# 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 (PNG / JPEG / AVIF / HEIF / JPEG-XL), 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 / Gemini 3 Pro** | ✅ Sparkle logo | ✅ SynthID v1 + v2 (default SDXL pipeline at native ~1024 px) | ✅ C2PA + EXIF | Alpha reversal + diffusion + metadata strip |
| **OpenAI DALL-E 3 / ChatGPT** | — | — | ✅ C2PA manifest | Metadata strip |
| **OpenAI ChatGPT Images 2.0** (gpt-image-2) | — | ⚠️ imperceptible pixel watermark (no public detector yet) | ✅ C2PA manifest (verified) | Diffusion regeneration + 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 (default profile, SDXL):

```text
image → resize to ~1024px (SDXL native) → encode to latent space (VAE)
      → add controlled noise (forward diffusion)
      → denoise (reverse diffusion, ~50 steps at strength 0.05)
      → decode back to pixels (VAE) → upscale to original resolution
```

SDXL is the default since May 2026: empirically defeats SynthID v2 on Gemini 3 Pro outputs, where the older SD-1.5 pipeline at 768 px did not. The SD-1.5 path was removed once it was verified not to handle v2.

**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]"
> ```
>
> To let `identify` decode the open Stable Diffusion / SDXL / FLUX invisible
> watermarks, install the `detect` extra (adds the `invisible-watermark` decoder):
>
> ```bash
> pip install -e ".[detect]"   # or: uv pip install -e ".[detect]"
> ```

#### 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
# Identify provenance: where an image was made + its watermark inventory.
# Aggregates C2PA, IPTC "Made with AI", embedded SD/ComfyUI params, EXIF/XMP
# generator tags (incl. inside AVIF/HEIF), the SynthID proxy, the visible Gemini
# sparkle, and (with the [detect] extra) the open SD/SDXL/FLUX invisible
# watermark into one verdict. Reports "unknown"
# (never "clean") when no signal is found, since stripped metadata is not proof
# of a clean origin. Add --json for machine-readable output.
remove-ai-watermarks identify image.png

# 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)
# --check also flags SynthID-bearing sources: a C2PA manifest signed by
# Google or OpenAI implies an invisible SynthID watermark in the pixels
# (both vendors pair the two). Adobe Firefly / Microsoft sign C2PA without
# SynthID, so they are reported as C2PA only.
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

## Roadmap

Tracked but not yet implemented:

- **SynthID-Image v2 automated regression test**. The default SDXL profile defeats v2 per manual checks against the [Gemini app](https://support.google.com/gemini/answer/16722517)'s "Verify with SynthID" feature on a Gemini 3 Pro output (May 2026). An automated end-to-end test would need either programmatic access to the [SynthID Detector portal](https://blog.google/innovation-and-ai/products/google-synthid-ai-content-detector/) (waitlist for media professionals and researchers) or an offline surrogate detector. The spectral phase-coherence surrogate from [reverse-SynthID](https://github.com/aloshdenny/reverse-SynthID) was evaluated and does not separate watermarked from cleaned real-content images (it only fires on controlled solid-color references at exact resolution), so it is not a usable oracle. Open.
- **Local SynthID *pixel* detector**. Not feasible today: Google's decoder is proprietary, and magnitude/carrier spectral methods do not separate real content (confirmed by three independent evaluations, including a from-scratch gpt-image pilot; see CLAUDE.md). Blocked on either (a) a programmatic generation path (OpenAI / Gemini API) to build a per-(model, resolution) labeled corpus at scale, or (b) a raw watermarked-output dataset. If data arrives, the next approach to try is a learned classifier on diverse content rather than a fixed carrier codebook.
- **Grow the SynthID reference corpus** (`data/synthid_corpus/`) with oracle-labeled samples per model and resolution (Gemini app for Google, openai.com/verify for OpenAI). Prerequisite for any pixel-detector attempt and for an automated removal-regression set.
- **Real non-PNG C2PA fixtures**. SynthID-source detection for JPEG / WebP / AVIF is currently covered only by synthetic byte blobs; replace with real vendor-emitted files to ground the binary-scan path.
- **Maintenance debt**. Bump transitive `idna` to 3.15 (blocks `uv-secure`); clear strict-pyright debt in `remove_ai_metadata` / `cli.py` (untyped piexif / PIL / click / rich) so `maintain.sh` can finish green.
- **AVIF / HEIF / JPEG-XL detection limits**. Removal strips top-level C2PA `uuid` and JUMBF `jumb` boxes. EXIF/XMP boxes inside these containers are not yet scrubbed (PNG and JPEG are fully covered).
- **Video pipeline (`noai-video`)**: per-frame inpainting and tracking for Sora 2 dynamic logo, Veo 3.1 badge, Kling, Runway. Separate package, not folded into this repo.

Won't fix:

- **Nightshade / Glaze / PhotoGuard removal**. These are defensive perturbations used by artists to protect their work from being scraped into AI training sets. Removing them attacks artists, not AI provenance. Out of scope.

## Legal

Watermarking and provenance for AI-generated content is now regulated in several jurisdictions. The table below summarises the May 2026 status. None of this is legal advice.

| Jurisdiction | Instrument | Status (May 2026) | Relevance |
| --- | --- | --- | --- |
| EU | AI Act, Article 50(2) | Marking obligations postponed to **2 December 2026** under the December 2025 omnibus agreement. Code of Practice finalising May/June 2026. | Removing mandated provenance markers with intent to deceive may be sanctioned under national implementations. |
| US (federal) | COPIED Act | Enacted 2025. | Criminalises removal of provenance information with intent to deceive about content origin. The tool itself is lawful; usage may not be. |
| US (state) | CA AB 2655, TX SB 751, similar | In force. | Content-specific (election deepfakes, sexual deepfakes). Not tool-specific. |
| China | Deep Synthesis Regulation, 2025 updates | In force. | Mandatory visible label for AI content. Removal is an administrative offence. |
| UK | Online Safety Act, 2025 transparency extension | In force. | Platform obligations, not user obligations. |

## Threat model

This tool defends already-distributed AI imagery against automatic detection systems (social-platform "Made with AI" labels, third-party classifiers, content-policy filters). It does **not** retroactively anonymise generation.

In particular, **SynthID-Image v2** (Google, deployed October 2025 with Gemini 3 Pro / Nano Banana Pro / Imagen 4 / Veo) embeds a **136-bit payload** ([arxiv 2510.09263](https://arxiv.org/abs/2510.09263)). The payload is believed to encode a user / session identifier. If the original watermarked file ever passed through a system controlled by the prompt originator (a saved Gemini account history, a screenshot uploaded to a Google product, a backup), Google retains the ability to link that original to the generating account. Stripping the watermark from a copy you possess does not erase Google's server-side record.

Use cases where the threat model fits:
- You generated the image yourself, want to publish it as your own work, and accept the consequences if Google ever publishes their detector logs.
- You are running a security / robustness evaluation.
- You are preserving art or historical record against false-positive "AI-generated" labels.

Use cases where the threat model **does not** fit:
- Generating an image, expecting that removing the watermark anonymises you to Google. It doesn't.
- Distributing AI-generated content while claiming human authorship. The watermark is one of several traceability layers.

This tool is intended for legitimate purposes such as:

- Privacy protection (removing metadata that leaks user account identifiers).
- Art preservation and fair-use research.
- Removing false-positive "Made with AI" labels from human-edited photographs.
- Security research and watermark robustness study.

Removing AI provenance markers to misrepresent AI-generated content as human-created may violate the laws above, the DMCA, and platform terms of service. Users are solely responsible for ensuring their use complies with all applicable laws. The authors do not condone use of this tool for deception, fraud, or any activity that violates applicable laws or regulations.

## License

MIT
