Release 0.31.0

Minor, not patch: the C2PA confidence mapping changed. A cryptographically bound
manifest now reports high confidence where 0.27.0 through 0.30.1 reported medium,
because the previous gate required a trust anchor no installation has. Consumers
that branch on `confidence` will see verdicts move on files whose bytes did not.

The provenance report stays at schema 1. No field was added, removed, renamed or
retyped, and the meaning of `confidence` is unchanged -- the value it carried for a
verified claim was wrong. Bumping the output schema instead would break every
pinned schema-1 consumer on a corrected value rather than a new shape.

pre-commit: 1) maintain.sh - exit 1 on uv-secure, lightning PYSEC-2026-3624 unchanged from 0.30.1, no fixed release exists, vulnerable API unreachable (no load_from_checkpoint in project or trustmark); ruff, ruff format, pyright src/, and 1394 tests passed separately; 2) /simplify - version bump only; 3) docs sync - no version refs outside pyproject, __init__.py, uv.lock; 4) CLAUDE.md - no change

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

.claude/rules/development.md

@@ -65,6 +65,15 @@ that is what actually happened.
 
 Use availability checks only for paths that actually load large models.
 
+A verdict branch must be reachable with the dependency stack as shipped. High-confidence
+C2PA attribution required `signingCredential.trusted`, a code the reader emits only when a
+trust anchor list is loaded, and none ships -- so the branch was dead in production for
+every vendor while a hand-built info dict stamping that code kept it green from 0.27.0
+through 0.30.0. Assert reachability against the committed fixtures rather than a synthesized
+status set; `TestIdentifyRealSamples::test_no_committed_fixture_reports_a_trusted_signer`
+is that guard. Read a capability the stack does not have as a missing input, never as a
+negative finding.
+
 ## One measurement, one gate seam
 
 A detector is split into a trust-level-blind scan and a verdict that applies the
@@ -98,6 +107,11 @@ record is byte-identical, and a green test suite does not establish that on its
 change that is meant to FIX detection is the exception that proves the rule: the diff
 must then be exactly the files you intended to change, named in advance.
 
+When the baseline is a published release rather than the previous commit, get it with
+`uv run --isolated --no-project --with 'remove-ai-watermarks[heif]==<version>' python <script>`:
+it resolves that release from PyPI without touching the working tree or the editable
+install. Quote the extra -- bare `pkg[extra]` is a glob in zsh.
+
 Two corollaries in `dwt_dct.py`, where "close enough" has an exact meaning. The bit test
 is `peak % 36 > 18.0` and for uint8 input the exact Haar value is a multiple of 0.5, so it
 lands ON the threshold once per 72 blocks and a 1-ulp difference flips real bits: leave the

README.md

@@ -44,6 +44,10 @@ removal.
 | Run visible, invisible, and metadata removal | `all` | Recommended |
 | Process a directory | `batch` | Depends on mode |
 
+Microsoft Paint and Photos InvisMark declarations are routed automatically to
+pixel regeneration. The `all` command removes both the hidden pixel watermark and
+its linked C2PA manifest; metadata stripping alone removes only the manifest.
+
 ## Installation modes
 
 | Need | Install |
@@ -202,8 +206,9 @@ remove-ai-watermarks invisible image.png -o clean.png --force
 ```
 
 Typography-heavy images can opt into the experimental verified-text post-pass.
-It requires manually reviewed strings and line boxes; it never trusts OCR as ground
-truth or runs automatically:
+It accepts manually reviewed strings and line boxes or an operator-verified
+geometry-only manifest; it never treats raw OCR output as ground truth or runs
+automatically:
 
 ```bash
 uv tool install --force "remove-ai-watermarks[text-restoration]"

docs/cli.md

@@ -41,9 +41,14 @@ remove-ai-watermarks identify image.png
 it also evaluates supported visible and invisible pixel signals. When no signal
 is found, it reports the origin as unknown. It does not claim the image is
 clean. For C2PA files, the text report shows asset integrity, claim-signature,
-signer-trust, and signer-validity results separately. An intact claim from an
-untrusted or expired signer is reported at medium confidence; a failed asset
-binding or signature does not confirm the claimed origin.
+signer-trust, and signer-validity results separately. Confidence follows the
+binding: an intact asset binding and claim signature is high confidence, and an
+unanchored or expired signer is reported as a caveat rather than a lower score,
+because no trust anchor list ships with the reader. A failed asset binding or
+signature, or a revoked signing credential, does not confirm the claimed origin. When a structured
+C2PA soft binding is present, the report also names its exact algorithm and
+signed value; removing the manifest does not remove the referenced pixel
+watermark or content fingerprint.
 
 Machine readable output:
 
@@ -157,6 +162,11 @@ remove-ai-watermarks metadata image.png --remove -o clean.png
 When `-o` is omitted, removal overwrites the source. Standard metadata is kept
 unless you pass `--remove-all`.
 
+A quiet `--check` or a successful `--remove` is not a clean verdict. The command
+only inspects and strips embedded AI metadata; a pixel watermark such as SynthID
+has no local decoder once that metadata proxy is gone. `identify` reports the
+same limit.
+
 The command also supports the audio and video containers listed in
 [supported signals](supported-signals.md). ffmpeg must be available for the
 non-ISOBMFF audio and video path.
@@ -225,6 +235,10 @@ remove-ai-watermarks video metadata input.mp4 --check
 remove-ai-watermarks video metadata input.mp4 --remove -o clean.mp4
 ```
 
+As with the generic `metadata` command, a quiet check or a successful strip is
+not a clean verdict: video SynthID is not decoded locally after the metadata
+proxy is gone.
+
 Supported containers are MP4, MOV, M4V, WebM, MKV, AVI, and FLV. The operation
 delegates to the same verified metadata scanner and stripper as the generic
 `metadata` command, so detection and removal stay in parity. Video and audio
@@ -408,7 +422,7 @@ the source with the Qwen VAE, blends 15% of that reconstruction into the normal
 only the reconstructed glyph cores through source-derived silhouettes. It does not
 run OCR or choose which strings are correct.
 
-Install the combined extra and run only with a manually reviewed manifest:
+Install the combined extra and run only with an operator-verified manifest:
 
 ```bash
 uv tool install --force "remove-ai-watermarks[text-restoration]"
@@ -416,11 +430,10 @@ remove-ai-watermarks invisible image.png -o clean.png \
   --pipeline qwen-zimage --text-manifest verified-lines.json --force
 ```
 
-``verified: true`` may also be set by an automated operator (a service) that
-attests machine-verified geometry: stability-gated detector boxes inside sane
-caps. The restoration pipeline consumes box/script geometry only - the ``text``
-field is advisory metadata and never reaches the pixels - so what verification
-must guarantee is the geometry, and a machine gate can.
+``verified: true`` may also be set by an automated operator that attests
+machine-verified geometry: stability-gated detector boxes inside sane caps. Such
+operators should use the geometry-only schema 2, which carries no transcription
+or script metadata.
 
 Since 0.27.1 the global 15% Qwen-VAE fidelity-anchor blend is off by default: it
 was measured to return detector-visible OpenAI SynthID on poster-scale manifests
@@ -428,16 +441,18 @@ was measured to return detector-visible OpenAI SynthID on poster-scale manifests
 0.27.0 research behavior; text-box fidelity lost by the default is well under one
 MAE point on the measured fixtures.
 
-The manifest is a JSON object with `schema_version: 1`, `verified: true`, decoded
-RGB dimensions, `source_pixel_sha256`, and a non-empty `lines` array. Each line has
-an integer `[x1, y1, x2, y2]` box, exact `text`, a non-empty `script`, and an optional
-angle from -30 to 30 degrees. Lines must be in top-to-bottom, left-to-right order.
+The manifest is a JSON object with `verified: true`, decoded RGB dimensions,
+`source_pixel_sha256`, and a non-empty `lines` array. Schema 1 is retained for
+manually reviewed annotations: each line has an integer `[x1, y1, x2, y2]` box,
+exact `text`, a non-empty `script`, and an optional angle from -30 to 30 degrees.
+Schema 2 is geometry-only: each line has the box and optional angle, with no
+required `text` or `script`. Lines must be in top-to-bottom, left-to-right order.
 The hash binds the annotations to decoded RGB geometry and pixels, so metadata-only
 container changes remain valid while a resized or edited source fails closed. The
 experimental helper
 `remove_ai_watermarks._internal.text_restoration.source_pixel_sha256` computes it.
 
-This mode is supported only by `qwen-zimage` at native untiled geometry with
+This mode is supported only by `qwen-zimage` at native geometry with
 `humanize=0`, `unsharp=0`, and adaptive polish disabled. `all` also accepts the flag,
 but its manifest must match the pixels entering the invisible stage; if visible-mark
 removal changes those pixels, the hash check rejects the run. One oracle verdict does

docs/known-limitations.md

@@ -407,7 +407,11 @@ remain identical because isolated decoder hits can otherwise be content noise.
 It accepts Variant P schemas 0-2. Variant Q requires a different model, and
 schema 3 is rejected at the measured precision threshold. Its NumPy 1.x runtime
 limits the extra to Python 3.11-3.12; the rest of the package remains supported
-through Python 3.14. The calibration history is in
+through Python 3.14. Through 0.29.0 the extra also resolves lightning 2.6.5
+(PYSEC-2026-3624, no fixed release yet); the vulnerable `load_from_checkpoint`
+path is unreachable here because TrustMark loads its checksummed checkpoints
+with plain `torch.load`. Bump lightning and cut a patch release when a fix
+ships. The calibration history is in
 [module internals](module-internals.md#metadata-and-provenance).
 
 External AI versus real image classifiers are out of scope. The project

docs/module-internals.md

@@ -42,6 +42,10 @@ Important contracts:
 - `invisible` writes no output when no supported local signal is found, unless
   `--force` is supplied.
 - The two no-signal conditions currently share exit code `2`.
+- A quiet `metadata --check` and a successful `metadata --remove` (same for
+  `video metadata`) end by repeating the `identify` limit: the pixel channel is
+  untouched and a watermark such as SynthID has no local decoder once its
+  metadata proxy is gone, so neither outcome is a clean verdict.
 - Hard processing and write failures exit with code `1`.
 - `all` can still write the completed visible and metadata stages when the
   diffusion dependencies are unavailable, but exits with code `1` so the
@@ -375,23 +379,68 @@ Structured extraction is limited to the active manifest and the ingredient
 manifests reachable from it. Validation is preserved as separate dimensions:
 asset binding integrity, claim signature, signer trust, and signer certificate
 validity. A matching asset hash and valid claim signature do not make an
-untrusted or expired signer trusted. `identify` therefore assigns high confidence
-only when all four dimensions validate, medium confidence to an intact but
-untrusted or unvalidated claim, and no origin verdict to a hash/signature failure.
-The failed claim remains in the marker inventory and keeps the removal gate
-fail-safe because a post-signing container edit can invalidate C2PA without
+untrusted or expired signer trusted, so those stay separate fields and separate
+caveats -- but they do not lower confidence. `identify` assigns high confidence
+when the asset binding and the claim signature both validate, medium confidence
+to a claim that validated nothing (fallback parsing, unknown dimensions), and no
+origin verdict to a binding failure, a signature failure, or a revoked signing
+credential. The failed claim remains in the marker inventory and keeps the removal
+gate fail-safe because a post-signing container edit can invalidate C2PA without
 removing a declared pixel watermark.
 
+Revocation reaches the disqualifying branch through `signer_validity`, not through
+binding or signature. A check that read only the latter two returned a confident AI
+verdict off a credential the issuer had disowned, with an empty `integrity_clashes`
+-- quieter than a hash mismatch on the same file. Certificate expiry is deliberately
+not disqualifying: an expired certificate does not imply the signed bytes changed,
+and a signature actually made outside validity already arrives as
+`claimSignature.outsideValidity`.
+
+One rule, one place. `_validation_fields` maps status codes to the four dimensions
+and also emits `c2pa_failed_codes`, the subset of failures that actually drove a
+dimension to invalid; `c2pa_info_has_invalid_credential` maps those dimensions to
+disqualified, and both the ingredient-reachability walk and the report consume that
+single path. The displayed reason comes from `c2pa_failed_codes` rather than a
+substring rescan of the full code list, so what a caller is shown as the cause
+cannot become a looser rule than the verdict it explains.
+
 The structured walk also treats an exact known AI product in a reachable
 `claim_generator` as an AI assertion. This covers update chains where the active
 manifest names only `c2pa-tool` while a validated ingredient names Dreamina, and
 Firefly chains that identify `Adobe_Firefly` without repeating a digital source
 type. Unreachable manifests remain excluded.
 
+Reachable `c2pa.soft-binding*` assertions retain their exact `alg` and bounded,
+printable block `value` in addition to the normalized vendor label. A block value
+without its algorithm is not surfaced because it cannot be attributed to a
+decoder or registry entry. `com.microsoft.invismark.1` uses that value as the
+pixel-watermark identifier in Microsoft Paint output. An InvisMark soft binding
+keeps the invisible-removal gate fail-safe even when the C2PA asset binding has
+since become invalid, because metadata damage does not prove the pixel carrier
+disappeared. `identify` retains the generic `soft_binding` signal for schema-1
+compatibility and adds `invismark` as the stable pixel-removal signal.
+Content-fingerprint soft bindings do not trigger pixel regeneration.
+
 The SDK default enables trust verification but supplies no production trust
 anchors. Consequently, an installation without an explicitly maintained C2PA
-trust bundle reports otherwise valid signer chains as untrusted and keeps their
-attribution at medium confidence. Shipping or fetching an official trust bundle
+trust bundle reports every otherwise valid signer chain as untrusted --
+`signingCredential.trusted` appears in no default installation, from any vendor.
+Confidence therefore must not depend on it. It did from 0.27.0 through 0.30.0, which made
+the high-confidence branch unreachable in production while a hand-built fixture
+stamping `signingCredential.trusted` kept it green in the suite; measured on the
+committed provenance fixtures, every C2PA file from OpenAI, Adobe and Black Forest
+Labs came back untrusted, and an intact manifest scored the same medium as a
+fallback parse that validated nothing. That collapsed the one distinction the
+official reader exists to draw.
+`tests/test_identify.py::TestIdentifyRealSamples::test_no_committed_fixture_reports_a_trusted_signer`
+is the reachability guard: it asserts the fixtures really are untrusted and still
+reach high confidence.
+
+Read `untrusted` here as a missing input, not a finding: nothing was checked,
+because there was nothing to check against. If a maintained trust bundle is ever
+configured, that stops being true and the confidence mapping in
+`_c2pa_credential_level` must be re-read, because only then does a failed trust
+check mean the signer was rejected. Shipping or fetching an official trust bundle
 requires a separate update, provenance, and availability policy; do not silently
 convert `signingCredential.untrusted` into trusted based on a vendor-name match.
 
@@ -960,9 +1009,10 @@ requires the same provider-oracle and identity evaluation as a model change.
 
 [`_internal/text_restoration.py`](../src/remove_ai_watermarks/_internal/text_restoration.py)
 implements the opt-in `vae-glyphs` stage. A versioned manifest carries manually
-reviewed strings and source-space line boxes, plus a SHA-256 over decoded RGB width,
-height, and pixels. Validation happens before model loading. The product never treats
-OCR confidence as verification.
+reviewed strings and source-space line boxes in schema 1, or verified source-space
+geometry alone in schema 2, plus a SHA-256 over decoded RGB width, height, and pixels.
+Validation happens before model loading. The library never treats OCR confidence as
+verification, and geometry-only operators do not need to invent text or script fields.
 
 When enabled, `QwenZImagePipeline` reconstructs the source once through its already
 loaded Qwen VAE, runs the ordinary global and face stages, blends 15% of the VAE
@@ -971,7 +1021,12 @@ The compositor derives binary source and candidate silhouettes, groups nearby li
 uses LaMa for the initial and residual-glyph erase passes, paints fresh silhouette
 edges, then copies the Qwen-VAE core with a 0.5-pixel feather. The evaluation script
 imports these same mask and compositing helpers so the two implementations cannot
-silently drift.
+silently drift. Silhouette crops start 12% of line height beyond each horizontal
+side, then expand each side independently while a foreground component anchored
+inside the detector box still reaches that boundary, up to one line height. They
+extend 8% above and 25% below. The anchored-component gate covers clipped leading
+flourishes, trailing punctuation, icons, and descenders without walking into
+disconnected decoration or background texture.
 
 The stage is deliberately narrower than the engine: it rejects `sdxl-zimage`, tiles,
 resolution caps, humanize, unsharp, and adaptive polish. Those combinations change

docs/python-api.md

@@ -160,9 +160,26 @@ print(report.c2pa_validation)
 
 `c2pa_validation`, when present, reports `integrity`, `signature`,
 `signer_trust`, and `signer_validity` independently, plus the reader status
-codes. A valid hash and signature with an untrusted or expired signer is a
-medium-confidence signed claim. A hash or signature failure does not confirm the
-claimed platform or AI origin. Fallback parsing reports unknown validation
+codes. A valid hash and signature is a high-confidence signed claim; an
+unanchored or expired signer appears in `caveats` and in these fields, not as a
+lower confidence, because the reader ships no trust anchors to check against and
+`signer_trust` is therefore a missing input rather than a finding. A hash or
+signature failure, or a revoked signing credential, does not confirm the claimed
+platform or AI origin.
+
+A consumer must read `integrity_clashes`. When a credential fails validation,
+`is_ai_generated` becomes `None`, because a claim that cannot be tied to these
+bytes cannot establish origin -- the manifest may have been transplanted from a
+real AI image onto anything -- and the failure is reported in
+`integrity_clashes` instead. That is a different question from whether an AI
+watermark is physically present in the pixels, which is what
+`has_invisible_target` answers, and it stays fail-safe `True` on the same file.
+Reading only `is_ai_generated` turns a broken vendor manifest into silence.
+
+`c2pa_validation["state"]` is the reader's own aggregate and is carried for
+diagnostics only; no verdict is derived from it, because it collapses a
+transplanted manifest and a merely expired certificate into one `Invalid`, and
+its `Trusted` level depends on anchors no default installation has. Fallback parsing reports unknown validation
 dimensions, while a raw marker in an unsupported or malformed container can
 leave `c2pa_validation` as `None`.
 
@@ -233,6 +250,11 @@ the schema 1 serializer remains available for rolling upgrades. Asking a release
 an unsupported schema raises `ValueError` rather than silently returning another
 shape.
 
+Microsoft InvisMark declarations emit both the compatible generic `soft_binding`
+signal and the additive `invismark` signal. Consumers should use `invismark` to route
+the image through pixel removal; the generic signal also covers content fingerprints
+that must not trigger regeneration.
+
 A record carries metadata regions, not the primary coded-pixel stream: marker
 segments before the JPEG scan, every PNG chunk except `IDAT`, RIFF chunks except the
 coded image, the ISOBMFF provenance boxes, the container's trailer, the parsed EXIF tags the
@@ -609,11 +631,16 @@ engine.remove_watermark(
 
 Install `remove-ai-watermarks[text-restoration]`. The manifest schema and safety
 constraints are documented in the CLI guide. The engine verifies its decoded RGB
-hash before loading the diffusion models and rejects SDXL, tiling, downscaling, and
-postprocessing combinations that were not evaluated. `InvisibleOptions` exposes the
+hash before loading the diffusion models and rejects SDXL, downscaling, and
+postprocessing combinations that were not evaluated. Tiling is allowed: the VAE
+donor uses the same overlapping tiles as the global pass. `InvisibleOptions` exposes the
 same field for `remove_all`; after a visible-stage edit, the manifest must be built
 against the staged pixels rather than the pristine source.
 
+Use manifest schema 1 for manually reviewed text plus script metadata. Automated
+operators that verify only text-region geometry should emit schema 2 lines with a
+`box` and optional `angle`; no placeholder transcription or script is required.
+
 Since 0.27.1 the mode's global 15% Qwen-VAE fidelity-anchor blend is **off by
 default** (`fidelity_anchor=False`): that whole-frame blend was measured to
 return detector-visible OpenAI SynthID on poster-scale manifests (official

docs/release-and-distribution.md

@@ -11,7 +11,7 @@ A release is complete only after all four published surfaces are verified:
 |---|---|---|
 | PyPI | The `remove-ai-watermarks` wheel and source distribution | `publish.yml` |
 | Homebrew | The `remove-ai-watermarks` formula in `wiltodelta/homebrew-tap` | `distribute.yml` |
-| Hugging Face Space | A factory rebuild of `wiltodelta/remove-ai-watermarks` against the new PyPI release | `distribute.yml` |
+| Hugging Face Space | The Space demo deployed on the new release through a requirements-pin bump in `wiltodelta/raiw-hf-space` | `raiw-hf-space` sync workflow; the pin bump is manual, `distribute.yml` only re-installs the pinned version |
 | ComfyUI Registry | A compatible release of `wiltodelta/ComfyUI-remove-ai-watermarks` with its own node version | `distribute.yml` and the node repository's workflows |
 
 The GitHub Release is the trigger and release record for this flow. Conda is
@@ -58,9 +58,17 @@ PyPI API token from the repository.
 waits for the matching source distribution to appear on PyPI, then:
 
 - updates the Homebrew tap formula URL and SHA-256;
-- triggers a factory rebuild of the Hugging Face Space;
+- triggers a factory rebuild of the Hugging Face Space: this re-installs the
+  version pinned in the Space repo, it does NOT upgrade the demo;
 - synchronizes, tests, versions, and publishes the ComfyUI nodes.
 
+Upgrading the Hugging Face Space is a separate, manual step: bump
+`remove-ai-watermarks[visible,heif]` in `wiltodelta/raiw-hf-space` (`pyproject.toml`,
+`uv lock`, and the re-exported `requirements.txt`), then push. That repository's
+`sync-to-hf.yml` mirrors the files onto the Space, which rebuilds on the new
+pin. Its callback smoke tests run in that repository's CI on every push; they
+are the scenario check for the demo surface.
+
 The workflow can also be started manually with an optional version input.
 
 If a distribution job fails because a repository or Hugging Face credential is
@@ -116,4 +124,20 @@ After publication, verify:
 - the Homebrew formula points to the new source distribution;
 - the distribution workflow completed successfully;
 - the ComfyUI Registry node requires the new library version;
+- the Hugging Face Space serves the new release: its `requirements.txt` pin
+  matches (raw file via the HF API), the runtime stage is `RUNNING` after the
+  rebuild, and one live `identify` and one live `visible` API call succeed
+  against the running Space;
 - a clean install can run `remove-ai-watermarks --version`.
+
+A clean-install check run immediately after publication can fail with "no
+version of remove-ai-watermarks==X.Y.Z" even though the simple index already
+lists it: uv serves its cached pre-release view of the index. Re-run with a
+fresh cache (`UV_CACHE_DIR=$(mktemp -d)`) before treating it as a release
+failure.
+
+The ComfyUI sync run can fail the same way on its own: it resolves the version
+from the PyPI JSON API (which updates first) but installs the test dependency
+with pip against the simple index, whose CDN edges lag by minutes. Rerun the
+failed `distribute.yml` comfyui job once the simple index lists the release;
+nothing about the release itself is wrong.

docs/supported-signals.md

@@ -88,9 +88,11 @@ The inspection and stripping code handles signals in these groups:
 unknown when evidence is absent. It never treats missing metadata as proof that
 an image is human made. C2PA presence alone is not a verified identity: the
 report distinguishes asset binding, claim signature, signer trust, and signer
-validity. High-confidence C2PA attribution requires all four; intact but
-untrusted or fallback claims are medium-confidence, while a failed binding or
-signature contributes no origin verdict.
+validity. High-confidence C2PA attribution requires an intact asset binding and
+claim signature; signer trust and certificate expiry are reported as their own
+dimensions and as caveats, since no trust anchor list ships to evaluate them.
+Fallback claims, which validate nothing, remain medium-confidence, while a failed
+binding or signature or a revoked credential contributes no origin verdict.
 
 ## File and container formats
 
@@ -138,6 +140,20 @@ all-media watermark policy, and current OpenAI C2PA carrying an explicit
 assert SynthID. After provenance metadata is removed, a local negative result
 is still inconclusive.
 
+Microsoft Paint can name `com.microsoft.invismark.1` in a C2PA soft-binding
+assertion. Inspection reports both that exact algorithm and its signed `value`,
+which Paint uses as the identifier carried by the pixel watermark, and emits an
+additive `invismark` signal so callers can select pixel removal without parsing
+the generic `soft_binding` detail. Photos uses a parallel local writer path.
+Metadata stripping removes only the embedded manifest; `invisible` and `all`
+guarantee the supported InvisMark removal contract by regenerating the pixel
+layer as well. The project has no validated local InvisMark decoder, so local
+inspection cannot independently verify the output. Microsoft's official
+[Content Provenance Detection API](https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/how-to-provenance-detection)
+is the external oracle: it reports pixel `Watermark` and embedded `C2PA` results
+separately; a control-positive, output-negative pair is the available per-file
+verification path.
+
 For MP4, MOV, and M4V, `video invisible` or the explicit
 `video all --invisible` option can regenerate the video through a VAE and strip
 source metadata. The shipped profile is oracle-certified, but it is not a local
@@ -158,6 +174,7 @@ not a universal clean verdict.
 | Google Gemini | Sparkle | Diffusion regeneration for SynthID | C2PA and related source signals |
 | Google Veo video | Veo diamond and legacy text | Oracle-certified VAE removal for SynthID | C2PA and related source signals |
 | OpenAI image generators | None registered | Diffusion regeneration for supported invisible signals | C2PA and generator provenance |
+| Microsoft Paint and Photos | None registered | External Microsoft oracle for InvisMark; no validated local decoder | Paint C2PA soft-binding algorithm and identifier |
 | Stable Diffusion and SDXL | None registered | Diffusion regeneration; optional open decoder | Embedded parameters and text metadata |
 | FLUX | None registered | Diffusion regeneration; optional open decoder | C2PA for supported sources |
 | Adobe Firefly | None registered | Optional TrustMark Variant P decoder | C2PA |

docs/verification-plan.md

@@ -186,10 +186,14 @@ report recall from the detector-sampled set.
 
 ## Tier D -- external oracles (manual, not automatable here)
 
-SynthID removal cannot be verified locally by design -- no public decoder exists. Each
-vendor has its own oracle and it covers only that vendor's content: `openai.com/verify` for
-OpenAI (more accessible, the automation candidate), the Gemini app for Google (manual,
-rate-limited). A quiet metadata proxy is **not** proof the pixel watermark is gone.
+Proprietary watermark removal cannot be verified locally by design -- no public decoder
+exists. Each vendor has its own oracle and it covers only that vendor's content:
+`openai.com/verify` for OpenAI, the Gemini app for Google, and Microsoft's
+[Content Provenance Detection API](https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/how-to-provenance-detection)
+for InvisMark. The Microsoft API reports `Watermark` and `C2PA` separately. It therefore
+needs a pixel-identical metadata-stripped control: the control must lose `C2PA` while
+remaining `Watermark`-positive before a candidate's negative result can be attributed to
+pixel regeneration. A quiet metadata proxy is **not** proof the pixel watermark is gone.
 
 Scope honestly: this tier certifies strength floors on a handful of images per vendor, and
 that is all it can do. See `docs/synthid.md`.

docs/watermarking-landscape.md

@@ -10,7 +10,7 @@ Who embeds what, and whether it is locally detectable (so we know which gaps are
   **Root cause and external confirmation (deep-research 2026-06-19, adversarially verified).** This is the SCHEME's ceiling, not our usage — there is no better decoder to adopt. The imwatermark maintainers state verbatim (both the ShieldMnt and Stability-AI READMEs) that the algorithm "cannot guarantee to decode the original watermarks 100% accurately even though we don't apply any attack." Independent measurement (WMAdapter, arXiv:2406.08337 Table 2) puts dwtDct at only **~0.79 bit accuracy on CLEAN images (~38/48 bits — already below our 44 gate)**, collapsing to ~0.50 (chance) under crop/JPEG. Two code-verified + locally-reproduced mechanisms drive the content-dependent failures: (1) the decoder reads each bit as the **highest-magnitude DCT coefficient per block**, so any content coefficient exceeding the encoded target flips the bit; (2) the default embed is in the **YUV chroma channel, which 8-bit-clamps on white/bright pixels** (a +36 chroma delta survives a white-fill round-trip as only +4, ~89% loss) — this is the mechanism behind the bright-flat / minimalist failures and the all-ones degenerate decode. No maintained fork or detector decodes this scheme reliably: the WAVES benchmark (arXiv:2401.08573) relegates DWT-DCT to supplementary appendix G.5 and targets Stable Signature / Tree-Ring / StegaStamp instead; learned encoder/decoder schemes reach ~0.98-0.99 clean but are a DIFFERENT watermark class (not what SDXL/FLUX stamp). `dwtDctSvd` does not help (SDXL embeds `dwtDct`; dwtDctSvd cannot decode it, and its clean accuracy ~0.72 is lower). **Authoritative conclusion: the open DWT-DCT mark cannot be turned from positive-only into a reliable real-world detector; keep it positive-only and rely on C2PA.** (Refuted along the way: that the library is unmaintained, and that it is robust to JPEG but only fails on geometric attacks — both did not survive verification.)
 
   Consequence for the FLUX hosted-output question (BFL Playground, FLUX.2 [pro] + FLUX.1 [dev], 2026-06-19): all samples carry the signed C2PA manifest (issuer "Black Forest Labs"); the open DWT-DCT decode returned `None`, but every available FLUX carrier (textured fox AND a minimalist-flat generation) failed the positive control (28/48), so the detector is blind on them and **whether BFL hosted output embeds the open pixel watermark is UNRESOLVED** (an earlier note here wrongly asserted it absent — overstated; a later note blamed "high texture" — also wrong, flat carriers fail too). What IS established: C2PA is the reliable FLUX identifier; the `_BITS_48` pattern is correct (round-trips on chatgpt/firefly/random). Resolving the hosted question needs a hosted FLUX carrier that first passes a >=44/48 positive control, which neither a textured nor a flat prompt produced — low priority (the open mark is only a stripped-metadata fallback).
-- **C2PA / IPTC (covered by the issuer/marker scan):** OpenAI, Google, Adobe Firefly, Microsoft (Designer + **Bing Image Creator** — collected 2026-05-24; Bing now runs Microsoft's own **MAI-Image** model, signs C2PA as "Microsoft", NOT OpenAI/DALL-E), **Stability AI** (collected from Brand Studio / DreamStudio successor; signs C2PA as "Stability AI Ltd", no SynthID, no imwatermark on its current Stable Image model — issuer added to `C2PA_ISSUERS`), and **Canva** (Magic Media signs C2PA as "Canva" + `trainedAlgorithmicMedia` with a generic `c2pa-rs` claim generator, no SynthID — issuer `b"Canva"` → "Canva (Magic Media)"; verified samples disproved the earlier assumption that Canva downloads always strip C2PA). Still unsampled: Getty, Shutterstock. Midjourney embeds NO C2PA and no invisible watermark (our `mj-*` sample carried only the IPTC tag).
+- **C2PA / IPTC (covered by the issuer/marker scan):** OpenAI, Google, Adobe Firefly, Microsoft (Copilot + Designer; Bing Image Creator collected 2026-05-24 still signs as "Microsoft" and now runs **MAI-Image**, NOT OpenAI/DALL-E), **Stability AI** (collected from Brand Studio / DreamStudio successor; signs C2PA as "Stability AI Ltd", no SynthID, no imwatermark on its current Stable Image model — issuer added to `C2PA_ISSUERS`), and **Canva** (Magic Media signs C2PA as "Canva" + `trainedAlgorithmicMedia` with a generic `c2pa-rs` claim generator, no SynthID — issuer `b"Canva"` → "Canva (Magic Media)"; verified samples disproved the earlier assumption that Canva downloads always strip C2PA). Still unsampled: Getty, Shutterstock. Midjourney embeds NO C2PA and no invisible watermark (our `mj-*` sample carried only the IPTC tag).
 
 **Samsung Galaxy AI** signs supported edits with C2PA and may carry the
 proprietary `genAIType` marker. The registered visible detector covers the
@@ -28,7 +28,7 @@ take precedence over issuer attribution. Supported mappings include Higgsfield
 AI, Topaz Labs Image API, and TikTok Ad Creative Toolbox; an unknown claim
 generator still falls back to the certificate issuer.
 
-**ByteDance Volcano Engine (Volcengine)** — the cloud behind Doubao / Jimeng — signs its AI image output with a cert from `certificate_center@volcengine.com` + `trainedAlgorithmicMedia` (issuer `b"volcengine"` → "ByteDance (Volcano Engine)", platform "ByteDance (Doubao / Jimeng / Volcano Engine)"); note this is the C2PA-signed surface, distinct from the XMP/PNG TC260 `AIGC` label Doubao also uses. ByteDance's **international brand (BytePlus / Seedream / Seededit)** signs the same content as **"Byteplus Pte. Ltd."**. The bare `volcengine` needle missed it, so BytePlus output was mis-attributed to "Adobe Firefly" through an incidental "Adobe XMP" toolkit string. Issuer `b"Byteplus"` now maps directly to "BytePlus (ByteDance)". ByteDance's consumer app **Dreamina** (the international Jimeng brand) signs as **"Bytedance Pte. Ltd."** with a `Dreamina/x.y` claim generator but, unlike the Volcano Engine surface, ships **no `trainedAlgorithmicMedia`**. Issuer `b"Dreamina"` maps to "ByteDance (Dreamina)" with **`asserts_ai=True`**. Registering the broader **issuer** `b"Bytedance Pte"` was deliberately avoided because that same entity also signs non-AI CapCut edits; keying on the `Dreamina` generator token is precise.
+**ByteDance Volcano Engine (Volcengine)** — the cloud behind Doubao / Jimeng — signs its AI image output with a cert from `certificate_center@volcengine.com` + `trainedAlgorithmicMedia` (issuer `b"volcengine"` → "ByteDance (Volcano Engine)", platform "ByteDance (Doubao / Jimeng / Dreamina / Volcano Engine)"); note this is the C2PA-signed surface, distinct from the XMP/PNG TC260 `AIGC` label Doubao also uses. ByteDance's **international brand (BytePlus / Seedream / Seededit)** signs the same content as **"Byteplus Pte. Ltd."**. The bare `volcengine` needle missed it, so BytePlus output was mis-attributed to "Adobe Firefly" through an incidental "Adobe XMP" toolkit string. Issuer `b"Byteplus"` now maps directly to "BytePlus (ByteDance)". ByteDance's consumer app **Dreamina** (the international Jimeng brand) signs as **"Bytedance Pte. Ltd."** with a `Dreamina/x.y` claim generator but, unlike the Volcano Engine surface, ships **no `trainedAlgorithmicMedia`**. Issuer `b"Dreamina"` maps to "ByteDance (Dreamina)" with **`asserts_ai=True`**. Registering the broader **issuer** `b"Bytedance Pte"` was deliberately avoided because that same entity also signs non-AI CapCut edits; keying on the `Dreamina` generator token is precise.
 - **EXIF/XMP/PNG-text generator tag (caught by `exif_generator`):** **Ideogram** writes EXIF `Make="Ideogram AI"` (collected 2026-05-24 — no C2PA, no SynthID, no imwatermark; the Make tag is the only signal). Additional verified generator stamps include **NovelAI** (`Software`, `Source`, and `Title` PNG text chunks), **Reve** (`Software` or XMP `CreatorTool` = `reve.com`), and **Aphrodite AI** (`Make` or `Software` = `Aphrodite AI`).
 - **App-export provenance and AIGC JSON:** supported ByteDance-family exports can place a JSON object in EXIF `ImageDescription` or `UserComment`, independently of C2PA or TC260. Exact `product` values for Doubao, Xinghui, and Dreamina are removable product provenance, but do not alone prove that the pixels were generated. Dreamina additionally requires `exportType=generation` for that verdict. A nested Aweme `aigc_type=1` or private ByteDance `aigc_label_type=1` / `2` is an AIGC disclosure; `0` is inconclusive and can occur on a Dreamina generation export. Plain Aweme, retouch, and `lv` exports are preserved. The lower-case private field is deliberately not interpreted as the normative TC260 `Label`, whose values `1` / `2` / `3` mean generated / possibly generated / suspected generated under [GB 45438-2025](https://www.tc260.org.cn/upload/2025-03-15/1742009439794081593.pdf).
 - **xAI / Grok — its own EXIF signature scheme, NOT C2PA (DETECTED by `metadata.xai_signature`, built 2026-05-26).**
@@ -65,7 +65,8 @@ payloads. Removal remuxes either container through ffmpeg with stream copy.
   DWT-DCT decoder only applies when the producing pipeline actually ran its
   encoder and the carrier remains decodable.
 - **Invisible but NOT locally detectable (proprietary, API/oracle only — same wall as SynthID):** Amazon Titan Image Generator + Nova Canvas (Bedrock `DetectGeneratedContent` API), Kakao (new SynthID image adopter, May 2026), NVIDIA Cosmos (SynthID video). No local detector possible; treat like SynthID.
-- **C2PA 2.4 "Durable Content Credentials" (April 2026; verified against the spec) raise the bar for metadata stripping.** 2.4 defines soft bindings (an invisible watermark or a content fingerprint) plus a server-side manifest repository and a new `c2pa.repository-receipt` assertion. Per the spec: "if a C2PA manifest is removed from an asset, but a copy of that manifest remains in a provenance store elsewhere, the manifest and asset may be matched using available soft bindings." So our local `metadata --remove` deletes the *embedded* manifest, but a fingerprint/watermark soft binding can still re-link the image to its manifest in a repository server-side. Stripping the file is becoming necessary-but-not-sufficient against durable provenance. (Our parsers target the stable embedded-manifest format documented in C2PA 2.1 §11; that format is unchanged in 2.4 -- the new pieces are repository/soft-binding infra, not the on-file box layout, so no parser change is implied.) Spec: https://spec.c2pa.org/specifications/specifications/2.4/specs/C2PA_Specification.html We now READ the soft-binding `alg` (`C2PA_SOFT_BINDINGS` / `soft_binding_vendors_in`) to name the forensic-watermark vendor, and locally DECODE the one open scheme, Adobe TrustMark (`trustmark_detector`); the rest (Digimarc/Imatag/Steg.AI/...) stay name-only (proprietary decoders).
+- **C2PA 2.4 "Durable Content Credentials" (April 2026; verified against the spec) raise the bar for metadata stripping.** 2.4 defines soft bindings (an invisible watermark or a content fingerprint) plus a server-side manifest repository and a new `c2pa.repository-receipt` assertion. Per the spec: "if a C2PA manifest is removed from an asset, but a copy of that manifest remains in a provenance store elsewhere, the manifest and asset may be matched using available soft bindings." So our local `metadata --remove` deletes the *embedded* manifest, but a fingerprint/watermark soft binding can still re-link the image to its manifest in a repository server-side. Stripping the file is becoming necessary-but-not-sufficient against durable provenance. (Our parsers target the stable embedded-manifest format documented in C2PA 2.1 §11; that format is unchanged in 2.4 -- the new pieces are repository/soft-binding infra, not the on-file box layout, so no parser change is implied.) Spec: https://spec.c2pa.org/specifications/specifications/2.4/specs/C2PA_Specification.html We now READ the soft-binding `alg` (`C2PA_SOFT_BINDINGS` / `soft_binding_vendors_in`) to name the forensic-watermark vendor, preserve the structured assertion's signed `value`, and locally DECODE the one open scheme, Adobe TrustMark (`trustmark_detector`); the rest stay name-only unless a compatible decoder is independently verified.
+- **Microsoft Paint and Photos InvisMark (reverse-engineered 2026-08-20):** Paint receives a per-generation GUID from remote prompt moderation, embeds it into locally generated pixels, and records the same value in `c2pa.soft-binding` under `com.microsoft.invismark.1`. The C2PA soft-binding registry independently identifies that algorithm as Microsoft Responsible AI InvisMark for image and video. Paint's 144-bit writer framing does not match the public repository's 100-bit pretrained checkpoint interface, so compatibility is not assumed. The parser reports the signed identifier; there is no validated local pixel decoder. Microsoft's external Content Provenance Detection API is the removal oracle because it reports `Watermark` separately from `C2PA`; a metadata-stripped, pixel-identical control must remain watermark-positive before an output-negative result is attributed to pixel removal. Sources: https://xusheng.dev/posts/reversing/mspaint_invisible_watermark/main/, https://github.com/c2pa-org/softbinding-algorithm-list/blob/main/softbinding-algorithm-list.json, and https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/how-to-provenance-detection
 - **Built in the dated batch:** soft-binding vendor detection, IPTC Photo
   Metadata AI-disclosure fields, C2PA detection and stripping for supported
   ISOBMFF video, the optional Adobe TrustMark decoder, and temporally stabilized

pyproject.toml

@@ -4,7 +4,7 @@
 # on it, including the ComfyUI node package. The console script below carries
 # the same weight, since users have it on PATH.
 name = "remove-ai-watermarks"
-version = "0.28.0"
+version = "0.31.0"
 description = "AI watermark remover for visible, invisible, and provenance marks in images and video"
 readme = "README.md"
 requires-python = ">=3.11,<3.15"

src/remove_ai_watermarks/__init__.py

@@ -32,7 +32,7 @@ _os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error")
 _warnings.filterwarnings("ignore", message=r".*ImageProcessorFast.*")
 
 
-__version__ = "0.28.0"
+__version__ = "0.31.0"
 
 __all__ = [
     "BatchSummary",

src/remove_ai_watermarks/_internal/c2pa.py

@@ -62,6 +62,16 @@ _SIGNATURE_FAILURES = frozenset(
         "claimSignature.outsideValidity",
     }
 )
+# A credential the issuer itself has disowned, disqualifying like a hash mismatch.
+# ``signingCredential.expired`` is deliberately absent: an expired certificate does not
+# imply the signed bytes changed, and a signature actually made outside validity already
+# arrives as ``claimSignature.outsideValidity`` above.
+_CREDENTIAL_FAILURES = frozenset(
+    {
+        "signingCredential.invalid",
+        "signingCredential.ocsp.revoked",
+    }
+)
 
 
 @dataclass(frozen=True)
@@ -293,21 +303,35 @@ def _status_codes(store: dict[str, Any]) -> tuple[list[str], list[str], list[str
 
 
 def _store_has_invalid_credential(store: dict[str, Any]) -> bool:
-    """Return whether this store's own active credential failed validation."""
-    _, _, failures = _status_codes(store)
-    return any(marker in code for code in failures for marker in _CONTENT_BINDING_FAILURE_MARKERS) or any(
-        code in _SIGNATURE_FAILURES for code in failures
-    )
+    """Return whether this store's own active credential failed validation.
+
+    Deliberately routed through the same codes -> dimensions -> disqualified path the
+    report uses, rather than re-testing the raw codes here. When this walk classified
+    failures on its own, adding one rule meant editing it and
+    :func:`c2pa_info_has_invalid_credential` in lockstep, and the ingredient
+    reachability walk was free to drift away from the verdict it feeds.
+    """
+    return c2pa_info_has_invalid_credential(_validation_fields(store))
 
 
 def c2pa_info_has_invalid_credential(info: dict[str, Any]) -> bool:
-    """Return whether parsed C2PA info reports a failed binding or signature."""
-    return info.get("c2pa_integrity") == "invalid" or info.get("c2pa_signature") == "invalid"
+    """Return whether parsed C2PA info reports a failed binding, signature, or credential."""
+    return (
+        info.get("c2pa_integrity") == "invalid"
+        or info.get("c2pa_signature") == "invalid"
+        or info.get("c2pa_signer_validity") == "invalid"
+    )
+
+
+def c2pa_info_has_invismark(info: dict[str, Any]) -> bool:
+    """Return whether parsed C2PA info declares Microsoft InvisMark."""
+    soft_bindings = info.get("soft_binding_vendors")
+    return isinstance(soft_bindings, list) and "Microsoft InvisMark" in soft_bindings
 
 
 def c2pa_info_has_removal_hint(info: dict[str, Any]) -> bool:
     """Return whether a C2PA AI or watermark claim should keep removal fail-safe."""
-    return bool(info.get("ai_source_kind") or info.get("synthid_watermark"))
+    return bool(info.get("ai_source_kind") or info.get("synthid_watermark") or c2pa_info_has_invismark(info))
 
 
 def _has_suffix(codes: list[str], suffixes: tuple[str, ...]) -> bool:
@@ -318,9 +342,16 @@ def _validation_fields(store: dict[str, Any]) -> dict[str, Any]:
     successes, informationals, failures = _status_codes(store)
     all_codes = list(dict.fromkeys([*successes, *informationals, *failures]))
 
-    binding_failed = any(marker in code for code in failures for marker in _CONTENT_BINDING_FAILURE_MARKERS)
+    disqualifying = [
+        code
+        for code in failures
+        if any(marker in code for marker in _CONTENT_BINDING_FAILURE_MARKERS)
+        or code in _SIGNATURE_FAILURES
+        or code in _CREDENTIAL_FAILURES
+    ]
+    binding_failed = any(marker in code for code in disqualifying for marker in _CONTENT_BINDING_FAILURE_MARKERS)
     binding_matched = _has_suffix(successes, _CONTENT_BINDING_MATCHES)
-    signature_failed = any(code in _SIGNATURE_FAILURES for code in failures)
+    signature_failed = any(code in _SIGNATURE_FAILURES for code in disqualifying)
     signature_validated = "claimSignature.validated" in successes
 
     if binding_failed:
@@ -343,7 +374,7 @@ def _validation_fields(store: dict[str, Any]) -> dict[str, Any]:
     else:
         signer_trust = "unknown"
 
-    if any(code in failures for code in ("signingCredential.invalid", "signingCredential.ocsp.revoked")):
+    if any(code in _CREDENTIAL_FAILURES for code in disqualifying):
         signer_validity = "invalid"
     elif "signingCredential.expired" in failures:
         signer_validity = "expired"
@@ -361,6 +392,10 @@ def _validation_fields(store: dict[str, Any]) -> dict[str, Any]:
         "c2pa_signer_trust": signer_trust,
         "c2pa_signer_validity": signer_validity,
         "c2pa_validation_codes": all_codes,
+        # The subset of failures that actually drove a dimension to invalid. Callers
+        # rendering "why" must use this rather than re-classifying the full code list,
+        # or the reason shown and the verdict reached stop being the same rule.
+        "c2pa_failed_codes": disqualifying,
     }
 
 
@@ -390,6 +425,7 @@ def _structured_manifest_fields(store: dict[str, Any]) -> dict[str, Any]:
     actions: list[str] = []
     source_types: list[str] = []
     soft_binding_algorithms: list[str] = []
+    soft_binding_values: list[str] = []
     claim_generator_asserts_ai = False
 
     def add_tool_matches(value: str, *, asserts_ai: bool = False) -> None:
@@ -431,9 +467,18 @@ def _structured_manifest_fields(store: dict[str, Any]) -> dict[str, Any]:
             label = assertion.get("label")
             data = assertion.get("data")
             if isinstance(label, str) and label.startswith("c2pa.soft-binding") and isinstance(data, dict):
-                algorithm = cast("dict[object, object]", data).get("alg")
+                soft_binding = cast("dict[object, object]", data)
+                algorithm = soft_binding.get("alg")
                 if isinstance(algorithm, str):
                     soft_binding_algorithms.append(algorithm)
+                    blocks = soft_binding.get("blocks")
+                    if isinstance(blocks, list):
+                        for block_value in cast("list[object]", blocks):
+                            if not isinstance(block_value, dict):
+                                continue
+                            value = cast("dict[object, object]", block_value).get("value")
+                            if isinstance(value, str) and value.isprintable() and len(value) <= 256:
+                                soft_binding_values.append(value)
             if not (isinstance(label, str) and label.startswith("c2pa.actions") and isinstance(data, dict)):
                 continue
             action_values = cast("dict[object, object]", data).get("actions")
@@ -491,11 +536,15 @@ def _structured_manifest_fields(store: dict[str, Any]) -> dict[str, Any]:
             info["synthid_watermark"] = synthid_verdict(", ".join(synthid))
 
     if soft_binding_algorithms:
+        soft_binding_algorithms = list(dict.fromkeys(soft_binding_algorithms))
         algorithms = "\n".join(soft_binding_algorithms).encode()
         soft_bindings = soft_binding_vendors_in(algorithms)
         if soft_bindings:
             info["soft_binding_vendors"] = soft_bindings
             info["soft_binding"] = ", ".join(soft_bindings)
+        info["soft_binding_algorithm"] = ", ".join(soft_binding_algorithms)
+    if soft_binding_values:
+        info["soft_binding_value"] = ", ".join(dict.fromkeys(soft_binding_values))
     return info
 
 
@@ -590,6 +639,7 @@ def _base_info(byte_count: int, *, fallback: bool = False) -> dict[str, Any]:
             c2pa_signer_trust="unknown",
             c2pa_signer_validity="unknown",
             c2pa_validation_codes=[],
+            c2pa_failed_codes=[],
         )
     return info
 

src/remove_ai_watermarks/_internal/constants.py

@@ -65,7 +65,7 @@ def _vendor(
 
 # Order is product priority when a manifest mentions more than one organization.
 C2PA_AI_VENDORS: tuple[C2paAiVendor, ...] = (
-    _vendor(b"Microsoft", "Microsoft", "Microsoft (Bing Image Creator / Designer)", "Microsoft"),
+    _vendor(b"Microsoft", "Microsoft", "Microsoft (Copilot / Designer)", "Microsoft"),
     _vendor(b"Adobe", "Adobe", "Adobe Firefly", "Adobe"),
     _vendor(
         b"OpenAI",
@@ -78,18 +78,25 @@ C2PA_AI_VENDORS: tuple[C2paAiVendor, ...] = (
     _vendor(b"Google", "Google LLC", "Google (Gemini / Imagen)", "Google", synthid=True),
     _vendor(b"Stability AI", "Stability AI", "Stability AI (Stable Image / DreamStudio)", "Stability AI"),
     _vendor(b"Black Forest Labs", "Black Forest Labs", "Black Forest Labs (FLUX)", "Black Forest Labs"),
-    _vendor(b"volcengine", "ByteDance (Volcano Engine)", "ByteDance (Doubao / Jimeng / Volcano Engine)", "ByteDance"),
+    _vendor(
+        b"volcengine",
+        "ByteDance (Volcano Engine)",
+        "ByteDance (Doubao / Jimeng / Dreamina / Volcano Engine)",
+        "ByteDance",
+    ),
     _vendor(
         "北京火山引擎科技有限公司",
         "ByteDance (Volcano Engine)",
-        "ByteDance (Doubao / Jimeng / Volcano Engine)",
+        "ByteDance (Doubao / Jimeng / Dreamina / Volcano Engine)",
         "ByteDance",
     ),
-    _vendor(b"Byteplus", "BytePlus (ByteDance)", "ByteDance (Doubao / Jimeng / Volcano Engine)", "ByteDance"),
+    _vendor(
+        b"Byteplus", "BytePlus (ByteDance)", "ByteDance (Doubao / Jimeng / Dreamina / Volcano Engine)", "ByteDance"
+    ),
     _vendor(
         b"Dreamina",
         "ByteDance (Dreamina)",
-        "ByteDance (Doubao / Jimeng / Volcano Engine)",
+        "ByteDance (Doubao / Jimeng / Dreamina / Volcano Engine)",
         "ByteDance",
         asserts_ai=True,
     ),
@@ -109,7 +116,7 @@ C2PA_IDENTITY_AI_ORGS = frozenset(vendor.org for vendor in C2PA_AI_VENDORS if ve
 C2PA_CLAIM_GENERATOR_PLATFORMS: tuple[tuple[str, str], ...] = (
     ("adobe_firefly", "Adobe Firefly"),
     ("firefly", "Adobe Firefly"),
-    ("dreamina", "ByteDance (Doubao / Jimeng / Volcano Engine)"),
+    ("dreamina", "ByteDance (Doubao / Jimeng / Dreamina / Volcano Engine)"),
     ("higgsfield ai", "Higgsfield AI"),
     ("topaz labs image api", "Topaz Labs"),
     ("tiktok ad creative toolbox", "TikTok Ad Creative Toolbox"),

src/remove_ai_watermarks/_internal/qwen_zimage_pipeline.py

@@ -1079,7 +1079,24 @@ class QwenZImagePipeline:
         donor = None
         if text_manifest is not None:
             self._progress("Reconstructing the verified text donor with the Qwen VAE...")
-            donor = self._qwen_vae_roundtrip(image)
+            if tile and max(image.size) > tile_size:
+                from remove_ai_watermarks._internal.tiling import run_tiled
+
+                donor = run_tiled(
+                    self._qwen_vae_roundtrip,
+                    image,
+                    tile_size,
+                    tile_overlap,
+                    lambda message: self._progress(
+                        message.replace(
+                            "Tiled diffusion",
+                            "Reconstructing the verified text donor",
+                            1,
+                        )
+                    ),
+                )
+            else:
+                donor = self._qwen_vae_roundtrip(image)
         global_strength = (
             resolution_adaptive_denoise(image.width, image.height) if strength is None else float(strength)
         )

src/remove_ai_watermarks/_internal/text_restoration.py

@@ -13,30 +13,35 @@ import cv2
 import numpy as np
 from PIL import Image
 
+from remove_ai_watermarks._internal.schema import require_schema_version
+
 if TYPE_CHECKING:
     from collections.abc import Sequence
     from pathlib import Path
 
     from numpy.typing import NDArray
 
-TEXT_MANIFEST_SCHEMA = 1
+TEXT_MANIFEST_SCHEMA = 2
+_SUPPORTED_TEXT_MANIFEST_SCHEMAS = frozenset({1, TEXT_MANIFEST_SCHEMA})
 FIDELITY_BLEND_ALPHA = 0.15
 GLYPH_FEATHER = 0.5
+GLYPH_SIDE_PAD_RATIO = 0.12
+GLYPH_SIDE_PAD_LIMIT = 1.0
 
 
 @dataclass(frozen=True)
 class VerifiedTextLine:
-    """One operator-verified source line in source-pixel coordinates."""
+    """One operator-verified source region in source-pixel coordinates."""
 
     box: tuple[int, int, int, int]
-    text: str
-    script: str
+    text: str | None = None
+    script: str | None = None
     angle: float = 0.0
 
 
 @dataclass(frozen=True)
 class VerifiedTextManifest:
-    """Text annotations cryptographically bound to one decoded RGB source."""
+    """Verified text regions cryptographically bound to one decoded RGB source."""
 
     source_pixel_sha256: str
     width: int
@@ -55,17 +60,20 @@ def source_pixel_sha256(image: Image.Image) -> str:
 
 
 def load_verified_text_manifest(path: Path, source: Image.Image) -> VerifiedTextManifest:
-    """Load and validate a manually verified manifest for exactly ``source``."""
+    """Load and validate an operator-verified manifest for exactly ``source``."""
     try:
         payload = json.loads(path.read_text(encoding="utf-8"))
     except (OSError, json.JSONDecodeError) as exc:
         raise ValueError(f"Cannot read text manifest {path}: {exc}") from exc
     if not isinstance(payload, dict):
         raise ValueError("Text manifest must be a JSON object")
-    if payload.get("schema_version") != TEXT_MANIFEST_SCHEMA:
-        raise ValueError(f"Text manifest schema_version must be {TEXT_MANIFEST_SCHEMA}")
+    schema_version = require_schema_version(
+        payload.get("schema_version"),
+        contract="text manifest",
+        supported=_SUPPORTED_TEXT_MANIFEST_SCHEMAS,
+    )
     if payload.get("verified") is not True:
-        raise ValueError("Text manifest must contain verified=true after manual review")
+        raise ValueError("Text manifest must contain verified=true after operator verification")
 
     rgb = source.convert("RGB")
     width = _manifest_integer(payload, "width")
@@ -82,7 +90,7 @@ def load_verified_text_manifest(path: Path, source: Image.Image) -> VerifiedText
     raw_lines = payload.get("lines")
     if not isinstance(raw_lines, list) or not raw_lines:
         raise ValueError("Text manifest lines must be a non-empty list")
-    lines = tuple(_load_line(item, width, height, index) for index, item in enumerate(raw_lines))
+    lines = tuple(_load_line(item, width, height, index, schema_version) for index, item in enumerate(raw_lines))
     if list(lines) != sorted(lines, key=lambda line: (line.box[1], line.box[0])):
         raise ValueError("Text manifest lines must be in top-to-bottom, left-to-right reading order")
     return VerifiedTextManifest(actual_hash, width, height, lines)
@@ -95,7 +103,7 @@ def _manifest_integer(payload: dict[str, Any], key: str) -> int:
     return value
 
 
-def _load_line(item: Any, width: int, height: int, index: int) -> VerifiedTextLine:
+def _load_line(item: Any, width: int, height: int, index: int, schema_version: int) -> VerifiedTextLine:
     if not isinstance(item, dict):
         raise ValueError(f"Text manifest line {index} must be an object")
     raw_box = item.get("box")
@@ -109,12 +117,15 @@ def _load_line(item: Any, width: int, height: int, index: int) -> VerifiedTextLi
     x1, y1, x2, y2 = box
     if not (0 <= x1 < x2 <= width and 0 <= y1 < y2 <= height):
         raise ValueError(f"Text manifest line {index} box is outside the source dimensions")
-    text = item.get("text")
-    script = item.get("script")
-    if not isinstance(text, str) or not text.strip():
-        raise ValueError(f"Text manifest line {index} text must be non-empty")
-    if not isinstance(script, str) or not script.strip():
-        raise ValueError(f"Text manifest line {index} script must be non-empty")
+    text: str | None = None
+    script: str | None = None
+    if schema_version == 1:
+        text = item.get("text")
+        script = item.get("script")
+        if not isinstance(text, str) or not text.strip():
+            raise ValueError(f"Text manifest line {index} text must be non-empty")
+        if not isinstance(script, str) or not script.strip():
+            raise ValueError(f"Text manifest line {index} script must be non-empty")
     angle_value = item.get("angle", 0.0)
     if isinstance(angle_value, bool) or not isinstance(angle_value, int | float):
         raise ValueError(f"Text manifest line {index} angle must be numeric")
@@ -185,28 +196,41 @@ def restore_verified_text(
     return Image.fromarray(restored)
 
 
-def _glyph_crop_box(box: tuple[int, int, int, int], width: int, height: int) -> tuple[int, int, int, int]:
-    """Widen the detector box so descenders stay inside the silhouette crop.
+def _glyph_crop_box(
+    box: tuple[int, int, int, int],
+    width: int,
+    height: int,
+    left_pad: int,
+    right_pad: int,
+) -> tuple[int, int, int, int]:
+    """Widen the detector box so glyph edges stay inside the silhouette crop.
 
-    Paddle line boxes sit 2-5 px above the true ink bottom on the poster
-    fixtures. The crop is the box itself, so those pixels never reached the
-    donor composite. Expand only on Y: 8% up, 25% down, clamped to the frame.
+    Detector boxes can stop inside a leading flourish or trailing punctuation,
+    and Paddle line boxes sit 2-5 px above the true ink bottom on the poster
+    fixtures. The crop is otherwise the box itself, so those pixels never reach
+    the donor composite. Start 12% sideways, expand each side independently
+    while foreground reaches its boundary, and pad 8% up and 25% down.
     """
     x1, y1, x2, y2 = box
     line_h = max(1, y2 - y1)
     pad_top = max(1, round(line_h * 0.08))
     pad_bot = max(2, round(line_h * 0.25))
-    return max(0, x1), max(0, y1 - pad_top), min(width, x2), min(height, y2 + pad_bot)
+    return (
+        max(0, x1 - left_pad),
+        max(0, y1 - pad_top),
+        min(width, x2 + right_pad),
+        min(height, y2 + pad_bot),
+    )
 
 
-def source_silhouette_mask(
+def _silhouette_crop_mask(
     source_rgb: NDArray[Any],
-    box: tuple[int, int, int, int],
-    angle: float = 0.0,
+    crop_box: tuple[int, int, int, int],
+    angle: float,
 ) -> NDArray[Any]:
-    """Recover a thresholded glyph shape without retaining source amplitudes."""
+    """Threshold one candidate crop so its horizontal boundaries can be tested."""
     height, width = source_rgb.shape[:2]
-    x1, y1, x2, y2 = _glyph_crop_box(box, width, height)
+    x1, y1, x2, y2 = crop_box
     gray = cv2.cvtColor(source_rgb[y1:y2, x1:x2], cv2.COLOR_RGB2GRAY)
     support = np.ones(gray.shape, dtype=np.uint8)
     if angle:
@@ -225,7 +249,7 @@ def source_silhouette_mask(
         background_luma = float(np.median(values))
     else:
         ring_pad = max(6, min(20, (y2 - y1) // 4))
-        rx1, ry1, rx2, ry2 = _clip_box((x1, y1, x2, y2), width, height, pad=ring_pad)
+        rx1, ry1, rx2, ry2 = _clip_box(crop_box, width, height, pad=ring_pad)
         context = cv2.cvtColor(source_rgb[ry1:ry2, rx1:rx2], cv2.COLOR_RGB2GRAY)
         ring = np.ones(context.shape, dtype=bool)
         ring[y1 - ry1 : y2 - ry1, x1 - rx1 : x2 - rx1] = False
@@ -239,6 +263,61 @@ def source_silhouette_mask(
     else:
         crop_mask = (gray.astype(np.float32) <= background_luma - threshold).astype(np.uint8) * 255
     crop_mask[support == 0] = 0
+    return crop_mask
+
+
+def _anchored_components_reach_sides(
+    crop_mask: NDArray[Any],
+    anchor_box: tuple[int, int, int, int],
+) -> tuple[bool, bool]:
+    """Whether anchored foreground reaches the left and right crop sides."""
+    count, labels, stats, _centroids = cv2.connectedComponentsWithStats(crop_mask, 8)
+    x1, y1, x2, y2 = anchor_box
+    anchor_counts = np.bincount(labels[y1:y2, x1:x2].reshape(-1), minlength=count)
+    anchor_counts[0] = 0
+
+    def reaches(edge_labels: NDArray[Any]) -> bool:
+        return any(
+            label != 0 and anchor_counts[label] >= max(3, round(stats[label, cv2.CC_STAT_AREA] * 0.1))
+            for label in np.unique(edge_labels)
+        )
+
+    return reaches(labels[y1:y2, :2]), reaches(labels[y1:y2, -2:])
+
+
+def source_silhouette_mask(
+    source_rgb: NDArray[Any],
+    box: tuple[int, int, int, int],
+    angle: float = 0.0,
+) -> NDArray[Any]:
+    """Recover a thresholded glyph shape without retaining source amplitudes."""
+    height, width = source_rgb.shape[:2]
+    box_x1, box_y1, box_x2, box_y2 = box
+    line_h = max(1, box_y2 - box_y1)
+    step = max(2, round(line_h * GLYPH_SIDE_PAD_RATIO))
+    limit = max(step, round(line_h * GLYPH_SIDE_PAD_LIMIT))
+    left_pad = right_pad = step
+    while True:
+        x1, y1, x2, y2 = _glyph_crop_box(box, width, height, left_pad, right_pad)
+        crop_mask = _silhouette_crop_mask(source_rgb, (x1, y1, x2, y2), angle)
+        core_y1 = max(0, box_y1 - y1)
+        core_y2 = min(y2 - y1, box_y2 - y1)
+        anchor_box = (max(0, box_x1 - x1), core_y1, min(x2 - x1, box_x2 - x1), core_y2)
+        can_expand_left = x1 > 0 and left_pad < limit
+        can_expand_right = x2 < width and right_pad < limit
+        left_anchored, right_anchored = (
+            _anchored_components_reach_sides(crop_mask, anchor_box)
+            if can_expand_left or can_expand_right
+            else (False, False)
+        )
+        left_touches = can_expand_left and left_anchored
+        right_touches = can_expand_right and right_anchored
+        if not left_touches and not right_touches:
+            break
+        if left_touches:
+            left_pad = min(limit, left_pad + step)
+        if right_touches:
+            right_pad = min(limit, right_pad + step)
     result = np.zeros((height, width), dtype=np.uint8)
     result[y1:y2, x1:x2] = crop_mask
     return result

src/remove_ai_watermarks/cli.py

@@ -904,10 +904,27 @@ def cmd_invisible(
 
 
 # ── Metadata operations ──
+def _print_metadata_not_a_clean_verdict() -> None:
+    """Repeat the identify empty-scan limit on metadata check and strip success.
+
+    ``metadata --check`` and ``metadata --remove`` answer a narrower question than
+    ``identify``: they report embedded AI metadata only. A quiet result used to
+    stop at "No AI metadata found" / "AI metadata stripped", which readers treat
+    as a clean-image verdict. The pixel channel is unchanged, and this project has
+    no local SynthID decoder, so the command must say so in the same words
+    ``identify`` already uses.
+    """
+    console.print(
+        "  This is not the same as 'clean': a pixel watermark such as SynthID cannot be\n"
+        "  detected here once its metadata proxy is absent."
+    )
+
+
 def _print_metadata_report(source: Path, has_ai: bool, metadata: dict[str, str]) -> None:
     """Render one metadata inspection result for the generic and video commands."""
     if not has_ai:
         console.print(f"  No AI metadata found in {source.name}")
+        _print_metadata_not_a_clean_verdict()
         return
 
     console.print(f"  Warning: AI metadata detected in {source.name}:")
@@ -974,6 +991,7 @@ def cmd_metadata(
         console.print("    the file could not be decoded, so it was copied through unchanged")
         raise SystemExit(1)
     console.print(f"  AI metadata stripped -> {out}")
+    _print_metadata_not_a_clean_verdict()
 
 
 # ── Video pipeline ──
@@ -1115,6 +1133,7 @@ def cmd_video_metadata(
         console.print(f"    still present: {', '.join(sorted(result.remaining))}")
         raise SystemExit(1)
     console.print(f"  AI metadata stripped -> {result.output}")
+    _print_metadata_not_a_clean_verdict()
 
 
 @cmd_video.command("invisible")

src/remove_ai_watermarks/identify.py

@@ -29,6 +29,7 @@ from typing import TYPE_CHECKING, Any, cast
 from remove_ai_watermarks._internal.c2pa import (
     c2pa_info_from_manifest_store,
     c2pa_info_has_invalid_credential,
+    c2pa_info_has_invismark,
     c2pa_info_has_removal_hint,
     cbor_text_after,
     extract_c2pa_info,
@@ -117,8 +118,13 @@ _SYNTHID_CAVEAT = (
     "decoded (proprietary decoder). Confirm via the Gemini app or openai.com/verify."
 )
 _C2PA_UNTRUSTED_CAVEAT = (
-    "The C2PA claim signature and asset binding validate, but the signer identity is not anchored "
-    "to a trusted credential here; treat the named platform as a signed claim, not a verified identity."
+    "The C2PA claim signature and asset binding validate, but no trust anchor list is configured here, "
+    "so the signer identity was never checked against one; treat the named platform as a signed claim, "
+    "not a verified identity."
+)
+_C2PA_EXPIRED_CAVEAT = (
+    "The C2PA signing certificate has expired and no trusted timestamp establishes when the claim was "
+    "signed. The binding to these bytes still holds; only the signing time is unproven."
 )
 _C2PA_UNVALIDATED_CAVEAT = (
     "The C2PA marker was parsed without cryptographic validation; treat its origin and watermark "
@@ -145,6 +151,10 @@ _C2PA_CLOUD_CAVEAT = (
     "It marks Content Credentials, not AI origin: the cloud manifest may describe a "
     "human edit, and reading it needs a network fetch this tool does not make."
 )
+_SOFT_BINDING_CAVEAT = (
+    "Removing the embedded C2PA manifest does not remove its soft binding: the named "
+    "watermark or fingerprint may remain in the pixels and re-link the asset to provenance."
+)
 _SAMSUNG_GENAI_CAVEAT = (
     "Samsung's genAIType marker shows a Galaxy AI editing tool (Generative Edit, "
     "Sketch to Image, ...) touched the image; it is an undocumented proprietary "
@@ -388,6 +398,8 @@ def evidence_from_metadata_record(
         "actions",
         "synthid_watermark",
         "soft_binding",
+        "soft_binding_algorithm",
+        "soft_binding_value",
     ):
         if key in c2pa_info:
             ai_metadata.setdefault(key, str(c2pa_info[key]))
@@ -467,17 +479,20 @@ class ProvenanceReport:
     # an open DWT-DCT decode) -- as opposed to a visible mark, provenance-only
     # TrustMark, or a weak medium-confidence hint (hf-job, Samsung genAIType). This
     # is the set of signals an invisible/diffusion scrub targets: a visible-only
-    # or no-signal image has it False. An intact C2PA AI claim from an untrusted
-    # signer is deliberately medium-confidence while this remains True; callers
-    # should gate on intent, not on the confidence string.
+    # or no-signal image has it False. Callers should gate on intent, not on the
+    # confidence string.
     ai_from_metadata: bool = False
     watermarks: list[str] = field(default_factory=list[str])
     signals: list[Signal] = field(default_factory=list["Signal"])
     caveats: list[str] = field(default_factory=list[str])
     # Contradictions between independent provenance signals (e.g. two different
     # AI vendors both claiming the image, or camera-capture credentials next to
-    # AI-generation markers). Non-empty means the provenance is internally
-    # inconsistent -- a strong tell of spoofed, transplanted, or laundered metadata.
+    # AI-generation markers), and credentials that failed validation. Non-empty means
+    # the provenance is internally inconsistent -- a strong tell of spoofed,
+    # transplanted, or laundered metadata. A failed credential sends
+    # ``is_ai_generated`` to None and lands here instead, so a consumer that reads
+    # only the verdict field turns a broken AI manifest into silence; see the
+    # ``integrity_clashes`` note in docs/python-api.md.
     integrity_clashes: list[str] = field(default_factory=list[str])
     # Orthogonal C2PA checks. A valid content binding does not make an untrusted
     # signer identity trusted, and an expired credential does not by itself mean the
@@ -540,15 +555,16 @@ def _c2pa_validation(info: dict[str, Any]) -> dict[str, Any] | None:
 
 
 def _c2pa_credential_level(info: dict[str, Any]) -> str:
-    """Return invalid, verified, or unverified for provenance attribution."""
+    """Return invalid, verified, or unverified for provenance attribution.
+
+    ``verified`` means the reader tied this manifest to these bytes: the hard binding
+    matched and the claim signature validated. Signer trust is deliberately NOT a
+    condition -- no trust anchors ship, so gating on it made this branch unreachable.
+    Read the trust-anchor paragraph in docs/module-internals.md before changing this.
+    """
     if c2pa_info_has_invalid_credential(info):
         return "invalid"
-    if (
-        info.get("c2pa_integrity") == "valid"
-        and info.get("c2pa_signature") == "valid"
-        and info.get("c2pa_signer_trust") == "trusted"
-        and info.get("c2pa_signer_validity") == "valid"
-    ):
+    if info.get("c2pa_integrity") == "valid" and info.get("c2pa_signature") == "valid":
         return "verified"
     return "unverified"
 
@@ -770,6 +786,7 @@ _AI_VENDOR_TOKENS: tuple[tuple[str, str], ...] = (
     ("google", "Google"),
     ("firefly", "Adobe"),
     ("adobe", "Adobe"),
+    ("copilot", "Microsoft"),
     ("bing", "Microsoft"),
     ("designer", "Microsoft"),
     ("microsoft", "Microsoft"),
@@ -1140,11 +1157,9 @@ def _identify_from_evidence(
     c2pa_validation = _c2pa_validation(info)
     c2pa_level = _c2pa_credential_level(info)
     c2pa_usable = c2pa_level != "invalid"
-    failed_c2pa_codes = [
-        str(code)
-        for code in cast("list[object]", info.get("c2pa_validation_codes", []))
-        if "mismatch" in str(code) or "invalid" in str(code)
-    ]
+    # The reader already named which failures moved a dimension; re-deriving that here
+    # by substring made the displayed reason a second, looser rule than the verdict.
+    failed_c2pa_codes = [str(code) for code in cast("list[object]", info.get("c2pa_failed_codes", []))]
     issuers = [info["issuer"]] if info.get("issuer") else _issuers_in(region)
     # Full AI generation (trainedAlgorithmicMedia) vs an AI-enhanced real photo
     # (compositeWithTrainedAlgorithmicMedia). The structured kind is parsed once in
@@ -1200,12 +1215,14 @@ def _identify_from_evidence(
                 Signal("c2pa", detail or "C2PA manifest present", "high" if c2pa_level == "verified" else "medium")
             )
             watermarks.append(f"C2PA Content Credentials ({', '.join(issuers) or 'unknown signer'})")
-            if c2pa_level == "unverified":
-                caveats.append(
-                    _C2PA_UNTRUSTED_CAVEAT
-                    if info.get("c2pa_integrity") == "valid" and info.get("c2pa_signature") == "valid"
-                    else _C2PA_UNVALIDATED_CAVEAT
-                )
+            if c2pa_level == "verified":
+                # A missing trust bundle is not a signer that failed against one.
+                if info.get("c2pa_signer_trust") != "trusted":
+                    caveats.append(_C2PA_UNTRUSTED_CAVEAT)
+                if info.get("c2pa_signer_validity") == "expired":
+                    caveats.append(_C2PA_EXPIRED_CAVEAT)
+            else:
+                caveats.append(_C2PA_UNVALIDATED_CAVEAT)
         # Record the AI-origin vendor for clash detection only when the source is
         # actually AI -- classify the issuer attribution / generator, NOT the
         # resolved `platform` (which may be a camera device token whose label,
@@ -1268,12 +1285,31 @@ def _identify_from_evidence(
     # the watermark itself can't be decoded; names whose watermark stamped the pixels.
     soft_binding = meta.get("soft_binding") or (", ".join(v) if (v := soft_binding_vendors_in(region)) else None)
     if soft_binding:
+        soft_binding_algorithm = meta.get("soft_binding_algorithm") or info.get("soft_binding_algorithm")
+        soft_binding_value = meta.get("soft_binding_value") or info.get("soft_binding_value")
+        soft_binding_details = "; ".join(
+            str(value) for value in (soft_binding, soft_binding_algorithm, soft_binding_value) if value
+        )
         signals.append(
             Signal(
-                "soft_binding", f"C2PA soft binding: {soft_binding}", "high" if c2pa_level == "verified" else "medium"
+                "soft_binding",
+                f"C2PA soft binding: {soft_binding_details}",
+                "high" if c2pa_level == "verified" else "medium",
             )
         )
-        watermarks.append(f"Forensic watermark soft binding ({soft_binding})")
+        if c2pa_info_has_invismark(info):
+            # Keep the generic soft-binding row for schema-1 compatibility, and add
+            # the pixel-removal target as its own stable signal for clients that need
+            # to select diffusion without parsing human-readable detail.
+            signals.append(
+                Signal(
+                    "invismark",
+                    f"Microsoft InvisMark pixel watermark: {soft_binding_details}",
+                    "high" if c2pa_level == "verified" else "medium",
+                )
+            )
+        watermarks.append(f"Forensic watermark soft binding ({soft_binding_details})")
+        caveats.append(_SOFT_BINDING_CAVEAT)
 
     # ── IPTC "Made with AI" (Meta etc.), only meaningful without C2PA ─
     iptc = any(m in head for m in IPTC_AI_MARKERS)

src/remove_ai_watermarks/invisible_engine.py

@@ -186,8 +186,10 @@ class InvisibleEngine:
             text_manifest: Operator-verified text lines bound to the decoded source
                 pixels. Enables the experimental Qwen-VAE ``vae-glyphs`` post-pass.
                 Requires the ``text-restoration`` extra and the ``qwen-zimage``
-                profile. Incompatible with tiling, downscaling, humanize, unsharp,
-                and adaptive polish because those combinations are not calibrated.
+                profile. Incompatible with downscaling, humanize, unsharp, and
+                adaptive polish. Tiling is supported: the VAE donor uses the same
+                overlapping tiles as the global pass, then glyph restore runs on
+                the blended full frame.
             fidelity_anchor: Blend 15% of the Qwen-VAE donor across the whole frame
                 before glyph restoration. OFF by default since 0.27.1: that global
                 blend was measured to return detector-visible OpenAI SynthID on
@@ -211,8 +213,6 @@ class InvisibleEngine:
                 raise ValueError("--text-manifest is supported only by the qwen-zimage profile")
             if max_resolution != 0:
                 raise ValueError("--text-manifest requires --max-resolution 0")
-            if tile:
-                raise ValueError("--text-manifest is not calibrated with --tile")
             if humanize > 0.0 or unsharp > 0.0 or adaptive_polish:
                 raise ValueError("--text-manifest requires humanize=0, unsharp=0, and adaptive polish disabled")
             from remove_ai_watermarks import region_eraser

src/remove_ai_watermarks/metadata.py

@@ -1160,6 +1160,8 @@ def get_ai_metadata(image_path: Path) -> dict[str, str]:
         "actions",
         "synthid_watermark",
         "soft_binding",
+        "soft_binding_algorithm",
+        "soft_binding_value",
         "c2pa_validation_source",
         "c2pa_validation_state",
         "c2pa_integrity",

src/remove_ai_watermarks/qwen_engine.py

@@ -1,4 +1,4 @@
-"""Qwen (Tongyi Qianwen, Alibaba) visible watermark detector/localizer.
+"""Qwen (Alibaba) visible watermark detector/localizer.
 
 Qwen stamps its generations with a visible "千问AI生成" text strip in the
 bottom-right corner -- the explicit AIGC label mandated by China's GB 45438-2025

src/remove_ai_watermarks/watermark_registry.py

@@ -20,7 +20,7 @@ Entries:
   - ``gemini`` -- Google Gemini / Nano Banana sparkle, bottom-right.
   - ``doubao`` -- ByteDance Doubao "豆包AI生成" text strip, bottom-right.
   - ``jimeng`` -- ByteDance Jimeng / Dreamina "★ 即梦AI" wordmark, bottom-right.
-  - ``qwen`` -- Alibaba Tongyi Qianwen "千问AI生成" text strip, bottom-right.
+  - ``qwen`` -- Alibaba Qwen "千问AI生成" text strip, bottom-right.
   - ``kling`` -- Kuaishou Kling "可灵AI 3.0" text strip, bottom-right.
   - ``yuanbao`` -- Tencent Yuanbao "元宝 / AI生成" two-line mark, bottom-right.
   - ``samsung`` -- Samsung Galaxy AI "Contenuti generati dall'AI" strip, bottom-left.
@@ -651,7 +651,7 @@ _REGISTRY: tuple[KnownMark, ...] = (
         "qwen",
         "Qwen 千问AI生成 text",
         "bottom-right",
-        platform="Alibaba Tongyi Qianwen (visible 千问AI生成 mark detected)",
+        platform="Alibaba Qwen (visible 千问AI生成 mark detected)",
         tc260_producer_codes=("91440101MA9Y9T4H7A",),
     ),
     _text_mark(

tests/test_cli.py

@@ -629,11 +629,13 @@ class TestMetadataCommand:
         result = runner.invoke(main, ["metadata", str(tmp_clean_png), "--check"])
         assert result.exit_code == 0
         assert "No AI metadata" in result.output
+        assert "not the same as 'clean'" in result.output
 
     def test_metadata_check_ai(self, runner, tmp_png_with_ai_metadata):
         result = runner.invoke(main, ["metadata", str(tmp_png_with_ai_metadata), "--check"])
         assert result.exit_code == 0
         assert "AI metadata detected" in result.output
+        assert "not the same as 'clean'" not in result.output
 
     def test_metadata_remove(self, runner, tmp_png_with_ai_metadata, tmp_path):
         output = tmp_path / "stripped.png"
@@ -649,6 +651,7 @@ class TestMetadataCommand:
         )
         assert result.exit_code == 0
         assert "stripped" in result.output
+        assert "not the same as 'clean'" in result.output
 
     def test_metadata_remove_reports_failure_when_the_strip_was_a_no_op(self, runner, tmp_path):
         """A file PIL cannot decode is copied through UNCHANGED by the fail-safe.

tests/test_identify.py

@@ -16,6 +16,8 @@ from unittest.mock import patch
 
 import pytest
 
+from remove_ai_watermarks._internal.c2pa import c2pa_info_from_manifest_store
+from remove_ai_watermarks._internal.constants import C2PA_AI_VENDORS, C2PA_CLAIM_GENERATOR_PLATFORMS
 from remove_ai_watermarks.identify import (
     ProvenanceEvidence,
     ProvenanceReport,
@@ -71,10 +73,77 @@ class TestProvenanceEvidence:
         report = identify_from_evidence(evidence)
 
         assert report.is_ai_generated is True
-        assert report.confidence == "medium"
+        # Intact binding and signature. The signer is not anchored, which is a missing
+        # input here (no trust bundle ships), not a finding against the credential.
+        assert report.confidence == "high"
         assert report.platform == "Adobe Firefly"
 
-    def test_fully_validated_c2pa_claim_is_high_confidence(self, tmp_path: Path):
+    def test_revoked_signing_credential_is_disqualifying(self, tmp_path: Path):
+        """A credential the issuer disowned cannot establish origin.
+
+        Revocation arrives on its own dimension, not as a binding or signature failure,
+        so a check that reads only those two returned an AI verdict off a dead cert with
+        an empty ``integrity_clashes`` -- quieter than a hash mismatch on the same file.
+
+        The evidence comes from :func:`c2pa_info_from_manifest_store`, not a hand-written
+        dict of what it is believed to emit, so the assertion follows the producer when
+        its contract changes.
+        """
+        path = tmp_path / "revoked.png"
+        info = c2pa_info_from_manifest_store(
+            {
+                "active_manifest": "created",
+                "validation_results": {
+                    "activeManifest": {
+                        "success": [
+                            {"code": "assertion.dataHash.match"},
+                            {"code": "claimSignature.validated"},
+                        ],
+                        "failure": [{"code": "signingCredential.ocsp.revoked"}],
+                    }
+                },
+                "manifests": {
+                    "created": {
+                        "signature_info": {"issuer": "OpenAI"},
+                        "assertions": [
+                            {
+                                "label": "c2pa.actions.v2",
+                                "data": {
+                                    "actions": [
+                                        {
+                                            "action": "c2pa.created",
+                                            "digitalSourceType": "trainedAlgorithmicMedia",
+                                        }
+                                    ]
+                                },
+                            }
+                        ],
+                    }
+                },
+            }
+        )
+        assert info["c2pa_signer_validity"] == "invalid"
+        evidence = ProvenanceEvidence(
+            path=path,
+            c2pa_info=info,
+            ai_metadata={},
+            scan=b"jumb c2pa OpenAI trainedAlgorithmicMedia",
+            iptc_ai_system=None,
+            aigc_label=None,
+            exif_generator=None,
+            xai_signature=False,
+            huggingface_job=None,
+            samsung_genai=None,
+        )
+
+        report = identify_from_evidence(evidence)
+
+        assert report.is_ai_generated is None
+        assert report.platform is None
+        assert report.confidence == "none"
+        assert any("revoked" in clash for clash in report.integrity_clashes)
+
+    def test_anchored_signer_is_also_high_confidence(self, tmp_path: Path):
         path = tmp_path / "validated.png"
         info = {
             "has_c2pa": True,
@@ -82,7 +151,7 @@ class TestProvenanceEvidence:
             "source_type": "trainedAlgorithmicMedia (AI-generated)",
             "ai_source_kind": "generated",
             "c2pa_validation_source": "reader",
-            "c2pa_validation_state": "Valid",
+            "c2pa_validation_state": "Trusted",
             "c2pa_integrity": "valid",
             "c2pa_signature": "valid",
             "c2pa_signer_trust": "trusted",
@@ -111,6 +180,7 @@ class TestProvenanceEvidence:
         assert report.is_ai_generated is True
         assert report.confidence == "high"
         assert report.platform == "OpenAI (ChatGPT / gpt-image / DALL-E / Sora)"
+        assert not any("not anchored" in caveat for caveat in report.caveats)
 
     def test_external_metadata_record_builds_equivalent_evidence(self, tmp_path: Path):
         path = tmp_path / "external.jpg"
@@ -552,7 +622,7 @@ class TestIdentifyRealSamples:
     def test_openai_chatgpt(self):
         r = identify(SAMPLES_DIR / "chatgpt-1.png", check_visible=False)
         assert r.is_ai_generated is True
-        assert r.confidence == "medium"
+        assert r.confidence == "high"
         assert r.platform
         assert "OpenAI" in r.platform
         assert any("C2PA" in w for w in r.watermarks)
@@ -621,15 +691,14 @@ class TestIdentifyRealSamples:
         # both invisible/metadata targets, so the diffusion scrub should run.
         assert has_invisible_target(SAMPLES_DIR / "chatgpt-1.png") is True
         assert has_invisible_target(SAMPLES_DIR / "mj-1.png") is True
-        # ai_from_metadata records scrub intent even when an untrusted signer
-        # makes the provenance verdict medium-confidence.
+        # ai_from_metadata records scrub intent independently of the confidence string.
         assert identify(SAMPLES_DIR / "chatgpt-1.png", check_visible=False).ai_from_metadata is True
 
-    def test_untrusted_but_intact_c2pa_is_medium_confidence(self):
+    def test_untrusted_but_intact_c2pa_is_high_confidence_with_a_caveat(self):
         report = identify(SAMPLES_DIR / "chatgpt-1.png", check_visible=False, check_invisible=False)
 
         assert report.is_ai_generated is True
-        assert report.confidence == "medium"
+        assert report.confidence == "high"
         assert report.ai_from_metadata is True
         assert report.c2pa_validation is not None
         assert report.c2pa_validation["source"] == "reader"
@@ -639,7 +708,30 @@ class TestIdentifyRealSamples:
         assert report.c2pa_validation["signer_trust"] == "untrusted"
         assert report.c2pa_validation["signer_validity"] == "expired"
         assert "assertion.dataHash.match" in report.c2pa_validation["codes"]
-        assert any("not anchored" in caveat for caveat in report.caveats)
+        # What was not established is said, not folded into the confidence string.
+        assert any("never checked against one" in caveat for caveat in report.caveats)
+        assert any("only the signing time is unproven" in caveat for caveat in report.caveats)
+
+    def test_no_committed_fixture_reports_a_trusted_signer(self):
+        """The reachability guard for :func:`_c2pa_credential_level`.
+
+        The SDK ships no production trust anchors, so ``signingCredential.trusted``
+        appears in no default installation. Gating high confidence on it made that branch
+        dead in production for every vendor while a hand-built dict kept it green in the
+        suite. These fixtures are the producer; if one ever comes back trusted, a bundle
+        got configured and the confidence mapping needs re-reading, not this assertion
+        deleted.
+        """
+        checked = 0
+        for path in sorted(SAMPLES_DIR.iterdir()):
+            report = identify(path, check_visible=False, check_invisible=False)
+            if report.c2pa_validation is None:
+                continue
+            checked += 1
+            assert report.c2pa_validation["signer_trust"] != "trusted"
+            if report.c2pa_validation["integrity"] == "valid" and report.c2pa_validation["signature"] == "valid":
+                assert report.confidence == "high", path.name
+        assert checked >= 3
 
     def test_hash_mismatch_does_not_confirm_origin_but_keeps_scrub_fail_safe(self, tampered_chatgpt_png: Path):
         report = identify(tampered_chatgpt_png, check_visible=False, check_invisible=False)
@@ -1143,6 +1235,39 @@ class TestIdentifySoftBinding:
         assert any("Digimarc" in w for w in r.watermarks)
         assert any(s.name == "soft_binding" for s in r.signals)
 
+    def test_invismark_signal_lists_signed_watermark_id(self, tmp_path: Path):
+        watermark_id = "83424621-03cb-40e3-9808-a9fae837156d"
+        record = {
+            "c2pa_store": {
+                "active_manifest": "paint",
+                "manifests": {
+                    "paint": {
+                        "assertions": [
+                            {
+                                "label": "c2pa.soft-binding",
+                                "data": {
+                                    "alg": "com.microsoft.invismark.1",
+                                    "blocks": [{"scope": "the entire image", "value": watermark_id}],
+                                },
+                            }
+                        ]
+                    }
+                },
+            }
+        }
+        evidence = evidence_from_metadata_record(record, path=tmp_path / "paint.png")
+
+        report = identify_from_evidence(evidence)
+
+        assert evidence.ai_metadata["soft_binding_value"] == watermark_id
+        signal = next(signal for signal in report.signals if signal.name == "soft_binding")
+        assert "com.microsoft.invismark.1" in signal.detail
+        assert watermark_id in signal.detail
+        invismark = next(signal for signal in report.signals if signal.name == "invismark")
+        assert "com.microsoft.invismark.1" in invismark.detail
+        assert watermark_id in invismark.detail
+        assert any("may remain in the pixels" in caveat for caveat in report.caveats)
+
 
 class TestIdentifyIptcAi:
     """IPTC 2025.1 AISystemUsed drives an AI verdict + platform attribution."""
@@ -1300,12 +1425,20 @@ class TestVendorOf:
     def test_registered_vendors_normalize(self):
         # Regression: these registered C2PA vendors returned None, so their claims never
         # entered clash detection (a coverage hole). They now normalize to one origin.
-        assert _vendor_of("ByteDance (Doubao / Jimeng / Volcano Engine)") == "ByteDance"
+        assert _vendor_of("Microsoft (Copilot / Designer)") == "Microsoft"
+        assert _vendor_of("Copilot") == "Microsoft"
+        assert _vendor_of("ByteDance (Doubao / Jimeng / Dreamina / Volcano Engine)") == "ByteDance"
         assert _vendor_of("Dreamina/1.2") == "ByteDance"
         assert _vendor_of("Canva (Magic Media)") == "Canva"
         assert _vendor_of("Black Forest Labs (FLUX)") == "Black Forest Labs"
         assert _vendor_of("Eleven Labs Inc.") == "ElevenLabs"
 
+    def test_bytedance_issuers_share_one_platform(self):
+        expected = "ByteDance (Doubao / Jimeng / Dreamina / Volcano Engine)"
+        platforms = {vendor.platform for vendor in C2PA_AI_VENDORS if vendor.needle == "ByteDance"}
+        assert platforms == {expected}
+        assert ("dreamina", expected) in C2PA_CLAIM_GENERATOR_PLATFORMS
+
 
 class TestIntegrityClashesHelper:
     def test_two_ai_vendors_clash(self):

tests/test_invisible_engine.py

@@ -61,7 +61,6 @@ class TestVerifiedTextMode:
         cases = (
             ("sdxl-zimage", {}, "qwen-zimage"),
             ("qwen-zimage", {"max_resolution": 1024}, "max-resolution 0"),
-            ("qwen-zimage", {"tile": True}, "not calibrated"),
             ("qwen-zimage", {"humanize": 1.0}, "humanize=0"),
             ("qwen-zimage", {"adaptive_polish": True}, "polish disabled"),
         )
@@ -129,6 +128,10 @@ class TestVerifiedTextMode:
 
         assert seen["fidelity_anchor"] is True
 
+        engine.remove_watermark(source, output, text_manifest=manifest, tile=True)
+
+        assert seen["tile"] is True
+
 
 class TestNativeOutputSize:
     """Model-side latent-grid rounding must not change the public output size."""

tests/test_metadata_internals.py

@@ -13,6 +13,8 @@ from PIL import Image
 from remove_ai_watermarks._internal.c2pa import (
     _parse_c2pa_chunk,
     c2pa_info_from_manifest_store,
+    c2pa_info_has_invismark,
+    c2pa_info_has_removal_hint,
     cbor_text_after,
     extract_c2pa_chunk,
     extract_c2pa_info,
@@ -209,7 +211,79 @@ class TestC2PA:
         assert info["ai_tool"] == "Dreamina"
         assert info["c2pa_identity_ai"] is True
 
-    def test_invalid_ingredient_does_not_taint_active_validation_or_supply_claims(self):
+    def test_structured_invismark_exposes_algorithm_and_watermark_id(self):
+        watermark_id = "83424621-03cb-40e3-9808-a9fae837156d"
+        store = {
+            "active_manifest": "paint",
+            "manifests": {
+                "paint": {
+                    "assertions": [
+                        {
+                            "label": "c2pa.soft-binding",
+                            "data": {
+                                "alg": "com.microsoft.invismark.1",
+                                "blocks": [
+                                    {
+                                        "scope": "the entire image",
+                                        "value": watermark_id,
+                                    }
+                                ],
+                            },
+                        }
+                    ]
+                }
+            },
+        }
+
+        info = c2pa_info_from_manifest_store(store)
+
+        assert info["soft_binding"] == "Microsoft InvisMark"
+        assert info["soft_binding_algorithm"] == "com.microsoft.invismark.1"
+        assert info["soft_binding_value"] == watermark_id
+
+    def test_soft_binding_value_requires_its_algorithm(self):
+        store = {
+            "active_manifest": "broken",
+            "manifests": {
+                "broken": {
+                    "assertions": [
+                        {
+                            "label": "c2pa.soft-binding",
+                            "data": {"blocks": [{"value": "not-attributable"}]},
+                        }
+                    ]
+                }
+            },
+        }
+
+        assert "soft_binding_value" not in c2pa_info_from_manifest_store(store)
+
+    def test_soft_binding_keeps_invisible_removal_fail_safe(self):
+        info = {"soft_binding_vendors": ["Microsoft InvisMark"]}
+
+        assert c2pa_info_has_invismark(info) is True
+        assert c2pa_info_has_removal_hint(info) is True
+
+    def test_content_fingerprint_does_not_trigger_invisible_removal(self):
+        info = {
+            "soft_binding": "Adobe (content fingerprint)",
+            "soft_binding_vendors": ["Adobe (content fingerprint)"],
+        }
+
+        assert c2pa_info_has_removal_hint(info) is False
+
+    @pytest.mark.parametrize(
+        "ingredient_failure",
+        [
+            # One exclusion rule, reached through two different dimensions: a broken
+            # binding and a credential the issuer disowned. The walk classified only the
+            # first for a while, so a revoked child manifest stayed reachable and kept
+            # donating its claim generator to the parent's attribution.
+            "assertion.dataHash.mismatch",
+            "signingCredential.ocsp.revoked",
+        ],
+    )
+    def test_invalid_ingredient_does_not_taint_active_validation_or_supply_claims(self, ingredient_failure: str):
         store = {
             "active_manifest": "update",
             "validation_results": {
@@ -220,13 +294,7 @@ class TestC2PA:
                     ],
                     "failure": [{"code": "signingCredential.untrusted"}],
                 },
-                "ingredientDeltas": [
-                    {
-                        "validationDeltas": {
-                            "failure": [{"code": "assertion.dataHash.mismatch"}],
-                        }
-                    }
-                ],
+                "ingredientDeltas": [{"validationDeltas": {"failure": [{"code": ingredient_failure}]}}],
             },
             "manifests": {
                 "update": {
@@ -234,11 +302,7 @@ class TestC2PA:
                     "ingredients": [
                         {
                             "active_manifest": "created",
-                            "validation_results": {
-                                "activeManifest": {
-                                    "failure": [{"code": "assertion.dataHash.mismatch"}],
-                                }
-                            },
+                            "validation_results": {"activeManifest": {"failure": [{"code": ingredient_failure}]}},
                         }
                     ],
                     "assertions": [],

tests/test_qwen_zimage_pipeline.py

@@ -659,6 +659,47 @@ def test_no_face_path_still_runs_verified_text_restoration(monkeypatch):
     restore.assert_called_with(source, anchor, donor, manifest.lines)
 
 
+def test_tiled_verified_text_runs_vae_donor_per_tile(monkeypatch):
+    from remove_ai_watermarks._internal import qwen_zimage_pipeline, text_restoration
+    from remove_ai_watermarks._internal.qwen_zimage_pipeline import QwenZImagePipeline
+    from remove_ai_watermarks._internal.text_restoration import VerifiedTextLine, VerifiedTextManifest
+
+    pipeline = object.__new__(QwenZImagePipeline)
+    pipeline.device = "cuda"
+    pipeline.progress_callback = None
+    source = Image.new("RGB", (96, 80), (10, 20, 30))
+    restored = Image.new("RGB", (96, 80), (130, 140, 150))
+    pipeline._qwen_vae_roundtrip = MagicMock(side_effect=lambda tile: tile)
+    pipeline._run_global = MagicMock(side_effect=lambda tile, _strength, _seed: tile)
+    monkeypatch.setattr(qwen_zimage_pipeline, "detect_faces", lambda _image: [])
+    restore = MagicMock(return_value=restored)
+    monkeypatch.setattr(text_restoration, "restore_verified_text", restore)
+    manifest = VerifiedTextManifest(
+        "0" * 64,
+        96,
+        80,
+        (VerifiedTextLine((4, 4, 20, 16), "Exact", "alphabetic"),),
+    )
+
+    result = pipeline.run(
+        source,
+        strength=0.1,
+        seed=0,
+        tile=True,
+        tile_size=64,
+        tile_overlap=16,
+        text_manifest=manifest,
+    )
+
+    assert result is restored
+    assert pipeline._qwen_vae_roundtrip.call_count > 1
+    assert pipeline._run_global.call_count > 1
+    restore.assert_called_once()
+    assert restore.call_args.args[0].size == (96, 80)
+    assert restore.call_args.args[1].size == (96, 80)
+    assert restore.call_args.args[2].size == (96, 80)
+
+
 def test_watermark_remover_dispatches_to_full_pipeline(tmp_path, monkeypatch):
     from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
 

tests/test_text_restoration.py

@@ -37,6 +37,17 @@ def _manifest(image: Image.Image) -> dict[str, object]:
     }
 
 
+def _geometry_manifest(image: Image.Image) -> dict[str, object]:
+    return {
+        "schema_version": 2,
+        "verified": True,
+        "source_pixel_sha256": source_pixel_sha256(image),
+        "width": image.width,
+        "height": image.height,
+        "lines": [{"box": [8, 8, 40, 24], "angle": 0.0}],
+    }
+
+
 def test_pixel_hash_ignores_container_metadata(tmp_path) -> None:
     image = Image.new("RGB", (48, 32), (10, 20, 30))
     plain = tmp_path / "plain.png"
@@ -63,6 +74,40 @@ def test_verified_manifest_is_bound_to_source_pixels(tmp_path) -> None:
     assert loaded.lines == (VerifiedTextLine((8, 8, 40, 24), "Exact text", "alphabetic", 0.0),)
 
 
+def test_geometry_manifest_needs_no_transcription_or_script(tmp_path) -> None:
+    source = Image.new("RGB", (48, 32), (10, 20, 30))
+    path = tmp_path / "regions.json"
+    path.write_text(json.dumps(_geometry_manifest(source)), encoding="utf-8")
+
+    loaded = load_verified_text_manifest(path, source)
+
+    assert loaded.lines == (VerifiedTextLine((8, 8, 40, 24), angle=0.0),)
+
+
+@pytest.mark.parametrize("field", ["text", "script"])
+def test_schema_one_still_requires_text_metadata(tmp_path, field) -> None:
+    source = Image.new("RGB", (48, 32), (10, 20, 30))
+    payload = _manifest(source)
+    del payload["lines"][0][field]
+    path = tmp_path / "lines.json"
+    path.write_text(json.dumps(payload), encoding="utf-8")
+
+    with pytest.raises(ValueError, match=field):
+        load_verified_text_manifest(path, source)
+
+
+@pytest.mark.parametrize("schema_version", [True, 0, 3])
+def test_manifest_rejects_unsupported_schema_versions(tmp_path, schema_version) -> None:
+    source = Image.new("RGB", (48, 32), (10, 20, 30))
+    payload = _geometry_manifest(source)
+    payload["schema_version"] = schema_version
+    path = tmp_path / "lines.json"
+    path.write_text(json.dumps(payload), encoding="utf-8")
+
+    with pytest.raises(ValueError, match="Unsupported text manifest schema"):
+        load_verified_text_manifest(path, source)
+
+
 @pytest.mark.parametrize(
     ("mutation", "message"),
     [
@@ -120,7 +165,7 @@ def test_restoration_uses_lama_and_qwen_vae_core(monkeypatch) -> None:
         Image.fromarray(source),
         Image.fromarray(candidate),
         Image.fromarray(donor),
-        (VerifiedTextLine((8, 8, 48, 28), "Exact text", "alphabetic"),),
+        (VerifiedTextLine((8, 8, 48, 28)),),
     )
 
     restored = np.asarray(result)
@@ -138,3 +183,17 @@ def test_silhouette_includes_descender_below_the_detector_box() -> None:
 
     assert mask[24, 20] == 255
     assert mask[16, 20] == 255
+
+
+def test_silhouette_includes_glyph_edges_beside_the_detector_box() -> None:
+    source = np.full((40, 70, 3), 240, dtype=np.uint8)
+    source[10:22, 20:50] = 20
+    source[14:18, 10:20] = 20  # leading flourish, left of the detector box
+    source[14:18, 50:58] = 20  # punctuation or icon edge, right of the box
+    source[14:18, 2:5] = 20  # separate decoration must stay outside the crop
+
+    mask = source_silhouette_mask(source, (20, 10, 50, 22))
+
+    assert mask[16, 12] == 255
+    assert mask[16, 56] == 255
+    assert mask[16, 3] == 0

tests/test_video.py

@@ -951,6 +951,7 @@ class TestVideoMetadataCli:
 
         assert result.exit_code == 0, result.output
         assert "AI metadata detected" in result.output
+        assert "not the same as 'clean'" not in result.output
 
     def test_remove_reports_output(self, tmp_path: Path):
         runner = CliRunner()
@@ -961,6 +962,7 @@ class TestVideoMetadataCli:
 
         assert result.exit_code == 0, result.output
         assert "AI metadata stripped" in result.output
+        assert "not the same as 'clean'" in result.output
         assert C2PA_UUID not in output.read_bytes()
 
     def test_rejects_image_input(self, tmp_clean_png: Path):

uv.lock

@@ -431,76 +431,117 @@ wheels = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.9"
+version = "3.5.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/bd/2a/23f34ec9d04624958e137efdc394888716353190e75f25dd22c7a2c7a8aa/charset_normalizer-3.4.9.tar.gz", hash = "sha256:673611bbd43f0810bec0b0f028ddeaaa501190339cac411f347ac76917c3ae7b", size = 152439, upload-time = "2026-07-07T14:34:58.454Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e5/3f/143b048436775b0f76ac3eec145c019e8173ccc2885c8f20319b996d5e83/charset_normalizer-3.5.1.tar.gz", hash = "sha256:6117b84ea48435e5356dc737f5121485c30920ba43375fa7b434fd753df0eac3", size = 171764, upload-time = "2026-08-15T08:20:44.807Z" }
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@@ -753,7 +794,7 @@ name = "cuda-bindings"
 version = "13.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder" },
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-    { name = "nvidia-nvjitlink", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
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+    { name = "nvidia-cuda-cupti", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
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