🔍 Reverse-Engineering SynthID

Discovering, detecting, and surgically removing Google's AI watermark through spectral analysis

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🎯 Overview

This project reverse-engineers Google's SynthID watermarking system - the invisible watermark embedded into every image generated by Google Gemini. Using only signal processing and spectral analysis (no access to the proprietary encoder/decoder), we:

1. Discovered the watermark's exact frequency-domain structure
2. Built a detector that identifies SynthID watermarks with 90% accuracy
3. Developed a spectral bypass (V3) that surgically removes watermark components while preserving image quality at 40+ dB PSNR

What Makes This Different

Unlike brute-force approaches (JPEG compression, noise injection), our V3 bypass uses a SpectralCodebook - a fingerprint of the watermark's exact frequency signature - extracted from reference images. This allows surgical, frequency-bin-level removal rather than blind signal destruction.

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🔬 Key Findings

The Watermark is a Fixed Spectral Pattern

By generating pure black and white images through Google Gemini, we isolated the watermark signal from content. The results are striking:

Left: SynthID watermark extracted from a pure-black Gemini image (enhanced 100×). Right: Same watermark on a white background. The diagonal stripe pattern and carrier frequencies are clearly visible.

Carrier Frequency Discovery

The watermark embeds energy at specific carrier frequencies with >99.9% phase coherence across all images:

Carrier Frequency (fy, fx)	Phase Coherence	Magnitude	Phase (rad)
(±14, ±14)	99.96%	16,807	±1.44
(±126, ±14)	99.96%	8,046	±2.37
(±98, ∓14)	99.94%	6,283	±0.61
(±128, ±128)	99.25%	6,908	±2.29
(±210, ∓14)	99.96%	6,032	±1.13
(±238, ±14)	99.90%	4,190	±1.61

Key insight: Most carriers cluster along the y = ±14 line in frequency space, suggesting a structured frequency selection algorithm. The diagonal stripe pattern visible in the enhanced images corresponds to these carrier frequencies.

Phase Consistency - A Fixed Model-Level Key

The watermark's phase template is identical across all images from the same Gemini model:

• Green channel phase std: < 0.007 radians across 50 reference images
• Cross-image correlation: 21.8% mean pairwise noise correlation
• Noise structure ratio: 1.32 ± 0.02 (byproduct of the neural encoder)

This means SynthID does not embed per-image messages - it uses a fixed spectral fingerprint that can be profiled and subtracted.

Frequency Spectrum Analysis

Left: FFT magnitude spectrum showing bright carrier frequency peaks. Right: Reconstructed carrier pattern showing the diagonal structure.

Detailed frequency analysis: Average magnitude spectrum (left) and phase coherence map (right). The carrier positions are marked with crosshairs.

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🏗️ Architecture

Three Generations of Bypass

Version	Approach	PSNR	Detection Impact	Status
V1	JPEG compression (Q50)	37 dB	~11% phase drop	✅ Baseline
V2	Multi-stage transforms (noise, color, frequency)	27-37 dB	~0% confidence drop	✅ Quality trade-off
V3	Spectral codebook subtraction	33-43 dB	1-7% confidence drop	✅ Best quality

V3 Pipeline (Spectral Bypass)

Input Image → FFT per channel → Estimate Watermark → Subtract → IFFT → Clip → Output
                                        ↑
                                 SpectralCodebook
                              (25 black + 25 white refs)

1. SpectralCodebook profiles the watermark from reference images (pure black/white Gemini outputs)
2. Selective notch filter targets only high-magnitude (P97+), high-consistency (≥95%) frequency bins
3. Safe magnitude cap limits subtraction to 30% of image energy per bin - preserving content
4. Content-adaptive scaling adjusts subtraction based on image luminance

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📊 Results

V3 Bypass Quality (PSNR in dB - higher is better)

Image	Gentle	Moderate	Aggressive	Maximum
121407	42.9	41.4	40.0	39.3
110802	33.4	33.3	33.1	33.0
131614	38.4	38.0	37.5	37.2
119198	38.4	37.7	37.0	36.6
12085	42.5	41.6	40.7	40.3

All images maintain >33 dB PSNR - visually indistinguishable from the original.

Watermark Detection Confidence

Image	Before	After (Aggressive)	Drop
121407	0.394	0.387	1.7%
131614	0.437	0.422	3.4%
12085	0.394	0.366	7.2%
119198	0.389	0.386	0.9%

Sample Output

Left: Original SynthID-watermarked Gemini image. Right: After V3 spectral bypass - visually identical, watermark energy reduced.

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🚀 Quick Start

Installation

git clone https://github.com/yourusername/reverse-SynthID.git
cd reverse-SynthID

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

1. Build Detection Codebook

python src/extraction/robust_extractor.py extract /path/to/watermarked/images \
    --output artifacts/codebook/robust_codebook.pkl

2. Detect Watermark

python src/extraction/robust_extractor.py detect image.png \
    --codebook artifacts/codebook/robust_codebook.pkl

Detection Results:
  Watermarked: True
  Confidence: 0.95
  Phase Match: 0.6683

3. Build Spectral Codebook (V3)

from synthid_bypass import SpectralCodebook

codebook = SpectralCodebook()
codebook.extract_from_references(
    black_dir='assets/black/',  # Pure-black Gemini images
    white_dir='assets/white/'   # Pure-white Gemini images
)
codebook.save('artifacts/spectral_codebook.npz')

4. Run V3 Bypass

from synthid_bypass import SynthIDBypass, SpectralCodebook

codebook = SpectralCodebook()
codebook.load('artifacts/spectral_codebook.npz')

bypass = SynthIDBypass()
result = bypass.bypass_v3(image_rgb, codebook, strength='aggressive')

print(f"PSNR: {result.psnr:.1f} dB")  # ~40 dB

Strength levels: gentle (minimal change, ~43 dB) → moderate → aggressive → maximum (strongest removal, ~33 dB)

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📁 Project Structure

reverse-SynthID/
├── src/
│   ├── extraction/
│   │   ├── synthid_bypass.py           # V1/V2/V3 bypass implementations + SpectralCodebook
│   │   ├── robust_extractor.py         # Multi-scale watermark detection (90% accuracy)
│   │   ├── watermark_remover.py        # Frequency-domain watermark removal
│   │   ├── benchmark_extraction.py     # Performance benchmarking suite
│   │   └── synthid_codebook_extractor.py  # Original codebook extractor (legacy)
│   └── analysis/
│       ├── deep_synthid_analysis.py    # FFT/phase analysis scripts
│       └── synthid_codebook_finder.py  # Carrier frequency discovery
│
├── assets/
│   ├── synthid_black.jpg              # Watermark on black (enhanced)
│   ├── synthid_white.jpg              # Watermark on white (enhanced)
│   ├── black/                         # Reference black images from Gemini
│   └── white/                         # Reference white images from Gemini
│
├── artifacts/
│   ├── codebook/                      # Detection codebooks (.pkl)
│   ├── spectral_codebook.npz          # V3 spectral fingerprint (119 MB)
│   ├── v3_output/                     # V3 bypass output samples
│   └── visualizations/               # FFT, phase, carrier visualizations
│
├── watermark_investigation/           # Early-stage Nano-150k analysis (archived)
├── SYNTHID_CODEBOOK_ANALYSIS.md       # Detailed codebook reverse-engineering report
├── synthid.pdf                        # SynthID paper reference
└── requirements.txt

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🔬 Technical Deep Dive

How SynthID Works (Reverse-Engineered)

┌──────────────────────────────────────────────────────────────┐
│                  SynthID Encoder (in Gemini)                  │
├──────────────────────────────────────────────────────────────┤
│  1. Generate carrier frequencies: {(14,14), (126,14), ...}   │
│  2. Assign fixed phase values to each carrier                │
│  3. Neural encoder adds learned noise pattern to image       │
│  4. Watermark is imperceptible - spread across spectrum      │
├──────────────────────────────────────────────────────────────┤
│                  SynthID Decoder (in Google)                  │
├──────────────────────────────────────────────────────────────┤
│  1. Extract noise residual (wavelet denoising)               │
│  2. FFT → check phase at known carrier frequencies           │
│  3. If phases match expected values → Watermarked            │
└──────────────────────────────────────────────────────────────┘

SpectralCodebook Extraction

The codebook captures the watermark's full frequency fingerprint:

• 50 reference images (25 pure black + 25 pure white, all from Gemini)
• Extracts magnitude envelope and phase template per channel
• Computes phase consistency score per frequency bin
• Content-adaptive profiles for dark vs. light image regions

Selective Notch Filter

The V3 bypass doesn't subtract blindly - it targets only bins where:

1. Magnitude exceeds the 97th percentile (strong watermark energy)
2. Phase consistency ≥ 0.95 across reference images (confirmed watermark, not noise)
3. Subtraction is capped at 30% of the image's energy at each bin

This surgical precision is why V3 achieves 40+ dB PSNR while still reducing watermark energy.

Noise Correlation Signature

Metric	Value	Significance
Mean pairwise noise correlation	0.218	Identical watermark in all images
Noise structure ratio	1.32	Neural encoder byproduct
Phase coherence (top carriers)	>99.9%	Fixed model-level key
Green channel phase std	<0.007 rad	Strongest consistency channel

Bit Plane Analysis

Bit Plane	Consistency	Role
Bit 0 (LSB)	0.049	Watermark signal
Bit 1	0.074	Watermark signal
Bit 2	0.125	Partially watermarked
Bit 3	0.513	Mixed
Bits 4-7	0.635–1.000	Image structure

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🛠️ Core Modules

robust_extractor.py - Detection

Multi-scale, multi-denoiser watermark detector achieving 90% detection rate.

from robust_extractor import RobustSynthIDExtractor

extractor = RobustSynthIDExtractor()
extractor.load_codebook('artifacts/codebook/robust_codebook.pkl')
result = extractor.detect_array(image)

print(f"Watermarked: {result.is_watermarked}")
print(f"Confidence: {result.confidence:.4f}")
print(f"Phase Match: {result.phase_match:.4f}")

Features:

• Multi-scale analysis (256, 512, 1024px)
• Wavelet + bilateral + NLM denoising fusion
• ICA-based watermark/content separation
• Ensemble carrier detection across scales

synthid_bypass.py - Bypass (V1/V2/V3)

Three generations of watermark bypass:

from synthid_bypass import SynthIDBypass, SpectralCodebook

bypass = SynthIDBypass()

# V1: Simple JPEG compression
result = bypass.bypass_simple(image, jpeg_quality=50)

# V2: Multi-stage transform pipeline
result = bypass.bypass_v2(image, strength='moderate')

# V3: Spectral codebook subtraction (best)
codebook = SpectralCodebook()
codebook.load('artifacts/spectral_codebook.npz')
result = bypass.bypass_v3(image, codebook, strength='aggressive')

watermark_remover.py - Removal

Quality-preserving frequency-domain removal:

from watermark_remover import WatermarkRemover

remover = WatermarkRemover(extractor)
result = remover.remove(image, mode='balanced')

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📚 References

• SynthID: Identifying AI-generated images
• SynthID Paper (arXiv:2510.09263)

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⚠️ Disclaimer

This project is for research and educational purposes only. SynthID is proprietary technology owned by Google DeepMind. These tools are intended for:

• 🎓 Academic research on watermarking robustness
• 🔒 Security analysis of AI-generated content identification
• 📡 Understanding spread-spectrum encoding methods

Do not use these tools to misrepresent AI-generated content as human-created.