Release 0.40.1 with Qwen symbol false-positive fix

docs/module-internals.md

@@ -1391,6 +1391,13 @@ The shared text engine handles `千问AI生成`; `QwenEngine` also scans a squar
 bottom-right region for Qwen Create's three-lobe symbol. Its 0.65 NCC gate was
 measured against the cleared provider fixture (0.848) and 132 decoded tracked
 controls other than that positive (maximum 0.516 on 2026-09-10, zero fires).
+NCC is not portable across OpenCV builds: issue 108 reported 0.81 on
+`data/fixtures/provenance/chatgpt-1.png` (sand and denim, no glyph) while this
+tree's OpenCV 4.11 scores 0.437 on the same file. A fire also requires
+binarized IoU of the bright residual with the three-lobe silhouette at or
+above 0.70; the true fixture stays at or above 0.75 down to 512 px, and
+chatgpt-1 sits at 0.55. `provenance_ncc_factor` remains 1.0, so OpenAI C2PA
+cannot relax this mark.
 The removal mask reuses the aligned three-lobe alpha and expands it by 6% of
 the detected side; a solid square was rejected because classical inpainting
 left a visible rectangular blur.

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 = "0.40.0"
+version = "0.40.1"
 description = "AI watermark remover for visible, invisible, and provenance marks in images and video"
 readme = "README.md"
 requires-python = ">=3.11,<3.15"

src/remove_ai_watermarks/__init__.py

@@ -33,7 +33,7 @@ _os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error")
 _warnings.filterwarnings("ignore", message=r".*ImageProcessorFast.*")
 
 
-__version__ = "0.40.0"
+__version__ = "0.40.1"
 
 __all__ = [
     "BatchItemResult",

src/remove_ai_watermarks/qwen_engine.py

@@ -109,6 +109,14 @@ _SYMBOL_MARGIN_FRAC = 0.012
 _SYMBOL_SIZE_FRAC = 0.03125
 _SYMBOL_LADDER = (0.75, 0.875, 1.0, 1.125, 1.25)
 _SYMBOL_NCC_THRESHOLD = 0.65
+# NCC alone is not portable: issue 108 reported 0.81 on
+# data/fixtures/provenance/chatgpt-1.png (sand/denim, no glyph) while this
+# OpenCV build scores 0.437 on the same file. True Qwen Create matches keep
+# binarized IoU >= 0.75 down to 512 px; that fixture sits at 0.55. Require
+# both the NCC gate and this shape overlap so a high-NCC blob cannot fire.
+_SYMBOL_SHAPE_IOU_THRESHOLD = 0.70
+_SYMBOL_TEMPLATE_MASK_FRAC = 0.25
+_SYMBOL_RESPONSE_MASK_FRAC = 0.35
 _SYMBOL_MIN_SHORT_SIDE = 256
 _SYMBOL_MASK_ALPHA = 4 / 255
 _SYMBOL_MASK_DILATE_FRAC = 0.06
@@ -165,6 +173,23 @@ class QwenEngine(TextMarkEngine):
         self._symbol_template = template
         self._symbol_template_scales: dict[int, NDArray[Any]] = {}
 
+    @staticmethod
+    def _symbol_shape_iou(response_crop: NDArray[Any], template: NDArray[Any]) -> float:
+        """Overlap of the bright residual with the three-lobe silhouette."""
+        if response_crop.size == 0 or template.size == 0:
+            return 0.0
+        template_peak = float(template.max())
+        response_peak = float(response_crop.max())
+        if template_peak <= 0.0 or response_peak <= 0.0:
+            return 0.0
+        template_mask = template > (template_peak * _SYMBOL_TEMPLATE_MASK_FRAC)
+        response_mask = response_crop > (response_peak * _SYMBOL_RESPONSE_MASK_FRAC)
+        union = int(np.logical_or(template_mask, response_mask).sum())
+        if union == 0:
+            return 0.0
+        intersection = int(np.logical_and(template_mask, response_mask).sum())
+        return intersection / union
+
     def _scaled_symbol_template(self, side: int) -> NDArray[Any]:
         """Return a cached square symbol template for a measured ladder size."""
         template = self._symbol_template_scales.get(side)
@@ -195,6 +220,7 @@ class QwenEngine(TextMarkEngine):
 
         best_score = 0.0
         best_region = (0, 0, 0, 0)
+        best_iou = 0.0
         for rung in _SYMBOL_LADDER:
             side = max(12, int(base * _SYMBOL_SIZE_FRAC * rung))
             if side >= search_side:
@@ -202,11 +228,15 @@ class QwenEngine(TextMarkEngine):
             template = self._scaled_symbol_template(side)
             scores = cv2.matchTemplate(response, template, cv2.TM_CCOEFF_NORMED)
             _minimum, score, _min_location, location = cv2.minMaxLoc(scores)
-            if score > best_score:
-                best_score = float(score)
-                best_region = (origin_x + location[0], origin_y + location[1], side, side)
+            ncc = float(score)
+            if ncc <= best_score:
+                continue
+            crop = response[location[1] : location[1] + side, location[0] : location[0] + side]
+            best_score = ncc
+            best_region = (origin_x + location[0], origin_y + location[1], side, side)
+            best_iou = self._symbol_shape_iou(crop, template)
         det.confidence = best_score
-        det.detected = best_score >= _SYMBOL_NCC_THRESHOLD
+        det.detected = best_score >= _SYMBOL_NCC_THRESHOLD and best_iou >= _SYMBOL_SHAPE_IOU_THRESHOLD
         det.region = best_region
         return det
 

tests/test_qwen_engine.py

@@ -20,6 +20,7 @@ from remove_ai_watermarks.qwen_engine import (
     _ALPHA_HEIGHT_FRAC,
     _ALPHA_WIDTH_FRAC,
     _LADDER,
+    _SYMBOL_SHAPE_IOU_THRESHOLD,
     QwenEngine,
     _alpha_template,
     _glyph_silhouette,
@@ -32,6 +33,7 @@ _BIG_MODE, _SMALL_MODE = 0.203, 0.124
 _MARGIN = 0.025  # measured right/bottom margin of the real mark
 _ROOT = Path(__file__).resolve().parents[1]
 _QWEN_CREATE = _ROOT / "data" / "fixtures" / "provenance" / "qwen-create-qwen-image-2.png"
+_CHATGPT_1 = _ROOT / "data" / "fixtures" / "provenance" / "chatgpt-1.png"
 
 
 def _compose(w: int, h: int, mode: float = _BIG_MODE, bg: float = 100.0):
@@ -146,6 +148,26 @@ class TestDetect:
         assert y > image.shape[0] * 0.90
         assert width == height
 
+    def test_chatgpt_1_fixture_does_not_fire_qwen(self):
+        # Issue 108: 0.40.0 reported visible_qwen 0.81 on this OpenAI C2PA
+        # beach photo and auto-inpainted sand. NCC on the three-lobe template
+        # is OpenCV-sensitive; the shape overlap must still refuse it.
+        from remove_ai_watermarks.identify import identify
+        from remove_ai_watermarks.image_io import imread
+
+        image = imread(_CHATGPT_1)
+        assert image is not None
+        det = QwenEngine().detect(image)
+        assert not det.detected
+        record = identify(_CHATGPT_1, check_visible=True, check_invisible=False)
+        assert all(signal.name != "visible_qwen" for signal in record.signals)
+
+    def test_symbol_shape_iou_rejects_a_solid_blob(self):
+        template = QwenEngine()._scaled_symbol_template(32)
+        blob = np.full_like(template, float(template.max()))
+        assert QwenEngine._symbol_shape_iou(blob, template) < _SYMBOL_SHAPE_IOU_THRESHOLD
+        assert QwenEngine._symbol_shape_iou(template, template) >= _SYMBOL_SHAPE_IOU_THRESHOLD
+
     def test_unaccepted_symbol_candidate_cannot_hide_detected_text(self):
         text_detection = TextMarkDetection(detected=True, confidence=0.45)
         symbol_detection = TextMarkDetection(detected=False, confidence=0.46)
@@ -176,6 +198,16 @@ class TestFootprintMaskAndRemoval:
         clean = cv2.GaussianBlur(np.full((640, 853, 3), 120, np.uint8), (5, 5), 0)
         assert QwenEngine().footprint_mask(clean, force=False) is None
 
+    def test_chatgpt_1_auto_remove_does_not_touch_pixels(self):
+        from remove_ai_watermarks.api import remove_visible_detailed
+        from remove_ai_watermarks.image_io import imread
+
+        image = imread(_CHATGPT_1)
+        assert image is not None
+        report = remove_visible_detailed(image, sensitivity="auto")
+        assert report.status == "no_watermark"
+        assert np.array_equal(report.image, image)
+
     def test_removes_qwen_create_symbol(self):
         image = cv2.imread(str(_QWEN_CREATE), cv2.IMREAD_COLOR)
         assert image is not None

uv.lock

@@ -3449,7 +3449,7 @@ wheels = [
 
 [[package]]
 name = "remove-ai-watermarks"
-version = "0.40.0"
+version = "0.40.1"
 source = { editable = "." }
 dependencies = [
     { name = "c2pa-python" },