Release 0.34.1

One change over 0.34.0: the Microsoft badge's provenance relaxation (0.7),
measured on the OCR-censused Microsoft-C2PA cohort -- 257 badge-less uploads
max 0.251, the relaxed band holds three faint badges and zero false fills.

.claude/rules/development.md

@@ -25,6 +25,16 @@ Boundary modules for cv2, Torch, and Diffusers may carry narrow per-file relaxat
 
 From a worktree, `uv run` imports the package from the MAIN checkout -- that is where the editable install points. A script measuring a worktree's edit must insert that worktree's `src` at `sys.path[0]` and assert `module.__file__` resolves inside it, or it silently compares unmodified code against itself.
 
+## Visible-mark example gallery
+
+Every registered mark carries a committed example: `data/fixtures/visible/<key>/example.png`
+(image marks) and `example.mp4` (video marks), regenerated by
+`scripts/render_visible_examples.py`. `tests/test_visible_examples.py` is the canary: it
+fails when a mark is registered without an example and when an engine stops detecting its
+own example. The examples are synthetic composites of the committed silhouettes -- user
+uploads never enter the repository. When the generator fails after a geometry or gate
+change, fix the generator (and the engine) together; do not hand-edit the binaries.
+
 ## Model-adjacent tests
 
 Do not classify an entire module as untestable because its main path downloads a model. Keep pure behavior covered without downloads, including:

data/README.md

@@ -7,6 +7,8 @@ data/
   fixtures/
     provenance/   Real format and provenance fixtures used by tests
                   (source records live in fixtures/README.md)
+    visible/      Synthetic per-mark example gallery (one committed example per
+                  registered visible mark; see fixtures/visible/README.md)
   calibration/
     <vendor>/     Minimal controlled inputs needed to rebuild detector assets
   synthid/

data/fixtures/visible/README.md

@@ -0,0 +1,41 @@
+# Visible-mark example gallery
+
+One committed example per registered visible mark, so the repository carries a
+working sample of everything it supports. `tests/test_visible_examples.py` holds
+both sides to it: a mark registered without an example fails the suite, and so
+does an engine that stops detecting its own example.
+
+## What these files are
+
+Every example is SYNTHETIC: `scripts/render_visible_examples.py` composites the
+mark's committed silhouette (the same font-rendered asset the detector matches)
+onto a deterministic generated base photo at the engine's measured geometry.
+No user upload and no vendor asset enters the repository: corpus files under
+`data/spaces/` are user content and stay out of git by policy, and the
+silhouettes themselves are our own renders (`scripts/render_vendor_silhouettes.py`).
+
+The examples demonstrate DETECTION geometry and house style, not vendor raster
+fidelity; real-world variants (fonts, opacities, sizes) are covered by the
+engines' calibration cohorts, which are local-only.
+
+## Regeneration
+
+    uv run python scripts/render_visible_examples.py
+
+The generator self-verifies: it fails (exit 1) if any registered mark does not
+detect on its own example, so regeneration is the fix point for drift.
+
+## Layout
+
+    <mark-key>/example.png   1536x2048..2048x2048 PNG, one per image mark
+    <mark-key>/example.mp4   960x540 90-frame clip, one per video mark
+                            (kling carries both: it is registered in both registries)
+
+Special cases: `gemini` composites the sparkle alpha map at the provider's
+configured position; `jimeng_pill` is the capture-less pill at the measured
+3:4 portrait geometry; `microsoft` is the opaque white pill with dark text
+holes (the discriminator its detector keys on). Video examples composite the
+detector's own synthetic template on every frame; where two marks share a
+shape family the example carries the discriminative variant (`veo` the legacy
+text form, `kling` the logo-plus-wordmark pair flush to the edge), because the
+temporal selection resolves cross-template ties by table order.

docs/module-internals.md

@@ -823,8 +823,12 @@ and 1200 non-overlapping no-signal controls. At the strict 0.38 gate, 15/17
 confirmed carriers fired (min 0.249, p50 0.519, p90 0.578, max 0.579), while
 0/1200 controls fired (p99 0.200, max 0.293). The provenance cohort produced
 78/343 fires, but that is not a recall measurement because provenance identifies
-the provider, not the presence of this visible layout. No provenance relaxation
-ships until that cohort is visually labeled.
+the provider, not the presence of this visible layout. That cohort was later
+labeled by an OCR badge census (2026-08-28: 86 badge carriers, 257 badge-less,
+badge-less max 0.251 / p99 0.213), which is what enabled the shipped provenance
+relaxation 0.7: the relaxed band [0.251, 0.38) holds three faint badges and
+zero false fills, re-verified as 3 band detections with 0/257 badge-less fires
+on the provenance path.
 
 The detector and removal mask must use compatible geometry. A detector that
 fires while producing an empty or misplaced mask is a removal failure even if

pyproject.toml

@@ -4,7 +4,7 @@
 # on it, including the ComfyUI node package. The console script below carries
 # the same weight, since users have it on PATH.
 name = "remove-ai-watermarks"
-version = "0.34.0"
+version = "0.34.1"
 description = "AI watermark remover for visible, invisible, and provenance marks in images and video"
 readme = "README.md"
 requires-python = ">=3.11,<3.15"

scripts/assets/visible-mark-candidates/README.md

@@ -20,6 +20,13 @@ ship; `scripts/vendor_mark_calibrate.py` is the candidate-detector harness.
 | `notebooklm_alpha.png` | NotebookLM wordmark (bottom-right) | 12 corpus files | locate geometry not yet fitted; POS max 0.12. |
 | `dola_alpha.png` | DolaAI on images (the video mark is registered) | 12 corpus files | POS 0.11-0.21 vs NEG max 0.30. |
 | `mindvideo_alpha.png` | MindVideo.AI (top-right) | 11 corpus files | POS 0.29-0.32 vs NEG max 0.30 -- borderline overlap, not shippable. |
+| `higgsfield_alpha.png` | HIGGSFIELD AI wordmark (bottom-right; the boxed `AI` variant shares the cohort) | 5 wordmark files (16 in the boxed-AI OCR cluster) | POS max 0.26 vs NEG max 0.22 -- no separation; the mark may be two-part (wordmark + boxed AI) and needs a composed template. |
+| `jianying_alpha.png` | 剪映AI (CapCut's CN sibling, bottom-right) | 2 corpus files | POS 0.29 vs NEG max 0.35. |
+| `capcut_alpha.png` | CapCut AI pill (top-left; likely pill class, not plain text) | 3 corpus files | POS max 0.15 vs NEG max 0.33 -- locate geometry not yet fitted for the pill form. |
+| `zsky_alpha.png` | MADE WITH zsky.ai (bottom-right) | 2 corpus files | POS 0.10 vs NEG max 0.27. |
+| `chromastudio_alpha.png` | ChromaStudio.ai (bottom-right) | 2 corpus files | POS 0.11 vs NEG max 0.29. |
+| `digenai_alpha.png` | DIGENAI (bottom-right) | 3 corpus files (one 2026-07-24 batch) | POS 0.16 vs NEG max 0.31. |
+| `gendo_alpha.png` | GendoAI (bottom-left) | 3 corpus files | POS 0.08 vs NEG max 0.32. |
 | `xinghui_alpha.png` | 星绘AI生成 (parked before this set) | -- | prior parking, unchanged. |
 | `qingyan_alpha.png` | 清言·AI生成 (parked before this set) | -- | prior parking, unchanged. |
 | `hailuo_alpha.png` | Hailuo AI image wordmark (parked before this set; the VIDEO label is registered) | -- | prior parking, unchanged. |

scripts/render_vendor_silhouettes.py

@@ -69,6 +69,15 @@ MARKS = {
     "notebooklm_alpha.png": "NotebookLM",
     "dola_alpha.png": "DolaAI",
     "mindvideo_alpha.png": "MindVideo.AI",
+    "higgsfield_alpha.png": "HIGGSFIELD AI",
+    "capcut_alpha.png": "CapCut AI",
+    "zsky_alpha.png": "MADE WITH zsky.ai",
+    "chromastudio_alpha.png": "ChromaStudio.ai",
+    "digenai_alpha.png": "DIGENAI",
+    "gendo_alpha.png": "GendoAI",
+    # CapCut's Chinese sibling, JianYing, stamps 剪映AI bottom-right (the
+    # international CapCut pill sits top-left).
+    "jianying_alpha.png": "剪映AI",
 }
 _REGISTERED = {f"{key}_alpha.png" for key in mark_keys()} & MARKS.keys()
 

scripts/render_visible_examples.py

@@ -0,0 +1,275 @@
+"""Render the visible-mark example gallery under data/fixtures/visible/.
+
+One committed example per registered image mark, so the repo carries a working
+sample of everything it supports and a canary test can hold both sides to it
+(mark registered without example; engine regressed on its canonical example).
+
+Every example is SYNTHETIC: a deterministic generated base photo with the mark's
+own committed silhouette composited at the engine's measured geometry. User
+uploads never enter the repository (data/spaces stays out of git), and no vendor
+asset is copied -- the silhouettes are the same font-rendered templates the
+detectors match against.
+
+Regenerate with:
+    uv run python scripts/render_visible_examples.py
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+from typing import Any
+
+import cv2
+import numpy as np
+
+_ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(_ROOT / "src"))
+
+from remove_ai_watermarks import watermark_registry as wr  # noqa: E402
+from remove_ai_watermarks.image_io import imread  # noqa: E402
+
+ASSETS = _ROOT / "src" / "remove_ai_watermarks" / "assets"
+OUT = _ROOT / "data" / "fixtures" / "visible"
+
+# Composite strength per key: glyph target luma for the light-overlay class.
+# Kling is a thin light-gray run (not near-white); Samsung is a faint overlay
+# expressed by SCALING ITS ALPHA to 0.38 toward full white (see below); everything
+# else is the bold near-white house style.
+_STRENGTH: dict[str, int] = {
+    "kling": 208,
+}
+_DEFAULT_STRENGTH = 238
+
+# Base geometry: one canonical size per mark where the engine's size modes
+# matter (qwen's big mode), otherwise a plain 3:2 landscape frame.
+# Samsung keeps the larger base: its overlay is faint (peak alpha ~0.38) and
+# the real marks live on ~2958px phone photos -- at 1536 the example falls to 0.39,
+# just under the engine's 0.40 gate.
+_SIZE: dict[str, tuple[int, int]] = {"qwen": (1536, 1536), "liblib": (1152, 1536), "samsung": (2048, 1536)}
+
+
+def base_photo(w: int, h: int, seed: int = 7) -> np.ndarray:
+    """A deterministic synthetic 'photo': gradient sky, soft blobs, mild noise."""
+    rng = np.random.default_rng(seed)
+    top, bottom = 96, 168
+    grad = np.linspace(top, bottom, h, dtype=np.float32)[:, None]
+    img = np.repeat(grad[:, :, None], w, axis=1)  # (h, w, 1)
+    for _ in range(5):
+        cx, cy = rng.uniform(0, w), rng.uniform(0, h)
+        r = rng.uniform(w * 0.12, w * 0.35)
+        blob = rng.uniform(-52, 52)
+        yy, xx = np.ogrid[:h, :w]
+        gauss = np.exp(-(((xx - cx) ** 2 + (yy - cy) ** 2) / (2 * (r * 0.55) ** 2))).astype(np.float32)
+        img = img + (blob * gauss)[:, :, None]
+    img = img + rng.normal(0, 1.6, img.shape).astype(np.float32)
+    return cv2.merge([np.clip(img, 0, 255).astype(np.uint8)] * 3)
+
+
+def _glyph_asset(name: str) -> np.ndarray:
+    at = imread(str(ASSETS / name), cv2.IMREAD_GRAYSCALE)
+    if at is None:
+        raise RuntimeError(f"missing silhouette asset: {name}")
+    return at.astype(np.float32) / 255.0
+
+
+def _composite_light(base: np.ndarray, alpha: np.ndarray, x: int, y: int, strength: int) -> np.ndarray:
+    out = base.copy()
+    h, w = alpha.shape[:2]
+    roi = out[y : y + h, x : x + w].astype(np.float32)
+    a3 = alpha[:, :, None] if alpha.ndim == 2 else alpha
+    out[y : y + h, x : x + w] = np.clip(roi * (1 - a3) + strength * a3, 0, 255).astype(np.uint8)
+    return out
+
+
+def _text_mark_example(key: str) -> np.ndarray:
+    engine = wr._engine(key)  # the generator drives the engine's own config
+    cfg = engine.config
+    w, h = _SIZE.get(key, (1536, 1152))
+    base = base_photo(w, h)
+    if key == "microsoft":
+        # Opaque white pill with dark text/sparkle holes, at the measured inset.
+        at = _glyph_asset("microsoft_alpha.png")
+        long_side = max(w, h)
+        pw = int(0.152 * long_side)
+        ph = max(4, int(pw / (at.shape[1] / at.shape[0])))
+        pad = int(0.010 * long_side)
+        pill = cv2.resize(at, (pw, ph))
+        x, y = w - pad - pw, pad
+        roi = base[y : y + ph, x : x + pw].astype(np.float32)
+        bright = (pill > 0.6)[:, :, None]
+        roi = np.where(bright, 245.0, 46.0)
+        base[y : y + ph, x : x + pw] = roi.astype(np.uint8)
+        return base
+    base_dim = {"short": min(w, h), "width": w, "long": max(w, h)}[cfg.scale_basis]
+    # Size the glyph ON a ladder rung: the continuous front ends sweep only the
+    # configured rungs, and a glyph sized between rungs collapses the NCC (the
+    # comb-collapse qwen's own two-rung ladder exists to avoid).
+    rung = max(cfg.ladder) if cfg.detect_frontend != "binary" else 1.0
+    gw = int(cfg.alpha_width_frac * base_dim * rung)
+    gh = max(4, int(cfg.alpha_height_frac * base_dim * rung))
+    loc = engine.locate(base)
+    # Corner-hugging placement: the yuanbao/runninghub anchor gates demote a match
+    # that does not hug the corner, and the real marks sit flush on the box's
+    # corner side (never centered).
+    if cfg.corner in ("br", "tr"):
+        x = loc.x + loc.w - gw
+    elif cfg.corner == "bc":
+        x = loc.x + (loc.w - gw) // 2
+    else:  # bl, tl: flush left
+        x = loc.x
+    y = loc.y if cfg.corner in ("tl", "tr") else loc.y + loc.h - gh
+    x, y = max(0, x), max(0, y)
+    at = _glyph_asset(f"{key}_alpha.png")
+    alpha = cv2.resize(at, (gw, gh))
+    if key == "samsung":  # faint overlay: peak alpha 0.38 toward FULL white
+        alpha = alpha * 0.38
+    return _composite_light(base, alpha, x, y, _STRENGTH.get(key, _DEFAULT_STRENGTH))
+
+
+def _gemini_example() -> np.ndarray:
+    from remove_ai_watermarks.gemini_engine import GeminiEngine, get_watermark_config, get_watermark_size
+
+    w, h = 1536, 1152
+    base = base_photo(w, h)
+    eng = GeminiEngine()
+    size = get_watermark_size(w, h)
+    alpha = eng.get_alpha_map(size)
+    cfg = get_watermark_config(w, h)
+    x, y = cfg.get_position(w, h)
+    return _composite_light(base, alpha.astype(np.float32), x, y, 255)
+
+
+def _pill_example() -> np.ndarray:
+    w, h = 1152, 1536  # the measured pill cohort is 3:4 portrait
+    base = base_photo(w, h)
+    at = _glyph_asset("jimeng_pill.png")
+    pw = max(24, int(0.161 * w))
+    ph = max(8, int(pw * at.shape[0] / at.shape[1]))
+    x, y = int(0.03 * w), int(0.03 * h)
+    alpha = cv2.resize(at, (pw, ph))
+    return _composite_light(base, alpha, x, y, 232)
+
+
+_BUILDERS: dict[str, Any] = {"gemini": _gemini_example, "jimeng_pill": _pill_example}
+
+
+def build(key: str) -> np.ndarray:
+    if key in _BUILDERS:
+        return _BUILDERS[key]()
+    return _text_mark_example(key)
+
+
+# ── Video mark examples ──────────────────────────────────────────────────────
+# One short clip per registered video mark: the detector's own synthetic
+# template composited at a scale inside its calibrated search profile, on every
+# frame of a generated base. The canary asserts the SHIPPED selection accepts
+# the clip (identify_video -> visible_mark), not just the per-frame detector.
+
+_VIDEO_FRAMES = 90
+_VIDEO_FPS = 30
+
+
+def _video_mark_frame(key: str, w: int, h: int) -> np.ndarray:
+    from remove_ai_watermarks.video_visible import _template_sources
+
+    base = base_photo(w, h, seed=11)
+    templates = _template_sources()
+    short = min(w, h)
+    if key == "sora":
+        tmpl, scale, x, y = templates["sora-icon"], 0.10, int(w * 0.72), int(h * 0.90)
+    elif key == "veo":
+        # The legacy "Veo" TEXT form: a perfect synthetic diamond also matches the
+        # Sora icon template (both are 4-point stars) and table order hands the
+        # tie to Sora, so the gallery carries the discriminative text variant.
+        tmpl = templates["veo-text"]
+        th = max(6, round(14 * short / 720))
+        tw = max(1, round(tmpl.shape[1] * th / tmpl.shape[0]))
+        x, y = w - tw - int(0.045 * w), h - th - int(0.045 * h)
+        return _composite_light(base, cv2.resize(tmpl, (tw, th)).astype(np.float32) / 255.0, x, y, 250)
+    elif key == "seedance":
+        tmpl, scale, x, y = templates["seedance"], 0.095, int(w * 0.74), int(h * 0.80)
+    elif key == "dola":
+        tmpl, scale, x, y = templates["dola"], 0.036, int(w * 0.70), int(h * 0.88)
+    elif key == "hailuo":
+        tmpl, scale, x, y = templates["hailuo"], 0.052, int(w * 0.34), int(h * 0.82)
+    elif key == "kling":
+        # The FULL mark: swirl logo left of the text run, flush against the
+        # bottom-right EDGE. The font arm is edge-gated (region must reach
+        # >=0.96W / >=0.94H), and a text-only composite away from the edge both
+        # fails that gate and cross-fires the Seedance detector.
+        tmpl = templates["kling-1"]
+        th = max(8, round(short * 0.040))
+        tw = max(1, round(tmpl.shape[1] * th / tmpl.shape[0]))
+        tx, ty = w - tw - 6, h - th - 6
+        out = _composite_light(base, cv2.resize(tmpl, (tw, th)).astype(np.float32) / 255.0, tx, ty, 250)
+        logo = templates["kling-logo"]
+        lh = max(6, round(short * 0.046))
+        lw = max(1, round(logo.shape[1] * lh / logo.shape[0]))
+        lx, ly = tx - lw - round(th * 0.5), h - lh - 6
+        return _composite_light(out, cv2.resize(logo, (lw, lh)).astype(np.float32) / 255.0, lx, ly, 250)
+    else:
+        raise ValueError(key)
+    th = max(8, round(short * scale))
+    tw = max(1, round(tmpl.shape[1] * th / tmpl.shape[0]))
+    alpha = cv2.resize(tmpl, (tw, th)).astype(np.float32) / 255.0
+    return _composite_light(base, alpha, x, y, 250)
+
+
+def build_video(key: str) -> None:
+    w, h = 960, 540
+    out_dir = OUT / key
+    out_dir.mkdir(parents=True, exist_ok=True)
+    path = out_dir / "example.mp4"
+    writer = cv2.VideoWriter(str(path), cv2.VideoWriter_fourcc(*"mp4v"), _VIDEO_FPS, (w, h))
+    if not writer.isOpened():
+        raise RuntimeError("mp4v writer unavailable")
+    frame = _video_mark_frame(key, w, h)
+    for _ in range(_VIDEO_FRAMES):
+        writer.write(frame)
+    writer.release()
+
+
+def verify_video(key: str) -> tuple[float, str | None, int]:
+    from remove_ai_watermarks.video import identify_video
+
+    rep = identify_video(OUT / key / "example.mp4", check_visible=True)
+    return float(rep.visible_detected_frames or 0), rep.visible_mark, rep.total_frames
+
+
+def render_videos() -> list[str]:
+    from remove_ai_watermarks.video import VIDEO_VISIBLE_MARKS
+
+    failures: list[str] = []
+    for key in VIDEO_VISIBLE_MARKS:
+        build_video(key)
+        frames, mark, total = verify_video(key)
+        status = "OK " if mark == key else "MISS"
+        print(f"{status} {key:10s} video: {mark} on {frames}/{total} frames -> data/fixtures/visible/{key}/example.mp4")
+        if mark != key:
+            failures.append(key)
+    return failures
+
+
+def main() -> None:
+    failures: list[str] = []
+    for mark in wr.known_marks():
+        key = mark.key
+        img = build(key)
+        out_dir = OUT / key
+        out_dir.mkdir(parents=True, exist_ok=True)
+        path = out_dir / "example.png"
+        cv2.imwrite(str(path), img)
+        det = wr.get_mark(key).detect(imread(str(path)), provenance=False)
+        status = "OK " if det.detected else "MISS"
+        print(f"{status} {key:12s} conf={det.confidence:.3f} -> {path.relative_to(_ROOT)}")
+        if not det.detected:
+            failures.append(key)
+    failures += render_videos()
+    if failures:
+        print(f"\nNOT DETECTED on their own examples: {failures}", file=sys.stderr)
+        raise SystemExit(1)
+
+
+if __name__ == "__main__":
+    main()

src/remove_ai_watermarks/__init__.py

@@ -32,7 +32,7 @@ _os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error")
 _warnings.filterwarnings("ignore", message=r".*ImageProcessorFast.*")
 
 
-__version__ = "0.34.0"
+__version__ = "0.34.1"
 
 __all__ = [
     "BatchSummary",

src/remove_ai_watermarks/microsoft_engine.py

@@ -22,10 +22,14 @@ status was not adjudicated, and 1200 non-overlapping no-signal controls:
     (aspect 3.73-3.89 over 720..1536 px), margins ~0.010/0.007 of the same basis. One size
     mode, so the shared 3-rung ladder is untouched and the locate box simply
     wraps the pill with NCC slack.
-  * STRICT ONLY (``provenance_ncc_factor`` 1.0): controls max 0.293 / p99
-    0.200; confirmed carriers p50 0.519 / p90 0.578 / max 0.579, with 15/17
-    above the 0.38 gate. Provenance alone does not relax the gate: only 78/343
-    files in that cohort crossed it, and the rest were not visually labeled.
+  * Provenance relaxation 0.7 (relaxed gate 0.266), enabled 2026-08-28 when the
+    cohort the strict-only note was waiting for became available: an OCR badge
+    census split the 343 Microsoft-C2PA uploads into 86 badge carriers and 257
+    true badge-less files (the watermark is a per-user opt-in, so 75% of MS
+    uploads carry none). Badge-less max 0.251 / p99 0.213, so the relaxed band
+    [0.251, 0.38) holds three genuine faint badges and zero false fills
+    (measured 3/3; doubao ships 0.7 on a 58%-precision band). Strict controls
+    max 0.293 / p99 0.200 vs the 0.38 gate.
   * Front-end "binary": the pill is a bold opaque overlay; the tophat blob is
     solid with dark-text holes, exactly the template's shape.
 """
@@ -88,7 +92,7 @@ _CONFIG = TextMarkConfig(
     min_gw=24,
     detect_frontend="binary",
     scale_basis="long",
-    provenance_ncc_factor=1.0,
+    provenance_ncc_factor=0.7,
 )
 
 

tests/test_microsoft_engine.py

@@ -76,9 +76,12 @@ class TestLocate:
 
 
 class TestConfig:
-    def test_strict_only_no_provenance_relaxation(self):
-        # Provenance names Microsoft, not the presence of this visible layout.
-        assert MicrosoftEngine().config.provenance_ncc_factor == 1.0
+    def test_provenance_relaxation_is_the_measured_07(self):
+        # The relaxed band was measured on the OCR-censused MS cohort: 257
+        # badge-less files max 0.251, so the 0.266 relaxed gate admits the three
+        # faint badges in [0.251, 0.38) with zero measured false fills. Do not
+        # move the factor without re-censusing the badge-less cohort.
+        assert MicrosoftEngine().config.provenance_ncc_factor == 0.7
 
     def test_long_scale_basis(self):
         assert MicrosoftEngine().config.scale_basis == "long"

tests/test_visible_examples.py

@@ -0,0 +1,57 @@
+"""The visible-mark example gallery is complete and self-consistent.
+
+Two failures this suite exists to catch:
+  * a mark registered without a committed example (the gallery lags the registry);
+  * an engine that no longer detects its own canonical example (the gallery is
+    generated from the engines' measured geometry, so this is a regression tripwire).
+
+The examples are SYNTHETIC (``scripts/render_visible_examples.py`` composites the
+committed silhouettes onto a generated base). User uploads never enter the repo.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from remove_ai_watermarks import watermark_registry as wr
+from remove_ai_watermarks.image_io import imread
+from remove_ai_watermarks.video import VIDEO_VISIBLE_MARKS, identify_video
+
+_ROOT = Path(__file__).resolve().parents[1]
+_GALLERY = _ROOT / "data" / "fixtures" / "visible"
+
+_IMAGE_KEYS = [m.key for m in wr.known_marks()]
+
+
+class TestGallery:
+    def test_every_registered_mark_has_an_example(self) -> None:
+        missing = [key for key in _IMAGE_KEYS if not (_GALLERY / key / "example.png").is_file()]
+        assert missing == [], f"registered without an example: {missing}; run scripts/render_visible_examples.py"
+
+    @pytest.mark.parametrize("key", _IMAGE_KEYS)
+    def test_engine_detects_its_own_example(self, key: str) -> None:
+        img = imread(str(_GALLERY / key / "example.png"))
+        assert img is not None, key
+        det = wr.get_mark(key).detect(img, provenance=False)
+        assert det.detected, f"{key}: confidence {det.confidence:.3f} on its own example"
+
+    def test_gallery_has_no_stray_directories(self) -> None:
+        known = set(_IMAGE_KEYS) | set(VIDEO_VISIBLE_MARKS) | {"README.md"}
+        extra = sorted(p.name for p in _GALLERY.iterdir() if p.name not in known)
+        assert extra == [], f"gallery holds unregistered examples: {extra}; remove or register them"
+
+
+class TestVideoGallery:
+    def test_every_registered_video_mark_has_an_example(self) -> None:
+        missing = [key for key in VIDEO_VISIBLE_MARKS if not (_GALLERY / key / "example.mp4").is_file()]
+        assert missing == [], f"video mark without an example: {missing}; run scripts/render_visible_examples.py"
+
+    def test_selection_accepts_each_example(self) -> None:
+        # The shipped temporal selection (not just the per-frame detector) must
+        # accept the clip: table order resolves cross-template ties, so the example
+        # must carry the discriminative variant of its mark.
+        for key in VIDEO_VISIBLE_MARKS:
+            rep = identify_video(_GALLERY / key / "example.mp4", check_visible=True)
+            assert rep.visible_mark == key, f"{key}: selection returned {rep.visible_mark!r}"

uv.lock

@@ -3169,7 +3169,7 @@ wheels = [
 
 [[package]]
 name = "remove-ai-watermarks"
-version = "0.34.0"
+version = "0.34.1"
 source = { editable = "." }
 dependencies = [
     { name = "c2pa-python" },