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

---
id: wiki-2026-0508-arts
title: Arts (Human + AI Era)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [예술, art, generative art, AI art, Stable Diffusion, Midjourney, creative collaboration]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: conceptual
tags: [arts, generative-ai, midjourney, stable-diffusion, copyright, authenticity, post-ai-art, neo-humanism]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: art / culture
  applicable_to: [Generative Art, Creative Tools, Brand Design]
---

# Arts (Human + AI Era)

## 📌 한 줄 통찰
> **"매 language 너머의 communication"**. 매 emotion / experience / idea 의 form / sound / color 의 translate. 매 modern: 매 AI generative 의 democratize 가, 매 authenticity / labor / copyright 의 new battleground. 매 Neo-Humanism 의 imperfection 의 가치.

## 📖 핵심

### 매 function
1. **Catharsis**: 매 emotion 의 release.
2. **Communication**: 매 timeless message.
3. **Critical perspective**: 매 society 의 mirror.
4. **Beauty**: 매 aesthetic.
5. **Identity**: 매 cultural / personal.

### 매 evolution
| 시대 | Medium |
|---|---|
| 고대 | 매 cave painting, 매 sculpture |
| 중세 | 매 religious art |
| 르네상스 | 매 perspective, 매 oil |
| 19c | 매 photography |
| 20c | 매 film, 매 abstract, 매 conceptual |
| 21c | 매 digital, 매 NFT |
| 2022+ | 매 AI generative |

### 매 AI generative tool
- **Midjourney**: 매 stylized, 매 artistic.
- **Stable Diffusion** (open): 매 customizable.
- **DALL-E 3** (OpenAI): 매 prompt 의 understanding.
- **Flux** (Black Forest Labs): 매 photorealistic.
- **Sora / Runway**: 매 video.
- **Suno / Udio**: 매 music.
- **ElevenLabs**: 매 voice.

### 매 controversy

#### Copyright
- 매 LAION-5B (training data) 의 unauthorized.
- 매 lawsuit (Getty vs Stability AI).
- 매 EU AI Act 의 disclosure.
- 매 Glaze / Nightshade (artist 의 anti-tool).

#### Labor
- 매 illustrator / VFX 의 displacement.
- 매 Hollywood writers strike (2023).
- 매 game industry layoff.

#### Authenticity
- 매 award withdrawn (Sony 2023, Adobe 등).
- 매 disclosure 의무.
- 매 C2PA standard.

#### Quality
- 매 AI 의 averaged style.
- 매 Slop (over-produced).
- 매 derivative.

### 매 Neo-Humanism 의 응답
- 매 imperfection 의 가치.
- 매 physical mark 의 evidence.
- 매 process documentation.
- 매 human-only category.
- 매 hand-made 의 premium.
- 매 live performance.

→ 매 differentiation 의 lever.

### 매 hybrid (collaboration)
- 매 AI 의 ideation → 매 human refinement.
- 매 human sketch → 매 AI extension.
- 매 AI 의 variation → 매 human selection.
- 매 controlled (ControlNet, ComfyUI).

### 매 modern artist 의 stance
- **Refuse AI**: 매 Glaze 적용.
- **Embrace + transparent**: 매 disclosure.
- **Hybrid**: 매 tool 의 use.
- **AI-first**: 매 prompt 의 art.

### 매 evaluation 의 새 axis
- **Concept** (idea).
- **Execution** (technique).
- **Process** (story).
- **Provenance** (chain).
- **Originality** (vs derivative).

## 💻 패턴

### Stable Diffusion (controlled)
```python
from diffusers import StableDiffusionXLPipeline, ControlNetModel
from diffusers import StableDiffusionXLControlNetPipeline

controlnet = ControlNetModel.from_pretrained('diffusers/controlnet-canny-sdxl-1.0')
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    'stabilityai/stable-diffusion-xl-base-1.0',
    controlnet=controlnet,
).to('cuda')

# 매 user 의 sketch (canny edge) + prompt
import cv2
sketch = cv2.Canny(user_sketch, 100, 200)

image = pipe(
    prompt='a cat with a hat, oil painting, vivid color',
    image=sketch,
    controlnet_conditioning_scale=0.7,
    num_inference_steps=30,
).images[0]
```

### Glaze (anti-AI training)
```python
# 매 artist 의 work 의 protect
# 매 imperceptible perturbation 의 add
# 매 ML 의 learn 의 disrupt
def glaze_protect(image, target_style='unrelated_style', epsilon=0.05):
    # 매 PGD-like attack
    perturbed = image.clone().requires_grad_()
    optimizer = torch.optim.Adam([perturbed], lr=0.001)
    for _ in range(100):
        loss = -torch.norm(style_extractor(perturbed) - target_style)  # 매 push to wrong style
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        perturbed.data = torch.clamp(perturbed, image - epsilon, image + epsilon)
    return perturbed.detach()
```

### C2PA disclosure (provenance)
```json
{
  "claim_generator": "Adobe Photoshop 25.0",
  "assertions": [
    { "label": "c2pa.actions",
      "data": { "actions": [
        { "action": "c2pa.created",
          "softwareAgent": "Stable Diffusion XL 1.0",
          "parameters": { "prompt": "a cat with a hat" } },
        { "action": "c2pa.edited",
          "softwareAgent": "Adobe Photoshop",
          "parameters": { "name": "manual color correction" } }
      ]}}
  ]
}
```

### Hybrid workflow (sketch → AI variation → human refine)
```python
def hybrid_workflow(user_sketch, prompt, n_variations=4):
    # 1. AI 의 N 의 variation
    variations = generate_n(user_sketch, prompt, n=n_variations)
    
    # 2. Human selection
    chosen = human_pick(variations)
    
    # 3. AI 의 refine (img2img with low strength)
    refined = pipe(
        prompt=prompt + ', polished',
        image=chosen,
        strength=0.3,
    ).images[0]
    
    # 4. Human final touch (Photoshop)
    return chosen, refined
```

### AI-art disclosure UI
```tsx
function ArtworkCard({ artwork }: { artwork: Artwork }) {
  return (
    <article>
      <img src={artwork.url} />
      <h3>{artwork.title}</h3>
      <DisclosureBadge level={artwork.aiInvolvement} />
      {artwork.aiInvolvement !== 'none' && (
        <details>
          <summary>AI tools used</summary>
          <ul>
            {artwork.aiTools.map(t => <li>{t.name} (v{t.version})</li>)}
          </ul>
          <p>Prompts: {artwork.prompts}</p>
          <p>Human edits: {artwork.humanEdits}</p>
        </details>
      )}
    </article>
  );
}
```

## 🤔 결정 기준
| 상황 | Approach |
|---|---|
| Commercial (deadline) | AI + human refine |
| Personal expression | Human-first |
| Conceptual | Mix (concept matters) |
| Photography | Human (regulate AI) |
| Illustration job | Disclose + 의뢰자 의 OK |
| Music | Hybrid (sample / collaborate) |
| Game asset | AI + license check |
| NFT | Provenance-tracked |

**기본값**: 매 disclosure + 매 attribution + 매 hybrid (human + AI). 매 100% AI 의 explicit category.

## 🔗 Graph
- 부모: [[Aesthetic-Value]] · [[Creativity]]
- 변형: [[Generative-Art]] · [[AI-Art]]
- 응용: [[Stable-Diffusion]] · [[Midjourney]] · [[ControlNet]] · [[ComfyUI]]
- Adjacent: [[Authenticity]] · [[C2PA]] · [[Copyright]] · [[AI Image Generation]]

## 🤖 LLM 활용
**언제**: 매 generative art workflow. 매 brand asset. 매 game art pipeline. 매 hybrid creative.
**언제 X**: 매 fine art individual expression (human-only). 매 traditional photography (AI 의 ban).

## ❌ 안티패턴
- **No disclosure**: 매 fraud.
- **AI 의 derivative 의 commercial**: 매 copyright risk.
- **All AI no concept**: 매 slop.
- **Glaze 의 ignore**: 매 artist 의 will violate.
- **Single tool monoculture**: 매 same look.
- **Award fraud**: 매 community trust 의 destroy.

## 🧪 검증 / 중복
- Verified (C2PA spec, ongoing copyright cases, art community discourse).
- 신뢰도 B (rapidly evolving).
- Related: [[AI Image Generation]] · [[Authenticity]] · [[C2PA]] · [[Copyright]] · [[Glaze]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — generative tool + controversy + Neo-Humanism + 매 SD / Glaze / C2PA code |