🔍 SynthID Watermark Reverse Engineering

Discovering Google's hidden AI watermark patterns through signal analysis

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🎯 Overview

This project reverse-engineers Google's SynthID watermarking technology by analyzing 250 AI-generated images from Gemini. Since the neural network encoder/decoder is proprietary, we use signal processing techniques to discover the watermark's structure.

Key Discovery

SynthID uses spread-spectrum phase encoding in the frequency domain—not LSB replacement or simple noise addition. The watermark embeds information through precise phase relationships at specific carrier frequencies.

🔬 Discovered Patterns

Carrier Frequency	Phase Coherence	Description
(±14, ±14)	99.99%	Primary diagonal carrier
(±126, ±14)	99.97%	Secondary horizontal
(±98, ±14)	99.94%	Tertiary carrier
(±128, ±128)	99.92%	Center frequency
(±210, ±14)	99.77%	Extended carrier
(±238, ±14)	99.71%	Edge carrier

Detection Metrics

• Noise Correlation: ~0.218 between watermarked images
• Structure Ratio: ~1.32
• Detection Threshold: correlation > 0.179

🖼️ Extracted Watermark Visualizations

Enhanced Visualization (500x Amplification)	Frequency Domain Carriers
False Color (HSV Encoding)	Phase Encoding Pattern

📁 Project Structure

synthid-demarker/
├── 📄 README.md                    # This file
├── 📋 requirements.txt             # Python dependencies
│
├── 💻 src/
│   ├── analysis/
│   │   ├── synthid_codebook_finder.py    # Pattern discovery
│   │   └── deep_synthid_analysis.py      # Frequency analysis
│   └── extraction/
│       └── synthid_codebook_extractor.py # Codebook extraction & detection
│
├── 🎯 artifacts/
│   ├── codebook/
│   │   ├── synthid_codebook.pkl          # Extracted codebook (9 MB)
│   │   └── synthid_codebook_meta.json    # Carrier frequencies
│   └── visualizations/                   # Watermark images
│
├── 📂 data/
│   └── pure_white/                       # 250 Gemini AI images
│
├── 📚 docs/
│   └── SYNTHID_CODEBOOK_ANALYSIS.md      # Technical documentation
│
└── 🖼️ assets/
    └── synthid-watermark.jpeg            # Cover image

🚀 Quick Start

Installation

git clone https://github.com/yourusername/synthid-demarker.git
cd synthid-demarker

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Detect Watermark

python src/extraction/synthid_codebook_extractor.py detect "path/to/image.png" \
    --codebook "artifacts/codebook/synthid_codebook.pkl"

Output:

Detection Results:
  Watermarked: True
  Confidence: 1.0000
  Correlation: 0.5355
  Phase Match: 0.9571
  Structure Ratio: 1.2753

Extract New Codebook

python src/extraction/synthid_codebook_extractor.py extract "data/pure_white/" \
    --output "./my_codebook.pkl"

Run Analysis

# Comprehensive pattern discovery
python src/analysis/synthid_codebook_finder.py

# Deep frequency analysis
python src/analysis/deep_synthid_analysis.py

🧠 How It Works

1. Pattern Discovery

Analyze noise patterns across multiple images to find consistent structures that persist despite varying image content.

2. Frequency Analysis

Use FFT to identify carrier frequencies where the watermark is embedded through phase modulation.

3. Phase Coherence

Measure phase consistency at carrier frequencies—high coherence indicates watermark presence.

4. Codebook Extraction

Build reference patterns from averaged signals across many watermarked images.

5. Detection

Compare test image against codebook using correlation, phase matching, and structure ratio metrics.

📊 Technical Details

Watermark Characteristics

• Embedding Domain: Frequency (FFT phase)
• Signal Strength: ~0.1-0.15 pixel values
• Carrier Count: 100+ frequency locations
• Robustness: Survives moderate compression

Detection Algorithm

def detect_synthid(image, codebook):
    # 1. Extract noise pattern
    noise = image - denoise(image)
    
    # 2. Check carrier phase coherence
    fft = fft2(noise)
    phase_match = check_phases(fft, codebook.carriers)
    
    # 3. Correlate with reference
    correlation = correlate(noise, codebook.reference)
    
    # 4. Apply decision thresholds
    is_watermarked = (
        correlation > 0.179 and 
        phase_match > 0.5 and 
        0.8 < structure_ratio < 1.8
    )
    
    return is_watermarked, confidence

📚 References

• SynthID: Identifying AI-generated images
• Arxiv Paper - SynthID-Image: Image watermarking at internet scale

⚠️ Disclaimer

This project is for research and educational purposes only. SynthID is proprietary technology owned by Google DeepMind. The extracted patterns and detection methods are intended for:

• Academic research on watermarking techniques
• Security analysis of AI-generated content identification
• Understanding spread-spectrum encoding methods

📄 License

Research and educational use only. See LICENSE for details.

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Made with 🔬 by reverse engineering enthusiasts