remove-ai-watermarks/docs/synthid.md

# SynthID-Image: technical reference

This document covers how Google SynthID for images works mechanically, what it
survives, what removes it, and the current deployment landscape. It is written
for engineers working on watermark detection and removal -- specifically to
inform decisions about strength settings, test methodology, and what oracle
results mean.

Primary sources are cited inline. Marketing-only claims are flagged separately
from independently-verified results.

---

## 1. Mechanism

### 1.1 Post-hoc, model-independent design

SynthID-Image is **not** baked into a diffusion model's weights. It is a
post-hoc, model-independent system: a separate encoder `f` is applied to an
already-generated image, and a separate decoder `g` reads it back.

> "We deliberately designed SynthID-Image as a post-hoc, model-independent
> approach, a choice largely based on deployment considerations."
> -- Gowal et al., arXiv:2510.09263

The formal definition from the paper:

> "A post-hoc watermarking scheme is a pair f, g consisting of an encoder
> function f: X -> X, which adds an identification mark, and a decoder
> function g: X -> {+-1}, which tries to detect if the mark is present."

This is the key architectural fact: **the generative model (Imagen, Gemini's
image model) is not modified**. The watermark is stamped onto the pixel output
after generation, by a separate neural network. This means:

- The watermark is in **pixel space**, not in the model's latent activations.
- Replacing the generative model does not remove the watermarking capability.
- The encoder/decoder pair can be updated independently of the generative model.

The paper does not disclose the internal architecture of the encoder/decoder
networks (layer types, capacity). The external variant SynthID-O is available
to partners; the production internal variant is not published.

### 1.2 How it differs from classical DWT-DCT watermarks

The open watermarks used by Stable Diffusion / SDXL / FLUX (via the
`imwatermark` library) use classical **DWT-DCT** frequency-domain embedding: a
fixed bit pattern is added to specific frequency coefficients of the image's
wavelet transform. This is fast, key-free, and locally detectable with a public
decoder.

SynthID-Image uses **jointly-trained deep learning models**:

> "SynthID uses two deep learning models -- for watermarking and identifying --
> that have been trained together on a diverse set of images. The combined model
> is optimised on a range of objectives, including correctly identifying
> watermarked content and improving imperceptibility by visually aligning the
> watermark to the original content."
> -- Google DeepMind blog, 2023

The practical difference for robustness: the deep learning encoder learns to
spread the signal across the image in a way that is optimized to survive a
specific perturbation distribution seen during training. Classical DWT-DCT
embeds in fixed, predictable frequency bins, making it brittle to any
operation that hits those bins (e.g., JPEG re-quantization wipes it cleanly at
quality <= 90).

### 1.3 Payload capacity

SynthID-O (the external/partnership variant) encodes:

- **136 bits** within a **512x512 pixel image**

For comparison (from the same paper):

| Method      | Bits | Resolution |
|-------------|------|------------|
| SynthID-O   | 136  | 512x512    |
| StegaStamp  | 100  | 400x400    |
| TrustMark   | 100  | 256x256    |
| WAM         | 32   | 256x256    |

The payload carries an identification mark (not a user-readable secret). The
paper separates watermark **detection** (is this watermarked?) from payload
**recovery** (what does the payload say?): the detection path is what oracles
like the Gemini app's "Verify with SynthID" exercise.

### 1.4 Where in the pipeline it lives

```
[Diffusion model]
       |
  raw pixel output
       |
  [SynthID encoder f]   <-- separate neural net, stamps the watermark
       |
  watermarked image
       |
  [served / downloaded]
       |
  [SynthID decoder g]   <-- separate neural net, run by Google's verifier only
       |
  present / not present
```

The VAE decoder of the diffusion model is **not** involved in watermarking.
Some in-generation watermark approaches (like the research method "Tree Ring")
inject the signal into the initial noise latent so it propagates through the
diffusion process and appears in the final image; SynthID-Image does not do
this -- it is applied after the VAE has already decoded latents to pixels.

---

## 2. Robustness

### 2.1 What the paper claims it survives (primary-source verified)

The SynthID-Image paper (arXiv:2510.09263) evaluates SynthID-O against **30
image transformations** grouped into 6 categories:

| Category    | Examples                                      |
|-------------|-----------------------------------------------|
| Color       | brightness, contrast, saturation, hue shifts  |
| Combination | combinations of multiple transforms           |
| Noise       | Gaussian noise, impulse noise, median filter  |
| Overlay     | text overlays, logos, stickers                |
| Quality     | JPEG compression, WebP, format conversion     |
| Spatial     | crop, resize, rotate, flip, padding           |

**TPR at 0.1% FPR -- SynthID-O vs. baselines (resized to 512x512):**

| Category         | SynthID-O | Best baseline (WAM) | Worst baseline (StegaStamp spatial) |
|------------------|-----------|---------------------|--------------------------------------|
| Identity (none)  | 100.00%   | 100.00%             | 100.00%                              |
| Aggregated       | 99.98%    | 90.62%              | ~70%                                 |
| Color            | 100.00%   | 81.29%              | ~75%                                 |
| Combination      | 99.96%    | 96.08%              | ~22%                                 |
| Noise            | 99.98%    | 100.00%             | ~92%                                 |
| Overlay          | 100.00%   | 100.00%             | 100.00%                              |
| Quality          | 99.99%    | --                  | ~89%                                 |
| Spatial (worst)  | 99.97%    | 76.04%              | 15.25%                               |

The "Spatial worst" row is the hardest case (aggressive crop + resize).
SynthID-O retains 99.97% TPR; StegaStamp collapses to 15.25%. This is where
the deep-learning approach gains the most over classical methods.

Google's marketing page states the watermark is:

> "designed to stand up to modifications like cropping, adding filters, changing
> frame rates, or lossy compression."
> -- deepmind.google/models/synthid/

The marketing claim is broadly consistent with the paper's numbers for these
specific categories.

**JPEG and format conversion specifically** fall under the "Quality" category,
where SynthID-O achieves 99.99% TPR. This is the empirical basis for the fact
that **GitHub-recompressed JPEGs from issue attachments are valid SynthID test
subjects**: the re-encoding does not remove the pixel watermark.

### 2.2 Stated limits (vendor claim, not independently verified)

> "SynthID isn't foolproof against extreme image manipulations."
> -- Google DeepMind blog, 2023

This is the only public failure-mode statement Google has made. No specific
perturbation type, threshold, or quantitative boundary is named. The
Limitations section of the paper (Section 10) was not recoverable from the
public HTML version of arXiv:2510.09263v1 due to a rendering failure in the
conversion (the body text of Section 10 is absent from the HTML).

**What is known empirically from our own oracle-verified testing.**

A controlled study (June 2026, clean v0.8.6 with text/face protection OFF,
native resolution on this repo's default SDXL pipeline) measured the minimum
img2img strength that removes the SynthID pixel watermark, verified per image on
the vendor's own oracle (openai.com/verify for OpenAI, the Gemini app "Verify
with SynthID" for Google). Each subject is archived in `data/synthid_corpus/` as a
pos original plus its minimum-clearing cleaned output (manifest `verified_via` =
`openai-verify` / `gemini-app`), EXCEPT one third-party image from issue #14, which
was oracle-verified but is not committed (third-party content stays out of the
public corpus).

| Vendor | Images | Resolution(s) | Pipeline | Removed at |
|--------|--------|---------------|----------|------------|
| OpenAI (gpt-image) | n=4 (3 archived + 1 external-only) | 1024x1536 .. 1600x1600 | native | **0.05** |
| Google (Gemini)    | n=4 | 2816x1536 -> capped 1536 | `--max-resolution 1536` | **0.15** (0.05 and 0.10 do NOT clear) |

**Two findings, both oracle-verified:**

1. **Vendor is the dominant factor, not resolution.** Google's SynthID is
   roughly 3x more robust than OpenAI's: at a comparable (small) working
   resolution, OpenAI clears at 0.05 while Google needs 0.15. This matches
   Google having hardened SynthID more aggressively over time.

2. **OpenAI SynthID removal is resolution-independent in the tested range.**
   All four OpenAI images (including a 1600x1600) cleared at 0.05.

**CORRECTION (supersedes the earlier "resolution dependence" claim).** A prior
version of this doc and CLAUDE.md stated that strength 0.30 failed to remove
SynthID on 1600x1600 gpt-image and that removal was resolution-dependent. That
was a **measurement artifact of a since-removed per-region re-scrub step** (issue
#14): on the dense-text infographics tested, that step could reconstitute SynthID
in text regions. Re-running the *same* 1600x1600 image on the clean current
pipeline removes SynthID at **0.05**. The "large images resist removal" conclusion
was false; the resistance was that region-rescrub shielding, since removed.

**Open / not locally testable:**

- **Native large Gemini (2816x1536, ~4.3 MP).** The Gemini floor of 0.15 was
  measured on the *capped* (`--max-resolution 1536`) path, which is the
  practical local route on Apple-Silicon (native 2816 OOMs / falls back to slow
  CPU on a 32 GB M-series). Native large Gemini was not measured here; the
  vendor and resolution effects would stack, so it plausibly needs >= 0.30 or a
  discrete GPU. Confirm on a CUDA box if needed.
- **Heavy JPEG compression** (quality < ~50-60): not oracle-tested; the DL
  approach is more robust than DWT-DCT but Google acknowledges limits at
  "extreme" manipulation.

### 2.3 Removal attacks and forensic detectability

The paper arXiv:2605.09203 ("Removing the Watermark Is Not Enough",
Goonatilake & Ateniese, 2026) evaluates 6 removal attacks against a ResNet-50
forensic detector. All attacks defeat the watermark verifier but are detected
by the forensic classifier:

| Attack          | Family           | AUROC  | TPR @ 1% FPR | TPR @ 0.1% FPR |
|-----------------|------------------|--------|--------------|----------------|
| UnMarker        | Distortion       | 0.9994 | 99.81%       | 98.28%         |
| WatermarkAttacker| Regeneration    | 0.9997 | 99.95%       | 99.38%         |
| CtrlRegen+      | Regeneration     | 0.9999 | 99.97%       | 99.64%         |
| NFPA            | Inversion/Pert.  | 0.9984 | 99.24%       | 62.10%         |
| Boundary Leak.  | Inversion/Pert.  | 0.9991 | 99.24%       | 88.34%         |
| WiTS            | Erosion          | 0.9999 | 99.80%       | 99.55%         |

The forensic detector is a standard ResNet-50 fine-tuned end-to-end; no exotic
architecture needed. The key finding:

> "These removers do not return images to a clean forensic state. They often
> trade an explicit watermark for an implicit watermark: a detectable artifact
> introduced by the removal process itself."

This means: even when our SDXL img2img pass defeats the SynthID pixel
watermark (oracle reads negative), the output may still be classifiable as
"an image that went through a removal pipeline" by an independent detector --
even if that detector is not trained on SynthID specifically. **Defeating the
verifier does not restore forensic deniability.**

CtrlRegen+ is the most detectable removal method (AUROC 0.9999), which is
notable because it is also the most powerful removal attack. The paper notes
that diffusion regeneration "leaves a strong reconstruction signature from the
diffusion prior."

---

## 3. Detectability and verifier access

### 3.1 No public local detector

The SynthID decoder is proprietary and not released:

> "SynthID-Image has been used to watermark over ten billion images and video
> frames across Google's services and its corresponding verification service is
> available to trusted testers."
> -- Gowal et al., arXiv:2510.09263

There is no public API, no released decoder weights, and no reproducible
algorithm for local detection. The verification service (SynthID Detector) is:

> "a verification portal" in early testing with "journalists and media
> professionals" on a waitlist
> -- deepmind.google/models/synthid/

The external variant SynthID-O is available "through partnerships" only. Our
tool cannot locally detect SynthID presence or absence -- this is by design,
not a gap we can fill.

### 3.2 How our tool detects SynthID (metadata proxy)

We detect SynthID indirectly: if the image's C2PA manifest is signed by a
known SynthID-using issuer (Google, OpenAI), we infer SynthID is present. This
is a **metadata proxy**, not a pixel watermark decode. It works while the C2PA
manifest is intact, and is silent once the manifest is stripped or the image
is re-encoded without C2PA (e.g., a screenshot, a social-media re-upload, or
after `metadata --remove`).

This is why:
- `identify` on a GitHub-recompressed issue attachment returns Unknown (C2PA is
  gone) even though the pixel SynthID is still present and detectable by
  openai.com/verify.
- A quiet `identify` output is not proof that SynthID was removed -- it only
  means the metadata signal is gone.

### 3.3 Oracle scope: each vendor detects only their own

From openai.com/research/verify (verbatim, verified 2026-05-31):

> "OpenAI generation signals will only be detected if the image was generated
> with our tools."
> "Content could also still be AI-generated by another company's model, which
> the tool currently does not detect."

SynthID technology is used by multiple vendors, but each verifier is keyed to
its own payload:

| Oracle                        | Detects          | Does NOT detect         |
|-------------------------------|------------------|-------------------------|
| Gemini app "Verify with SynthID" | Google SynthID | OpenAI SynthID          |
| openai.com/research/verify    | OpenAI SynthID   | Google SynthID          |

A Google-SynthID image reads clean on openai.com/verify. An OpenAI image reads
clean in the Gemini oracle. They are different payloads within the same
framework.

---

## 4. Adoption and current state (as of June 2026)

### 4.1 Google products

Google has watermarked **over 10 billion** images and video frames. The
deployment split by surface matters for our tool:

| Surface                              | SynthID pixel | C2PA metadata | Visible sparkle |
|--------------------------------------|---------------|---------------|-----------------|
| Gemini app (generated images)        | YES           | YES (Google)  | YES             |
| Gemini API / AI Studio / Nano Banana | YES           | NO            | YES             |

The Gemini API surface is a key blind spot: it embeds the pixel watermark and
the visible sparkle but **no C2PA or IPTC at all**. Our `identify` returns
Unknown on API-generated images unless the visible sparkle is detected (via
`check_visible=True`) or the user runs the Gemini app oracle.

### 4.2 OpenAI

OpenAI confirmed SynthID adoption (Help Center, updated 2026-05-21):

> "ChatGPT images include both C2PA metadata and SynthID watermarks."

This is time-gated: pre-rollout ChatGPT/gpt-image images carry C2PA without
SynthID. Our C2PA proxy therefore over-reports SynthID presence on old images
(hence the `_OPENAI_CAVEAT` hedging flag in the codebase).

### 4.3 Other vendors

- **Kakao** (South Korea): SynthID adopter as of May 2026 (Google announcement)
- **NVIDIA Cosmos**: SynthID for video (not still images; different pipeline)
- **Meta AI**: does NOT use SynthID; uses IPTC `digitalSourceType` marker instead

### 4.4 Version evolution (v1 vs v2 hardening)

Google has not publicly documented version numbers for the SynthID image
watermark in a way that maps to our testing observations. What is known
empirically from oracle tests:

- **Before May 2026 (Gemini)**: strength 0.05 removed the watermark
- **May 2026 (Gemini)**: strength 0.05 insufficient; 0.10 required
- **Current (Gemini, June 2026)**: on the capped 1536 path, 0.05 and 0.10 do
  NOT clear; 0.15 clears (n=4, Gemini app oracle). See section 2.2.
- **OpenAI (June 2026)**: clears at 0.05 across 1024-1600 (n=4, clean v0.8.6).
  The earlier "0.30 still detected on 1600x1600" report (issue #14) was the
  text-protection bug, not a hardening of the watermark -- see the correction in
  section 2.2.

Google has hardened SynthID relative to OpenAI's (vendor gap measured at ~3x
strength), but the year-over-year "0.05 -> 0.10 -> 0.30" progression above
conflates a real hardening trend with the now-debunked region-rescrub artifact;
treat only the section 2.2 controlled numbers as authoritative.

---

## 5. Practical implications for this tool

### 5.1 Preserving content means regenerating it, never copying it

**Core rule:** SynthID is a pixel-amplitude pattern, so any approach that FREEZES
or RESTORES original pixels in a region re-introduces the watermark there. Early
region-based text/face "protection" (since removed) proved this: restoring the
original face pixels guaranteed SynthID survived in faces, and even a per-region
high-resolution re-scrub from an upscaled crop could be insufficient to destroy
the payload, reconstituting SynthID in text. The lesson held and shaped the
current design: **content is preserved by REGENERATING it under structural
conditioning, never by copying original pixels.**

Both preservation features below are **EXPERIMENTAL and opt-in (off by default)**;
the plain `default` SDXL img2img pass is the shippable path.

- **Text + structure:** `--pipeline controlnet` (SDXL img2img + a canny ControlNet,
  experimental/opt-in) conditions the regeneration on the edge map, so text and
  structure stay sharp while every pixel is still regenerated. Text legibility is
  better than plain img2img at the same strength (text stays readable where plain
  garbles it). **BUT removal efficacy at the low vendor-adaptive strength is CONTENT ×
  PIPELINE dependent and NEITHER pipeline clears all content -- oracle-validated
  2026-06-04 (8 OpenAI images, strength 0.10/0.15, max-res 1536).** The survivors FLIP
  by content type: **photoreal** (a 9-face grid, a bracelet product photo) SURVIVES
  controlnet but CLEARS `default`; **flat graphic** (a logo/poster with large flat
  color fills) SURVIVES `default` but CLEARS controlnet; a flat **text** card cleared
  under both. Why: controlnet's dense edge map keeps the regen too close to the
  original on photoreal (so SynthID survives) but freely repaints flat fills (so it
  clears them); plain img2img at low strength perturbs photoreal texture enough but
  barely touches flat fills. **Root cause = insufficient STRENGTH, not the pipeline:
  the vendor-adaptive 0.10 is NOT universally sufficient (the June numbers below held
  for the content they were measured on). The robust fix is a HIGHER strength,
  oracle-revalidated per content type (controlnet can be cranked harder without losing
  structure; a lower `controlnet_conditioning_scale` also frees the regen on
  photoreal).** So neither `--pipeline controlnet` nor plain `default` is a drop-in
  removal guarantee at today's strength -- pick by what you must PRESERVE (controlnet
  for text/structure), then raise strength until the oracle reads clean. (The earlier
  "reads clean on the oracle" claim held only for the one flat/text-background case it
  was checked on; it does not generalize.)
- **Face identity:** canny holds face *structure* but not *identity*. Shipped as the
  optional `--restore-faces` GFPGAN post-pass (`face_restore.py`, the `restore`
  extra, experimental/opt-in, off by default). It runs GFPGAN on the ORIGINAL
  faces and feather-composites the restored face REGIONS into the cleaned image.
  **WARNING (oracle-confirmed 2026-06-04): this pass can RE-INTRODUCE SynthID into
  the face regions -- the earlier "GFPGAN re-synthesizes from a StyleGAN2 prior ->
  scrubs SynthID -> oracle-confirmed clean" claim was WRONG.** At the default fidelity
  weight `0.5` GFPGAN blends ~half the ORIGINAL (watermarked) face pixels with the
  prior, and SynthID is robust to that partial blend, so the composited face carries
  the watermark back in -- over the diffusion-cleaned face. Confirmed by a clean A/B:
  `gemini_3` read SynthID-detected after controlnet @ 0.20/0.25 WITH restore, but
  NOT-detected after the same controlnet @ 0.20 with `--no-restore-faces` (only
  restore differed). Content-dependent (a second face image cleared WITH restore),
  which is why a single-image check earlier read "clean". **Fix directions (not yet
  done): run GFPGAN on the diffusion-CLEANED image not the original; or drop the
  weight well below 0.5; or leave restore OFF for removal -- each needs oracle
  re-validation.** Commercial-
  safe (GFPGAN Apache-2.0 + RetinaFace MIT); the CodeFormer alternative is
  NON-COMMERCIAL and is not shipped. (An IP-Adapter FaceID approach was tried and
  REMOVED -- it needs high denoise strength and corrupts faces at removal strength;
  see `docs/controlnet-removal-pipeline-research.md`.)

### 5.2 Strength setting

There is no single permanent correct strength, but the controlled June 2026
study (section 2.2) gives empirical floors:

- **OpenAI**: 0.05 clears across 1024-1600 (n=4) -- **but content-dependent, NOT
  universal.** The follow-up oracle pass (2026-06-04, 8 images) found a flat-graphic
  OpenAI logo/poster still SynthID-detected after `default` at 0.10, and photoreal
  images still detected after controlnet at 0.10/0.15: at low strength the
  low-change regions (large flat fills under `default`, dense edges under controlnet)
  are not perturbed enough. So the 0.05 floor held only for the n=4 content it was
  measured on; treat it as a lower bound, not a guarantee, and raise + oracle-recheck
  per content type (see §5.1 controlnet bullet).
- **Google (capped 1536)**: 0.15 (n=4); 0.05 and 0.10 do not clear.
- **Google native 2816**: not locally measured; likely needs >= 0.30 (vendor +
  resolution stack). Use a GPU or `--max-resolution 1536`.

The default is **vendor-adaptive** (`watermark_profiles.resolve_strength` +
`vendor_for_strength`): the tool reads the C2PA issuer on the original input and
picks `OPENAI_STRENGTH` 0.10 / `GEMINI_STRENGTH` 0.15 / `UNKNOWN_STRENGTH` 0.15.
This uses the vendor signal we DO have locally (the C2PA SynthID proxy) to avoid
the overkill of a single high default on OpenAI images, without needing a local
pixel detector. An explicit `--strength` always wins. If the watermark still
survives (e.g. a large native Gemini beyond the capped-1536 validation), raise
toward 0.30 then 0.35-0.40 (0.40 visibly corrupts dense text), using the lowest
value that reads clean on the oracle.

### 5.3 Test methodology

- **GitHub-recompressed JPEGs from issue attachments are valid SynthID test
  subjects.** JPEG re-encoding removes C2PA metadata but does NOT remove the
  SynthID pixel watermark (verified June 2026 on issue #14 pic3). Do not
  dismiss these as "not faithful originals" for SynthID-removal tests.
- **The correct oracle for OpenAI images is openai.com/verify**, not the Gemini
  app. The two oracles detect different payloads.
- **A quiet `identify` output after processing is not proof of removal.** It
  means the metadata proxy is gone. The pixel watermark state is unknown without
  an oracle check.
- **After removal, the output may carry forensic artifacts** detectable by an
  independent classifier even if the vendor oracle reads negative. Defeating the
  verifier is not the same as being forensically indistinguishable from clean
  content (arXiv:2605.09203).

### 5.4 Strength vs forensic detectability: the tradeoff

Higher img2img strength removes the watermark but introduces detectable
regeneration artifacts. The Goonatilake & Ateniese paper shows the strongest
diffusion-based removers are simultaneously the most forensically detectable
(AUROC up to 0.9999). The tradeoff is unavoidable with current diffusion-based
approaches: defeating the vendor's verifier is not the same as being clean.

### 5.5 Oracle validation log -- 2026-06-04 OpenAI pass

Eight OpenAI `gpt-image` originals run through both pipelines and checked on
openai.com/verify (the OpenAI SynthID oracle). `--max-resolution 1536`; strength
is the vendor-adaptive default (`vendor_for_strength`): images with an OpenAI C2PA
manifest get `OPENAI_STRENGTH` 0.10, the one without C2PA falls to
`UNKNOWN_STRENGTH` 0.15. "detected" = SynthID still found (removal FAILED);
"clean" = SynthID not detected.

| image | content type | size | strength | `--auto`/controlnet | `default` |
|---|---|---|---|---|---|
| typography card | flat text | 1122x1402 | 0.10 | clean | clean |
| raiw.cc poster | flat graphic (logo + flat fills) | 1024x1536 | 0.10 | clean | **detected** |
| 9-face grid | photoreal | 1448x1086 | 0.10 | **detected** | clean |
| bracelet product photo | photoreal | 1600x1600 | 0.15 | **detected** | clean |

(The other four cleared under both and are omitted.) **Reading:** at this strength
NEITHER pipeline removes SynthID on all content -- the survivors flip by content
type. Photoreal survives controlnet / clears `default`; flat graphic survives
`default` / clears controlnet; flat text clears both.

**Follow-up: removal near the threshold is NON-DETERMINISTIC (seed-dependent).**
Re-running the two photoreal survivors through controlnet at an explicit
`--strength 0.15` (`--auto`, same `--max-resolution 1536`) cleared BOTH on the
oracle (SynthID not detected). But the bracelet had SURVIVED controlnet at the
SAME 0.15 in the first pass (it was the no-C2PA image, so its vendor-adaptive
strength was already 0.15) -- same pipeline + strength + resolution, only the
random (unset) seed differed between runs. So **0.15 is the borderline floor for
controlnet photoreal, not a robust guarantee**: at the threshold the same
image+settings can pass or fail run-to-run. img2img runs with `seed=None` (random)
unless `--seed` is passed, so a removal SERVICE gets a coin-flip near threshold and
has no local SynthID detector to self-verify.

**Controlnet strength ladder on the two photoreal images (oracle, `--auto`,
`--max-resolution 1536`):**

| controlnet strength | 9-face grid | bracelet photo |
|---|---|---|
| 0.10 | detected | (was 0.15) |
| 0.15 | clean | **non-deterministic** (survived pass 1, clean pass 2) |
| **0.20** | **clean** | **clean** |

**Recommended robust controlnet strength = 0.20** (0.05 of margin above the 0.15
non-deterministic borderline); both photoreal survivors cleared at 0.20. Honest
caveat: 0.20 is one confirming run WITH margin, not an N-run repeatability proof --
for a removal service, add a little more margin or validate repeatability, since
there is no local SynthID detector to self-check. **Implications:** (1) the
content×pipeline table above conflates a borderline/non-deterministic 0.15 result
with deterministic content behavior -- the photoreal-survives-controlnet effect is
solid at 0.10 but at 0.15 it is near-threshold noise; (2) for reliable removal pick
a strength with MARGIN above the borderline (controlnet >= 0.20), not exactly on
it; (3) **engineering follow-up for raiw.cc: the controlnet pipeline should use a
HIGHER vendor strength than `default` (it currently shares `resolve_strength`) --
e.g. controlnet floor 0.20 -- calibrated per vendor/content on the GPU worker where
batches are cheap. The shared 0.10/0.15 is tuned for `default`, not controlnet.**
Source images are private (faces / product shots), not committed; reproduce on any
photoreal + flat-graphic gpt-image pair, varying the seed, and re-checking the
oracle.

**Gemini pass + the face-restore re-introduction (2026-06-04).** Four Gemini
originals via `--auto` (controlnet) at `--max-resolution 1024`, checked on the
Gemini "Verify with SynthID" oracle (Google content needs the Google oracle, not
openai.com/verify):
- Most cleared at controlnet 0.15-0.25; `gemini_3` (a large central FACE, +restore)
  stayed **SynthID-detected at controlnet 0.15, 0.20 AND 0.25** -- raising strength
  did not crack it.
- **Root cause was the face-restore pass, not strength/resolution.** `gemini_3` at
  controlnet 0.20 with `--no-restore-faces` read **SynthID-NOT-detected** (clean
  A/B, only restore differed). GFPGAN runs on the ORIGINAL watermarked face and at
  weight 0.5 blends ~half its pixels back, re-introducing SynthID into the
  composited face over the diffusion-cleaned result (see §5.1 face-identity bullet).
- (Side note: reducing the processing resolution does NOT weaken SynthID -- it is
  robust to downscaling by design, and the study's resolution trend says LOWER
  processing res needs LESS strength, so 1024 was never the wall.)

**Certified controlnet floors (Modal GPU sweep `raiw-app/modal_cert.py` + oracle,
restore OFF, <= 1536, each vendor on its own oracle):** OpenAI **0.20** (2 photoreal x
seed {1,2,3} = 6/6 clean; the 0.15-flipper is seed-robust at 0.20) and Gemini **0.30**
(0.20 detected -> 0.30 clean on 2/2 seeds). OpenAI 0.20 transfers to prod
(resolution-independent); Gemini 0.30 holds only <= 1536 -- Gemini is
resolution-sensitive and raiw.cc runs NATIVE, so cap Gemini <= 1536 + use 0.30 or
native-calibrate (~0.35+). See `docs/controlnet-removal-pipeline-research.md` for the
table.

**Net for raiw.cc:** (1) controlnet needs a higher, per-vendor strength than
`default` -- CERTIFIED OpenAI 0.20 / Gemini 0.30 (above); add a controlnet-specific
schedule to `resolve_strength`, do not reuse the default ladder; (2) the
`--restore-faces` pass is now SynthID-safe by construction (the GFPGAN-on-original
path that re-added SynthID was removed 2026-06-04; the shipped restore is
PhotoMaker-V2, identity-as-embedding, see `synthid-robust-identity-research.md`); (3)
removal near threshold is seed-non-deterministic -> FIX the prod seed (kills the
coin-flip; ship a deterministic certified config).

---

## References

1. Gowal et al. (2025). **SynthID-Image: Image watermarking at internet scale.**
   arXiv:2510.09263. https://arxiv.org/abs/2510.09263

2. Google DeepMind. **Identifying AI-generated images with SynthID.** Blog post,
   2023. https://deepmind.google/blog/identifying-ai-generated-images-with-synthid/

3. Google DeepMind. **SynthID.** Product page.
   https://deepmind.google/models/synthid/

4. Goonatilake & Ateniese (2026). **Removing the Watermark Is Not Enough:
   Forensic Stealth in Generative-AI Watermark Removal.** arXiv:2605.09203.
   https://arxiv.org/abs/2605.09203

5. OpenAI. **Verify tool for AI-generated images.** openai.com/research/verify.
   Accessed 2026-05-31.