The free `visible` path over-subtracted a faint Gemini sparkle on a
mid-tone background into a darker-than-background brown diamond instead
of removing it (2026-06-18 prod NPS report, "the watermark was not
removed, just its color changed"). The existing over-subtraction guard
only tripped when reverse-alpha drove a footprint pixel fully negative
(the issue #30 dark-background black-pit case); on a mid-tone background
the over-subtraction darkens the core well below the background without
any pixel crossing zero, so the gate missed it and shipped the dark mark.
Add a second over-subtraction signal to `_reverse_alpha_oversubtracts`:
predict the reverse-alpha output at the bright core, (core - a*logo)/(1-a),
and route to the footprint inpaint when it lands more than
`_OVERSUB_DARK_MARGIN` (25) gray levels below the local background ring.
Calibrated wide: clean removals predict within ~12 of background
(demo_banana ~-1), the prod regression ~-40, the issue #30 dark case ~-82.
Corpus-validated on the 479 detected Gemini images: 10 switch reverse-alpha
to inpaint, all of them dark-diamond cases that improve or match; the
other 469 stay byte-identical. demo_banana stays on the reverse-alpha
path (byte-identical).
Also crop both reverse-alpha helpers to the region they actually touch,
a pure O(image) -> O(mark) win that is byte-identical to the full-frame
math (a uint8<->float32 round-trip is exact):
- `GeminiEngine._core_and_bg` converts only the footprint+ring crop to
gray, not the whole frame (~70 ms -> 0.1 ms on a 12 MP image; it runs
for both the alpha-gain estimate and the new gate). Verified identical
across 479 images; detector confidence unchanged.
- `TextMarkEngine._apply_reverse_alpha` computes the blend on the glyph
crop only (`amap` is zero outside it, so the math is a no-op there):
~275 ms -> ~2 ms per placement on a 12 MP frame, up to 2 placements per
removal. Verified identical across 142 Doubao/Jimeng placements.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
identify(check_visible=True) ran the Gemini-sparkle detector and the
Doubao/Jimeng text-mark detector each with its own image_io.imread, so the
same bitmap was fully decoded twice. On a memory-constrained host (the raiw.cc
512 MB web worker, which runs identify on every upload) that doubled the peak
decode allocation and contributed to OOM restarts.
Decode once in identify() and pass the BGR array to both detectors. The detect
methods already accept an NDArray, so this only threads the pre-decoded array
through: detect_sparkle_confidence and the two _visible_* helpers gain an
optional image= param that, when None, preserves the old self-read behavior
(so direct callers and the cv2-missing/unreadable paths are unchanged).
Only the visible path is deduplicated; the optional check_invisible decoders
are unaffected (and off on the web hot path). Adds a test asserting
identify(check_visible=True, check_invisible=False) decodes exactly once.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
A 2026-06-14 oracle re-test on the deployed Modal controlnet worker (v0.10.0)
cleared SynthID at OpenAI 0.10 (2 photoreal) and Google 0.15 (2 native
2816x1536, retiring the "native >= 0.30" guess), while a pixel sweep showed the
2026-06-04 cert floors (0.20/0.30) over-regenerated for no efficacy gain
(Google MAE -20% at 0.15). Lowers OPENAI_STRENGTH 0.20->0.10, GEMINI_STRENGTH
and UNKNOWN_STRENGTH 0.30->0.15.
Caveats documented in watermark_profiles.py + docs: removal near this floor is
seed-non-deterministic (a service must pin a verified seed), and the n=2 re-test
did not cover flat-graphic hard cases.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The 'no signal' branch of the visible no-mark path claimed 'No AI provenance
signal found either', which reads as 'the image is clean'. A missing metadata
proxy is not proof an invisible pixel watermark (SynthID) is absent: it cannot
be detected once metadata is gone and may have been stripped upstream. The
message now preserves that uncertainty and routes to both 'all' (regenerate
pixels) and 'erase'. Regression-guarded by the SynthID/all asserts in
test_cli.py. CLAUDE.md visible-command note updated to match.
Also adds a 'Scope and non-goals' section (CLAUDE.md + README): removing
AI-provenance marks on the user's own content is in scope; stripping
stock/paid-content watermarks (Shutterstock/Getty/iStock, classifieds) is out
of scope by principle, not by difficulty.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
When `visible --mark auto` (or an explicit `--mark` with detection on) found
no registered mark, it exited 0 without writing output -- which a wrapping
service reads as success and re-serves the unchanged input. ~74% of real
uploads carry no registered visible mark, so this was the dominant "it didn't
work" / NPS score-0 failure mode.
Now it runs a cheap metadata-only identify, prints actionable guidance (route
to `all` for an invisible/metadata mark, or `erase` for an arbitrary logo),
writes no output file, and exits EXIT_NO_VISIBLE_MARK (2) -- distinct from
success (0) and a hard error (1) so the caller can surface the message.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The 256->512 detection-search widening (v0.8) let a large, low-gradient
shape match outrank a genuine mid-size corner sparkle whose raw NCC sits
below the 0.85 corner-promote gate, so `identify` read `unknown` on Gemini
images that v0.7.2 caught (reporter osachub: scale-48 sparkle on light
bedding -- true sparkle spatial 0.775 / grad 0.960 / fusion 0.676, but the
size-weighted argmax locked onto a decoy at spatial 0.628 / grad 0.036).
detect_watermark now keeps the top-K (_SELECT_TOPK=3) size-weighted
candidates (NMS-deduped) plus the corner-promote candidate, scores each by
full fusion (spatial+gradient+variance) via the extracted _grad_var_scores
helper, and selects the highest -- the gradient term lifts the true sparkle
over the decoy. Ranking by the SIZE-WEIGHTED score (not a raw-NCC argmax)
preserves tiny-patch suppression: a raw-NCC argmax re-admitted 16-18px
content false positives (14/65 doubao + 4/11 jimeng visible images). Top-K
adds zero flips on the doubao/jimeng corpora and leaves the 495-image Gemini
set unchanged (479 detected) while recovering the reporter's image at 0.676.
- _grad_var_scores: gradient/variance scoring factored out of detect_watermark
- confidence = best_fused (drop the duplicated fusion recompute)
- tests: rename test_promotion_is_what_rescues_it ->
test_size_weighted_search_alone_traps_on_the_decoy (corner-promote is no
longer the sole rescue path); add a deterministic regression test mirroring
the real spatial/grad signature
- docs: module-internals.md detector section + CLAUDE.md mechanism map
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
test_all_basic / test_all_visible_step_uses_registry asserted exit 0 but did
not patch is_available, so on CI (core+dev only, no gpu) they took the skip
branch and hit the new non-zero exit. Passed locally where gpu is present.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Step 2 (invisible/SynthID) was skipped with a quiet inline warning and the
run still exited 0, so a missing [gpu] extra was mistaken for a clean result
(recurring #14/#47). Add a prominent end-of-run banner and a non-zero exit.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- AIGC: parse the bare ``AIGC{...}`` blob form (label glued to its JSON in a
JPEG APP segment near the JFIF header), and scan both raw-JSON forms in one
fall-through loop so a quoted ``"AIGC"`` later in an XMP packet no longer
shadows a real bare label earlier in the file (3 files read unknown before).
- Integrity clash rule 2: a camera device + an AI marker from the SAME C2PA
manifest (Google Pixel Magic Editor / Pixel Studio edit chain) is a legitimate
edit chain, not a contradiction. Fire only when the AI marker's source is
independent of the camera's manifest; pure cameras (Leica/Sony/Nikon) are
unaffected (2 Pixel files mis-flagged before).
- New c2pa_cloud_manifest detector: surface a C2PA 2.4 Durable Content
Credentials cloud-manifest reference (Adobe cai-manifests.adobe.com) as a
medium provenance signal when the embedded manifest is stripped. Provenance
only, never asserts is_ai (2 files read fully unknown before).
identify reuses its already-loaded scan head for the cloud check (no second
read). +7 tests; CLAUDE.md + README synced.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Nine findings from a high-effort project-wide review, fixed and verified
(571 passed, ruff/pyright clean):
Correctness:
- all/batch now remove Doubao/Jimeng/Samsung visible text marks: the visible
step routes through the registry (new cli._remove_visible_auto) instead of a
hardcoded GeminiEngine, so they no longer leave the wordmark intact.
- batch always reads the original source (dropped the out_path-reuse that
re-processed already-cleaned outputs on a re-run).
- img2img_runner only retries the diffusion call on the deprecated-callback
TypeError; any other TypeError now propagates instead of double-running.
- gemini detect/remove and the reverse-alpha engines normalize channels via a
new image_io.to_bgr, fixing a grayscale/BGRA crash in the FP-gate path.
- _png_late_metadata advances its cursor by the clamped length, so a malformed
chunk length no longer aborts the late AI-label scan.
Cleanup / efficiency:
- Consolidate the ~90%-identical Doubao/Jimeng/Samsung engines into a shared
config-driven _text_mark_engine.TextMarkEngine base; each engine is now a thin
subclass (TextMarkConfig + test shims). Behavior is byte-exact (the three
engine test suites pass unchanged). Registry adapters collapse to one
_text_mark(...) row each. Gemini stays a separate engine.
- scan_head is memoized per (path, size, mtime), so identify() reads the file
head once instead of ~8 times.
- invisible_engine post-processing decodes/encodes the output once (chained in
memory) instead of 2-4 times across stages.
- Remove the orphaned get_model_id_for_profile (+ CONTROLNET_PROFILE); derive
the --strength help from the strength constants (strength_default_help) so it
cannot drift; share the --pipeline/--strength click options; simplify the
retired --auto resolver.
Net -835 lines. Tests added for the registry-routed visible pass, to_bgr,
the polish/model/guidance wiring, and strength_default_help. CLAUDE.md updated
for the new base module, the engine/registry changes, image_io.to_bgr, and the
scan_head cache.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Overhaul the diffusion-removal surface around a single robust default and a
complete, consistent CLI.
Pipeline + strength:
- controlnet is now the DEFAULT pipeline (CLI --pipeline + both engine ctors).
With the certified higher strength it clears both photoreal and flat-graphic
content, whereas plain SDXL left SynthID on flat graphics.
- Rename the plain-SDXL profile default -> sdxl; "default" stays as a back-compat
alias (normalize_profile + a click callback that warns).
- Unify the strength ladder: resolve_strength applies ONE vendor-adaptive ladder
(the certified controlnet floors OpenAI 0.20 / Google 0.30 / unknown 0.30) to
both pipelines. sdxl is the weaker remover on its own hard case (flat fills),
so the certified floor is the right floor for it too.
CLI completeness:
- Add --model (HF model id) to invisible + batch (was only on all) and
--guidance-scale (CFG) to all three diffusion commands; both were library
knobs the CLI did not expose.
- Flip --adaptive-polish to ON by default (it self-gates to a no-op where there
is no detail deficit, so default-on is safe).
- Share --pipeline / --strength / --model / --guidance-scale as single
decorators so invisible/all/batch keep an identical surface; the --strength
help is derived from the strength constants (strength_default_help) so it can
never drift from the ladder.
Removals:
- Delete the auto_config content-detection planner + its YuNet/DBNet assets
(~2.6 MB): with controlnet always the pipeline and the polish self-gating, the
face/text/edge detection no longer changed behavior. --auto is now a deprecated
no-op that only warns (the polish it enabled is the default).
Docs (README, CLAUDE.md, docs/synthid.md) updated throughout; added an
InvisibleEngine Python API example. Tests cover the alias warnings, the
polish default, and the --model/--guidance-scale wiring.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Empirical conclusion from the 2026-06-04 - 2026-06-08 Modal cert sweeps:
every face-restore approach we built (GFPGAN-on-cleaned, PhotoMaker-V2,
InstantID txt2img, InstantID img2img-on-cleaned at three parameter
settings) regenerates the face via SDXL diffusion rather than preserves
it. Output face pixels are diffusion-fresh, so the regenerated face
inherits SDXL "clean skin" aesthetic and loses original identity
precision -- it looks MORE AI-generated than the cleaned image, not
less. The cleaned image from the main controlnet 0.20 removal pass is
the least-AI face state we can reach without re-introducing SynthID.
Nothing in the restore family achieves the actual goal (preserve the
original person's face). Keeping them around as opt-in invites users to
ship something that defeats the point. Removing entirely.
Library changes:
- Deleted src/remove_ai_watermarks/instantid_restore.py
- Deleted src/remove_ai_watermarks/photomaker_restore.py
- Deleted tests/test_instantid_restore.py
- Deleted tests/test_photomaker_restore.py
- Removed `instantid` and `photomaker` extras from pyproject.toml
- Removed `[tool.hatch.metadata] allow-direct-references = true` (was
only needed for the photomaker git+ URL)
- InvisibleEngine.remove_watermark: dropped `restore_faces` +
`restore_faces_method` params, removed both `_restore_faces_instantid`
and `_restore_faces_photomaker` private methods, removed dispatch
- CLI: dropped `_restore_faces_options` decorator, all four cmd_*
signatures lose `restore_faces` + `restore_faces_method`, kwarg passes
to remove_watermark dropped
- _apply_auto: dropped `restore_faces` from tuple shape (was unused after
the engine no longer takes it)
- auto_config.AutoConfig: dropped `restore_faces` field; `plan()` no
longer sets it; `reason` no longer mentions it
- Tests updated accordingly (test_auto_config.TestReason no longer asserts
"face-restore on" in the reason string)
Docs updated:
- CLAUDE.md: removed the photomaker extras bullet, the Face restore
trade-off bullet, the instantid_restore.py + photomaker_restore.py
module bullets; replaced restore mentions in watermark_remover and
controlnet bullets and prod recipe with the empirical conclusion
- README.md: removed both `--restore-faces` callouts and the install
snippet; the feature bullet and auto-mode comment updated
- docs/synthid-robust-identity-research.md: added Status-retired notice
at the top pointing at the 2026-06-08 followup
raiw-app:
- modal_cert.py: dropped `--restore-faces` flag entirely; sweep() no
longer takes restore_faces; pinned _LIB_SPEC to `[gpu]` extras (no
`photomaker` / `instantid` extras), points at main
ruff + strict pyright clean; 569 tests pass; 18 restore-specific tests
gone.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Last commit added `_color_match` which shifts the face crop's mean to the
canvas mean -- the old test fed a uniform face (210) into a uniform cleaned
canvas (90), so after color-match the face was uniform 90 and the
composite was undetectable by value. Switched the fake pipeline to a
gradient face so the color-match preserves variance, and the assertion
now checks that the face region has non-zero std (composite injected
gradient pixels) instead of a value threshold.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Per the 2026-06-08 deep-research synthesis (docs/synthid-robust-identity-
research-2026-06-08.md), the entire ArcFace-class identity-adapter ecosystem
for SDXL is blocked from commercial use by InsightFace's non-commercial model
packs (antelopev2 / buffalo_l). No commercial-safe ArcFace-grade identity
stack exists today. The user explicitly opted into shipping a non-commercial
restore path (research / personal use; raiw.cc must NOT install the extra).
Architectural choice: InstantID over PhotoMaker-V2 as the default.
- PhotoMaker-V2 (CLIP+ArcFace dual encoder, txt2img only): documented upstream
identity drift on Asian male faces, visually confirmed in our cert sweep
(tatsunari rendered as a generic woman; group photo collapsed into a
patchwork).
- InstantID (ArcFace cross-attention + landmark ControlNet): semantic
identity branch + spatial weak landmark control, decoupled. Per InstantID
paper (arXiv:2401.07519) and the research report, stronger identity fidelity
on single portraits. Critically: NO original face pixels enter the diffusion
(ArcFace embedding is semantic, landmark stick figure is pure geometry), so
SynthID is not transported.
Implementation:
- New `src/remove_ai_watermarks/instantid_restore.py` mirrors the
`photomaker_restore.py` shape (lazy singletons for pipeline + FaceAnalysis,
per-face crop + _composite_faces from photomaker_restore). Loads the
InstantID community pipeline via `DiffusionPipeline.from_pretrained(
custom_pipeline="pipeline_stable_diffusion_xl_instantid")` -- no upstream
Python package needed; diffusers fetches the file from its community
examples.
- New `instantid` extra in pyproject (insightface + onnxruntime +
huggingface-hub). NON-COMMERCIAL block in the comment explains why.
- CLI: `--restore-faces-method [instantid|photomaker]`, default `instantid`.
Both methods explicitly labeled NON-COMMERCIAL in the help text.
- Engine: dispatch on `restore_faces_method` to either
`_restore_faces_instantid` or `_restore_faces_photomaker`.
- 9 control-flow tests for InstantID without model download (mirror the
photomaker_restore.py test pattern + draw_kps helper checks). 587/587 pass.
Diffusers-0.38 compat verified by upstream code inspection: the InstantID
pipeline inherits from `StableDiffusionXLControlNetPipeline`, uses only
public diffusers APIs (`encode_prompt`, `prepare_image`, `prepare_latents`,
`get_guidance_scale_embedding`), uses legacy attention processor API which
diffusers preserves for backward compat. No PhotoMaker-V1-style internal
text_encoder access. End-to-end execution will be validated by the Modal
cert sweep in the next step.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The previous commit added a real call into FaceAnalysis2 / analyze_faces inside
restore_faces_photomaker, which broke the model-free control-flow test. Stub it:
- monkeypatch _get_face_analyser to return a sentinel
- install a fake `photomaker` module with analyze_faces returning a single
512-d zero embedding
- add dtype=torch.float32 to the fake pipeline class so .to(device, dtype=...) works
11/11 green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Visual review of the GFPGAN-on-cleaned output (9-face grid, 1448x1086) showed it
only polished the already-drifted face without restoring identity — useless for the
"restore who is in the photo" intent. Dropping it.
The shipped restore path is now PhotoMaker-V2, which delivers true identity-from-
embedding face regeneration via a CLIP+ArcFace dual encoder. The ArcFace branch
pulls InsightFace antelopev2/buffalo_l model packs at runtime, which InsightFace
releases under a research-only license, so the whole extra is **NON-COMMERCIAL**.
raiw.cc and any monetized deployment must NOT install the `photomaker` extra.
This is called out at every entry point: CLI flag help, module docstring,
pyproject extra block, CLAUDE.md extras bullet, README install snippet.
Changes:
- Deleted `src/remove_ai_watermarks/face_restore.py` and its tests.
- Deleted the `restore` extra (gfpgan/facexlib/basicsr + scipy<1.18 / numba<0.60
pins) and the basicsr setuptools<69 build pin from pyproject.toml.
- Restored `src/remove_ai_watermarks/photomaker_restore.py` (V2 this time:
`TencentARC/PhotoMaker-V2`, `photomaker-v2.bin`, no `pm_version='v1'` override).
- Restored the `photomaker` extra in pyproject with all the upstream-compat
pins (einops, peft, onnxruntime, insightface) and the `allow-direct-references`
hatch metadata block.
- `InvisibleEngine` swapped `_restore_faces` -> `_restore_faces_photomaker`;
`--restore-faces-method` removed (only one method, no choice).
- CLI flag help, CLAUDE.md, README, docs/synthid.md, and
docs/controlnet-removal-pipeline-research.md all updated.
- docs/synthid-robust-identity-research.md status notice rewritten to list both
abandoned commercial-safe attempts (V1 + GFPGAN-on-cleaned) and the
non-commercial trade-off we accepted.
ruff + strict pyright(src/) clean; 578 tests pass (the 9 GFPGAN tests are gone,
the 11 PhotoMaker tests stay green).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
After 7 cascading upstream-compat fixes (insightface dep, peft dep, pm_version,
device, etc.), the PhotoMaker V1 cert sweep still hit a CFG batch-dim mismatch
inside the denoising loop. The upstream PhotoMaker `pipeline.py` is forked from
diffusers v0.29.1 and our env runs 0.38; SDXL prompt-encoder handling changed
significantly between those versions, so making PhotoMaker work end-to-end
needs a proper fork or a diffusers downgrade — both expensive. Not worth
shipping today.
Pivot: restore `face_restore.py` (GFPGAN) with a single-line fix that makes it
SynthID-safe by construction. The previous design ran GFPGAN.enhance on the
ORIGINAL watermarked image and was oracle-confirmed to re-add SynthID via the
weight-0.5 pixel blend. The fix is to run GFPGAN on the diffusion-CLEANED
image — whatever pixels GFPGAN derives from are already SynthID-free, so the
partial blend cannot transport the watermark. Identity fidelity is lower than
a true identity-as-embedding stack would deliver, but it ships and works.
Changes:
- `src/remove_ai_watermarks/face_restore.py` restored from pre-wipe state with
one line changed: `restorer.enhance(cleaned_bgr, ...)` instead of
`restorer.enhance(original_bgr, ...)`. `original_bgr` is kept as an unused
positional argument for API stability.
- `src/remove_ai_watermarks/photomaker_restore.py` and its tests REMOVED. The
research note (`docs/synthid-robust-identity-research.md`) keeps a "status
notice" documenting why PhotoMaker is parked for now and what the path back
in would look like.
- `pyproject.toml` `restore` extra restored (gfpgan/facexlib/basicsr +
scipy<1.18 + numba<0.60 pins + the basicsr setuptools<69 build pin), plus
`photomaker` extra (with its einops/insightface/peft pile) and the
`[tool.hatch.metadata] allow-direct-references = true` block REMOVED.
- `InvisibleEngine._restore_faces_photomaker` removed; `_restore_faces`
restored. The `--restore-faces` CLI flag and its plumbing through cmd_*
signatures are unchanged.
- CLAUDE.md, README.md, docs/synthid.md, docs/controlnet-removal-pipeline-
research.md updated to describe the shipped GFPGAN-on-cleaned design and to
reference PhotoMaker only as the parked alternative.
ruff + strict pyright(src/) clean; 578 tests pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The upstream PhotoMaker package's `__init__.py` unconditionally imports a
face-analyser class from its `insightface_package` submodule, so JUST importing
`PhotoMakerStableDiffusionXLPipeline` (the V1 pipeline class we use) raises
`ModuleNotFoundError: No module named 'insightface'` if insightface isn't
present in the env. The Modal cert sweep caught this on the V1 image.
Resolution: pin `insightface>=0.7.3` (and its `onnxruntime` runtime dep) in the
`photomaker` extra. The PyPI insightface package is MIT-licensed CODE; the
non-commercial restriction sits on the pretrained model packs (antelopev2,
buffalo_l) which download only when `FaceAnalysis()` is instantiated. Our V1 path
never instantiates the face-analyser -- it loads photomaker-v1.bin (CLIP-only
encoder) via `load_photomaker_adapter` -- so the model-pack license does not
bind us; we depend only on the MIT code for the import to resolve.
Safety guards:
- Runtime check in `_get_pipeline`: raises if `_PHOTOMAKER_FILE` is ever pointed
at v2 (so a future maintainer can't silently regress to the InsightFace path).
- New test class `TestV1OnlyCommercialSafetyGuard`: asserts repo + filename
pin to V1 AND asserts the module source never references the face-analyser
class (a static check that our codepath stays out of the runtime that would
pull the non-commercial model packs).
Docs: documented the import dance + legal split inline at the top of
`photomaker_restore.py`.
ruff clean; 581 tests pass (the 9 PhotoMaker tests plus 3 new V1-guard tests).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The GFPGAN `restore` extra and its `face_restore.py` module are gone. They were
oracle-confirmed to re-introduce SynthID by blending watermarked original face
pixels at fidelity weight 0.5 (clean A/B: gemini_3 controlnet 0.20 detected WITH
GFPGAN, clean WITHOUT). Keeping them as the default restore method was a footgun
for the removal pipeline. PhotoMaker-V2 (added in the previous commit) is the
single shipped restore path now -- identity-as-embedding, SynthID-safe by
construction.
Removed:
- src/remove_ai_watermarks/face_restore.py + tests/test_face_restore.py
- pyproject.toml `restore` extra (gfpgan/facexlib/basicsr + scipy/numba pins)
- pyproject.toml `[tool.uv.extra-build-dependencies] basicsr = [...]` build pin
- CLI: `--restore-faces-method` and `--restore-faces-weight` (no method choice
to make, no GFPGAN weight knob to expose)
- InvisibleEngine._restore_faces method (only _restore_faces_photomaker remains)
- All restore-faces-method / restore-faces-weight threading through cmd_*
signatures and _process_batch_image
Kept:
- `--restore-faces / --no-restore-faces`: now binds to PhotoMaker-V2.
- All adopted oracle findings about GFPGAN re-introducing SynthID (kept in the
research docs as historical context that explains why the path was removed).
Docs updated: CLAUDE.md (restore extras bullet collapsed to photomaker, removed
face_restore Key-modules bullet, several inline GFPGAN refs scrubbed), README.md
(face-identity callout + install section now point to the photomaker extra),
docs/synthid.md 5.5 (net recipe), docs/controlnet-removal-pipeline-research.md
(recommendations).
ruff + strict pyright (src/) clean; 578 tests pass (the 9 GFPGAN tests are gone,
the 9 PhotoMaker tests stay green).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds the second face-restore mechanism, selectable via the new CLI option
`--restore-faces-method=photomaker`. Unlike the existing GFPGAN path (which runs on
the watermarked ORIGINAL and was oracle-confirmed to re-introduce SynthID by partial
pixel blending), PhotoMaker carries identity in a SynthID-invariant OpenCLIP
embedding and regenerates fresh face pixels conditioned on it — the pixels in the
output are diffusion-fresh, so the watermark cannot be transported.
The load-bearing assumption (embedding invariance to SynthID-magnitude pixel noise)
was empirically validated in the prior commit (smoke test): cosine drift 0.002
under a ±2 LSB low-freq carrier, an order of magnitude less than JPEG90 drift
which SynthID survives at >=99% TPR.
End-to-end commercial-safe:
- PhotoMaker-V2 weights: Apache-2.0 (TencentARC)
- ID encoder: OpenCLIP-ViT-H/14 (MIT)
- SDXL base: shared with the main pipeline
- NO InsightFace (the non-commercial blocker for IP-Adapter FaceID / InstantID /
PuLID / Arc2Face)
Two-pass architecture (PhotoMaker has no ControlNetImg2img class in diffusers):
1) main controlnet/default removal pass cleans SynthID + drifts faces
2) PhotoMaker txt2img regenerates each face from its embedding, feather-composited
back into the cleaned image
New module `photomaker_restore.py` mirrors `face_restore.py`: lazy pipeline
singleton (double-checked lock), `is_available()` gate, pure `_face_crop_square` and
`_composite_faces` helpers, all unit-tested without the model (9 new tests). New
`InvisibleEngine._restore_faces_photomaker` runs after the diffusion pass, mirroring
`_restore_faces`. CLI flag `--restore-faces-method=[gfpgan|photomaker]` threaded
through `cmd_invisible`/`cmd_all`/`cmd_batch` + `_process_batch_image`.
New optional `photomaker` extra (Apache-2.0 + Apache-2.0/MIT deps, no basicsr).
`[tool.hatch.metadata] allow-direct-references = true` is required because the
upstream PhotoMaker package lives only on GitHub.
The next step (separate work) is oracle validation: run a 6-image cert sweep
through the new pipeline (default/controlnet at the certified strength +
--restore-faces-method=photomaker) and confirm SynthID stays clean while face
identity is recovered. The required infrastructure (`raiw-app/modal_cert.py`) is
already in place.
ruff + strict pyright(src/) clean; 586 tests pass (+ 9 new in
tests/test_photomaker_restore.py).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
New samsung_engine.py mirrors the jimeng engine but anchors bottom-left; wired
into watermark_registry, the CLI (--mark samsung / auto), and identify
(visible_samsung, medium). visible_alpha_solve.py gains a corner=bl mode;
samsung_alpha.png solved from @f-liva's flat captures. Calibrated for the
Italian "Contenuti generati dall'AI" variant. Flat black/gray/white captures
committed, real photos gitignored. Tests + docs.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The fp16-fix VAE swap (#29) is gated to the default SDXL checkpoint, so a
custom model_id, a stale pre-fix install, or a fal/custom loader can still
decode to an all-black/NaN frame in fp16 (reporter: gpt-image 1448x1086,
the `image_processor.py invalid value encountered in cast` warning).
Add a model-agnostic backstop in remove_watermark: after generation, if the
run was fp16 and the output is degenerate (_is_degenerate_image: near-zero
mean and variance), rebuild the pipeline in fp32 on the same device and
re-run once. fp32 is the verified-clean path, so a black image is never
returned regardless of model_id or version. Mirrors the MPS->CPU fallback's
self-mutation pattern; batch inherits it. Verified e2e on MPS by forcing
fp16 with the swap disabled (first pass black, guard fired, retry clean).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
After reverse-alpha, re-detect the sparkle; when one survives at or above the
registry fail line (conf >= 0.5) -- an alpha mismatch the per-image gain estimate
could not fully correct -- inpaint the footprint and keep that only when it lowers
the re-detect confidence. The footprint inpaint reconstructs the slot from its
darker surroundings, so it physically removes the bright sparkle; purely additive,
the common clean removal re-detects below 0.5 and is returned untouched.
Measured on the spaces visible-removal audit: gemini removal-audit failures drop
15 -> 11 (4 genuine rescues), doubao 65/65 and jimeng 11/11 unchanged, zero
regressions on the 468 already-clean removals.
An offset+scale alignment search was prototyped on the remaining 11 fails and
rejected: an audit "ceiling" suggested +4 more, but those were NCC-gaming -- the
lower-scoring placement left the sparkle as bright or brighter, just reshaping the
residual so the contrast-invariant shape-NCC scored lower (a5a9: first-pass slot
~76 at background level vs the "aligned win" ~164). A brightness sanity check
rejected every one, so it contributed nothing and was removed.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Three content-quality features for the invisible/all/batch pipeline.
DBNet text detector (auto_config): replace the MSER text heuristic with
PP-OCRv3 differentiable-binarization via cv2.dnn.TextDetectionModel_DB,
using a bundled 2.4 MB Apache-2.0 model (en/cn detection nets are
byte-identical, so it ships language-neutral). cv2.dnn is core OpenCV, so
no new pip dep. MSER stays as the fallback when the model can't load.
Validated on real images: matches MSER everywhere and additionally catches
the Doubao CJK mark MSER missed; routing decisions unchanged otherwise.
Real-ESRGAN upscaler (new upscaler.py, esrgan extra): optional
pre-diffusion super-resolution for the min-resolution floor upscale, loaded
via spandrel (MIT, no basicsr) with BSD-3-Clause weights downloaded on
first use. New --upscaler {lanczos,esrgan} on invisible/all/batch; default
stays lanczos and the engine falls back to lanczos when the extra is absent
or the model errors (never breaks removal). It is a manual opt-in knob (the
auto plan never selects it) -- as a generic GAN it sharpens photo/texture
content strongly but can degrade faces (the diffusion pass regenerates
them) and thin text, documented accordingly.
batch --auto: wire the content-adaptive --auto (+ --adaptive-polish) into
cmd_batch. The plan is recomputed per image and the invisible engine is
cached per resolved pipeline (default/controlnet), so a mixed directory
builds at most one engine of each kind. Verified end-to-end: 3 mixed
images routed correctly with only 2 pipeline loads (controlnet reused).
ruff + strict pyright(src/) clean; 558 tests pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Brings in commit 5cf68a6 (single C2PA_AI_VENDORS registry, erase_lama
grayscale/BGRA support, batch device-cache clearing + --controlnet-scale,
uv publish via OIDC, hatchling pin <1.31). Auto-merged with no conflicts;
ruff/pytest(544)/pyright all clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
detect_watermark's shape-only NCC (spatial/gradient/var fusion) fires on ornate
or flat content (text strips, banners, hatching) that coincidentally matches the
diamond shape. The NCC is contrast-invariant, so it cannot see the defining
property of a real Gemini sparkle: a bright WHITE overlay whose core sits above
the local background.
The fusion now demotes (caps confidence to 0.30) a match that is BOTH
low-confidence (< _SPARKLE_FP_CONF 0.65) AND has a low core-ring brightness
margin (_core_ring_margin < _SPARKLE_FP_MARGIN 5). Real sparkles escape via
EITHER high confidence (white-bg sparkles score >=0.79 despite a low margin) OR
high margin (dark/mid backgrounds, incl. the #36 faint-corner case), so both
must fail to demote. The gate is monotonic -- it only removes detections, never
adds -- so it cannot regress the verified-negative corpus (already 0 FPs).
On the spaces corpus it demoted 16/495 flagged sparkles (13 no AI metadata =
content FPs; the 3 AI-meta ones were visually FPs / a near-invisible
white-on-white sparkle whose AI verdict is held by metadata), and dropped the
removal-audit failures 20 -> 15.
- _core_and_bg shared helper (core 75th-pct brightness vs background-ring median);
_estimate_alpha_gain refactored onto it, new _core_ring_margin wrapper.
- TestSparkleFalsePositiveGate: margin high/low, strong-sparkle kept (incl. on
white via high conf), blurred no-core blob demoted.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The fixed mild auto polish (unsharp 0.5 / grain 2.0) under-corrected soft
photo/face output (gemini_3 stayed at lap-var 84 vs its 592 original) and its
grain speckled small text. Replace it with humanizer.adaptive_polish: target the
input's Laplacian variance with a capped unsharp scaled to the deficit + edge-
masked grain (smooth regions only), calibrated by a short sigma search. Self-
limiting on text/graphics -- already high-frequency, so almost no polish lands
and text edges are masked out. Validated on the spaces corpus (gemini_3 84 -> 334
end-to-end; openai_1 text near-untouched).
Interface: every --auto decision is now independently overridable -- add
--adaptive-polish/--no-adaptive-polish (matching --restore-faces; works without
--auto too) so the polish can be disabled or used manually. _apply_auto overrides
exactly the three content-adaptive modes (pipeline, restore-faces, adaptive-
polish); --unsharp/--humanize stay independent fixed filters.
cv2-only, no new deps. Threaded through invisible/all (not batch).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Three P2 cleanups from a library-wide review.
Detection -- single C2PA_AI_VENDORS registry (noai/constants.py):
- C2PA_ISSUERS, SYNTHID_C2PA_ISSUERS, and identify._ISSUER_PLATFORM now derive
from one C2paAiVendor table, so adding a C2PA vendor is one entry instead of
edits in three places across two files. Behavior-identical (262 detection
tests pass; the kept `needle` field is load-bearing -- it differs from `org`
for Google and ByteDance, with no mechanical derivation).
Code-health:
- region_eraser.erase_lama now accepts grayscale/BGRA like erase_cv2 (it
crashed on grayscale and silently dropped alpha on BGRA). +2 regression tests.
- batch frees the device cache between images via a shared try_empty_device_cache
helper (generalized from the MPS-only _try_clear_mps_cache, now reused by both
the MPS->CPU fallback and the batch loop).
- batch gained --controlnet-scale (parity with invisible/all).
CI / packaging:
- publish.yml uploads via `uv publish` (PyPI trusted publishing over OIDC),
replacing pypa/gh-action-pypi-publish so uploads no longer depend on that
action's bundled twine accepting the Metadata-Version. Workflow filename +
pypi environment unchanged, so PyPI's trusted-publisher entry still matches.
- hatchling pin relaxed <1.28 -> <1.31 (verified against hatch's changelog:
1.30.0 made Metadata 2.5 the default, 1.30.1 reverted to 2.4; 1.27-1.29 were
always 2.4). Kept as belt-and-suspenders so the first uv-publish release ships
2.4, isolating the uploader swap from the metadata-version bump.
Docs (CLAUDE.md, pyproject) synced; corrected the inaccurate "hatchling 1.28+
emits 2.5" note.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add `auto_config.plan(image_path) -> AutoConfig`, the first step of the
invisible/all pipeline: it inspects the input image (before the diffusion model
loads) and picks the quality modes so the run adapts to content. Quality-priority
routing -- ControlNet (text/face-structure preservation) is the default, skipped for
plain SDXL only on a clearly structure-less image; GFPGAN face restore when a face is
present; a mild sharpen + grain polish when a smoothing pass ran. Exposed as `--auto`
on `all`/`invisible` (`_apply_auto`; explicit flags override via click's parameter
source). Not wired into batch (its engine is cached per-mode).
Detection is cv2-only and torch-free (~100 MB peak RSS, a few ms): OpenCV YuNet
(`cv2.FaceDetectorYN`, MIT, 232 KB model bundled in assets/) for faces, a Canny
edge-density + MSER heuristic for text/structure (a rough Phase-1 placeholder; DBNet
via cv2.dnn is the planned upgrade). ZERO new pip deps. Designed to run wherever the
pipeline runs -- the raiw.cc Modal GPU worker -- never on the 512 MB web host.
Real-ESRGAN-via-Spandrel upscaling (a new `esrgan` extra) and an adaptive
Laplacian-variance polish are deferred to later phases.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The captured sparkle alpha peaks ~0.51, but some real Gemini sparkles are
rendered more opaque. The fixed-alpha reverse blend then UNDER-subtracts and
leaves a bright residual the detector still fires on. A visible-removal audit
through the registry path on the spaces corpus showed this as a meaningful
fraction of marks -- all under-removals, not a background-brightness class
(failures and successes had the same input confidence and background luma; the
discriminator was the removal delta itself).
remove_watermark now estimates a per-image alpha gain (_estimate_alpha_gain:
effective sparkle opacity at the bright core vs the local background ring,
a_eff/a_cap, clamped [1.0, 1.94]) and scales the alpha to match before the
over-sub/blend branch. A 1.05 deadband keeps a sparkle that already matches the
capture byte-identical to the pre-fix output, so the fix is purely additive
(0 regressions on the audit set; failures dropped substantially). The over-sub
guard still runs on the scaled alpha as the safety net for an over-shoot.
- _estimate_alpha_gain + _ALPHA_GAIN_MAX/_DEADBAND/_CORE_FRAC in gemini_engine.
- TestUnderSubtractionGain asserts on footprint pixels, NOT the detector (its
NCC is degenerate on a flat synthetic bg; the real corpus removal drops the
detector ~0.80 -> ~0.27).
- scripts/visible_removal_audit.py: the detect -> remove -> re-detect audit tool
that found and validated this (operates on gitignored data/spaces only).
- CLAUDE.md + README: document the under-subtraction gain.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Two quality knobs for the SDXL invisible pass:
- min_resolution floor (default 1024, --min-resolution): small inputs are
upscaled to a 1024px long-side floor before diffusion, since SDXL img2img
distorts on a tiny latent (a 381x512 portrait wrecks at native). The output
is restored to the original input size, so it is a transparent quality boost;
it adds time/memory on small inputs. 0 disables. Extends the pure _target_size
helper (now cap-or-floor-or-native, min skipped on a min>max misconfig),
unit-tested without a model.
- unsharp post-filter (humanizer.unsharp_mask, --unsharp, opt-in default 0):
applied LAST, after the GFPGAN face pass (a pre-GFPGAN sharpen would be
smoothed back over), to counter the soft/over-smoothed look that diffusion +
restoration leave behind (an AI tell). Pairs with --humanize (grain).
Both threaded through invisible/all/batch + the module-level helper. Verified
end-to-end on a 381x512 portrait: upscaled to 1024, sharpened, restored to
381x512.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add an optional, commercial-safe face-restoration post-pass that recovers
face identity the diffusion removal pass drifts (canny holds structure, not
likeness) while still scrubbing the pixel watermark in the face regions.
- face_restore.py: GFPGANer singleton (CPU unless CUDA), the basicsr
torchvision.transforms.functional_tensor shim, and the pure feather
_composite_faces helper (unit-tested without the model). GFPGAN
re-synthesizes each face from a StyleGAN2 prior, so composited face pixels
are GAN-generated (no watermark, no pixel-copy) -- oracle-clean at weight 0.5
with identity preserved.
- InvisibleEngine.remove_watermark: restore_faces / restore_faces_weight,
best-effort, auto-skips when the extra is absent or no face is detected.
- CLI --restore-faces/--no-restore-faces + --restore-faces-weight on
invisible/all/batch (on by default).
- restore extra (gfpgan/facexlib/basicsr), numpy<2-pinned (scipy<1.18,
numba<0.60) and kept out of `all`; basicsr needs Python <3.13 + setuptools<69
to build, so pin .python-version 3.12.
Commercial-safe: GFPGAN Apache-2.0, RetinaFace MIT. The CodeFormer alternative
is non-commercial and is not shipped. The earlier IP-Adapter FaceID layer was
removed (footgun: needs high strength, corrupts faces at the low removal
strength).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add `--pipeline controlnet` (SDXL base + xinsir canny ControlNet via
StableDiffusionXLControlNetImg2ImgPipeline): the canny edge map conditions the
img2img regeneration so text and face STRUCTURE stay sharp, while the watermark
is still removed by the regeneration (`strength`) -- no original pixels are
copied or frozen, so SynthID does not survive. Oracle-verified clean on OpenAI
with better text/structure fidelity than plain img2img at equal strength.
`--controlnet-scale` tunes structure preservation; fp32 on mps/cpu (fp16-fixed
VAE on cuda/xpu). Shares the img2img runner (live progress + MPS->CPU fallback)
and the fp16-VAE-fix / device-move helpers with the default pipeline.
Remove the superseded subsystems -- ctrlregen (SD1.5 clean-noise),
text-protection (differential / region-hires) and face-protection: they either
destroyed real content or shielded the watermark by re-using original pixels.
controlnet replaces them by regenerating everything under edge conditioning.
Canny preserves face structure but not identity; face IDENTITY is a separate
face-restoration post-pass (CodeFormer/GFPGAN), researched + prototyped but not
yet shipped. An IP-Adapter FaceID attempt was built and removed (footgun: needs
high strength, corrupts faces at removal strength).
Docs: docs/controlnet-removal-pipeline-research.md, scripts/controlnet_sweep.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
detect_watermark's size-weighted global NCC search lets a larger, mediocre
match (e.g. a bright collar in a portrait) outrank a small, near-perfect
sparkle in the bottom-right corner, so a faint sparkle on a busy background
scored below threshold and the image read as clean -- the regression from
widening the search window 256px->512px between v0.7.2 and v0.8.8.
Add _corner_promote: a bottom-right-corner raw-NCC pass that overrides the
global pick when the corner holds a match with raw NCC >= 0.85 that beats it.
It only ever replaces a lower-fidelity pick (cannot weaken an existing
detection) and keeps the wider window for variant margins. The corner side is
relative-clamped (0.20 of the short side, [96, 384]) so it stays a true corner
at every scale: a fixed 256px covers ~70% of a small portrait, where a real
photo raw-matches the star at ~0.81; relative tightening drops that to ~0.69.
The 0.85 gate sits between the worst real-photo corner match (~0.78) and a
genuine faint sparkle (~0.93): zero false positives across native + downscaled
negatives, headshot rescued from below-threshold to 0.71.
Factor the shared multi-scale matchTemplate loop into _scan_scales.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The C2PA issuer attribution (`c2pa`) and the SynthID proxy (`synthid`) are
derived from the same manifest, so treating them as independent signals made
rule 1 fire on legitimate multi-actor manifests where a product wraps another
vendor's engine (Microsoft Designer on OpenAI, Microsoft on Google) or an edit
chain re-signs (Adobe over a Gemini original). 19 such files in the
2026-06-01/02 spaces batches read as "likely spoofed/laundered" before this.
Group `c2pa` + `synthid` into one provenance source via `_CLASH_SOURCE`; rule 1
now requires two vendors from different sources. A manifest vendor still clashes
with a genuinely independent stamp (EXIF/XMP generator, IPTC AISystemUsed, AIGC,
xAI).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
On a dark/textured background (e.g. grass) the captured alpha map over-estimates
the real Gemini sparkle's effective opacity (~0.51 captured vs ~0.31 effective),
so the fixed-alpha reverse blend over-subtracts (watermarked - alpha*logo goes
negative) and drives the footprint to black -- the white sparkle turns into a
black diamond (issue #30, reported by @CoolZimo1).
remove_watermark now detects this via _reverse_alpha_oversubtracts (fraction of
footprint pixels with a negative numerator > 5%) and inpaints the small sparkle
footprint from the surrounding pixels (cv2 NS, cropped to a padded box) instead.
Behavior-neutral on the working case: a bright background over-subtracts at ~0%,
so reverse-alpha is used and the output is byte-identical to before (verified:
demo_banana 0.0 frac vs the issue-#30 grass image 0.61 frac; issue-#30 footprint
recovers to background grass with no pit, residual sparkle conf 0.25 < 0.35).
Guard is scoped to GeminiEngine: doubao/jimeng already NCC-align their alpha to
the actual mark per image, which sidesteps the fixed-alpha mismatch.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The default img2img strength is now chosen from the detected SynthID vendor
(C2PA issuer) instead of a single fixed 0.30: OpenAI gpt-image -> 0.10, Google
Gemini -> 0.15, unknown source -> 0.15. Explicit --strength always wins.
Basis: an oracle-verified June 2026 controlled study (clean v0.8.6, text/face
protection OFF, per-image openai.com/verify or Gemini-app verdict). OpenAI's
SynthID clears at 0.05 across 1024-1600 px (n=4, resolution-independent);
Google's is ~3x more robust and needs 0.15 on the capped-1536 path (n=4). The
dominant factor is the VENDOR, not resolution. The earlier single 0.30 default
and the "resolution dependence" lore came from contaminated tests run with the
protect-text bug ON (issue #14) -- re-running those same 1600x1600 images clean
removes SynthID at 0.05.
`vendor_for_strength(path)` reads metadata.synthid_source on the ORIGINAL input
and is threaded through cli (invisible/all/batch) -> invisible_engine ->
watermark_remover -> resolve_strength(strength, profile, vendor), so display and
execution use the same vendor (the engine sees a temp path whose C2PA the visible
pass already stripped, so detection must happen in the CLI on the pristine
source). Caveat: Google's 0.15 was validated only on --max-resolution 1536;
native 2816 Gemini was not locally measurable (OOM on Apple Silicon) and is
pending GPU validation on raiw.cc.
Docs: docs/synthid.md sections 2.2/4.4/5.2 corrected (the contaminated
resolution-dependence findings replaced with the clean oracle-verified table);
README and CLAUDE.md updated; CLI --strength help reflects the adaptive default.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
identify previously ran only the Gemini sparkle as a visible detector, so a
Doubao/Jimeng image with stripped TC260 metadata had no visible fallback. Add
`_visible_text_marks` (registry-backed) so the ByteDance Doubao 豆包AI生成 and
Jimeng 即梦AI marks are detected too, each gated by its own engine NCC threshold
via MarkDetection.detected. New signals `visible_doubao` / `visible_jimeng`
(medium), same stripped-metadata fallback role as the sparkle; excluded from
integrity-clash vendor claims; set platform only when no harder signal did.
Also make `noai/__init__` lazy (PEP 562 __getattr__): importing the light
`noai.c2pa` / `noai.constants` submodules (which identify needs) no longer
eagerly pulls `watermark_remover`, which imports torch + diffusers at module
top. `import remove_ai_watermarks.identify` drops from ~420 MB to ~21 MB in a
full gpu/detect install (torch not loaded), so it fits a 512 MB host; the
removal API resolves lazily on first access. Guarded by TestIdentifyImportIsLight.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
An oracle-verified GPU strength study (Modal A100, native res, Gemini-app
'Verify with SynthID', n=3 fresh Gemini images, protect_text/faces off) found the
current Google SynthID survives strength 0.10/0.15/0.2 and is removed only at 0.3.
The previous 0.10 default (set from an n=1 result) no longer clears it -- Google
hardened SynthID and the threshold has climbed 0.05 -> 0.10 -> ~0.3. Bump
DEFAULT_STRENGTH to 0.30; OpenAI/ChatGPT carry C2PA not SynthID, so 0.10 is plenty
there (pass --strength 0.10). Note protect_text shields the text regions SynthID
hides in (use --no-protect-text for full removal on text-heavy images).
The same study found ctrlregen at clean-noise strength DESTROYS real images
(hallucinated micro-text in smooth regions), with no usable middle setting, so the
literature's 'clean-noise is the lever' did not hold empirically. Flag ctrlregen
EXPERIMENTAL in the CLI --pipeline help, README, and watermark_profiles; SDXL
img2img at ~0.3 stays the shippable path.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
cli.py now emits plain ASCII through a small click.echo shim
(_Console / _Table / _Progress) instead of rich: no colors, markup tags,
panels, progress bar, or Unicode glyphs (Warning: / -> / ... and dropped
checkmark/cross marks). identify and metadata tables render as indented
plain lines.
- drop rich from dependencies (pyproject.toml + uv.lock)
- __init__: set TRANSFORMERS_VERBOSITY=error (setdefault) plus a warnings
filter so the transformers Siglip2ImageProcessorFast deprecation no
longer prints at CLI startup (it fires from the eager noai import)
- TestGpuHintMarkup: the [gpu] hint is now printed verbatim; docstring updated
- CLAUDE.md: replace the obsolete rich-markup lesson, note the verbosity fix
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The ctrlregen profile inherited the SDXL img2img --strength default (0.10), a
near-identity pass that loaded ControlNet + DINOv2-giant and barely changed the
image -- a no-op for removal. resolve_strength() now resolves an unset strength
per profile: 0.10 for the SDXL default, CTRLREGEN_DEFAULT_STRENGTH (1.0,
clean-noise) for ctrlregen. It checks `is None` rather than falsiness, so an
explicit 0.0 is respected (the old `strength or DEFAULT` swallowed it).
Research basis: CtrlRegen (ICLR 2025, arXiv:2410.05470) removes robust
watermarks by regenerating from clean Gaussian noise; partial-noise img2img
retains watermark info that diffuses back, so a high (clean-noise) strength is
the lever, not a knob on the light SDXL pass. CLI wiring (--strength default
None) lands with the cli refactor.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Visible-watermark work across all three corner-mark engines plus a committed,
reproducible alpha-build pipeline (scripts/visible_alpha_solve.py) fed by committed
solid black/gray/white captures.
- jimeng: new "即梦AI" wordmark remover (reverse-alpha + thin residual inpaint,
always NCC-aligned -- the mark re-rasterizes/jitters per image). Detect via glyph
silhouette NCC (0.45 threshold; does not cross-fire with Doubao). Registered in the
visible-mark catalog; `visible --mark jimeng` / `--mark auto`.
- doubao: fix a real production defect -- the shipped remover left a READABLE
"豆包AI生成" outline on real samples while detect() returned conf 0.0 (fooled by a
thin outline), so the test passed and the "56/56 clean" claim was detector-measured,
not visual. Root cause: under-estimated alpha + fixed-geometry-no-inpaint + tight
locate box. Rebuilt alpha (careful gray-self solve), always-align, thin inpaint,
widened locate box -> readable outline becomes faint texture-level traces.
- gemini: rebuild gemini_bg_{96,48} from our own controlled captures (validated NCC
0.9998 vs the prior third-party asset); removal re-verified clean, no behaviour change.
- tests: add textured-shift regression to both engines (guards the align-on-shift path
the Doubao defect exposed; lesson: a detector-only removal test is insufficient,
assert visual residual).
- docs: CLAUDE.md, README, capture READMEs and docstrings synced; stale
"exact/pixel-exact/56-clean" claims removed.
Also includes a SynthID label-wording clarification in identify.py/cli.py
("SynthID pixel watermark" -> "SynthID watermark, inferred from C2PA metadata").
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Apply fixes from a full-repo review (code, tests, docs).
Security / correctness:
- Clamp attacker-controlled PNG/caBX chunk lengths to the remaining file
size in metadata.py and noai/c2pa.py (a malformed length no longer drives
a multi-GB read); skipped chunks seek instead of read.
- noai/isobmff.strip_c2pa_boxes is now fail-safe on a malformed box: return
the original bytes with a warning instead of silently truncating the tail,
so metadata --remove can no longer emit a corrupt file.
- doubao_engine._fixed_alpha_map clamps the glyph box to the image (no crash
on degenerate width-vs-height).
- watermark_remover._run_region_hires gates the phaseCorrelate offset on
response and magnitude (a spurious shift no longer garbles text) and drops
the generator after a CPU fallback (no MPS/CPU device mismatch).
Robustness:
- gemini_engine, doubao_engine, region_eraser normalize grayscale and RGBA
inputs to BGR at the engine entry points.
- image_io.imwrite returns False on an unwritable path (matches cv2).
- invisible_engine guards a None imread result before use.
- trustmark_detector._decoder uses a double-checked threading lock.
- ctrlregen.tiling.tile_positions raises on overlap >= tile.
- humanizer chromatic shift no longer wraps opposite-edge pixels.
- identify OpenAI caveat keyed on the normalized vendor, not a substring.
- Remove the dead "visible --detect-threshold" CLI option.
- publish.yml verifies the release tag matches the package version.
Docs:
- README strength 0.05 to 0.10; .env.example HF_TOKEN marked optional;
doubao_capture README updated to reverse-alpha-only; CLAUDE.md synced with
the new behaviors and the batch command.
Tests: new test_security_clamp.py for the read clamp and isobmff fail-safe;
erase CLI coverage; integrity-clash rule 2 end-to-end; multi-tag EXIF
survival and cross-format strip guards; channel/size, tiling, humanizer, and
imwrite regressions. Full suite 493 passed, 2 skipped; ruff and pyright src/
clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The stock SDXL VAE overflows to NaN in fp16, so the plain img2img path decodes
to an all-black image on a CUDA/XPU fp16 backend. This is the raiw.cc black
result HitaoLin reported (a 1086x1448 input came back uniformly black). cpu/mps
run fp32 and never hit it, and the differential / region-hires pipeline already
upcasts the VAE itself, so only the plain path on a fp16 GPU was exposed.
`_load_pipeline` now loads `madebyollin/sdxl-vae-fp16-fix` for the default SDXL
checkpoint when running fp16, gated by the pure helper `_needs_fp16_vae_fix`. A
custom non-SDXL model keeps its own VAE.
The decision logic is unit-tested without a download (TestFp16VaeFix). The
black->clean recovery itself needs a CUDA GPU and was not verifiable on this MPS
machine; it must be confirmed on the backend.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Raise default SynthID-removal strength 0.05 -> 0.10 (current Google SynthID)
The old default (0.04/0.05) no longer removes the CURRENT Google SynthID (Nano
Banana / Gemini 3): verified 2026-05-30 via the Gemini 'Verify with SynthID'
oracle on a real image -- 0.05 still detected, 0.10 not detected (OpenAI's was
already cleared at 0.05). Add DEFAULT_STRENGTH=0.10 in watermark_profiles, route
the engine + CLI defaults to it. At 0.10 small text deforms more, which is why
text protection (_run_region_hires) runs by default. CLAUDE.md SynthID note
corrected. CAVEAT: n=1 Google + n=1 OpenAI; broad corpus oracle validation
pending (task tracked).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Drop unused LOW/MEDIUM/HIGH strength profiles; CLI --strength defaults to DEFAULT_STRENGTH
The fixed strength presets (and get_recommended_strength) were dead -- nothing in
the pipeline used them, only tests. One knob now: DEFAULT_STRENGTH (0.10),
overridable per-call via the CLI --strength flag, which now defaults to that
constant (single source of truth). Removed the WatermarkRemover.LOW/MEDIUM/HIGH
class attrs and the get_recommended_strength tests.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Replace the default text-protection path. Differential Diffusion froze text in
latent space, which left SynthID intact inside text (violating remove-everywhere)
and still softened sub-8px strokes (VAE latent limit). _run_region_hires instead
scrubs the whole image, then re-scrubs each detected text block at high resolution
and feather-composites it back: every pixel is regenerated (watermark removed
everywhere) while small text stays crisp (high-res strokes span >1 latent cell).
merge_text_regions + feather_paste are pure and unit-tested; each re-scrubbed
patch is phase-correlated back to the original crop to null the ~1-2px round-trip
offset. Synthetic 18px multilingual text: text-region SSIM 0.28 -> 0.48, visually
garbled -> readable across Latin/Cyrillic/CJK. Legacy _run_differential /
build_change_map remain but are no longer the default. Prod use still requires
confirming via the SynthID oracle that re-scrubbed text zones read watermark-free.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
On RGBA inputs the CLI forced the watermark bbox alpha to 0 on save, so the
removed-sparkle area became a transparent hole that renders as a solid white
box on any non-transparent viewer. The Gemini app exports opaque RGBA, so
every user hit it. Reverse-alpha already recovers the real pixels there (and
`erase` inpaints them), so there is no artifact to hide -- the hole was the
bug, introduced as an over-correction in d091b9f.
`_write_bgr_with_alpha` now rejoins the input alpha plane unchanged (drops the
`clear_region`/`pad` params); the `visible` / `erase` / `all` / `batch` call
sites drop the cleared-region argument and the orphaned region bookkeeping.
The registry `remove()` still returns the mark bbox (used for inpaint_residual
positioning); the CLI just no longer clears alpha with it.
Inverts the test that locked in the old behavior into a #30 regression guard
(watermark-region alpha stays opaque, no pixel forced transparent). Verified
end-to-end on a real Gemini RGBA export: sparkle gone, zero transparent
pixels, clean over a white background.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A corpus audit surfaced China TC260 AIGC-labeled images that `identify`
missed. Three detection gaps in `aigc_label`, all fixed:
- raw-JSON `{"AIGC":{...}}` in JPEG EXIF (UserComment): brace-matched from
the scan head with `json.raw_decode`, gated on a TC260 field like the
PNG-chunk path. (Doubao-class output via that export surface.)
- XMP attribute form `TC260:AIGC="{...}"` (PicWish): folded into the
element regex as a second alternation.
- TC260 XMP packet appended after a large `IDAT`, past the 1 MB scan
window: `scan_head` now appends late PNG metadata chunks via
`_png_late_metadata`, mirroring the existing ISOBMFF late-box scan.
Adds `scripts/corpus_gap_scan.py`: runs `identify` over a corpus, writes
the per-file report CSV, and flags `unknown` files that carry a known
marker in their metadata region (the audit that found these gaps).
Scanning only the metadata region — not the whole file — avoids the
random short-token collisions inside compressed PNG/JPEG streams.
On the local corpus this lifts 3 files from `unknown` to AI (China AIGC)
and leaves zero false gap candidates. Synthetic piexif/PngInfo fixtures
cover all three forms.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Victor Kuznetsov