huggingface / huggingface/diffusers
AI-generated image provenance metadata (EU AI Act deepfake disclosure)
- 主要言語
- Python
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- 平均マージ
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- マージ済み PR(30日)
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説明
## Context
Diffusers generates images that get saved and distributed. Under EU AI Act Article 50 (August 2, 2026), AI-generated images disclosed publicly must carry transparency metadata. Deepfake disclosure becomes mandatory.
Currently, when you run:
```python
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
image = pipe("a sunset over mountains").images[0]
image.save("output.png")
```
The saved PNG has no metadata indicating it's AI-generated. A recipient has no way to know its origin.
## The Gap
Photos from cameras carry EXIF (camera model, GPS, timestamp). AI-generated images carry nothing. There's no standard EXIF/XMP field for:
- `AIGenerated: True`
- `AIModel: stable-diffusion-xl-base-1.0`
- `AIPrompt: "a sunset over mountains"` (optional)
- `GeneratedAt: 2026-03-28T12:00:00Z`
## Possible Approach
Diffusers could optionally embed provenance into saved images:
```python
image.save("output.png",
pnginfo=PngInfo() # already supported
)
# Could include AI provenance in PNG tEXt chunks or EXIF
```
This could be:
1. **Opt-in flag** — `pipe(..., embed_provenance=True)`
2. **Default on, opt-out** — more aggressive but helps compliance
3. **Separate utility** — `diffusers.utils.embed_provenance(image, model_name=...)`
## Why Diffusers Specifically
- Diffusers is the most popular image generation library
- Images are the primary target of EU AI Act deepfake provisions
- Pillow (which diffusers uses) already supports custom EXIF/XMP writing
- C2PA exists but requires Certificate Authority infrastructure — a lightweight EXIF-based approach would have much broader adoption
## Existing Formats
- [C2PA](https://c2pa.org/) — media provenance with PKI (heavy)
- [AKF](https://akf.dev) — lightweight JSON provenance for 20+ formats including images
- Custom EXIF/XMP — simplest, most compatible
Not advocating for any specific format — the important thing is that diffusers outputs carry *some* provenance. What does the team think?
コントリビューションガイド
調査の方向性
Issueに示されているパイプライン出力とPillow's image.save/PngInfoのパスから始めます。リポジトリのファイルやテストは指定されていません。提案されているオプトイン、デフォルト、ユーティリティのアプローチを、C2PAおよびカスタムメタデータ形式と比較します。完了には、合意された来歴形式とAPIの動作に加え、保存されたPNGメタデータのカバレッジが必要です。
索引モデルが issue の本文から書いたものです。
評価
- 技術スタック
- python
- 領域
- computer-vision, machine-learning
- issue の種類
- 機能追加
- 難易度
- 5/5
- 見積もり時間
- 1週間以上
- 活発さ
- 静か
- 明瞭さ
- 説明が足りない
- 初心者へのやさしさ
- 30/100