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Merge remote-tracking branch 'origin/main' into docs/arxiv-paper-review
# Conflicts: # docs/installation.md # docs/supported-signals.md # docs/verification-plan.md
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@@ -296,13 +296,13 @@ from the gate. The full-clip oracle floor is
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detected while `0.15` did not.
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[`video_visible.py`](../src/remove_ai_watermarks/video_visible.py) implements
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the first pixel stages for Sora, Veo, Seedance, Dola, Hailuo, and Kling. The
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the first pixel stages for Sora, Veo, Seedance, Dola, Hailuo AI, and Kling AI. The
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Sora detector searches a normalized frame with a fully synthetic
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mascot-and-text silhouette at several scales. The Veo detector uses separate
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synthetic silhouettes for the current four-point diamond and legacy `Veo`
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text. Seedance uses a synthetic rounded boxed-`AI` silhouette, while Dola uses
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an OpenCV-font `Dola AI` silhouette. Hailuo uses a synthetic waveform,
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MINIMAX/Hailuo text, separator, and ring. Kling combines synthetic font
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an OpenCV-font `Dola AI` silhouette. Hailuo AI uses a synthetic waveform,
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MINIMAX/Hailuo AI text, separator, and ring. Kling AI combines synthetic font
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variants with a ring approximation of its swirl; the logo path rescues
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wordmarks whose version or font differs, while the edge and white-label gates
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reject recurring scene texture. All fixed-mark searches are bounded to the
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@@ -323,23 +323,23 @@ Every per-frame result is untrusted. Each provider's floors, minimum-run policy,
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fill padding and mask style are one row in `VISIBLE_MARK_POLICIES`, and every mark
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enters the same `stabilize_localizations` entry point; the recurrence
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implementation underneath knows nothing about providers. That policy row also
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carries `accepts_provenance`, which forces `provenance=False` for Hailuo and Kling
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carries `accepts_provenance`, which forces `provenance=False` for Hailuo AI and Kling AI
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— they have no metadata that could confirm them, and the guarantee used to be
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structural (their wrappers took no `provenance` parameter at all). Provenance can relax a low-contrast run only
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after recurring visual evidence exists. Sora transition frames follow the
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nearest confirmed moving position only with Sora provenance. Seedance, Dola,
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Hailuo, and Kling additionally require candidates to remain anchored to the
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Hailuo AI, and Kling AI additionally require candidates to remain anchored to the
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start of a run. This rejects slowly drifting scene details that still have
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high frame-to-frame overlap. Hailuo and Kling do not infer provenance from
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high frame-to-frame overlap. Hailuo AI and Kling AI do not infer provenance from
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technical encoder tags; their confirmed public samples carried no provider
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metadata.
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Removal runs in a second decode pass. Sora, legacy Veo text, Dola text,
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Seedance, Hailuo, and Kling use box masks. Seedance deliberately fills the
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Seedance, Hailuo AI, and Kling AI use box masks. Seedance deliberately fills the
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complete localized box: a synthetic outline mask passed repeat detection but
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left part of the real translucent border visible during visual end-to-end
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review. Hailuo expands beyond the matched core to cover both provider icons.
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Kling expands around the wordmark or swirl to include the version and optional
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review. Hailuo AI expands beyond the matched core to cover both provider icons.
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Kling AI expands around the wordmark or swirl to include the version and optional
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`PRO` suffix. The square Veo diamond uses a synthetic shape mask so transparent
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corners do not erase unrelated pixels. Every mask goes through the shared
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`watermark_registry.fill` backends. ffmpeg encodes the changed video stream and
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@@ -451,7 +451,9 @@ For an AI C2PA claim, a recognized product in `claim_generator` takes precedence
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over the certificate issuer: an application can sign through an upstream model
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provider without becoming that provider's product. Only exact product mappings
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receive this precedence; an unknown claim generator still falls back to issuer
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attribution.
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attribution. An unmapped issuer org reads as unknown-signer C2PA with no platform;
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that is how Ideogram was surfaced (4 corpus uploads signed "Ideogram, Inc",
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2026-08-08) before its vendor row was added on 2026-08-27.
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### Metadata scanning and stripping
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@@ -1121,9 +1123,9 @@ capture-less pill is too weak to attribute.
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The set of marks that veto the pill is DERIVED from the registry rows: every mark
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under the same label regime (`tc260`) belonging to a different product. It used to
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be a hand-written list of keys, and that list drifted -- LibLibAI was registered
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alongside RunningHub and Baidu, both of which were added to it, and LibLibAI was
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not, so a confident LibLibAI detection did not suppress the pill the way its two
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be a hand-written list of keys, and that list drifted -- LiblibAI was registered
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alongside RunningHub and Baidu, both of which were added to it, and LiblibAI was
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not, so a confident LiblibAI detection did not suppress the pill the way its two
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siblings did. Marks outside the TC260 regime (Gemini, Samsung) are deliberately
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not vetoers: neither can put `jimeng` into `provenance`, so neither can enable the
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arm it would be vetoing.
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@@ -1175,6 +1177,33 @@ be represented by the shared base:
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- [`runninghub_engine.py`](../src/remove_ai_watermarks/runninghub_engine.py)
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- [`baidu_engine.py`](../src/remove_ai_watermarks/baidu_engine.py)
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- [`liblib_engine.py`](../src/remove_ai_watermarks/liblib_engine.py)
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- [`microsoft_engine.py`](../src/remove_ai_watermarks/microsoft_engine.py)
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The measured Microsoft badge variant (2026-08-27 registration) is the first
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`tr`-corner mark and the first `long`-side scale basis: the pill tracks the
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render dimension, so a
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1024x1536 portrait carries the same pill as 1536x1024, and a width basis
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undersized the template by the aspect ratio (portrait carriers fell to
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0.15-0.32 NCC until the basis was measured). The silhouette is a white pill with
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its synthetic internal shapes knocked out - the holes are what separate it from
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any other bright rounded corner element (a plain white pill scores below the gate
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in the tests). It does not claim coverage of Microsoft's other documented icon,
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wording, or position variants.
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The 2026-08-27 rerun used the registered engine through
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`scripts/registered_mark_calibrate.py`, rather than a copied detector
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configuration. The manifest kept three evidence classes separate: 17 visually
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confirmed carriers, 343 Microsoft-provenance files without a visual adjudication,
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and 1200 non-overlapping no-signal controls. At the strict 0.38 gate, 15/17
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confirmed carriers fired (min 0.249, p50 0.519, p90 0.578, max 0.579), while
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0/1200 controls fired (p99 0.200, max 0.293). The provenance cohort produced
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78/343 fires, but that is not a recall measurement because provenance identifies
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the provider, not the presence of this visible layout. That cohort was later
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labeled by an OCR badge census (2026-08-28: 86 badge carriers, 257 badge-less,
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badge-less max 0.251 / p99 0.213), which is what enabled the shipped provenance
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relaxation 0.7: the relaxed band [0.251, 0.38) holds three faint badges and
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zero false fills, re-verified as 3 band detections with 0/257 badge-less fires
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on the provenance path.
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The detector and removal mask must use compatible geometry. A detector that
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fires while producing an empty or misplaced mask is a removal failure even if
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@@ -1193,7 +1222,7 @@ were briefly skipped there for exactly that reason.
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A mark whose removable footprint differs from what the detector localizes
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overrides `_footprint_rect` (which policy) and `_extend_match_box` (how far the
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box grows), not the whole `footprint_mask`. Baidu extends right to the corner tag
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and LibLibAI extends left to the triangle logo; both inherit every guard around
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and LiblibAI extends left to the triangle logo; both inherit every guard around
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that arithmetic.
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Yuanbao uses the polarity-independent `contrast` front end because its standard
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@@ -1395,11 +1424,13 @@ Validation happens before model loading. The library never treats OCR confidence
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verification, and geometry-only operators do not need to invent text or script fields.
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When enabled, `QwenZImagePipeline` reconstructs the source once through its already
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loaded Qwen VAE, runs the ordinary global and face stages, blends 15% of the VAE
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reconstruction into that clean result, and calls the shared restoration compositor.
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The compositor derives binary source and candidate silhouettes, groups nearby lines,
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uses LaMa for the initial and residual-glyph erase passes, paints fresh silhouette
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edges, then copies the Qwen-VAE core with a 0.5-pixel feather. The evaluation script
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loaded Qwen VAE, runs the ordinary global and face stages, and calls the shared
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restoration compositor. The optional fidelity anchor first blends 15% of the VAE
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reconstruction into the clean result; it is off by default because the blend returned
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detector-visible OpenAI SynthID in the measured poster fixtures. The compositor derives
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binary source and candidate silhouettes, groups nearby lines, uses LaMa for the initial
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and residual-glyph erase passes, paints fresh silhouette edges, then copies the Qwen-VAE
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core with a 0.5-pixel feather. The evaluation script
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imports these same mask and compositing helpers so the two implementations cannot
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silently drift. Silhouette crops start 12% of line height beyond each horizontal
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side, then expand each side independently while a foreground component anchored
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@@ -1524,6 +1555,44 @@ sizes the curve's own values verify clean, which is what a resolution-scaled
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requirement would predict. There is no measured size at which the shipped curve fails,
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so it is left alone.
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### Meta Content Seal boundaries for qwen-zimage
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Measured 2026-08-26 against the anonymous oracle `meta.ai/identification`
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(verdicts read from the settled page text; corpus and full row-level provenance in
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`data/contentseal/manifest.csv`). Muse Image carries no C2PA, so
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`vendor_for_strength` returns None for it and these images ride the default
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resolution-adaptive curve; there is deliberately no `meta` entry in the flat vendor
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map, because no provenance signal can establish that cohort automatically and an
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unreachable constant would be dead code.
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| source (2.56 MP generation) | detected at | clean from |
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|---|---|---|
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| gen_lighthouse_watercolor | 0.0525 | 0.06 |
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| gen_fox_forest | 0.03 | 0.0375 |
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| gen_night_city | 0.03 | 0.0375 |
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| gen_studio_mug | -- | 0.03 |
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| gen_text_poster | -- | 0.015 |
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Full spread: worst first-clean boundary (0.0525, 0.06] on lighthouse, easiest
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source already clean at 0.015. Following the same derivation as the OpenAI and
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Microsoft floors (worst clean boundary plus one full observed cross-source
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spread): 0.06 + (0.0525 - 0.015) = 0.0975, rounded up to **0.1**. Shipped as
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`QWEN_ZIMAGE_META_STRENGTH` with two routing paths: auto mode routes a file
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whose only provenance is the standalone AI IPTC tag onto the cohort
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(`vendor_for_strength` checks C2PA issuers first, so Google/OpenAI/Microsoft
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evidence always wins, and the tag's other users ship no invisible watermark
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this profile targets), and `--vendor meta` / `InvisibleOptions.vendor` names the
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cohort explicitly on stripped files, implying the scrub runs (naming the cohort
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asserts the watermark is present). sdxl-zimage has no measured Meta rung and an
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explicit meta vendor there falls to the conservative unknown 0.25. The default
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resolution-adaptive curve (~0.1305 at 2.56 MP) also clears every measured
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source, so default behavior needed no change. Oracle verdicts carry a
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generation ID and creation timestamp embedded in the watermark payload; both
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survived the 512 px resize and JPEG q85 rows, so payload recovery outlives the
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detection threshold. Oracle session limits are per-IP, server-side, and
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sliding-window: clearing cookies and storage does not reset them, and a burst
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exhausts the window minutes after it reopens.
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### Static prompt embeddings
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Both stages prompt with module constants, and at CFG 1.0 DiffSynth's
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