Main's claim_generator_platform and c2pa_vendor_platform now use the
branch's whole-word registry match, which replaces
claim_generator_word_platform. The branch's provenance veto covers every
video mark and made main's Veo-over-Sora override unreachable, so the
override is removed. Main's credential rule carries into the veto: an
invalid credential vetoes nothing. Provenance that confirms the mark's
own vendor also vetoes nothing. Over 78 local videos with a C2PA AI
claim, the only verdict changes against main are Kling/Hailuo false
positives on Veo and FLUX clips that the veto now removes. C2PA verdicts
over 1,379 files are unchanged against the pre-merge branch.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Collect provider samples from Higgsfield, Runway and Dreamina plus Google
Photos, Gemini, Grok and YouTube re-encodes into data/captures/2026-09 with
a hash-checked manifest, and use them to fix attribution and false positives:
- Attribute aggregator outputs: Higgsfield job metadata, Dreamina's
re-signed manifests, Runway's own C2PA signer, TC260 video producers.
- Veto any visible video mark when a valid C2PA claim names another vendor
(a Gemini Omni clip read as Sora on steam), and gate Kling and Hailuo
matches on edge, anchor and white-label evidence.
- Veto the Gemini sparkle when trusted provenance names another vendor.
- Report SynthID as present for Google Photos AI edits: Google's checker
found it on all four measured edits, though manifests record no action.
- Add Vidu video and Wan image marks, TC260 audio placements, and record
the AIGC key in metadata records.
- Document the research sweep, the Dreamina badge decision, and verified
Google statements on SynthID in Photos.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
- Lock OpenCV 5 and cap NumPy below 2.4 through uv constraint-dependencies so
PaddleX 3.7 keeps resolving; Dependabot ignores mirror the cap.
- OpenCV 5 draws Hershey putText glyphs differently, so the Dola and Kling
video templates ship as committed OpenCV 4 renders with a recipe script, and
the OpenArt alpha recipe now refuses any OpenCV but 4.x.
- Break Sora/Veo cross-matches in favor of Veo under valid Google AI-video
provenance, and require the full Sora mascot shape for mascot-only matches.
- Let the Doubao engine detect marks tinted by saturated backgrounds.
- Share C2PA claim-generator, credential-level and vendor-platform helpers,
JPEG suffixes and the DCT basis instead of per-module copies.
- Log model download failures, document the optional GITHUB_TOKEN, mark the
photo and source classifier model cards archived, and refresh the lock.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Repair image and metadata publication contracts, detection routing, research
artifact validation, release verification, and the published agent skill.
Refresh the dependency lock and reconcile documentation with verified behavior.
Add regression coverage and record the completed audit and corrected VideoSeal
measurements. Preserve the existing Accelerate and Lightning versions.
Validation: Ruff and Pyright pass; 2358 tests pass with 3 skipped. The maintenance
gate exits 2 on the documented Accelerate and Lightning security findings.
bytedance generator class (surgical six-head provider.pt), tc260 group
veto, stable-name receipt-gate.npz with the threshold inside the
artifact, snapshot-tolerant loader, weights pinned to revision
4c276376 (published via photo-classify-freeze-2026-09-07 release).
Provider classes now name the GENERATOR that rendered the pixels, never
the brand. bytedance joins openai/google/muse-image as the shared
ByteDance lineage (Doubao and Jimeng render with one model family;
measured: separate heads cross-fire 32/22 at 3.5x mass, the surgical
union holds 83.9% on the frozen test cell while openai/google/meta/no_ai
stay byte-identical to run1). tc260 stops being a public value: its head
remains as the group veto for the rest of China's generators (without
it, 207/379 China test rows would be falsely named openai or
muse-image), and the loader tolerates provider snapshots that predate
bytedance. The receipt gate moves model-side: stable-name artifact
receipt-gate.npz carries its threshold, head and threshold always load
from one file, and the publish script stages the stable gate from the
weights source. Microsoft stays metadata-only: 144 of its 253 bank rows
name an OpenAI model as their C2PA softwareAgent and none names a
Microsoft renderer.
pre-commit: 1) maintain.sh - exit 1 (uv-secure: lightning 2.6.5 via
trustmark, PYSEC-2026-3624 no fix released; per docs/development.md the
core checks ran separately: ruff clean, pyright 0 errors, 2059 passed
3 skipped); 2) /simplify - single-pass four angles, clean, one noted
skip (gate-test npz staging duplication); 3) docs sync - grepped
bytedance/receipt-gate.npz/provider values over README, docs, skill,
rules: no stale references; 4) CLAUDE.md - improver: no changes,
revise: no additions (learnings live in canonical docs)
The code-to-code seams were guarded and the code-to-docs seam was not, so it
drifted the way an unguarded seam does. docs/cli.md named none of
--adaptive-polish, --controlnet-scale, --detect/--no-detect, --humanize,
--strength, --tile-size or --tile-overlap; --no-detect is the escape hatch the
CLI's own --sensitivity help recommends. docs/python-api.md named 9 of the 16
InvisibleOptions fields.
The new test reads the live Click tree and the live dataclass rather than a
hand-kept list. Group spellings by the Click parameter, not by string surgery:
a boolean pair can be spelled --strip-metadata/--keep-metadata or
--keep-standard/--remove-all, and deriving the partner from a --no- prefix
splits those and demands both halves. Mutation-tested both ways -- removing a
flag from cli.md and adding a field to InvisibleOptions each fail it.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* Add rigorous SynthID research and evaluation harness
* Record SynthID cross-epoch validation results
* Reset SynthID detector hypotheses to paired training
* Add paired wavelet and spectral SynthID probes
* Record closed-corpus SynthID detector ablations
* Add registered SynthID phase probing
* Add periodic SynthID tile probing
* Add calibrated SynthID pixel detector
* Harden scale-registered SynthID detection
* Record SynthID phase and channel rescue findings
* Add production SynthID routing and OpenAI verification
* Reframe periodic pixel route as pipeline lattice; confirm and harden detection
The frozen periodic experts read an origin-anchored generation-pipeline lattice destroyed by a crop off the tile grid, not the crop-robust SynthID mark. Route the pixel result as an experimental pipeline_lattice signal kept out of the watermark inventory, and carry the crop sensitivity in every verdict envelope.
Add split-patch phase/amplitude/codeword confirmation for registered-v3, affine-lattice and cyclostationary research probes, and timeout/retry/error-taxonomy hardening for the official OpenAI verification path.
* Record pipeline-lattice reframing and registered-v3 phase lock
Document that the pixel route detects an origin-anchored lattice rather than the watermark, and record the registered-v3 two-pixel phase-lock measurement: a 2px diagonal crop killed all 36 tested detections (28 foreign-generator, 8 Google) with recovery only at offsets that are multiples of four. Update README, CLI, Python API, supported-signals, known-limitations, module-internals, the SynthID reference, and the detector research plan.
* Split SynthID campaign docs by detector, classifiers, and removal
Keep Model 1 as a research AI-versus-camera result outside the library.
Retire the mixed oracle-ladders page to a routing hub and mark the plan
file as a chronological archive.
* Keep 2026-08-22 SynthID campaign results in research docs only
Drop Bayer, Model 1, and lattice re-check numbers from user guides and
module internals. The library surface is unchanged.
* Unplug lattice detector from package, expand research docs
* Remove lattice detector sources from the public package
* Document SynthID GitHub sweep findings
* Document general AI classifier sweep
* Document modern-negative taxonomy audit and classifier doc split
Split general AI-classifier research into ai-generated-image-classifiers.md
and record the modern-negative campaign: Unsplash/Openverse date-clean cells,
contamination controls, quarter-hard paired result, closed linear veto and
taxonomy-continuation arms.
pre-commit: 1) maintain.sh - exit 1, known uv-secure lightning advisory with no upstream fix (same triage as main); core checks separately green (ruff, format, pyright, 1564 tests); 2) /simplify - docs-only single pass, no findings; 3) docs sync - split references updated everywhere, none stale; 4) CLAUDE.md - routing line already updated, compliant
* Record wild-AI audit, Meta provider class, and stock-negative expansion
The wild vendor-flagged AI cell (300 stratified rows) puts Model 1 recall at
69.7% on unknown-renderer stock AI; the stock-negative harvest triples the
modern fashion/product cells and confirms the combined-pool veto control;
the Meta muse-image corpus doubles to 132 rows with its margin sweep; a
per-channel cv2 reference fixes the latent fold test under cv2 4.10.0.
pre-commit: 1) maintain.sh - exit 1, known uv-secure lightning advisory with no upstream fix; core checks separately green (ruff, format, pyright, 1665 tests); 2) /simplify - docs-only single pass, no findings; 3) docs sync - new run references point at the gitignored research store, none stale; 4) CLAUDE.md - compact, no changes needed
* Close representation-change campaign: both candidates falsified
Pre-registered gates (AI-test >=1772, fresh OI <=50, Kodak 0, FLUX >=288,
EvalGEN >=95) rejected DINOv2-L ridge head (1427/1847, 162/3000, 1/24, 29/300,
5/100) and the paired-reconstruction FFT tower (14/1847, 12/3000, 0/24, 0/300,
0/100; contract learned at 0.949 pairwise separation but no transfer to
production generators). Campaign verdict: general classifier beyond local
scale as pursued; product stays on documented remainder.
pre-commit: 1) maintain.sh - exit 1, known uv-secure lightning advisory with no upstream fix; core checks green minutes ago (ruff, format, pyright, 1665 tests); 2) /simplify - docs-only single pass, no findings; 3) docs sync - verdict mirrored in data/research/REPRESENTATION-CAMPAIGN.md, no stale refs; 4) CLAUDE.md - no changes
* Record full failure map, H2 router falsification, and H3 process detector closure
20 measured negative domains with batch-2 (memes, whiteboards, tickets, book
pages). H2 domain router falsified: 97.9% of AI-test looks structured, every
family 94-100%. H3 process detector on 369 contrastive pairs from three
generators (OpenAI gpt-image-1-mini, Meta muse-image-1.0, Gemini flash-image)
achieves 0.909 pairwise AUC on training pairs but transfers at AUC 0.55-0.60
to production AI; human receipts score higher than any AI family. The
generator-specific spectral trace does not generalize across three generators,
closing the process-trace family in its current form.
pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1665 tests); 2) /simplify - docs + research scripts, single pass, no findings; 3) docs sync - all artifacts in data/research/, no stale refs; 4) CLAUDE.md - no changes
* Record H4-H6 research hypotheses: MLP probe, spectral invariant, TTA voting
H4 (MLP on frozen CLIP): 98.6% AI-test recall but 15.8% photo FPR — same
trade-off curve as quarter-hard. H5 (spectral invariant from CVPR 2025):
2.4% recall, the 1/f^alpha profile does not discriminate current generators.
H6 (TTA voting): fixes museum FP but not documents/UI/fashion, loses 9% recall.
All three falsified against frozen gates; ten total closed hypotheses now
document the CLIP-L representation limit from every angle.
Also records the provider cascade across all 20 domains (69.8% stray on
documents mirrors the detector failure map) and the full Y-phase campaign
PSNR data awaiting oracle rate-limit reset.
pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1665 tests); 2) /simplify - docs-only single pass, no findings; 3) docs sync - all artifacts in data/research/, no stale refs; 4) CLAUDE.md - no changes
* Fix Y-phase frequency convention, record H4-H6 and campaign results
The initial Y-phase campaign targeted FFT bins 16-32 instead of spatial
periods 16-32 px (1/32 to 1/16 cycles/px). After the fix, the full-octave
Y-phase scramble reproduces the known kill (not_detected at 20.5 dB on s1
at native 1122x1402). Partial sub-bands are visually invisible (42.5 dB)
but stay detected: the mark is distributed across the full octave, not
concentrated. Point intervention is not viable within this frequency band.
H4 MLP probe: 98.6% recall, 15.8% FPR (same trade-off wall).
H5 spectral invariant: 2.4% recall (1/f^alpha does not discriminate).
H6 TTA voting: fixes museum FP but not documents/UI/fashion.
pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (1665 tests, verified minutes ago); 2) /simplify - docs-only; 3) docs sync - all artifacts synced; 4) CLAUDE.md - no changes
* Add TC260 fifth class and per-class margin calibration to provider cascade
Five-head cascade (openai/google/tc260/meta/no_ai): openai 68.8%, google
76.6%, tc260 63.8%, meta 71.8% mean recall at uniform margin 0.50.
Per-class margins calibrated against 1.67% photo budget: openai 0.31,
google 0.29, tc260 0.39, meta 0.55. TC260 test recall 71.4% at calibrated
margins on 196 held-out images. Chinese-AIGC is a viable pixel class.
pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (verified within the hour); 2) /simplify - research script, single pass; 3) docs sync - artifacts in data/research/; 4) CLAUDE.md - no changes
* Record universal detector v3-v8: diverse training shifts the curve
Fresh MLP on frozen CLIP embeddings with 49 negative domains (12,258 images)
and 5,043 AI positives achieves 93.0% AI-test recall with 39/49 domains at
zero false positives. Digital art FP dropped from 99.3% (Model 1) to 24.8%,
fashion from 62-71% to 12%, UI from 99.3% to 12%. Photo FPR 0.7%. The
improvement comes from training data diversity on frozen embeddings, not
representation change (v5 fine-tune proved this by undoing the gains).
Also adds .local-eval to ruff exclude so the gate covers only tracked files.
pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1731 tests); 2) /simplify - docs + research; 3) docs sync - all artifacts in data/research/; 4) CLAUDE.md - no changes
* Add three-tier ensemble detector: DEFINITELY/POSSIBLY/LIKELY_HUMAN
Combines Model 1 and v8 into calibrated confidence tiers. DEFINITELY_AI
(both models agree) achieves 79.6% recall at 0.50% photo FPR with 46/49
domains at zero FP. The union reaches 93.3% recall at 1.80% photo FPR
with Kodak 0/24. First detector working across ALL content types with
per-tier calibrated error rates.
pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1731 tests); 2) /simplify - docs + ensemble; 3) docs sync - artifacts in data/research/; 4) CLAUDE.md - no changes
* Add provider v2: diversity training eliminates document leakage
Same diversity-training insight applied to the provider cascade. Adding
3,769 structured negative features to training drops document/receipt
leakage from 69.8% to 5.1%, UI from 10% to 4.3%, fashion from 5-15% to
0-3%. AI recall improves: OpenAI 84% (was 75%), Google 88.8% (was 79%).
Photo leakage 1.4%. Worst regression: memes 47.9% (was 27%).
pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1731 tests); 2) /simplify - provider v2; 3) docs sync - artifacts in data/research/; 4) CLAUDE.md - no changes
* Add universal v11: full taxonomy, 136 domains, 124 at zero FP
Master data plan mapped 148 visual domains. Massive collection (3,343
images, 73 domains from Openverse) plus gap-filler (724 images from
Safebooru anime/manga) expanded training to 19,768 images across 136
negative domains. Universal v11 (MLP-512 + Model 1 ensemble):
DEFINITELY 90.3% recall at 0.50% photo FPR, ANY 95.6% recall,
124/134 domains at exactly 0% FP, worst FP 13.8% (digital art).
New domains opened: anime, manga, chibi, mecha, vintage photos,
banknotes, newspapers, magazines, movie posters, antique maps,
watercolor/oil originals, patterns, cosmetics, real estate, 3D
renders, and 60+ more.
pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1731 tests); 2) /simplify - docs + research; 3) docs sync - artifacts in data/research/; 4) CLAUDE.md - no changes
* Confirm representation limit: v12 fine-tune destroys structured FP despite 6x data
Fine-tuning CLIP tower on 19,518 images achieves 95.1% standalone recall
but returns to 100% FP on art/UI and 14/59 zero-FP domains (vs 124/134
for frozen v11). This is a representation property, not a data issue.
Frozen CLIP + MLP (v11) is the only working architecture. Also records
provider v3 trained on all negative domains.
pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1731 tests); 2) /simplify - docs + research; 3) docs sync - artifacts in data/research/; 4) CLAUDE.md - no changes
* Document v13 plateau: 128/136 zero FP, frozen-CLIP ceiling confirmed
v13 with top-plan expansion (1,169 new images) improves domain coverage
to 128/136 at zero FP and reduces fashion FP to 5.3%, but DEFINITELY
recall plateaus at 89.6% and art FP at 15.9%. The frozen CLIP + MLP
architecture has reached its ceiling.
pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (verified minutes ago, 1731 tests); 2) /simplify - docs-only; 3) docs sync - v13 recorded; 4) CLAUDE.md - no changes
* Document the 2026-08-31 photo-classifier freeze.
Record the AI-versus-camera contract, Meta v3 hold-out, Flickr UI
replacement, and that receipts stay out of the supported domain.
* Scrub Spaces catalog details from classifier research docs
* Record receipt layout-gate probe for the universal detector
The head was trained on mixed Chinese AIGC producers that share the
TC260 mark. openai and google stay provider classes. muse-image stays
a model class.
openai, google, and tc260 stay provider classes. The Muse Image head
is public as muse-image, not meta. The freeze file still keys that
head meta_muse_image.
Ship Model 1 then gated Model 2 as an explicit CLI and library call.
identify, has_invisible_target, and all do not import it. Weights stay
on Hugging Face wiltodelta/raiw-models at a pinned revision.
Extract TwoStageZImagePipeline shared base from QwenZImagePipeline so all three
profiles are siblings, not a chain. Add ChromaZImagePipeline on that base with
diffusers ChromaImg2ImgPipeline bf16, neutral calibrated prompt, guidance 5.0,
four effective steps with Diffusers truncation compensation, no Canny.
Flat vendor floors from the 2026-08-29/30 oracle calibration across OpenAI
(0.09), Microsoft InvisMark (0.125), Google (0.40), Meta Content Seal (0.17),
seed-stability verified on Meta, full-path face-stage interaction clean through
the Gemini oracle. Research record in docs/chroma1-engine-research.md with the
matched-strength addendum showing Chroma1 regenerates better per unit strength
but its boundaries scatter wider than qwen's. Fix text-manifest guard to reject
chroma-zimage at the boundary, update all profile references from 'both' to
'all three' across src, docs, README, and python-api.
Tiled diffusion was never provider-oracle calibrated with verified text
restoration: the tiled VAE donor path ran anyway and produced results no
oracle had certified. The combination is now rejected at both the
pipeline and the engine seam (ValueError with the reason), and the CLI
help no longer implies support. The invisible help is generalized and
the metadata container list corrected (MKA/OGA/Opus/AAC).
scripts/contentseal_transforms.py reproduces the deterministic crop,
resize, and JPEG variants of the Content Seal corpus from manifest.csv,
hash-verifying every output; its README gains scripts/README.md context
and new data tests. The corpus README is honest about the one crop the
daily oracle limit left unchecked, and the eval CSVs carry the updated
verdicts. The byte-scan SynthID suppression hoists its soft-binding
lookup so the guard is computed once.
Staged on top of 0.33.1; no version bump in this commit.
Full Meta Muse Image support in the invisible-removal path:
- QWEN_ZIMAGE_META_STRENGTH = 0.1: derived by the standard
worst-boundary-plus-cross-source-spread method over five oracle-bracketed
generations (data/contentseal/manifest.csv)
- Auto mode: vendor_for_strength routes a file whose only provenance is the
standalone AI IPTC trainedAlgorithmicMedia tag onto the meta cohort; C2PA
issuers win first, so Google/OpenAI/Microsoft routing is unchanged. Muse
WebP outputs place the XMP in a tail chunk, so the scan uses the shared
chunk-aware metadata.scan_head rather than a plain head read
- Explicit override: --vendor on invisible/all/batch and
InvisibleOptions.vendor name the cohort on stripped files; naming a cohort
asserts the watermark is present, so the no-signal gate treats it like
--force at both the CLI and API seams
- sdxl-zimage has no measured Meta rung: an explicit meta vendor falls to
the conservative unknown 0.25 rather than inventing one
- identify emits a Content Seal caveat pointing at the removal path
- The legacy visible 'Imagined with AI' mark stays unregistered: a dedicated
sample hunt (newsroom mockups, community posts, press screenshots, dead
imagine.meta.com, broken Wayback captures) found no pixel-verifiable
capture, and the registry rule forbids encoding a corner without one.
erase --region remains its removal path; outcome recorded in the landscape
Co-Authored-By: Claude Fable 4.5 <noreply@anthropic.com>
High-confidence C2PA attribution required signingCredential.trusted, a status code
the reader emits only when a trust anchor list is loaded. None ships, so from 0.27.0
through 0.30.0 the branch was unreachable in production for every vendor: an intact,
cryptographically bound manifest scored the same medium as a fallback parse that
validated nothing, which collapsed the one distinction the official reader exists to
draw. A hand-built info dict stamping that code kept the branch green in the suite.
Confidence now follows the binding. Signer trust and certificate expiry stay visible
as their own dimensions and as caveats, because a trust list that was never
configured is a missing input, not a finding against the credential. Every committed
provenance fixture with a reader result and an intact binding now reaches high
confidence, and test_no_committed_fixture_reports_a_trusted_signer guards the
reachability itself rather than a synthesized status set.
Revocation joins binding and signature failures as disqualifying. It arrives only on
signer_validity, so a check reading the other 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. Expiry stays non-disqualifying: it does not imply
the signed bytes changed, and a signature genuinely made outside validity already
arrives as claimSignature.outsideValidity.
The rule now lives in one place. _validation_fields maps status codes to the four
dimensions and names the failures that moved one; c2pa_info_has_invalid_credential
maps dimensions to disqualified. The ingredient-reachability walk and the
user-visible reason both consume that path instead of re-classifying raw codes, so
adding this one rule no longer means editing three layers in lockstep.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Tile the Qwen VAE donor the same way as the global pass. Glyph restore already runs on the blended full frame, so the old tile ban was an artificial gate.
Move the draft-annotation logic (PaddleOCR line detection, word-box
grouping, three script-chosen recognition engines, crop-jitter
stability gate) from the evaluation-only scripts into the installable
package, with lazy paddle imports and a text-draft extra (CPU, no
torch). draft_text_lines() returns accepted (crop-stable, NEVER
ground-truth-correct - precision on the reference posters was 90.0% /
94.4%) and rejected proposals; source_pixel_sha256 is re-exported for
manifest building. scripts/infer_text_lines.py now dogfoods the
package module instead of loading the eval script by path.
The whole-frame 15% Qwen-VAE blend returned detector-visible OpenAI
SynthID on poster-scale manifests through the engine text-manifest
path (official Content Provenance API, 2026-08-19: restored detected
x6 with the anchor, clean x6 without it; base outputs clean x6;
pixel-identical stripped controls detected, proving the pixel channel).
Add fidelity_anchor=False to remove_watermark and InvisibleOptions and
--fidelity-anchor on the CLI to reproduce the 0.27.0 research
behavior. Text-box MAE cost of the new default is under one point on
all three fixtures (11.60->11.72, 7.79->7.86, 7.57->8.13).
Three gaps found while measuring the record path against the file path, each one
a signal the library could not see:
WebP stores `XMP ` after the pixels, so on any WebP above the scan window a fixed
read stops short of the label. `_riff_late_metadata` steps over the coded image to
reach it, the RIFF analogue of the existing PNG and ISOBMFF readers. Three corpus
files hid an IPTC "Made with AI" tag and a C2PA `trainedAlgorithmicMedia` there.
The decoder-backed fallback now covers only what it is actually for -- metadata the
raw bytes do not spell, such as a compressed PNG `zTXt` packet.
A C2PA reader failure returned the same `None` as a file with no manifest, so a
verdict could fall back to the raw byte scan with no trace anywhere. Failures now
log at warning and only genuine ones do: a file without credentials never reaches
that branch, and an unsupported container is demoted to debug through the reader's
own `C2paError.NotSupported`. The first corpus run with it found a truncated PNG.
`scan_dataset.py` never registered the pillow-heif opener it declares as a
dependency, so every HEIC was scanned as unreadable -- no EXIF, and a pixel layer
that was 397 of 406 features NaN instead of 136.
`_riff_late_metadata` caps its total like `isobmff.scan_c2pa_region` does. Clamping
each chunk to the bytes remaining is not enough on its own: one chunk can declare a
length spanning most of the file, and this runs on the memoized verdict path over
images from arbitrary sources.
Also lands `identify_metadata_record` and `ProvenanceReport.to_dict()`, the
one-call entry point and the versioned JSON contract for the record path.
Record-vs-file equality holds over 3,478 corpus images, and the eight files these
fixes recovered still report AI.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
`collect_metadata_record` returns a JSON-safe record carrying an image's
provenance metadata regions -- never its pixels -- and the existing
`evidence_from_metadata_record` + `identify_from_evidence` build the verdict
from it without opening the file. The contract is equality with
`identify(path, metadata only)`, verified over the tracked fixtures and over a
local corpus of 3,478 images (every file carrying a rare signal, plus a random
slice): zero differences.
Three placements defeated earlier drafts and each is now a rule with a test:
the `scan_head` buffer is the head CONCATENATED with late metadata, so a
structural walk must read the raw head instead; Samsung splits its evidence
between a post-EOI trailer and the coded scan; and PIL's info keys must be
emitted in the file path's candidate order, since the first token match wins.
Also fix a real detection gap found while establishing that equality: a label
the decoder can read but a raw byte scan cannot -- a compressed PNG `zTXt`
packet, or a WebP XMP chunk past the scan window -- was invisible to
`identify`. Eight corpus files carrying a China TC260 AIGC label or an IPTC
"Made with AI" tag were reported as no signal at all.
`scripts/detection_timing.py` and its report script measure the metadata path
per method; they write outside the repository and are read-only over a dataset.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
`InvisibleOptions` promises in its docstring that every default mirrors
`InvisibleEngine`. Two fields made that promise cost something to keep: `force` is
not an engine parameter at all, and `controlnet_scale` was a third spelling of the
engine's `controlnet_conditioning_scale`. The mirror test carried an exception
table for each. This removes both, so the comparison needs no exceptions -- a field
that needs one is a field that belongs somewhere else.
`force` decides WHETHER the engine runs, which is settled before it is built, so it
joins `backend` and `sensitivity` as a parameter of `remove_all` and `remove_batch`
and is threaded to `_run_invisible` as its own argument. `controlnet_scale` takes
the engine's own name; the click option stays `--controlnet-scale` and is now
translated exactly once instead of at three forwarding sites.
Safe to do today: both symbols landed after 0.25.0 and have never been published.
The forwarding turned out to be the weaker half. A defaults comparison cannot see a
hardcoded literal at the seam, and `_run_invisible` passed the entire suite with
`controlnet_conditioning_scale` pinned to a constant. Each of the two knobs also
reaches the engine through TWO paths -- `remove_all` versus `remove_batch(mode="all")`
for `force`, `_run_invisible` versus `_batch_engine` for the scale -- and guarding one
left the other free to hardcode with a green suite. So:
* `test_every_field_arrives_at_the_engine_with_the_caller_s_value` drives the real
seam with all 13 fields set off their defaults; mutating any one of them to its
default fails it.
* `test_force_reaches_the_scrub_gate_in_every_scrubbing_mode` and
`test_batch_controlnet_scale_flows_to_the_cached_engine` are parametrized over
both modes, so neither path can be pinned alone.
Also fixes an order-dependent test surfaced by the added tests reshuffling the xdist
shards. `test_visible_path_decodes_file_once` counted every `image_io.imread` in the
process, but the Gemini engine loads its own bundled capture assets on first
construction, so the count was 3 on a cold engine and 1 on a warm one and the test
passed only when an earlier test happened to build the engine first. It now counts
decodes of the SOURCE, which is the invariant it exists for, and still fails when the
shared decode is broken. The production path was never wrong: the source bitmap is
decoded exactly once.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The visible-mark path had grown three copies of one ladder sweep, four
near-identical `detect` arms, and four hand-rolled `footprint_mask` overrides;
mark knowledge sat in five hand-maintained tables across three modules; and the
flagship `all`/`batch` pipeline existed only in cli.py, written twice with
divergent behavior.
Detection is now one measurement. `_ladder_best` replaces the three sweeps,
`_scan`/`_verdict` replace the four arms, and the winning box travels to the
mask on `TextMarkDetection.match_box` instead of being swept a second time.
`detect_both` returns the strict and relaxed verdicts from one scan, which
halves the arbiter's perception cost (260 -> 130 matchTemplate calls on a 2048²
image, verdicts identical field for field). A per-mark demotion goes in the new
`_post_gate` hook, never in a `detect` override -- an override is invisible to
the single-pass path, which is how the RunningHub and Yuanbao anchor gates
briefly stopped applying.
Everything about a mark is now one registry row: product, label regime, the
platform sentence `identify` reports, the metadata signals that confirm it, and
its TC260 producer codes. `identify._VISIBLE_MARK_PLATFORM`, the signal mapping
in `api.visible_provenance`, `_PRODUCT_OF` and the pill veto are derived from
those rows.
`api.remove_all` / `api.remove_batch` are the library form of the `all` and
`batch` commands; the CLI is a wrapper that owns console text and exit codes.
Progress is a `(stage, detail)` pair of stable tokens, so the CLI keys its
wording off structure rather than parsing the library's prose back.
Two intentional behavior changes, both verified against a recorded 811-image
sample of detector verdicts, removal-mask hashes, arbiter decisions and
`identify` reports:
* A TC260 label now relaxes the vendor its `ContentProducer` names rather than
ByteDance's pair on every China-AIGC image. 333 of 811 samples move; on 185
of them the previously relaxed pair was simply the wrong vendor, and the
mark actually present never reached the relaxed gate its own
`provenance_ncc_factor` was calibrated for.
* A confident LibLibAI detection suppresses the Jimeng pill, like every other
TC260 product's mark. It was registered alongside RunningHub and Baidu, both
of which were added to the hand-written veto list, and it was not. 1 sample
moves, and it is exactly the co-firing case.
Nothing else in that record changes: detector verdicts, mask hashes and
`identify` verdicts are byte-identical, and all 200 calibration constants are
untouched.
Also: `aigc_label` and friends plus `extract_c2pa_info` are memoized on
(path, mtime_ns, size) -- size because this package rewrites in place; the
native TC260 container readers route on magic bytes instead of the file
extension, so a mislabeled AVI or FLV is no longer invisible; `identify` shares
one pixel decode between the DWT-DCT and visible stages (TrustMark keeps its own
Pillow decode, which is not substitutable); and the six `stabilize_*` video
wrappers collapse into one policy table.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
An adversarial review of 52b2c11 (five independent audits, each finding put to
two skeptics, plus a completeness critic) found four defects that commit
introduced and several stale claims it should have caught.
The install hint no longer installs -- again. Folding five hints into one
INVISIBLE_EXTRA constant dropped the shell quoting the originals had, so the
printed remediation was `pip install remove-ai-watermarks[qwen-zimage]`. Bare
brackets are a glob in zsh, the macOS default shell: it dies with "no matches
found" before pip runs. That is the exact failure 52b2c11 existed to stop
producing, reintroduced in a different form by a bulk replace. The constant is
quoted now, and a test asserts the quotes rather than the bare substring -- the
old assertions passed either way, which is why nothing caught it.
Three tests were not guarding what they claimed:
- The commit's headline behaviour change, per-profile polish resolution inside
the engine, had no test at all. Rebinding resolve_adaptive_polish to the
pre-commit `bool(value)` left the full suite green. Now covered by a test that
drives the real engine and observes whether humanizer.adaptive_polish ran;
that mutation now fails it.
- TestAvailability still asserted the pre-commit (torch, diffusers) contract, so
in a diffusion-only environment it was simply wrong, and comparing each gate to
a tuple copied from itself could never catch the two gates disagreeing -- the
drift the shared REMOVAL_MODULES was introduced to prevent. Replaced with a
test that simulates each module's absence and requires BOTH gates to close.
- Both CUDA-refusal guards skipped in every environment, including CI: they were
gated on the diffusion stack, which no CI job installs. The refusal fires
before any torch attribute is read, so they now run everywhere; only the dtype
assertion keeps its skip.
Also: the retired-knob test covered `invisible` but not `all` or `batch`, though
all three declared those options separately; and smoke_matrix.py still called
remove_watermark(region=...), a parameter 52b2c11 deleted, with the resulting
TypeError swallowed into a skip by a broad except.
Stale documentation the previous sweep missed: known-limitations still described
an MPS out-of-memory fallback and a lighter-pipeline escape that no code can
produce; module-internals declared Canny thresholds of 100/200 as a compatibility
contract while the code uses 13/64, attributed enable_model_cpu_offload to
deleted profiles, and still warned that the engine and CLI defaults differ (this
commit's predecessor made them identical); cli.md gated `all` on the `diffusion`
extra; python-api claimed "cuda" was the only accepted explicit device when
"auto" is too. The claim that `device` is not a parameter was wrong in both
module-internals and .claude/rules/development.md -- it is one, deliberately, and
now says so. `--cpu-offload` help and the pipeline's CUDA guard both still
pointed at MPS.
Not fixed here, reported instead -- both are outside this repo:
- ComfyUI-remove-ai-watermarks nodes.py:332 passes num_inference_steps and
guidance_scale (plus min_resolution/upscaler from bf4bfc1). distribute.yml's
comfyui job runs on every release and fails the release if the node sync fails,
so 0.25.0 needs that node updated first.
- raiw-app modal_app.py:422-425 forwards the same two kwargs into
remove_watermark. Latent: it is pinned to 1a77e24 and nothing supplies a value
today, so it fires on the next pin bump.
pre-commit: 1) maintain.sh - exit 0 (1093 tests, Pyright 0 errors, no
vulnerabilities); 2) /simplify - not re-run, this commit is the applied output of
a five-dimension adversarial review; 3) docs sync - grepped MPS/mps, the extras
names and every symbol touched across README, docs/, scripts/, .claude/; updated
6 docs; 4) CLAUDE.md - corrected the device claim in .claude/rules/development.md
and added the shell-quoting rule
Verified by execution, not assertion: smoke_matrix --quick 51 pass / 0 fail,
_knob_rows driven directly 10 pass / 0 fail / 7 skip (no CUDA), the install hint
rendered and round-tripped through zsh, and each new test confirmed to fail
under the mutation it is meant to catch.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The CLI still advertised --model, --steps, --guidance-scale, --device and a
deprecated --auto. Each pinned a value the two surviving profiles fix -- the
model stack, the per-stage distilled schedule, CFG 1.0, CUDA -- so the only
outcome any of them had was an error raised several frames below the caller,
under a message naming an internal profile. A flag whose sole result is a
refusal is worse than no flag: it advertises a capability that does not exist,
and it lets a wrapper thread a value that will silently do nothing. They are
gone from the parser, from InvisibleEngine, and from WatermarkRemover, so the
failure is now a TypeError or a Click "No such option" at the point the caller
can act on.
The install hint was wrong in the same way. is_available() checked torch and
diffusers, then told the user to install [diffusion] -- which contains neither
DiffSynth nor the Z-Image face stage both profiles run. Following the advice
produced a second, different failure. The module list and the extra name now
live once in watermark_profiles (REMOVAL_MODULES, INVISIBLE_EXTRA) and are read
by both the CLI gate and the remover's precondition, which cannot drift apart
because they are the same tuple.
The adaptive-polish default moved out of the argument parser. It was resolved by
reading Click's parameter source, which put per-profile data in the CLI layer,
left the engine declaring the opposite default (False vs True) so a library
caller and a CLI caller on one profile got different output, and lost the polish
entirely for anything that supplies the flag non-interactively. The flag is now
tri-state (default=None) and resolve_adaptive_polish owns the per-profile
answer. The seed follows the same rule: the CLI stopped pre-resolving it.
Dead code removed with it: six scan_*_video wrappers and the _scan_video helper
none of them had a caller for, PNG_METADATA_KEYS, feather_region_composite and
the remover region path that was only reachable from a no-caller convenience
wrapper, remove_watermark_batch on both layers, try_empty_device_cache, the
_generate/_run_qwen_zimage pass-through pair, self.model_id, and the _internal
PEP 562 shim that no caller ever went through. get_device now answers cuda or
cpu only: mps and xpu travelled one frame to the same CUDA-only refusal while
costing a device probe each, and that refusal now names the resolved device, so
device=None on a CUDA-less host says 'cpu' rather than 'None'. The XPU wheel
index went with them.
Docs: README, cli, installation, python-api, supported-signals,
known-limitations and module-internals all still described the removed profiles,
the CPU/MPS/XPU ladder, a `default`->`sdxl` alias, and the wrong extra.
known-limitations still listed the retired SDXL strength ladder as current.
scripts/smoke_matrix.py and real_examples_e2e.py drove --device mps.
Next release is 0.25.0, not a patch: this removes public parameters and
narrows a published extra on top of the released 0.24.0.
pre-commit: 1) maintain.sh - exit 0 (1091 tests, Pyright 0 errors, no
vulnerabilities); 2) /simplify - 4 agents, 11 findings applied, 2 skipped
(dropping the `device` parameter entirely, which raiw-app pins; folding
diffsynth into the `diffusion` extra, which video-only callers do not need);
3) docs sync - grepped every removed identifier across README, docs/, scripts/,
.claude/; updated 9 docs; 4) CLAUDE.md - added the no-error-only-knobs rule to
.claude/rules/development.md
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The min-resolution floor lifted small inputs toward SDXL's ~1024 training size,
and Real-ESRGAN was an optional way to do that lifting. Both surviving profiles
run at native geometry, so the engine forced the floor to 0 on every path; the
floor never fired, `upscaling` was never true, and nothing downstream of it could
execute. Gone: upscaler.py, _esrgan_upscale, the min_resolution and upscaler
parameters, _target_size's floor branch, --min-resolution, --upscaler,
_warn_if_esrgan_unavailable and the `esrgan` extra. max_resolution stays and is
now the only lever on geometry; it can only scale down.
scripts/smoke_matrix.py was the one live consumer and neither gate saw it -
Pyright is scoped to src/ and Ruff cannot resolve its function-local import - so
`--diffusion` would have died at import. Its knob rows were written for the
removed profiles besides (--pipeline sdxl, --steps 20, --guidance-scale 5.0,
--device mps), so they are rewritten rather than patched: most now assert a knob
is REJECTED, which is the coverage worth having when the CLI accepts a value the
library refuses several layers down. Accepted-knob rows skip without CUDA, so the
row count is host-dependent and verification-plan.md no longer claims a fixed 68.
This removes a public module, a CLI option and a published extra, so the next
release is 0.25.0, not a patch.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
qwen-zimage becomes the default and sdxl-zimage the only alternative. The
controlnet, sdxl, qwen and default profiles are gone, and with them the CPU and
MPS paths for invisible-watermark removal: neither matched the two-stage
recipe's face preservation, so keeping them advertised a quality this library no
longer delivers. Visible-mark removal and every identify command still run
anywhere.
Retired names are rejected rather than remapped. Silently routing --pipeline
sdxl onward would run an old script at a different strength, on a different
model, at a different quality, and report success.
CUDA is now checked when the remover is constructed instead of when the model
loads. Auto-detection cheerfully returned mps on a Mac, so the failure arrived
several layers down, after the dependency check and the pipeline import, in a
message naming whichever internal pipeline happened to raise. _DEVICES collapses
to {"cuda"} and the cpu/mps float32 branch goes with it.
resolve_strength stays total. It briefly returned None for qwen-zimage, meaning
"ask the resolution curve", which pushed a branch onto both callers and left one
of the two strength policies outside the strength module; the CLI copy had
already grown an `or 0.0` guarding a path its own comment called unreachable. It
now takes the image size and answers for both profiles, so the displayed value
cannot drift from the executed one.
Deletion fallout removed with it: img2img_runner and progress.py (the MPS
recovery path and its progress monitor had no callers left), viable_steps, the
fp16 degenerate-output retry, the fp16 VAE fix, and the Qwen img2img call
builders. try_empty_device_cache moved into watermark_remover rather than
leaving a module whose docstring outlived its code. _HAS_DIFFUSERS routes
through optional_deps.module_available, which is what the rest of the library
uses and what correctly rejects a pruned namespace remnant.
--steps, --guidance-scale and --model now have exactly one legal value each and
are still accepted at parse time, then rejected in remove(). Their help text
says so, but validating them beside the option would be better.
Not addressed, and worth its own decision: invisible_engine forces
min_resolution to 0 for both profiles, so the --min-resolution floor, --upscaler,
_esrgan_upscale, upscaler.py and the esrgan extra are all unreachable.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
