refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-deepfake-detection
+title: Deepfake Detection
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Deepfake Detection, Synthetic Media Detection, AI-Generated Content Detection]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [security, ml, forensics, deepfake, detection]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: PyTorch
+---
+
+# Deepfake Detection
+
+## 매 한 줄
+> **"매 generative model 의 fingerprint 의 수확"**. 2017 FakeApp 의 등장 이후 detection 의 cat-and-mouse race 가 시작되었고, 2026 modern detector 는 frequency-domain artifacts, biological signals (PPG, eye blink), 그리고 self-supervised representation 의 ensemble 의 통해 95%+ AUC 의 달성 — but cross-model generalization 의 여전히 매 open problem.
+
+## 매 핵심
+
+### 매 Detection 패러다임
+- **Frequency-domain**: GAN/Diffusion 의 upsampling artifact (DCT spectrum 의 grid pattern, FFT 의 high-freq 결손).
+- **Biological signal**: heart-rate (rPPG), micro-expression, eye blink frequency 의 unnatural pattern.
+- **Identity consistency**: face embedding 의 video-level temporal drift.
+- **Self-supervised**: CLIP/DINOv2 feature 의 OOD detection.
+
+### 매 Generation 종류
+- **Face swap**: DeepFaceLab, FaceFusion, Roop.
+- **Face reenactment**: First Order Motion Model, LivePortrait (2024).
+- **Full-body**: Wav2Lip, SadTalker, EMO (Alibaba 2024).
+- **Diffusion-based**: Stable Video Diffusion, Sora (OpenAI 2024), Veo 3 (Google 2025).
+
+### 매 응용
+1. Newsroom 의 fact-checking pipeline (Reuters, AP).
+2. Social platform 의 watermark + detection (Meta, TikTok, X).
+3. Identity verification (KYC, banking — Persona, Onfido).
+4. Forensic 증거 분석 (court-admissible chain of custody).
+
+## 💻 패턴
+
+### Frequency-domain CNN (Frank et al. baseline)
+```python
+import torch
+import torch.nn as nn
+from torch.fft import fft2, fftshift
+
+class FrequencyDeepfakeDetector(nn.Module):
+    def __init__(self, num_classes=2):
+        super().__init__()
+        self.backbone = nn.Sequential(
+            nn.Conv2d(1, 32, 3, padding=1), nn.ReLU(),
+            nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(),
+            nn.AdaptiveAvgPool2d(8), nn.Flatten(),
+            nn.Linear(64 * 64, num_classes),
+        )
+
+    def forward(self, x):  # x: (B, 3, H, W) RGB
+        gray = x.mean(1, keepdim=True)
+        spec = fftshift(fft2(gray)).abs().log1p()
+        return self.backbone(spec)
+```
+
+### rPPG-based liveness (heart-rate from face video)
+```python
+import numpy as np
+from scipy.signal import butter, filtfilt
+
+def extract_rppg(face_frames, fps=30):
+    # POS algorithm — Wang et al. 2017
+    rgb_signal = np.stack([f.reshape(-1, 3).mean(0) for f in face_frames])
+    rgb_norm = rgb_signal / rgb_signal.mean(0)
+    proj = rgb_norm @ np.array([[0, 1, -1], [-2, 1, 1]]).T
+    s = proj[:, 0] + (proj[:, 0].std() / proj[:, 1].std()) * proj[:, 1]
+    b, a = butter(4, [0.7, 4.0], btype='band', fs=fps)
+    return filtfilt(b, a, s - s.mean())
+
+def is_live(rppg, fps=30):
+    fft = np.abs(np.fft.rfft(rppg))
+    freqs = np.fft.rfftfreq(len(rppg), 1/fps) * 60  # BPM
+    peak_bpm = freqs[fft.argmax()]
+    return 50 <= peak_bpm <= 180  # 매 plausible HR range
+```
+
+### CLIP-based zero-shot detector
+```python
+import open_clip
+import torch
+
+model, _, preprocess = open_clip.create_model_and_transforms(
+    'ViT-L-14', pretrained='laion2b_s32b_b82k')
+tokenizer = open_clip.get_tokenizer('ViT-L-14')
+
+prompts = ["a real photograph", "an AI-generated image",
+           "a deepfake", "a synthetic face"]
+text = tokenizer(prompts)
+text_features = model.encode_text(text)
+text_features /= text_features.norm(dim=-1, keepdim=True)
+
+def score(image_pil):
+    img = preprocess(image_pil).unsqueeze(0)
+    img_feat = model.encode_image(img)
+    img_feat /= img_feat.norm(dim=-1, keepdim=True)
+    sims = (img_feat @ text_features.T).softmax(-1)
+    return sims[0, 1:].sum().item()  # 매 fake probability
+```
+
+### Temporal consistency (face embedding drift)
+```python
+from facenet_pytorch import InceptionResnetV1
+
+embedder = InceptionResnetV1(pretrained='vggface2').eval()
+
+def temporal_drift(face_crops):
+    embs = embedder(torch.stack(face_crops))
+    embs = embs / embs.norm(dim=-1, keepdim=True)
+    consec_sim = (embs[:-1] * embs[1:]).sum(-1)
+    # 매 swapped face 의 unnatural jitter 의 detect
+    return 1.0 - consec_sim.mean().item()
+```
+
+### Watermark verification (C2PA / SynthID)
+```python
+import hashlib
+from cryptography.hazmat.primitives.asymmetric import ed25519
+
+def verify_c2pa_manifest(manifest_bytes, signature, public_key):
+    try:
+        public_key.verify(signature, manifest_bytes)
+        return True
+    except Exception:
+        return False  # 매 manifest 의 tampered 또는 missing
+```
+
+### Ensemble fusion (production)
+```python
+def ensemble_decision(image, video_clip):
+    scores = {
+        'freq': freq_detector(image),
+        'clip': clip_detector(image),
+        'rppg': 1.0 - is_live_score(video_clip),
+        'temporal': temporal_drift(extract_faces(video_clip)),
+    }
+    weights = {'freq': 0.3, 'clip': 0.25, 'rppg': 0.25, 'temporal': 0.2}
+    return sum(w * scores[k] for k, w in weights.items())
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Real-time KYC | rPPG + active liveness challenge |
+| Static image forensic | Frequency CNN + CLIP zero-shot |
+| Video newsroom | Ensemble (freq + temporal + watermark) |
+| Cross-generator generalization | Self-supervised foundation model |
+| High-stakes legal | Multi-modal + chain-of-custody + C2PA |
+
+**기본값**: ensemble of frequency + foundation-model + watermark verification.
+
+## 🔗 Graph
+- 부모: [[Computer Vision]]
+- 응용: [[Content Moderation]]
+- Adjacent: [[C2PA]]
+
+## 🤖 LLM 활용
+**언제**: feature engineering 의 brainstorm, dataset curation script, false-positive 분석.
+**언제 X**: production detection model 의 직접 inference (LLM 의 vision 의 reliable detector 의 X — specialized model 의 사용).
+
+## ❌ 안티패턴
+- **Single-detector reliance**: GAN-trained detector 의 diffusion-generated content 의 fail.
+- **No cross-generator eval**: train/test 의 same generator 의 inflated metric.
+- **Ignoring compression artifacts**: JPEG/H.264 의 frequency signal 의 destroy.
+- **Adversarial blindness**: detector 의 adversarial perturbation 의 robust 의 X.
+- **Watermark-only**: open-source generator 의 watermark 의 strip.
+
+## 🧪 검증 / 중복
+- Verified (FaceForensics++ benchmark, DFDC, Frank et al. ICML 2020, C2PA spec v2.1).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — frequency/biological/CLIP detection patterns + ensemble |