README.md

metadata

title: SynthID Watermark Remover
emoji: 🔬
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 6.2.0
app_file: app.py
pinned: false
license: mit
tags:
  - research
  - ai-safety
  - watermark-removal
  - diffusion
  - controlnet

🔬 SynthID Watermark Remover

A research tool demonstrating the removal of invisible SynthID watermarks from AI-generated images using diffusion-based reconstruction techniques.

🎯 Overview

This application implements the technique described in the SynthID-Bypass research by 00quebec. It demonstrates that pixel-space watermarks embedded by Google's SynthID technology can be disrupted through careful re-processing with diffusion models.

🔧 How It Works

The core technique involves three key steps:

1. Structural Extraction: Uses Canny edge detection to create a structural map of the image
2. Low-Denoise Diffusion: Applies multiple passes of low-strength denoising to "re-noise" the image, replacing the watermark-carrying pixels
3. ControlNet Guidance: Preserves the original composition and structure using ControlNet conditioning

This process effectively "launders" the pixels - keeping semantic and structural information while replacing the low-level noise that carries the watermark.

🚀 Usage

1. Upload an AI-generated image with a SynthID watermark
2. Adjust settings if needed (default values work well for most images)
3. Click "Remove Watermark" and wait for processing
4. Download the processed image

Advanced Settings

• Denoise Strength (0.05-0.3): Lower values preserve more detail but may leave watermark traces
• Inference Steps (10-50): More steps = better quality but slower processing
• Guidance Scale (5.0-15.0): Controls how strongly the model follows the prompt
• ControlNet Scale (0.5-1.0): Strength of structural preservation

⚠️ Ethical Considerations & Disclaimer

This tool is provided for educational and AI safety research purposes only.

• ❌ Do NOT use for malicious purposes
• ❌ Do NOT use to circumvent copyright
• ❌ Do NOT use to misrepresent content origin
• ✅ DO use for research and understanding watermark robustness
• ✅ DO use to develop better watermarking techniques

This proof-of-concept is presented "as-is" and without warranty.

🔬 Research Background

This implementation demonstrates a fundamental challenge in synthetic media detection: watermarks embedded in pixel space are vulnerable to reconstruction-style attacks. The research shows that:

• SynthID watermarks are not deterministic (different noise patterns each time)
• Low-denoise diffusion can replace watermark-carrying noise
• Structural guidance (ControlNet) prevents content degradation
• Multiple passes ensure complete watermark removal

🛠️ Technical Details

Models Used:

• Stable Diffusion v1.5 (base diffusion model)
• ControlNet Canny (structural preservation)
• DDIM Scheduler (quality optimization)

Processing Pipeline:

1. Image preprocessing and resizing
2. Canny edge extraction
3. 3-pass low-denoise diffusion
4. ControlNet-guided reconstruction

📚 Credits & References

• Original Research: 00quebec/Synthid-Bypass
• Related Paper: Hu, Y., et al. (2024). "Stable signature is unstable: Removing image watermark from diffusion models." arXiv:2405.07145
• SynthID: Google DeepMind

🤝 Contributing

This is a research tool. If you develop techniques that:

• Defeat this bypass method
• Create more robust watermarking
• Improve the removal process

Please contribute to the broader AI safety dialogue!

📄 License

MIT License - See LICENSE file for details

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Remember: The goal of this research is to improve AI safety, not to undermine it. Use responsibly and ethically.