Document both fixes in CLAUDE.md (the metadata.py and watermark_remover.py
bullets). Also add a --backend flag to the visible-removal audit script so a
realistic quality pass can run the production MI-GAN fill instead of cv2
(removal SUCCESS is backend-independent, but only migan/lama reflect the
recovered-region quality a user actually gets).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Full-corpus strip audit surfaced 24 china_aigc survivors on real uploads:
- 19 JPEG carried the bare AIGC{...} blob in APP11 (0xEB). That marker's branch
in _jpeg_app_carries_ai only tested for a C2PA/JUMBF manifest and RETURNED, so
a bare AIGC there slipped past the generic AIGC check. The specific
C2PA(APP11)/XMP(APP1)/IPTC(APP13) checks now fall through to the generic
_is_aigc_exif_value drop, which runs for every APP marker they did not claim.
- PNG carried the {"AIGC":{...}} block in a STANDARD text chunk (Description).
_is_ai_key keeps that key, so removal now also drops a text value carrying an
AIGC block (_is_aigc_exif_value broadened to accept str; wired into the PNG
re-save value filter), in parity with aigc_label's detection.
Verified on the corpus: decodable china_aigc survivors 27 -> 0; the 3 remaining
are truncated files the strip fail-safe (v0.15.1) copies through by design.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
PIL raises OSError decoding a truncated file, which crashed remove_ai_metadata
(the PNG/WebP PIL re-save path) -- a direct library caller like a web worker
500s on a partial upload. ~0.2% of the real upload corpus is truncated. The
strip now probes decodability first and, on failure, copies the input through
unchanged and returns rather than raising (we cannot strip what we cannot parse),
mirroring strip_c2pa_boxes' fail-safe. identify already handled these.
The CLI `metadata --remove` on an unreadable file therefore now exits 0 with the
input passed through, not a clean error (exit 1) -- `visible`, which must decode
to remove a mark, still exits 1. Test updated to the per-command contract.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Bug fixes (each with a regression test):
- metadata strip parity across every marker placement: IPTC digitalSourceType
in XMP, the Samsung post-EOI trailer, the China TC260 AIGC block in EXIF
UserComment, a bare AIGC block in a non-standard APP segment, and the ISOBMFF
EXIF path (AIGC + xAI) are all now stripped -- anything a scanner flags, the
strip reaches
- Samsung genAIType detected when its trailer sits past the 512 KB scan window
(file-tail read on large photos)
- crashes on edge inputs: Gemini detector on images with a short side < 16px,
footprint_mask on a zero-size ndarray, the humanizer on chromatic_shift >=
width, and the CLI on unreadable/corrupt/empty input (clean error, not a
traceback)
- WebP written losslessly (cv2 quality 101), not lossy at 100
- the IPTC digitalSourceType algorithmicMedia (procedural, not trained on
sampled data) is no longer flagged as AI-generated, so clean procedural
content is not scrubbed
- c2pa source-type: compositeWithTrainedAlgorithmicMedia is checked before the
bare algorithmicMedia token, so an AI-enhanced composite is not misclassified
Detection:
- integrity-clash coverage now normalizes ByteDance / Canva / ElevenLabs /
Black Forest Labs, so a transplanted manifest next to an independent
conflicting stamp is caught; the generic China TC260 AIGC label is attributed
to a co-present TC260 vendor, so a legit Doubao image (its own C2PA + TC260
label) does not clash (corpus-validated: 0 new clashes on 5000 carriers)
CLI:
- batch exits non-zero (with a warning) when any image errors or a GPU-missing
SynthID scrub is skipped, and copies the input through so the output dir stays
complete -- it used to always exit 0 and could silently drop files
Perf:
- GeminiEngine reused as a process-wide singleton with a precomputed template
ladder: -24% on the identify sparkle path, detection byte-identical
Internal: one shared _ai_exif_targets rule set feeds both EXIF scrubbers so
their coverage cannot drift; docs synced; maintain.sh hardened so the uv-secure
internal teardown crash no longer aborts the gate (still fails on a real finding).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
erase_migan fed the whole frame to the ONNX model, so peak RSS scaled with the
upload (~0.6 GB at 4 MP up to ~2.4 GB at 25 MP). Mirror erase_lama: crop a padded
region around the mask (pad = max(256, 2*bbox)), feed only that crop (at native
resolution -- MI-GAN accepts arbitrary dims, unlike LaMa's fixed 512 square), and
paste only masked pixels back. Peak RSS is now bounded by the mark size
(~0.6-0.9 GB), so a memory-tight host (a 1-2 GB web worker) can run MI-GAN on a
25 MP upload.
Fill quality is unchanged: verified by eye on real Gemini/Doubao marks plus a
ground-truth reconstruction sweep -- a tighter view if anything reduces the GAN's
hallucination of large background structure.
Extract the shared padded-crop-box math into _padded_crop_box (used by both
erase_lama and erase_migan).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Two AI-provenance metadata types mined from the retained corpus that
identify previously read as no-signal:
- Dreamina (ByteDance's international Jimeng brand) signs C2PA as
"Bytedance Pte. Ltd." with a "Dreamina/x.y" claim generator and NO
digitalSourceType, so the generator name is the only AI signal. Add a
C2paAiVendor row with a new asserts_ai flag (identity-AI: presence
asserts AI without trainedAlgorithmicMedia) plus the derived
C2PA_IDENTITY_AI_ORGS view, folded into identify's c2pa_is_ai. Keyed on
the Dreamina generator token, not the "Bytedance Pte" issuer, so non-AI
CapCut edits signed by the same entity stay unattributed. 7/7 corpus
files now attribute to ByteDance.
- Tencent Cloud's TC260 AIGC variant uses a ServiceProvider/ServiceUser
schema (vs the producer-side ContentProducer schema), embedded in EXIF
ImageDescription; add those field names to _TC260_FIELDS so the generic
{"AIGC":{...}} gate accepts it. 11/11 corpus files now flagged.
Test-first: reproducing tests in test_identify.py / test_metadata.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The FP gate demotes a low-gradient match, but a real FAINT sparkle also has soft
edges, so metadata-stripped faint sparkles were dropped. Keep a low-grad match
that is a strong (conf >= 0.52), bright, near-WHITE-core sparkle: a real sparkle
core is white, a clean bright corner that shape-matches (sky/sun) is colored
(_core_saturation). Recovers ~14/20 stripped faint sparkles under the DEFAULT
strict/auto (no metadata, no flag) at ~1.25% clean false-fire (baseline 0.55%);
the ~0.51-scoring bright-background FPs stay demoted (below 0.52).
A learned classifier on the same features measured WORSE than the tuned gate
(tier-1: MLP 86.7% recall vs the gate's 90.8% at equal false-fire), so the
heuristic stays; a patch-CNN with richer features is roadmapped P2 with low
expected value -- the precision/recall wall is fundamental (deep-research +
tier-1 both confirm it).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Full-dataset validation of reverse-alpha (v0.12.1) vs the current localize->fill:
doubao/jimeng identical (100% coverage + clearance across all backends); gemini
strict coverage a few points below reverse-alpha (the FP tightening), every
missed mark recovered under assume_ai, clearance ~98% both, no outside-box
damage. Clearance is fill-independent (cv2/MI-GAN/LaMa all strip the mark shape);
the difference is visual fill quality on textured/structured backgrounds -- LaMa
best, MI-GAN can ghost/hallucinate, cv2 smears -- which motivates auto = LaMa >
MI-GAN > cv2. Added to module-internals, known-limitations, and the CLAUDE.md
compact list.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The auto backend now resolves best-first: LaMa (highest quality, recovers the
textured/structured backgrounds the classical fill smears) > MI-GAN > cv2. Both
learned backends share the same onnxruntime availability check, so auto cannot
tell them apart and always prefers the better one; a memory-tight deployment
that cannot afford LaMa's ~4.7 GB peak pins MI-GAN explicitly via
`--backend migan` / `backend="migan"` (the deployment's call, not the library's).
cv2 stays the no-deps floor and now emits a one-time quality warning when auto
falls back to it, since it smears texture/structure.
Motivated by a v0.12.1 reverse-alpha vs 0.14 localize->fill head-to-head:
reverse-alpha recovered structured backgrounds more cleanly than any inpaint;
LaMa closes most of that gap, MI-GAN can ghost/hallucinate, cv2 is weakest.
doubao/jimeng removal is identical between versions; gemini strict coverage is
4pp lower (all recovered via assume_ai) with cleaner clearance and no
outside-box damage.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Candidate carries only the fields the arbiter reads (key, label,
detected_strict, detected_relaxed, features); location/region/confidence were
vestigial from the removed best_auto_mark max-by-confidence path.
- resolve_backend returns preferred_inpaint_backend() directly (typed Literal)
instead of an identity ternary.
- colour/normalise/behaviour -> US spelling across code comments and docs.
No behavior change.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Resolve 10 code-review findings on the v0.14.0 localize->fill path, several
release-blocking:
- gemini: build the removal mask from the decision's provenance-aware region
instead of a strict internal re-detect. A relaxed/assume_ai sparkle was
re-demoted by the FP gate into a None mask and reported removed while left in
the image; this also drops the redundant double-detect.
- registry: report a mark removed only when a fill actually happened (remove()
returns a None region for an empty mask), so a no-op is never claimed.
- api/cli: add write_noop so the CLI `visible` no-mark path writes nothing and
cannot clobber a pre-existing -o file (was write-then-unlink -> data loss);
create output.parent; skip the same-file copy (SameFileError on in-place).
- cli: catch the missing migan/lama backend RuntimeError on the visible/all
paths (matches `erase`); route the single-mark relaxation through the shared
resolve_relax instead of an inline copy.
- metadata: keep_standard=False no longer takes the AI-only lossless JPEG
short-circuit (it left standard metadata); defer a malformed-marker JPEG to
the PIL fallback instead of reporting a partial strip as complete.
- invisible: register the HEIF opener before Image.open (HEIC --force) and
RGB-convert before the PNG temp (CMYK JPEG).
- pill: normalize via to_bgr so a 4-channel BGRA array cannot crash cvtColor.
Regression tests for each; docs synced (resolve_relax, write_noop,
best_auto_mark -> detect_marks).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Replace reverse-alpha removal with localize -> fill (template-free mask + one
shared cv2/MI-GAN/big-LaMa fill) for every mark; drops the colour-shift / dark-pit
failure modes, version-robust to a moved or re-rendered mark
- Separate perception/decision/action: engines report Candidates, a pure
decide(candidates, Context) arbiter owns all policy (sensitivity + provenance +
pill gate), remove_auto_marks orchestrates -- behavior-preserving (corpus 46/46/92)
- Three orthogonal knobs replace --method: --backend cv2|migan|lama,
--sensitivity auto|strict|assume-ai, provenance (auto from metadata)
- Add high-level api.remove_visible / visible_provenance (lazy top-level re-export);
visible --mark auto delegates to it so CLI and library share ONE path
- Read+write HEIC/AVIF on the pixel path via pillow-heif; imwrite preserves the input
format at max quality (JPEG q100/4:4:4); a no-op copies the original bytes verbatim
- Lossless byte-level JPEG metadata strip (no DCT re-encode); consolidate the two
remove_ai_metadata into one, delete legacy noai/cleaner + best_auto_mark
- Bump 0.13.0 -> 0.14.0
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Verified 0.13.0 pill removal on a 32k real-upload corpus. The metadata-OR-wordmark
gate was only ~1/3 precise: TC260 metadata confirms Jimeng-class provenance, not pill
presence, so the weak edge-NCC detector's false fires (textured ceilings/walls, where
inpaint visibly smears) were admitted whenever metadata was present.
Split into two arms (_keep_pill): the reliable bottom-right wordmark (~94% precise,
survives metadata stripping) removes the pill unrestricted; the metadata-only arm
removes it ONLY when the top-left footprint is flat enough for an invisible inpaint
(PillEngine.footprint_is_flat, median-Sobel <= _FLAT_TEXTURE_MAX). Keeps real
flat-scene pills and harmless flat false fires; leaves the damaging textured false
fires untouched. Corpus: 270 -> 118 removals, ~90 true preserved, damaging FP -> ~0.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Add the Jimeng-basic top-left "AI生成" pill as a CAPTURE-LESS mark
(pill_engine.py): synthetic-silhouette edge-NCC detect + inpaint-only removal.
Gated in remove_auto_marks: kept only when Jimeng is confirmed (TC260 metadata
OR the bottom-right "★ 即梦AI" wordmark fired -- the wordmark keeps recall on
metadata-STRIPPED uploads) AND Doubao did not fire.
- Add an inpaint-fallback removal path + MI-GAN ONNX backend (migan extra, MIT,
~28 MB / ~1 GB peak -- droplet-friendly) alongside big-LaMa. New
--method auto|reverse-alpha|inpaint (shared across visible/all/batch) and
erase --backend migan; footprint_mask on each engine.
- auto is deterministic: reverse-alpha for capture marks (recovers exact pixels,
lighter -- measured cleaner than MI-GAN on structured backgrounds) and inpaint
only for the capture-less pill.
- --mark auto now removes EVERY detected mark in one pass (remove_auto_marks),
so a Jimeng-basic image's top-left pill AND bottom-right wordmark both clear.
- Bump 0.12.1 -> 0.13.0.
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Per user decision 2026-06-22: synthetic font-rendered alpha reconstruction is
rejected as below the quality bar; the reverse-alpha alpha map must be solved
from real controlled flat captures (visible_alpha_solve.py). Meta AI, more
Samsung locales, and any Grok visible mark are parked until captures exist.
Future sessions must not propose synthetic or derive assets from the corpus.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Mined from the retained corpus 2026-06-22 (open-world EXIF/PNG-text/XMP scan,
minus the registry): three AI image generators that stamp a plain generator
name and no C2PA, so identify read them as no-signal -- and under the P0#5
no-signal skip would have skipped the scrub.
- NovelAI (anime SD): PNG tEXt Software/Source/Title. exif_generator now reads
PNG text chunks (via img.info), not only EXIF/XMP.
- Reve (reve.com): EXIF Software / XMP CreatorTool. Token is the full
"reve.com", not bare "reve" (would false-fire on "forever"/"reverie").
- Aphrodite AI: EXIF Make / Software.
Detection/removal parity: NovelAI stamps an AI-shaped VALUE under a non-AI KEY
(Title/Source), which _is_ai_key alone keeps. New _is_ai_value drops a text
chunk by value-token match on removal, mirroring exif_generator -- else the
cleaned file still read as NovelAI (verified on a real corpus file).
Tests: TestExifGenerator gains NovelAI PNG-text, Reve, Reve-not-overmatched,
Aphrodite, and a NovelAI detect/remove parity regression. Docs synced
(module-internals, watermarking-landscape, CLAUDE.md).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Regenerating pixels removes SynthID / open watermarks but degrades a real
photo, so running it on a clean image is the dominant paid score-0 cause on
no-watermark uploads. Gate invisible/all/batch on identify.has_invisible_target:
when no invisible AI signal is locally detectable and --force is unset, skip the
regeneration. Per-command semantics:
- invisible: write no output, exit EXIT_NO_INVISIBLE_SIGNAL (2)
- all: skip step 2 but keep visible-removed pixels + strip metadata, exit 0
- batch: skip the scrub; copy the input through in invisible mode
A skip never claims the image is clean (a pixel SynthID is undetectable once its
metadata proxy is gone); the message says so and routes to --force. The gate
fails safe (a detector error runs the removal).
has_invisible_target wraps identify(check_visible=False, check_invisible=True)
and returns the new ProvenanceReport.ai_from_metadata field (the confidence==high
union), so the raiw.cc worker can reuse the same gate. Gate placed before engine
construction so the skip path is cheap; shared via cli._should_skip_invisible_scrub.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- watermark_remover: _build_qwen_kwargs now passes explicit height/width (via
_qwen_target_size, floored to /16). Without it QwenImageImg2ImgPipeline defaults to
1024x1024 and silently squishes non-square inputs, distorting the scene and garbling text.
- watermark_profiles: resolve_strength gains a `pipeline` arg + a Qwen strength ladder
(_QWEN_VENDOR_STRENGTH, Gemini 0.25), so `--pipeline qwen` gets its certified floor
automatically; retires the manual "pass --strength 0.25 for Gemini on qwen" workaround.
- fidelity_metrics: replace per-face nearest matching (collided on multi-face images when a
variant dropped a face, corrupting the identity metric) with a collision-free one-to-one
assignment (assign_faces_one_to_one). lapvar/LPIPS were always bbox-anchored and immune.
Regression-guarded by tests/test_fidelity_matching.py.
- docs: record the measured outcomes of the qwen-improvement arc. The Qwen ControlNet
face-fix is CLOSED (no permissive Qwen detail/tile ControlNet exists; canny carries edges,
not skin grain). The `--pipeline auto` router + faces+text mixed dual-pass were prototyped
and DROPPED (controlnet wins faces AND display text: abba CER 0.114 vs qwen 0.379).
Z-Image-Turbo was tried and dropped (same regeneration limits). qwen stays a manual opt-in;
controlnet is the default for everything.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Update CLAUDE.md and docs/module-internals.md for: ProvenanceReport.ai_source_kind
(generated vs enhanced) and the shared GEMINI_SPARKLE_TRUST_CONF; the text-mark
over-subtraction guard; noai/tiling.feather_region_composite + the region-targeted
WatermarkRemover.remove_watermark(region=) path; the new C2PA vendor rows (Volcano
Engine Chinese legal name, ElevenLabs) and the documented TikTok/PixelBin
exclusion. Record the rejected gemini-gate-lowering experiment.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Oracle seed-repeat + floor refinement (2026-06-20, data/qwen_in):
- OpenAI floor 0.10 is SEED-ROBUST: 0.05 and 0.075 still detected; 0.10 clean on
seeds 0-4 (5/5) -> a random seed is safe.
- Gemini floor lowered 0.30 -> 0.25 (0.20 still detected, 0.25 clean on both
images). Single-seed (seed 0): the Gemini oracle rate-limits volume seed-repeat,
so pin a seed in prod rather than relying on seed-robustness there.
Re-measured fidelity at the certified floors (controlnet 0.15 vs Qwen 0.25 for
Gemini): faces still favor controlnet (ArcFace 0.546 vs 0.382, lapvar 0.62 vs
0.40); the short-CJK text case is now a TIE (gemini_1 0.037 vs 0.037 -- the earlier
Qwen 0.000 was at 0.30, not the floor). Qwen's text win holds on substantial
Latin/mixed text (OpenAI 0.385 vs 0.241 / 0.341 vs 0.290). Update watermark_profiles
comment, CLAUDE.md, module-internals, known-limitations.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The face fidelity numbers cited an equal-strength compare (both 0.15), but Qwen at
0.15 does NOT clear Gemini SynthID -- so that output is un-scrubbed and the compare
is invalid. Per the methodology rule (compare fidelity only between outputs where
SynthID is removed in BOTH), restate faces at each pipeline's scrub floor
(controlnet 0.15 / Qwen 0.30): ArcFace identity 0.546 vs 0.331, lapvar 0.62 vs 0.40,
face LPIPS 0.09 vs 0.19 -- controlnet still wins faces, conclusion unchanged. Drop
the "equal strength" framing in CLAUDE.md / module-internals / known-limitations.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- data/qwen_in/: a stable, committed set of 4 AI-generated images (OpenAI +
Google, carrying SynthID/C2PA -- same class as data/samples fixtures) used to
compare the controlnet/sdxl/qwen pipelines for fidelity. Two text-multi-script
(incl. RU/CJK), one EN poster, one face grid. README documents the set + the
ground-truth workflow. data/ is sdist-excluded so the wheel is unaffected.
- scripts/fidelity_metrics.py: switch text OCR from EasyOCR to PaddleOCR
(PP-OCRv6, higher accuracy esp. CJK, single multilingual stack); split into
`ocr` (seed a {basename: text} ground truth) and `compare` (--ground-truth for
a clean CER vs the hand-verified reference instead of noisy OCR-vs-OCR). Spatial
IoU-NMS keeps the best-scoring read per line so wrong-script models don't inject
garbage over Cyrillic/CJK.
- Oracle methodology: validate the OpenAI arm FIRST (openai.com/verify is more
accessible and the strongest Playwright/Chrome-MCP automation candidate; the
Gemini app is more manual). Recorded in CLAUDE.md + docs/synthid.md.
Ground-truth JSON (data/qwen_in/ground_truth.json) lands in a follow-up once
hand-verified.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add scripts/fidelity_metrics.py: an objective eval harness comparing
watermark-removal outputs against the original (reference) across four groups
-- OCR character error rate (EasyOCR), ArcFace identity cosine (insightface),
face texture (LPIPS + Laplacian-variance ratio), and whole-image LPIPS/SSIM/
PSNR. PEP 723 inline deps so it stays out of the package / uv.lock; metrics
self-gate (faces only where faces, text only where text).
The metrics overturned an eyeball conclusion: at EQUAL strength Qwen beats
controlnet on TEXT (OpenAI typography 0.10: OCR CER 0.25 vs 0.37) but controlnet
beats Qwen on FACES (gemini_3, 18 faces, 0.15 each: Laplacian-variance retention
0.62 vs 0.41, face LPIPS 0.09 vs 0.13 -- Qwen smooths faces MORE; ArcFace
identity ~tied). So Qwen is the better TEXT-preserving remover, not a universal
fidelity win. Correct the earlier "qwen keeps faces faithful where controlnet
plasticizes" claim in CLAUDE.md, module-internals.md, known-limitations.md, README.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A third diffusion pipeline alongside sdxl/controlnet: Qwen-Image (20B MMDiT,
Apache-2.0 code AND weights) img2img. The scrub still comes from the img2img
strength; Qwen preserves text (incl. CJK) and structure markedly better than
SDXL at the scrub floor, so it over-regenerates real photos far less (directly
targets the controlnet over-regeneration that degrades real uploads).
- watermark_profiles: QWEN_MODEL_ID, normalize_profile accepts "qwen".
- WatermarkRemover: _load_qwen_pipeline (bf16, loads Qwen base unless --model
overridden, clear ImportError if diffusers lacks the class), _run_qwen (no
MPS fallback -- 20B is CUDA/cloud-class), dispatch in _generate_one/preload,
pure _build_qwen_kwargs (true_cfg_scale, not guidance_scale).
- Shared _base_load_kwargs() across all three loaders (dtype + token).
- CLI --pipeline gains "qwen"; invisible_engine threads it through.
- scripts/qwen_scrub_prototype.py: standalone PEP 723 GPU experiment.
Prototype oracle floors (Modal A100-80GB, single seed, controls SynthID-positive,
PENDING seed-repeat cert): OpenAI clears at strength ~0.10, Gemini at ~0.30 (0.20
still detected), with CJK text + faces faithful where controlnet plasticizes. The
Gemini floor is higher than the shared default ladder, so pass an explicit
--strength for Gemini on this pipeline until a Qwen-specific ladder is certified.
The model-running path is CUDA-only (untestable locally); unit tests cover the
pure call-shape (_build_qwen_kwargs) and profile normalization without torch.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add a lossless alternative to the --max-resolution downscale for large
images that OOM on MPS/GPU: regenerate in overlapping, feather-blended
tiles at native resolution.
- noai/tiling.py: pure plan_tiles (uniform tiles, last flush to edge) +
feather_weights (strictly-positive separable taper -> partition-of-unity
blend) + run_tiled (per-tile generate callable, decoupled from the
pipeline). Unit-tested without the model.
- WatermarkRemover.remove_watermark: refactor _generate into _generate_one
+ a tiled branch that engages only when --tile is set and the long side
exceeds tile_size (ControlNet canny is rebuilt per tile).
- Thread tile/tile_size/tile_overlap through InvisibleEngine and the
invisible/all/batch CLI commands via a shared _tile_options decorator.
Verified end-to-end on the real SDXL pipeline (forced 2x2 tiling on a
1024px sample, MPS): non-degenerate output, no gross seam at tile borders.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Closes a documented coverage gap (P2#9): an AI Software/Make/Artist/ImageDescription
token in an EXIF item (its TIFF bytes live in mdat/idat) survived remove_ai_metadata
because the top-level box stripper and (absent pillow-heif) the PIL EXIF reader can't
reach it. New isobmff.blank_ai_exif_tokens finds EXIF TIFF blocks by their II/MM
byte-order header, validates each with piexif (a coincidental II/MM run in pixels
won't parse as a TIFF IFD, so it's ignored), and overwrites any AI_GENERATOR_TOKENS-
bearing value with same-length spaces -- so box sizes and iloc offsets stay valid and
the coded image is untouched (mirrors blank_ai_xmp_packets; no iinf/iloc surgery, no
exiftool dep). Camera/editor EXIF without an AI token is preserved. Wired into
remove_ai_metadata's ISOBMFF path. Covers the realistic AI-generator-token case; xAI-
signature-in-meta-box-EXIF (Grok is JPEG-only) stays out.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Final characterization after a positive-control sweep. The imwatermark dwtDct
round-trip fails (28-39/48, below the 44 gate) not on "high texture" as a prior
note claimed, but on a broad carrier class: the FLUX fox, doubao, a minimalist-FLAT
FLUX generation, AND a clean synthetic bright-flat fill with NO watermark all fail
identically. The degenerate all-ones decode is therefore a CARRIER ARTIFACT, not a
watermark (the no-watermark synthetic image reproduces it; a double-embed test shows
no interference). detect_invisible_watermark is positive-only: trust a hit, treat a
None as inconclusive unless a same-carrier positive control first recovers >=44.
Consequence: whether BFL hosted FLUX embeds the open DWT-DCT is unresolvable with
this detector on the available carriers (textured AND flat FLUX both fail the
control). C2PA stays the reliable FLUX signal. Low priority to chase further.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Earlier notes asserted BFL hosted output has no open DWT-DCT watermark. That was
overstated: the test carriers were high-texture fox images where a clean
encode->decode round-trip of a KNOWN-embedded watermark recovers only 28-35/48
bits (below the safe 44 gate), so the detector would miss a present mark there --
the None is inconclusive, not proof of absence.
Verified positive-control (2026-06-19): imwatermark dwtDct round-trips 48/48 on
synthetic carriers and on chatgpt-1.png (48/48) / firefly-1.png (45/48), but
FAILS on flux-1.png (28/48) and doubao-1.png (39/48). So invisible_watermark
detection is a positive-only signal: trust a hit, treat a miss on busy content as
inconclusive. Affects all open SD/SDXL/FLUX DWT-DCT detection. C2PA stays the
reliable FLUX identifier; whether BFL hosted embeds the open mark is unresolved
(needs a low-texture hosted sample).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
flux-1.png / flux-1.jpg are real Black Forest Labs FLUX.2 [pro] Playground
outputs (signed C2PA, issuer "Black Forest Labs" + trainedAlgorithmicMedia,
manifests verified to contain no personal data). flux-1.jpg is the first
committed JPEG-with-C2PA fixture, exercising the c2pa-python non-PNG reader path
end to end. Regression tests assert both attribute to "Black Forest Labs (FLUX)".
Also documents the verified finding (n=2, 2026-06-19): BFL's hosted output carries
the signed C2PA manifest but NOT the open invisible-watermark DWT-DCT (decodes to
degenerate all-ones, chance-level vs the FLUX reference) -- the open pixel mark is
dev-inference-code-optional only. So a hosted FLUX.2 image is identified by C2PA
alone, with no open-pixel fallback once C2PA is stripped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
extract_c2pa_info now uses the c2pa-python Reader first (any container, whole
manifest store incl. ingredient manifests), falling back to the hand-rolled caBX
parser for blobs the validator rejects (synthetic/partial, broken wheel). The
issuer/source-type/SynthID/soft-binding registry scan is shared by both paths
(_populate_registry_fields), so the return-dict contract is unchanged. Also
replaces the dead `from c2pa import has_c2pa_metadata` import in metadata.py with
a real Reader presence check. c2pa-python added as a core dep (MIT/Apache, ~+5MB
RSS, no torch; wheels cover the CI matrix).
Validated on the full local spaces corpus (25,725 imgs): 0 regressions; 384
manifests newly parsed (379 non-PNG JPEG/WebP + 2 PNGs the byte-scanner missed);
3 false Adobe/Microsoft->Google attributions fixed via real-manifest parsing.
The docs/module-internals.md section for this change already landed in 41f6797.
Co-Authored-By: Claude Opus 4.8 <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>
actions/checkout@v4 ran on the deprecated Node 20; bump to v6 to match
test.yml/publish.yml. Document the dismissed Dependabot torch alert
(GHSA-rrmf-rvhw-rf47, not_used: no torch.jit usage, gpu-extra-only, no patch).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
distribute.yml fans a published GitHub Release out to the channels that
would otherwise be manual: it waits for the sdist on PyPI, bumps the
Homebrew formula (HOMEBREW_TAP_TOKEN) and factory-rebuilds the HF Space
(HF_TOKEN). PyPI stays on publish.yml; conda-forge on its autotick bot.
Co-Authored-By: Claude Opus 4.8 <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>
Empirical conclusion from the 2026-06-04 - 2026-06-08 cert sweeps:
every shipped face-restore method (GFPGAN-on-cleaned, PhotoMaker-V2,
InstantID txt2img, InstantID img2img-on-cleaned at three parameter
settings) regenerates the face from an ArcFace embedding via SDXL
diffusion. Output face pixels are diffusion-fresh, which makes the
regenerated face look MORE AI-generated than the cleaned image (gloss,
symmetric pores, SDXL "clean skin" aesthetic) regardless of license.
The cleaned image from the main controlnet 0.20 removal pass is the
LEAST-AI state we can reach without re-introducing SynthID; any restore
on top trades original-look for embedding-driven regeneration. The
fundamental issue is structural: ArcFace encodes "general look" at 512
dimensions, SDXL decodes that into pixels with the inherent SDXL
aesthetic. Stronger identity push (higher strength + IP-Adapter scale)
makes the face closer to the embedding but more AI-looking; weaker push
leaves identity to drift further. No parameter setting recovers original
identity AND looks less AI than cleaned.
Production conclusion: do not ship `--restore-faces` in any monetized
deployment. The extras (`instantid`, `photomaker`) stay in the library
for research / personal use where users explicitly want regeneration.
Documented at every entry point:
- CLAUDE.md: new "Face restore trade-off" bullet + every restore mention
rewritten to "REGENERATES, does NOT recover"; controlnet bullet updated
- README.md: feature bullet + callout + secondary mention all updated
- docs/synthid-robust-identity-research-2026-06-08.md: appended
"Empirical follow-up" section documenting the InstantID sweep phases
(Phase 1 txt2img v1/v2/v3, Phase 2 img2img defaults + stronger params)
- docs/controlnet-removal-pipeline-research.md: updated restore-faces
bullet to reflect the empirical conclusion
- CLI help: `_restore_faces_options` docstring + `--restore-faces` /
`--restore-faces-method` help text all updated
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
