AI Image Metadata Cleaner
README.md
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# 🛡️ AI Image Metadata Cleaner (MCP Server)
**High-performance Model Context Protocol (MCP 2.x) server designed to strip AI provenance tags (C2PA, EXIF, XMP, IPTC), disrupt invisible frequency watermarks, and simulate authentic camera sensor physics.**
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## 💡 Purpose
Modern generative AI tools (Midjourney, DALL-E, Adobe Firefly, Canva, Stable Diffusion) embed cryptographic provenance manifests (**C2PA / JUMBF**), invisible spatial watermarks, and prompt parameters into every generated image.
Major platforms like **LinkedIn**, **Twitter/X**, and algorithmic newsfeeds automatically read these tags to **label, flag, or suppress organic reach** of content.
The purpose of **AI Image Metadata Cleaner** is to give creators and developers complete sovereign control over their media by:
1. **Scrubbing 100% of embedded tracking provenance** and generator signatures.
2. **Disrupting invisible frequency-domain watermarks** without visual quality loss.
3. **Restoring physical camera realism** (CMOS sensor grain, optical PSF) so images blend seamlessly with authentic photography.
---
## 🎯 Real-World Use Cases
| Use Case | Description |
|---|---|
| **LinkedIn Posts & Profile Media** | Prevent LinkedIn from automatically attaching "AI-generated" labels, reducing reach penalties or triggering account restriction reviews. |
| **Social Media & Content Marketing** | Publish AI-assisted blog banners, promotional graphics, and tech memes without algorithmic downranking across platforms. |
| **Privacy & Anti-Fingerprinting** | Strip embedded generation prompts, seed parameters, workflow graphs (ComfyUI), and system timestamps before sharing publicly. |
| **Portfolio & Headshot Naturalization** | Transform synthetic, plastic-looking portraits into authentic-looking shots by adding realistic CMOS photon grain and lens falloff. |
| **Batch Asset Pipeline Automation** | Automatically sanitize entire folders of marketing assets or user uploads via the `batch_sanitize_directory` tool. |
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## ⚡ How Platforms Detect AI Images
1. **C2PA / Content Credentials Metadata:** Embedded in JPEG `APP11` (JUMBF) segments, PNG `c2pa`/`caPt` chunks, and XMP packets declaring synthetic origin.
2. **Invisible / Frequency-Domain Watermarks:** Encoded in latent frequency space (e.g. Google SynthID, latent diffusion watermarks).
3. **Statistical AI Footprints:** Unnaturally smooth surfaces with 0 variance (absence of camera sensor photon noise) and transposed-convolution grid harmonics.
4. **Suspicious Zero Metadata:** Heuristic checkers often flag files with 0 EXIF. Injected authentic camera EXIF (iPhone 15 Pro, Sony A7 IV) passes authenticity audits.
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## 🛠️ MCP Tools
| Tool | Type | Description |
|---|---|---|
| `anti_ai_sanitize` | **Flagship** | Full pipeline: strips C2PA/EXIF, executes 0.3% micro-resample, injects luminance-adaptive CMOS grain, and embeds realistic camera EXIF. |
| `strip_metadata_only` | Utility | Fast, lossless metadata stripping (EXIF, XMP, IPTC, C2PA) without altering pixel values. |
| `inspect_image_metadata` | Audit | Deep diagnostic inspection scanning for C2PA byte manifests, AI keywords, and risk assessment. |
| `inject_camera_profile` | Stealth | Injects authentic camera EXIF into clean JPEG images. |
| `get_camera_profiles` | Info | Returns list of supported camera profiles and optical specifications. |
| `batch_sanitize_directory` | Automation | Batch-sanitizes an entire directory of images with parallel processing. |
---
## 📷 Supported Realistic Camera Profiles
- `iphone_15_pro`: Apple iPhone 15 Pro (iOS 17.5.1, 24mm f/1.78, ISO 80, 1/120s)
- `sony_a7iv`: Sony Alpha A7 IV (FE 24-70mm F2.8 GM II, f/2.8, 50mm, ISO 160)
- `samsung_s24_ultra`: Samsung Galaxy S24 Ultra (6.3mm f/1.7, ISO 100)
- `canon_r6`: Canon EOS R6 Mark II (RF24-105mm F4 L IS USM, ISO 200)
---
## 🚀 Installation & Setup
### Requirements
- Python 3.10+
- `uv` (recommended) or `pip`
```bash
# Clone the repository
git clone https://github.com/satyamkumar420/ai-image-meta-cleaner.git
cd ai-image-meta-cleaner
# Create and activate virtual environment
uv venv .venv
source .venv/bin/activate
# Install dependencies
uv pip install -r requirements.txt
```
### Running Tests
```bash
pytest -v
```
---
## ⚙️ MCP Configuration
Add this server to your `mcp_config.json` (Antigravity, Claude Desktop, Cursor):
```json
{
"mcpServers": {
"meta-cleaner": {
"command": "/path/to/ai-image-meta-cleaner/.venv/bin/python",
"args": [
"-u",
"/path/to/ai-image-meta-cleaner/server.py"
],
"env": {
"PYTHONPATH": "/path/to/ai-image-meta-cleaner"
}
}
}
}
```
---
## ☕ Support & Sponsor
If you find **AI Image Metadata Cleaner** helpful and want to support its maintenance:
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---
## 📄 License
This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.
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