remove-ai-watermarks/README.md

# Remove-AI-Watermarks

Remove **visible** and **invisible** AI watermarks from images generated by Google Gemini (Nano Banana), ChatGPT / DALL-E, Stable Diffusion, Adobe Firefly, Midjourney, and other AI models.

Strips SynthID, C2PA Content Credentials, EXIF/XMP "Made with AI" labels, and visible sparkle overlays — all in one command.

## Features

- **Visible watermark removal** — Gemini / Nano Banana sparkle logo via reverse alpha blending (fast, offline, deterministic)
- **Invisible watermark removal** — SynthID, StableSignature, TreeRing via diffusion-based regeneration
- **AI metadata stripping** — EXIF, PNG text chunks, C2PA provenance manifests, XMP DigitalSourceType
- **"Made with AI" label removal** — removes the metadata that triggers AI labels on Instagram, Facebook, X (Twitter)
- **Analog Humanizer** — film grain and chromatic aberration to bypass AI image classifiers
- **Smart Face Protection** — automatic extraction and blending of human faces to prevent AI distortion
- **Batch processing** — process entire directories
- **Detection** — three-stage NCC watermark detection with confidence scoring

> **Try it online** — don't want to install anything? Use [raiw.cc](https://raiw.cc), a free web service powered by this library.

## Examples

| Before (Watermarked) | After (Cleaned) |
| --- | --- |
| ![Before](demo_banana_before.png) | ![After](demo_banana_after.png) |

## Supported models

| AI model | Visible watermark | Invisible watermark | Metadata | Our approach |
| --- | --- | --- | --- | --- |
| **Google Gemini / Nano Banana** | ✅ Sparkle logo | ✅ SynthID | ✅ C2PA + EXIF | Alpha reversal + diffusion + metadata strip |
| **OpenAI DALL-E 3 / ChatGPT** | — | — | ✅ C2PA manifest | Metadata strip |
| **Stable Diffusion (AUTOMATIC1111, ComfyUI)** | — | ✅ DWT / steganographic | ✅ PNG text chunks | Diffusion regeneration + metadata strip |
| **Adobe Firefly** | — | — | ✅ Content Credentials (C2PA) | Metadata strip |
| **Midjourney** | — | — | ✅ EXIF + XMP (prompt, model, seed) | Metadata strip |
| **StableSignature** (Meta) | — | ✅ In-model watermark | — | Diffusion regeneration |
| **TreeRing** | — | ✅ Latent space watermark | — | Diffusion regeneration |

> Visible watermarks (logo overlays) are currently used only by Google Gemini / Nano Banana. Other services rely on invisible watermarks and/or metadata. Our diffusion-based regeneration works against any invisible watermark in pixel or frequency domain.

## How it works

### Removing the Gemini / Nano Banana sparkle watermark

Google Gemini (internally codenamed **Nano Banana**) adds a visible sparkle logo to generated images using alpha blending:

```text
watermarked = α × logo + (1 − α) × original
```

We reverse this with a known alpha map (extracted from Gemini / Nano Banana output on a pure-black background):

```text
original = (watermarked − α × logo) / (1 − α)
```

A three-stage NCC (Normalized Cross-Correlation) detector finds the watermark position and scale dynamically, so it works even if the image was resized or cropped. After removal, residual sparkle-edge artifacts are cleaned via gradient-masked inpainting.

**Speed**: ~0.05s per image. No GPU needed.

### Removing SynthID and other invisible watermarks

Google embeds **SynthID** into every image generated by Gemini / Nano Banana. Other AI services use StableSignature, TreeRing, and similar schemes. These imperceptible frequency-domain patterns survive cropping, resizing, and JPEG compression.

The removal pipeline:

```text
image → downscale to 768px → encode to latent space (VAE)
      → add controlled noise (forward diffusion)
      → denoise (reverse diffusion, ~2 steps at strength 0.02)
      → decode back to pixels (VAE) → upscale to original resolution
```

The key insight: even minimal noise injection (strength 0.02 = 2% perturbation) breaks the watermark signal while preserving visual quality. The diffusion model acts as a learned image prior — it reconstructs the image faithfully while destroying the watermark pattern.

**Face Protection**: before diffusion, YOLO detects people in the image and extracts them. After diffusion, the original faces are blended back with a soft elliptical mask to prevent AI distortion of facial features.

**Analog Humanizer**: optional film grain and chromatic aberration injection that makes the output indistinguishable from a photo of a screen, defeating AI-generated image classifiers.

### Stripping C2PA, EXIF, and "Made with AI" metadata

AI tools embed generation metadata that social platforms use to show "Made with AI" labels:

- **EXIF tags** — prompt, seed, model hash, sampler settings (Stable Diffusion, Midjourney)
- **XMP DigitalSourceType** — `trainedAlgorithmicMedia` tag used by Instagram, Facebook, and X (Twitter) to show "Made with AI"
- **PNG text chunks** — ComfyUI workflows, AUTOMATIC1111 parameters
- **C2PA Content Credentials** — cryptographic provenance manifests from Google Imagen, OpenAI DALL-E, Adobe Firefly

The cleaner parses each layer, removes AI-related fields, and preserves standard metadata (Author, Copyright, Title).

## Installation

### Recommended

Install as an isolated CLI tool — no need to manage virtual environments:

```bash
# Using pipx (https://pipx.pypa.io)
pipx install git+https://github.com/wiltodelta/remove-ai-watermarks.git

# Or using uv (https://docs.astral.sh/uv)
uv tool install git+https://github.com/wiltodelta/remove-ai-watermarks.git
```

To update to the latest version:

```bash
pipx upgrade remove-ai-watermarks

# or
uv tool upgrade remove-ai-watermarks
```

### Install from repository

**Prerequisites:** Python 3.10+ and `pip` (or [`uv`](https://docs.astral.sh/uv/)).

```bash
# 1. Clone the repository
git clone https://github.com/wiltodelta/remove-ai-watermarks.git
cd remove-ai-watermarks

# 2. Install the package in editable mode
pip install -e .

# Or, if you use uv:
uv pip install -e .
```

After installation the `remove-ai-watermarks` command is available system-wide.

> **Note**: The base install covers visible watermark removal and metadata stripping.
> For invisible watermark removal (SynthID etc.), install GPU dependencies:
>
> ```bash
> pip install -e ".[gpu]"   # or: uv pip install -e ".[gpu]"
> ```

#### Invisible watermark removal

Invisible removal uses diffusion models and a GPU for reasonable speed.

```bash
# On first run, the model (~2 GB) will be downloaded automatically.
# Device is auto-detected: CUDA (Linux/Windows) > MPS (macOS) > CPU.
# To force a device: --device cuda / --device mps / --device cpu

# Optional: set a HuggingFace token for gated/private models
cp .env.example .env
# Edit .env and set HF_TOKEN=hf_your_token_here
```

#### Developer setup

```bash
# Install with dev dependencies (pytest, ruff, pyright)
pip install -e ".[dev]"
# Or with uv:
uv pip install -e ".[dev]"

# Run tests
pytest

# Run linters
./maintain.sh
```

## Usage

### CLI

```bash
# Remove all watermarks from a single image (visible + invisible + metadata)
remove-ai-watermarks all image.png -o clean.png

# Process an entire directory
remove-ai-watermarks batch ./images/ --mode all
```

#### Individual commands

```bash
# Visible watermark only (Gemini / Nano Banana sparkle) — fast, offline
remove-ai-watermarks visible image.png -o clean.png

# Invisible watermark only (SynthID etc.) — requires GPU
remove-ai-watermarks invisible image.png -o clean.png --humanize 4.0

# Check / strip AI metadata (C2PA, EXIF, "Made with AI" labels)
remove-ai-watermarks metadata image.png --check
remove-ai-watermarks metadata image.png --remove

# Batch with a specific mode
remove-ai-watermarks batch ./images/ --mode visible
```

### Python API

```python
from remove_ai_watermarks.gemini_engine import GeminiEngine
import cv2

engine = GeminiEngine()
image = cv2.imread("watermarked.png")

# Detect
result = engine.detect_watermark(image)
print(f"Detected: {result.detected} (confidence: {result.confidence:.1%})")

# Remove
clean = engine.remove_watermark(image)
cv2.imwrite("clean.png", clean)
```

### Metadata stripping

```python
from remove_ai_watermarks.metadata import has_ai_metadata, remove_ai_metadata
from pathlib import Path

if has_ai_metadata(Path("image.png")):
    remove_ai_metadata(Path("image.png"), Path("clean.png"))
```

## Requirements

- Python ≥ 3.10
- **Visible removal / metadata**: CPU only, no GPU required
- **Invisible removal**: GPU recommended (CUDA or MPS), works on CPU (slow)

## Troubleshooting

**SSL certificate error** (`CERTIFICATE_VERIFY_FAILED`):

```bash
# Install certifi (the tool auto-detects it)
pip install certifi

# macOS only: run the Python certificate installer
/Applications/Python\ 3.*/Install\ Certificates.command
```

**First run is slow** — this is expected. The tool downloads model weights (~2 GB) on first launch. Subsequent runs use cached models.

## Credits

- [noai-watermark](https://github.com/mertizci/noai-watermark) by mertizci — invisible watermark removal engine
- [GeminiWatermarkTool](https://github.com/allenk/GeminiWatermarkTool) by Allen Kuo (MIT) — visible watermark removal algorithm
- [CtrlRegen](https://github.com/yepengliu/CtrlRegen) by Liu et al. (ICLR 2025) — controllable regeneration pipeline
- NeuralBleach (MIT) — analog humanizer technique

## ⚠️ Disclaimer

This tool is provided for **educational and research purposes only**.

Removing AI watermarks to misrepresent AI-generated content as human-created
may violate applicable laws, including the U.S. Digital Millennium Copyright Act
(DMCA) and the COPIED Act. Users are solely responsible for ensuring their use
complies with all applicable laws and platform terms of service.

The authors do not condone the use of this tool for deception, fraud,
or any activity that violates applicable laws or regulations.

## License

MIT