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

How it works

1. Visible watermark removal

Gemini embeds a sparkle logo using alpha blending:

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

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

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.

2. Invisible watermark removal

AI services (Google SynthID, StableSignature, TreeRing) embed imperceptible patterns in the frequency domain. These survive cropping, resizing, and JPEG compression.

The removal pipeline:

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.

3. AI metadata stripping

AI tools embed generation metadata in multiple layers:

EXIF tags — prompt, seed, model hash, sampler settings
PNG text chunks — ComfyUI workflows, Stable Diffusion parameters
C2PA manifests — Google Imagen, OpenAI DALL-E, Adobe Firefly provenance

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

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

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Remove-AI-Watermarks

Features