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.

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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.

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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."

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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.

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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.

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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 -- SynthID is removed everywhere. Verified better than plain img2img at the same strength (text stays legible where plain garbles it), and the controlnet background scrub reads clean on the oracle.
• 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: GFPGAN RE-SYNTHESIZES each face from a StyleGAN2 prior (GAN pixels, not original -> scrubs SynthID) at a low fidelity weight (--restore-faces-weight, default 0.5). Oracle-confirmed clean in face regions with identity preserved. 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). 0.30 is large overkill here.
• 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.

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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.