_samples[:3])
+    prompt = f"Target author samples:\n{target_blob}\n\nRewrite preserving semantics:\n{code}"
+    return llm_call(prompt)
+```
+
+### Detection of Obfuscated Code
+```python
+def obfuscation_signal(code: str) -> float:
+    """High score → likely autoformatted/normalized."""
+    feats = ast_node_freq(code)
+    entropy = -sum((c/sum(feats.values())) * np.log2(c/sum(feats.values())) for c in feats.values())
+    return 1.0 - entropy / np.log2(len(feats))  # uniform → 0, peaked → 1
+```
+
+### Defensive Pre-commit Hook
+```bash
+#!/usr/bin/env bash
+# .git/hooks/pre-commit
+ruff format --quiet .
+python -m style_neutralizer **/*.py
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Anonymous OSS contribution | LLM rewrite + autoformat |
+| Whistleblower | Full mimicry to public author |
+| Defensive (detection) | Char n-gram + AST RF |
+| Research baseline | Caliskan 2015 features |
+
+**기본값**: autoformat + LLM neutralization for adversarial; char n-gram TF-IDF + RF for detection.
+
+## 🔗 Graph
+- 변형: [[Code Obfuscation]]
+- Adjacent: [[Differential Privacy]]
+
+## 🤖 LLM 활용
+**언제**: style neutralization, mimicry attack, defensive paraphrase.
+**언제 X**: ground-truth authorship verification 에 LLM judgment 단독 사용.
+
+## ❌ 안티패턴
+- **Autoformatter 만 의존**: AST/lexical features 는 그대로 leak.
+- **Identifier rename only**: control-flow signature 가 식별 가능.
+- **Single-pass LLM rewrite**: subtle idioms 잔존 — multi-pass 필요.
+- **Train/test 동일 repo**: leakage — author-disjoint split 필수.
+
+## 🧪 검증 / 중복
+- Verified (Caliskan 2015 USENIX Sec, Abuhamad 2018 CCS).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — AST features, attack/defense patterns |