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

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 resolution-dependent carrier frequency structure
2. Built a detector that identifies SynthID watermarks with 90% accuracy
3. Developed a multi-resolution spectral bypass (V3) that achieves 75% carrier energy drop, 91% phase coherence drop, and 43+ dB PSNR on any image resolution

We're actively collecting pure black and pure white images generated by Nano Banana Pro to improve multi-resolution watermark extraction.

If you can generate these:

• Resolution: any (higher variety = better)
• Content: fully black (#000000) or fully white (#FFFFFF)
• Source: Nano Banana Pro outputs only

1. Generate a batch of black/white images by attaching a pure black/white image into Gemini and prompting it to "recreate this as it is"
2. Place them in new folders:
   • gemini_black_nb_pro/ (for black)
   • gemini_white_nb_pro/ (for white)
3. Open a Pull Request 🚀

These reference images are critical for:

• Carrier frequency discovery
• Phase validation
• Improving cross-resolution robustness

Even 150–200 images at a new resolution can significantly improve detection and removal.

Unlike brute-force approaches (JPEG compression, noise injection), our V3 bypass uses a multi-resolution SpectralCodebook - a collection of per-resolution watermark fingerprints stored in a single file. At bypass time, the codebook auto-selects the matching resolution profile, enabling surgical frequency-bin-level removal on any image size.

SynthID embeds carrier frequencies at different absolute positions depending on image resolution. A codebook built at 1024x1024 cannot directly remove the watermark from a 1536x2816 image - the carriers are at completely different bins.

This is why the V3 codebook stores separate profiles per resolution and auto-selects at bypass time.

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

• Green channel carries the strongest watermark signal
• Cross-image phase coherence at carriers: >99.5%
• Black/white cross-validation confirms true carriers via |cos(phase_diff)| > 0.90

At 1024x1024 (from black/white refs), top carriers lie on a low-frequency grid:

At 1536x2816 (from random watermarked content), carriers are at much higher frequencies:

Input Image (any resolution)
       │
       ▼
  codebook.get_profile(H, W)  ──► exact match? ──► FFT-domain subtraction
       │                                             (fast path)
       └─ no exact match ──────► spatial-domain resize + subtraction
                                  (fallback path)
       │
       ▼
  Multi-pass iterative subtraction (aggressive → moderate → gentle)
       │
       ▼
  Anti-alias → Output

1. SpectralCodebook stores resolution-specific profiles (carrier positions, magnitudes, phases)
2. Auto resolution selection picks the exact profile or the closest match
3. Direct known-signal subtraction weighted by phase consistency and cross-validation confidence
4. Multi-pass schedule catches residual watermark energy missed by previous passes
5. Per-channel weighting (G=1.0, R=0.85, B=0.70) matches SynthID's embedding strength

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

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

From the CLI:

python src/extraction/synthid_bypass.py build-codebook \
    --black gemini_black \
    --white gemini_white \
    --watermarked gemini_random \
    --output artifacts/spectral_codebook_v3.npz

Or from Python:

from src.extraction.synthid_bypass import SpectralCodebook

codebook = SpectralCodebook()

# Profile 1: from black/white reference images (1024x1024)
codebook.extract_from_references(
    black_dir='gemini_black',
    white_dir='gemini_white',
)

# Profile 2: from watermarked content images (1536x2816)
codebook.build_from_watermarked('gemini_random')

codebook.save('artifacts/spectral_codebook_v3.npz')
# Saved with profiles: [1024x1024, 1536x2816]

from src.extraction.synthid_bypass import SynthIDBypass, SpectralCodebook

codebook = SpectralCodebook()
codebook.load('artifacts/spectral_codebook_v3.npz')

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

print(f"PSNR: {result.psnr:.1f} dB")
print(f"Profile used: {result.details['profile_resolution']}")
print(f"Exact match: {result.details['exact_match']}")

From the CLI:

python src/extraction/synthid_bypass.py bypass input.png output.png \
    --codebook artifacts/spectral_codebook_v3.npz \
    --strength aggressive

Strength levels: gentle (minimal, ~45 dB) > moderate > aggressive (recommended) > maximum

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

reverse-SynthID/
├── src/
│   ├── extraction/
│   │   ├── synthid_bypass.py              # V1/V2/V3 bypass + multi-res SpectralCodebook
│   │   ├── robust_extractor.py            # Multi-scale watermark detection
│   │   ├── watermark_remover.py           # Frequency-domain watermark removal
│   │   ├── benchmark_extraction.py        # Benchmarking suite
│   │   └── synthid_codebook_extractor.py  # Legacy codebook extractor
│   └── analysis/
│       ├── deep_synthid_analysis.py       # FFT / phase analysis scripts
│       └── synthid_codebook_finder.py     # Carrier frequency discovery
│
├── gemini_black/                          # 100 pure-black Gemini images (1024x1024)
├── gemini_white/                          # 100 pure-white Gemini images (1024x1024)
├── gemini_random/                         # 88 watermarked content images (1536x2816)
│
├── artifacts/
│   ├── spectral_codebook_v3.npz           # Multi-res V3 codebook [1024x1024, 1536x2816]
│   ├── codebook/                          # Detection codebooks (.pkl)
│   └── visualizations/                    # FFT, phase, carrier visualizations
│
├── assets/                                # README images and early analysis artifacts
├── watermark_investigation/               # Early-stage Nano-150k analysis (archived)
└── requirements.txt

┌──────────────────────────────────────────────────────────────┐
│                  SynthID Encoder (in Gemini)                  │
├──────────────────────────────────────────────────────────────┤
│  1. Select resolution-dependent carrier frequencies           │
│  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            │
└──────────────────────────────────────────────────────────────┘

The codebook captures watermark profiles at each available resolution:

• 1024x1024 profile: from 100 black + 100 white pure-color Gemini outputs
  • Black images: watermark is nearly the entire pixel content
  • White images (inverted): confirms carriers via cross-validation
  • Black/white agreement (|cos(phase_diff)|) filters out generation bias
• 1536x2816 profile: from 88 diverse watermarked content images
  • Content averages out across images; fixed watermark survives in phase coherence
  • Watermark magnitude estimated as avg_mag x coherence^2

The bypass uses direct known-signal subtraction (not a Wiener filter):

1. Confidence = phase_consistency x cross_validation_agreement
2. DC exclusion — soft ramp suppresses low-frequency generation biases
3. Per-bin subtraction = wm_magnitude x confidence x removal_fraction x channel_weight
4. Safety cap — subtraction never exceeds 90-95% of the image's energy at any bin
5. Multi-pass — decreasing-strength schedule (aggressive → moderate → gentle) catches residual energy

SpectralCodebook — multi-resolution watermark fingerprint:

codebook = SpectralCodebook()
codebook.extract_from_references('gemini_black', 'gemini_white')  # adds 1024x1024 profile
codebook.build_from_watermarked('gemini_random')                   # adds 1536x2816 profile
codebook.save('codebook.npz')

# Later:
codebook.load('codebook.npz')
profile, res, exact = codebook.get_profile(1536, 2816)  # auto-select

SynthIDBypass — three bypass generations:

bypass = SynthIDBypass()

result = bypass.bypass_simple(image, jpeg_quality=50)           # V1
result = bypass.bypass_v2(image, strength='aggressive')          # V2
result = bypass.bypass_v3(image, codebook, strength='aggressive') # V3 (best)

Multi-scale watermark detector (90% accuracy):

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}, Confidence: {result.confidence:.4f}")

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

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.