Visible watermark removal — Gemini 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
Analog Humanizer — film grain and chromatic aberration injection to bypass AI image classifiers
Smart Face Protection — automatic extraction and blending of human faces to prevent AI distortion
High-Res Upscaler — prevents resolution degradation during invisible watermark removal
Batch processing — process entire directories
Detection — three-stage NCC watermark detection with confidence scoring

Examples

Before (Watermarked)	After (Cleaned)

Installation

Install from repository

Prerequisites: Python 3.10+ and pip (or uv).

# 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.

Invisible watermark removal

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

# 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

# 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

# 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

# Visible watermark only (Gemini 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 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

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

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):

# 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 by mertizci — invisible watermark removal engine
GeminiWatermarkTool by Allen Kuo (MIT) — visible watermark removal algorithm
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

README.md

Remove-AI-Watermarks

Features