2nd/10_Wiki/Topics/AI_and_ML/Authenticity.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-authenticity	Authenticity	10_Wiki/Topics	verified	self	진정성 AI authenticity content provenance C2PA deepfake detection	none	B	0.85	applied	authenticity ethics branding ai-disclosure c2pa watermark provenance deepfake		2026-05-10	pending	language applicable_to ethics / cryptography Brand Strategy Content Provenance AI Disclosure

Authenticity

📌 한 줄 통찰

"매 진짜 의 힘". 매 internal value + 매 external action 의 일치. 매 deepfake 시대 의 가장 큰 differentiator. 매 AI 의 "I'm an AI" 의 honesty 의 trust 의 maximum.

📖 핵심

매 layer

1. Internal consistency: 매 self 의 honesty.
2. Relational transparency: 매 mask X.
3. Moral courage: 매 cost 가 있어도 매 belief.
4. Vulnerability: 매 weakness 의 share (Brené Brown).
5. Provenance: 매 origin 의 verify 가능.

매 modern context

1. Brand authenticity: 매 marketing 의 가장 큰 lever (Edelman Trust Barometer).
2. Influencer: 매 BeReal, 매 unfiltered.
3. AI content: 매 disclosure 의 default.
4. Deepfake era: 매 provenance 의 의무.
5. Whistleblower / journalism: 매 source verify.

매 AI 시대 의 challenge

• Generative content: 매 image / voice / video 의 indistinguishable.
• Personalized deepfake: 매 target 의 specific.
• Voice cloning: 매 3 second 로 OK.
• Synthetic media: 매 election interference.
• Bot persona: 매 Twitter / Reddit 의 manipulation.

매 verification standard

C2PA (Coalition for Content Provenance)

• 매 cryptographic signature 의 manifest.
• 매 camera → edit → publish 의 chain.
• 매 Adobe / Microsoft / Sony 의 backing.

IPTC Photo Metadata

• 매 EXIF 의 extension.
• 매 capture / edit history.

Watermark (visible / invisible)

• 매 SynthID (Google).
• 매 statistical watermark in LLM output.

Blockchain provenance

• 매 NFT 의 origin.
• 매 immutable timestamp.

매 detection

• Deepfake detection: 매 ML 기반 (FaceForensics++).
• Voice deepfake: 매 spectral analysis.
• AI-text detection: 매 GPTZero, 매 Originality.ai (매 false positive 많음).
• Reverse image search: 매 source 의 trace.

매 ethical 권장

• AI 사용 의 disclose.
• Synthetic content 의 watermark.
• Source 의 verify.
• Persona 의 honest (no false biography).
• Vulnerability OK.

💻 패턴 (응용 — provenance + disclosure)

C2PA manifest (구조)

{
  "claim_generator": "Adobe Photoshop 25.0",
  "format": "image/jpeg",
  "instance_id": "xmp:iid:abc123",
  "claim": {
    "title": "My Photo",
    "format": "image/jpeg",
    "assertions": [
      { "label": "c2pa.actions",
        "data": { "actions": [{ "action": "c2pa.created" },
                              { "action": "c2pa.edited",
                                "parameters": { "name": "color-correct" } }] } },
      { "label": "c2pa.training-mining",
        "data": { "entries": { "c2pa.ai_generative_training": { "use": "notAllowed" } } } }
    ]
  },
  "signature": "..."
}

AI disclosure (UI)

<Message author="AI Assistant">
  <DisclosureBadge>AI-generated</DisclosureBadge>
  {content}
</Message>

→ 매 message-level explicit.

LLM watermark (Aaronson scheme)

# 매 generation 의 token 선택 의 cryptographic hash 의 bias
def watermark_logits(logits, prev_token, key):
    h = hash(prev_token + key)
    bias = derive_bias(h, vocab_size)  # 매 small bias
    return logits + bias

# Detection
def detect_watermark(text, key):
    score = sum(check(token, prev, key) for prev, token in pairs(text))
    return score > THRESHOLD

Content authentication (verify chain)

def verify_c2pa(image_path):
    manifest = read_c2pa_manifest(image_path)
    if not manifest: return 'unverified'
    
    if not verify_signature(manifest): return 'tampered'
    
    chain = manifest.get('chain', [])
    for step in chain:
        if not verify_step(step): return 'broken_chain'
    
    return f'authentic, {len(chain)} edits tracked'

Persona honesty

const aiPersona = {
  name: 'Aria',
  identity: 'AI assistant',  // 매 honest
  // ❌ NOT biography: 'Born in Seattle, 25 years old'
  greeting: "Hi! I'm Aria, an AI. How can I help?",
  responseToHumanQuestion: () =>
    "I'm an AI — I don't have personal experiences, but I can help you think through this.",
};

🤔 결정 기준

상황	적용
Brand strategy	Vulnerability + consistency + transparency
AI agent	Identity disclose + persona honest
Generative content	C2PA + watermark + disclosure
Journalism	Source verify + provenance
Marketing	Genuine story > polished
Influencer	Behind-the-scenes + flaws OK

기본값: 매 disclose + provenance + vulnerability.

🔗 Graph

• 부모: Ethics · Branding · Trust
• 변형: Vulnerability-Brown · Moral-Courage · Brand-Authenticity
• 응용: C2PA · Content-Provenance · SynthID · AI-Disclosure
• Adjacent: Deepfake · Anthropomorphism · EU-AI-Act · Watermarking

🤖 LLM 활용

언제: 매 brand / agent persona design. 매 content provenance system. 매 AI disclosure policy. 언제 X: 매 fake "vulnerability" 의 manipulation. 매 manufactured 'authentic' marketing.

❌ 안티패턴

• Performative vulnerability: 매 fake 의 share.
• Fake AI persona biography: 매 deception.
• No disclosure: 매 trust 의 long-term destroy.
• Watermark 의 옵션 (audit 없이): 매 disable 의 OK.
• C2PA 의 partial: 매 missing step 의 invalidate.
• "매 perfect = 매 authentic": 매 polished 의 manufactured 의 의심.

🧪 검증 / 중복

• Verified (C2PA spec, Edelman Trust Barometer, Brown).
• 신뢰도 B.
• Related: C2PA · Anthropomorphism · AI-Disclosure · Deepfake.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — layers + C2PA + watermark + AI disclosure