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https://github.com/wiltodelta/remove-ai-watermarks.git
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Victor Kuznetsov
85b18af804
The wild vendor-flagged AI cell (300 stratified rows) puts Model 1 recall at 69.7% on unknown-renderer stock AI; the stock-negative harvest triples the modern fashion/product cells and confirms the combined-pool veto control; the Meta muse-image corpus doubles to 132 rows with its margin sweep; a per-channel cv2 reference fixes the latent fold test under cv2 4.10.0. pre-commit: 1) maintain.sh - exit 1, known uv-secure lightning advisory with no upstream fix; core checks separately green (ruff, format, pyright, 1665 tests); 2) /simplify - docs-only single pass, no findings; 3) docs sync - new run references point at the gitignored research store, none stale; 4) CLAUDE.md - compact, no changes needed
788 lines
28 KiB
Python
788 lines
28 KiB
Python
"""Runtime tests for the positive-only SynthID periodic carrier detector."""
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from __future__ import annotations
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import hashlib
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from pathlib import Path
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import numpy as np
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import pytest
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import synthid_runtime.synthid_detector as detector
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from PIL import Image
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@pytest.fixture(scope="module")
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def supported_images(tmp_path_factory: pytest.TempPathFactory) -> tuple[Path, Path]:
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"""Create supported-geometry positive and negative synthetic fixtures."""
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directory = tmp_path_factory.mktemp("synthid-detector")
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template, *_model = detector._load_template()
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scaled_tile = np.rint(template / np.max(np.abs(template)))
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marked = np.full((detector.MODEL_HEIGHT, detector.MODEL_WIDTH, 3), 128, dtype=np.float64)
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marked += np.tile(scaled_tile, (128, 128, 1))
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positive = directory / "positive.png"
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negative = directory / "negative.png"
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Image.fromarray(np.clip(np.rint(marked), 0, 255).astype(np.uint8), "RGB").save(positive)
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Image.new("RGB", (detector.MODEL_WIDTH, detector.MODEL_HEIGHT), (128, 128, 128)).save(negative)
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return positive, negative
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@pytest.fixture(scope="module")
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def registered_scale_positive(tmp_path_factory: pytest.TempPathFactory) -> Path:
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"""Create a strong period-12.8 carrier by shrinking a period-16 source."""
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import cv2
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directory = tmp_path_factory.mktemp("synthid-registered")
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template, *_model = detector._load_template()
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scaled_tile = template / np.max(np.abs(template)) * 40.0
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source = np.tile(scaled_tile, (64, 64, 1)) + 128.0
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pixels = cv2.resize(
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np.clip(np.rint(source), 0, 255).astype(np.uint8),
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(819, 819),
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interpolation=cv2.INTER_AREA,
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)
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path = directory / "period-12.8-positive.png"
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Image.fromarray(pixels, "RGB").save(path)
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return path
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@pytest.fixture(scope="module")
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def opponent_registered_positive(tmp_path_factory: pytest.TempPathFactory) -> Path:
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"""Create a strong period-10 opponent-color fallback fixture."""
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import cv2
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directory = tmp_path_factory.mktemp("synthid-opponent-registered")
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template, *_model = detector._load_template()
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scaled_tile = template / np.max(np.abs(template)) * 40.0
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source = np.tile(scaled_tile, (128, 128, 1)) + 128.0
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pixels = cv2.resize(
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np.clip(np.rint(source), 0, 255).astype(np.uint8),
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(1280, 1280),
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interpolation=cv2.INTER_AREA,
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)
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path = directory / "period-10-positive.png"
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Image.fromarray(pixels, "RGB").save(path)
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return path
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@pytest.fixture(scope="module")
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def opponent_period8_positive(tmp_path_factory: pytest.TempPathFactory) -> Path:
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"""Create a strong period-8 fallback fixture without native JPEG block edges."""
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import cv2
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directory = tmp_path_factory.mktemp("synthid-opponent-period8")
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template, *_model = detector._load_template()
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scaled_tile = template / np.max(np.abs(template)) * 40.0
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source = np.tile(scaled_tile, (128, 128, 1)) + 128.0
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pixels = cv2.resize(
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np.clip(np.rint(source), 0, 255).astype(np.uint8),
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(1024, 1024),
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interpolation=cv2.INTER_AREA,
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)
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path = directory / "period-8-positive.png"
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Image.fromarray(pixels, "RGB").save(path)
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return path
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@pytest.fixture(scope="module")
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def fine_opponent_registered_positive(tmp_path_factory: pytest.TempPathFactory) -> Path:
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"""Create a strong period-7.68 carrier missed by the coarse period grid."""
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import cv2
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directory = tmp_path_factory.mktemp("synthid-fine-opponent-registered")
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template, *_model = detector._load_template()
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scaled_tile = template / np.max(np.abs(template)) * 40.0
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source = np.tile(scaled_tile, (144, 144, 1)) + 128.0
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pixels = cv2.resize(
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np.clip(np.rint(source), 0, 255).astype(np.uint8),
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(1106, 1106),
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interpolation=cv2.INTER_AREA,
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)
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path = directory / "period-7.68-positive.png"
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Image.fromarray(pixels, "RGB").save(path)
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return path
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def test_bundled_model_is_the_frozen_calibrated_artifact() -> None:
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model = Path(detector.__file__).parent / detector.MODEL_FILENAME
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assert hashlib.sha256(model.read_bytes()).hexdigest() == (
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"ee7838da8542c206c3403284b68e98f0ac99429e82f262c1a438f50a638b488b"
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)
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@pytest.mark.parametrize(
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("width", "height"),
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[(1000, 1000), (1001, 1000), (3000, 6000), (768, 1364)],
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)
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def test_supported_geometry_uses_the_challenged_pixel_count_range(width: int, height: int) -> None:
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assert detector._geometry_supported(width, height)
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@pytest.mark.parametrize(
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("width", "height"),
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[(999, 1000), (3001, 6000), (64, 32)],
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)
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def test_geometry_outside_the_challenged_pixel_count_range_is_unsupported(
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width: int,
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height: int,
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) -> None:
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assert not detector._geometry_supported(width, height)
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@pytest.mark.parametrize(
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("width", "height", "supported"),
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[
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(500, 500, True),
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(4000, 2500, True),
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(256, 977, True),
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(499, 500, False),
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(4001, 2500, False),
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(255, 981, False),
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(64, 3907, False),
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(32, 7813, False),
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],
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)
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def test_registered_geometry_uses_its_measured_pixel_count_range(
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width: int,
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height: int,
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supported: bool,
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) -> None:
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assert detector._registered_geometry_supported(width, height) is supported
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@pytest.mark.parametrize(
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("width", "height", "supported"),
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[
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(1000, 1000, True),
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(4000, 2500, True),
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(767, 1304, False),
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(1000, 999, False),
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(4001, 2500, False),
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],
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)
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def test_opponent_registered_geometry_uses_its_frozen_domain(
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width: int,
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height: int,
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supported: bool,
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) -> None:
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assert detector._opponent_registered_geometry_supported(width, height) is supported
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@pytest.mark.parametrize(
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("width", "height", "supported"),
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[
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(1000, 1000, True),
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(2500, 2000, True),
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(767, 1304, False),
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(1000, 999, False),
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(2501, 2000, False),
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],
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)
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def test_fine_opponent_registered_geometry_uses_its_frozen_domain(
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width: int,
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height: int,
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supported: bool,
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) -> None:
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assert detector._fine_opponent_registered_geometry_supported(width, height) is supported
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@pytest.mark.parametrize(
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("width", "height", "supported"),
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[
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(4883, 2048, True),
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(3072, 5504, True),
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(2048, 4882, False),
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(2047, 6000, False),
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(3001, 6000, False),
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],
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)
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def test_large_geometry_requires_multiple_calibrated_windows(
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width: int,
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height: int,
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supported: bool,
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) -> None:
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assert detector._large_geometry_supported(width, height) is supported
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def test_large_window_starts_cover_both_edges_on_carrier_phase() -> None:
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starts = detector._large_window_starts(5504)
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assert starts == (0, 2048, 3456)
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assert all(start % detector.LARGE_PHASE == 0 for start in starts)
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assert starts[-1] + detector.LARGE_WINDOW == 5504
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def test_large_components_apply_the_portrait_alias_guard_only_to_its_geometry() -> None:
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values = {
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"minimum_fixed_score": 0.28,
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"minimum_red_green_spatial": 0.95,
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"minimum_blue_yellow_spatial": 0.85,
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"minimum_blue_yellow_mid_band": -0.30,
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"maximum_green_mid_band": 0.061,
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}
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portrait = detector.LargeImageComponents(width=3072, height=5504, **values)
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landscape = detector.LargeImageComponents(width=5504, height=3072, **values)
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assert portrait.decision_score < detector.LARGE_THRESHOLD
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assert landscape.decision_score > detector.LARGE_THRESHOLD
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def test_large_red_green_gate_mutation_changes_the_real_verdict(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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width, height = 4883, 2048
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image = np.broadcast_to(np.zeros((1, 1, 3), dtype=np.uint8), (height, width, 3))
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components = detector.LargeImageComponents(
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width=width,
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height=height,
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minimum_fixed_score=0.28,
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minimum_red_green_spatial=detector.LARGE_RED_GREEN_SPATIAL_MIN,
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minimum_blue_yellow_spatial=0.85,
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minimum_blue_yellow_mid_band=-0.30,
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maximum_green_mid_band=0.0,
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)
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monkeypatch.setattr(detector, "is_available", lambda: True)
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monkeypatch.setattr(detector, "_load_template", lambda: (np.zeros((16, 16, 3)), 1.0, 0, 0, 0, 0))
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monkeypatch.setattr(detector, "large_image_components", lambda *_args: components)
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baseline = detector.detect_synthid("unused.png", image=image)
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monkeypatch.setattr(
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detector,
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"LARGE_RED_GREEN_SPATIAL_MIN",
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float(np.nextafter(components.minimum_red_green_spatial, np.inf)),
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)
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mutated = detector.detect_synthid("unused.png", image=image)
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assert baseline.status == "detected"
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assert baseline.detector == detector.LARGE_DETECTOR_ID
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assert mutated.status == "indeterminate"
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def test_uncalibrated_narrow_large_geometry_is_unsupported() -> None:
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image = np.broadcast_to(np.zeros((1, 1, 3), dtype=np.uint8), (11_000, 1000, 3))
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result = detector.detect_synthid("unused.png", image=image)
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assert result.status == "unsupported"
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assert result.detector == detector.LARGE_DETECTOR_ID
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assert result.score is None
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def test_registered_mode_rejects_a_side_too_short_for_quadrants(tmp_path: Path) -> None:
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path = tmp_path / "too-narrow.png"
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Image.new("RGB", (32, 7813), "white").save(path)
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result = detector.detect_synthid(path, register_scale=True)
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assert result.status == "unsupported"
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assert result.score is None
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assert result.detector == detector.REGISTERED_DETECTOR_ID
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def test_detects_supported_periodic_carrier(supported_images: tuple[Path, Path]) -> None:
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positive, _negative = supported_images
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result = detector.detect_synthid(positive, register_scale=False)
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assert result.status == "detected"
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assert result.detected is True
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assert result.score is not None
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assert result.score > result.threshold
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assert result.to_dict()["detector"] == detector.DETECTOR_ID
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def test_detects_unregistered_non_divisible_geometry_in_size_range(tmp_path: Path) -> None:
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width, height = 1001, 1000
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template, *_model = detector._load_template()
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scaled_tile = np.rint(template / np.max(np.abs(template)))
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repeats_y = (height + scaled_tile.shape[0] - 1) // scaled_tile.shape[0]
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repeats_x = (width + scaled_tile.shape[1] - 1) // scaled_tile.shape[1]
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carrier = np.tile(scaled_tile, (repeats_y, repeats_x, 1))[:height, :width]
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pixels = np.clip(np.rint(carrier + 128.0), 0, 255).astype(np.uint8)
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path = tmp_path / "non-divisible-positive.png"
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Image.fromarray(pixels, "RGB").save(path)
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result = detector.detect_synthid(path, register_scale=False)
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assert result.status == "detected"
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assert (result.width, result.height) == (width, height)
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assert result.score is not None
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assert result.score > result.threshold
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def test_registered_mode_detects_a_rescaled_carrier(registered_scale_positive: Path) -> None:
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fixed = detector.detect_synthid(registered_scale_positive, register_scale=False)
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default = detector.detect_synthid(registered_scale_positive)
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registered = detector.detect_synthid(registered_scale_positive, register_scale=True)
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assert fixed.status == "unsupported"
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assert default == registered
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assert registered.status == "detected"
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assert registered.score is not None
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assert registered.score > registered.threshold
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assert registered.threshold == detector.REGISTERED_THRESHOLD
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assert registered.detector == detector.REGISTERED_DETECTOR_ID
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def test_registered_mode_falls_back_to_the_opponent_color_expert(
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monkeypatch: pytest.MonkeyPatch,
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opponent_registered_positive: Path,
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) -> None:
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import synthid_runtime._synthid_registered as registered_detector
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monkeypatch.setattr(registered_detector, "registered_score", lambda *_args: 0.0)
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result = detector.detect_synthid(opponent_registered_positive, register_scale=True)
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assert result.status == "detected"
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assert result.detector == detector.OPPONENT_REGISTERED_DETECTOR_ID
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assert result.score is not None
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assert result.score >= result.threshold
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def test_opponent_registered_threshold_mutation_changes_the_real_verdict(
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monkeypatch: pytest.MonkeyPatch,
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opponent_registered_positive: Path,
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) -> None:
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import synthid_runtime._synthid_registered as registered_detector
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monkeypatch.setattr(registered_detector, "registered_score", lambda *_args: 0.0)
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baseline = detector.detect_synthid(opponent_registered_positive, register_scale=True)
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assert baseline.score is not None
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assert baseline.detector == detector.OPPONENT_REGISTERED_DETECTOR_ID
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monkeypatch.setattr(
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detector,
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"OPPONENT_REGISTERED_THRESHOLD",
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float(np.nextafter(baseline.score, np.inf)),
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)
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mutated = detector.detect_synthid(opponent_registered_positive, register_scale=True)
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assert mutated.status == "indeterminate"
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assert mutated.detector == detector.REGISTERED_DETECTOR_ID
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def test_opponent_fallback_recovers_period8_without_codec_grid(
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monkeypatch: pytest.MonkeyPatch,
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opponent_period8_positive: Path,
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) -> None:
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import synthid_runtime._synthid_registered as registered_detector
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monkeypatch.setattr(registered_detector, "registered_score", lambda *_args: 0.0)
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result = detector.detect_synthid(opponent_period8_positive, register_scale=True)
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assert result.status == "detected"
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assert result.detector == detector.OPPONENT_REGISTERED_DETECTOR_ID
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def test_fine_opponent_fallback_recovers_off_grid_period(
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monkeypatch: pytest.MonkeyPatch,
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fine_opponent_registered_positive: Path,
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) -> None:
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import synthid_runtime._synthid_registered as registered_detector
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monkeypatch.setattr(registered_detector, "registered_score", lambda *_args: 0.0)
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monkeypatch.setattr(registered_detector, "opponent_registered_score", lambda *_args: 0.0)
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result = detector.detect_synthid(fine_opponent_registered_positive, register_scale=True)
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assert result.status == "detected"
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assert result.detector == detector.FINE_OPPONENT_REGISTERED_DETECTOR_ID
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assert result.score is not None
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assert result.score >= detector.FINE_OPPONENT_REGISTERED_THRESHOLD
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|
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def test_fine_opponent_threshold_mutation_changes_the_real_verdict(
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monkeypatch: pytest.MonkeyPatch,
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fine_opponent_registered_positive: Path,
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) -> None:
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import synthid_runtime._synthid_registered as registered_detector
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monkeypatch.setattr(registered_detector, "registered_score", lambda *_args: 0.0)
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monkeypatch.setattr(registered_detector, "opponent_registered_score", lambda *_args: 0.0)
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baseline = detector.detect_synthid(fine_opponent_registered_positive, register_scale=True)
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assert baseline.score is not None
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assert baseline.detector == detector.FINE_OPPONENT_REGISTERED_DETECTOR_ID
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monkeypatch.setattr(
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detector,
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"FINE_OPPONENT_REGISTERED_THRESHOLD",
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float(np.nextafter(baseline.score, np.inf)),
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)
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mutated = detector.detect_synthid(fine_opponent_registered_positive, register_scale=True)
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assert mutated.status == "indeterminate"
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assert mutated.detector == detector.REGISTERED_DETECTOR_ID
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def test_fine_opponent_selector_recovers_the_fractional_period(
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fine_opponent_registered_positive: Path,
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) -> None:
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import synthid_runtime._synthid_registered as registered_detector
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template, sigma, *_model = detector._load_template()
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pixels = np.asarray(Image.open(fine_opponent_registered_positive).convert("RGB"), dtype=np.uint8)
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components = registered_detector.fine_opponent_registered_components(pixels, template, sigma)
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assert components.selected_period == pytest.approx(7.68, abs=0.01)
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assert components.fine_decision_score >= detector.FINE_OPPONENT_REGISTERED_THRESHOLD
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assert components.candidate_count >= 100
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def test_period8_codec_veto_threshold_mutation_changes_real_components(
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monkeypatch: pytest.MonkeyPatch,
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opponent_period8_positive: Path,
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) -> None:
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import synthid_runtime._synthid_registered as registered_detector
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template, sigma, *_model = detector._load_template()
|
|
pixels = np.asarray(Image.open(opponent_period8_positive).convert("RGB"), dtype=np.uint8)
|
|
components = registered_detector.opponent_registered_components(pixels, template, sigma)
|
|
assert components.decision_score >= detector.OPPONENT_REGISTERED_THRESHOLD
|
|
assert components.red_green_p8_edge_ratio is not None
|
|
assert components.blue_yellow_p8_edge_ratio is not None
|
|
monkeypatch.setattr(registered_detector, "OPPONENT_REGISTERED_MAX_P8_EDGE_RATIO", 0.9)
|
|
|
|
assert components.decision_score == 0.0
|
|
|
|
|
|
def test_opponent_registered_period_band_and_codec_veto_are_required() -> None:
|
|
from synthid_runtime._synthid_registered import OpponentRegisteredComponents
|
|
|
|
values = {
|
|
"spectral_score": 0.8,
|
|
"fixed_score": 0.32,
|
|
"red_green_spatial": 0.9,
|
|
"blue_yellow_spatial": 0.8,
|
|
"candidate_count": 3,
|
|
"red_green_p8_edge_ratio": None,
|
|
"blue_yellow_p8_edge_ratio": None,
|
|
}
|
|
matching = OpponentRegisteredComponents(10.0, 10.0, **values)
|
|
period8 = OpponentRegisteredComponents(
|
|
8.0,
|
|
8.0,
|
|
**{
|
|
**values,
|
|
"red_green_p8_edge_ratio": 1.0,
|
|
"blue_yellow_p8_edge_ratio": 1.0,
|
|
},
|
|
)
|
|
codec_alias = OpponentRegisteredComponents(
|
|
8.0,
|
|
8.0,
|
|
**{
|
|
**values,
|
|
"red_green_p8_edge_ratio": 1.2,
|
|
"blue_yellow_p8_edge_ratio": 1.2,
|
|
},
|
|
)
|
|
|
|
assert matching.decision_score > detector.OPPONENT_REGISTERED_THRESHOLD
|
|
assert period8.decision_score > detector.OPPONENT_REGISTERED_THRESHOLD
|
|
assert codec_alias.base_decision_score > detector.OPPONENT_REGISTERED_THRESHOLD
|
|
assert codec_alias.decision_score == 0.0
|
|
|
|
|
|
def test_registered_threshold_mutation_changes_the_real_verdict(
|
|
monkeypatch: pytest.MonkeyPatch,
|
|
registered_scale_positive: Path,
|
|
) -> None:
|
|
baseline = detector.detect_synthid(registered_scale_positive, register_scale=True)
|
|
assert baseline.score is not None
|
|
mutated_threshold = float(np.nextafter(baseline.score, np.inf))
|
|
monkeypatch.setattr(detector, "REGISTERED_THRESHOLD", mutated_threshold)
|
|
|
|
mutated = detector.detect_synthid(registered_scale_positive, register_scale=True)
|
|
|
|
assert mutated.status == "indeterminate"
|
|
assert mutated.threshold == mutated_threshold
|
|
|
|
|
|
def test_registered_period_thresholds_cover_the_bounded_search() -> None:
|
|
from synthid_runtime._synthid_registered import _period_threshold
|
|
|
|
assert _period_threshold(7.5) == pytest.approx(0.3770629524888979)
|
|
assert _period_threshold(12.0) == pytest.approx(0.19794247706938645)
|
|
assert _period_threshold(24.5) == pytest.approx(0.3142958338390489)
|
|
with pytest.raises(ValueError, match="outside"):
|
|
_period_threshold(7.49)
|
|
|
|
|
|
def test_registered_amplitude_threshold_mutation_changes_the_real_verdict(
|
|
monkeypatch: pytest.MonkeyPatch,
|
|
registered_scale_positive: Path,
|
|
) -> None:
|
|
import synthid_runtime._synthid_registered as registered_detector
|
|
|
|
baseline = detector.detect_synthid(registered_scale_positive, register_scale=True)
|
|
assert baseline.status == "detected"
|
|
monkeypatch.setattr(
|
|
registered_detector,
|
|
"_PERIOD_THRESHOLDS",
|
|
((7.5, 24.5, float("inf")),),
|
|
)
|
|
|
|
mutated = detector.detect_synthid(registered_scale_positive, register_scale=True)
|
|
|
|
assert mutated.status == "indeterminate"
|
|
|
|
|
|
def test_registered_spectral_candidate_disagreement_blocks_decision() -> None:
|
|
from synthid_runtime._synthid_confirmation import RegisteredConfirmationComponents
|
|
from synthid_runtime._synthid_registered import RegisteredComponents
|
|
|
|
confirmation = RegisteredConfirmationComponents(12.8, 0.5, 0.2, 0.5, 8, 8)
|
|
matching = RegisteredComponents(0.5, 0.25, 12.8, 12.8, 0.15, confirmation)
|
|
mismatching = RegisteredComponents(0.5, 0.25, 12.8, 12.9, 0.15, confirmation)
|
|
unconfirmed = RegisteredComponents(0.5, 0.25, 12.8, 12.8, 0.15)
|
|
|
|
assert matching.decision_score == pytest.approx(2.0)
|
|
assert mismatching.decision_score == pytest.approx(0.0)
|
|
assert unconfirmed.base_decision_score == pytest.approx(2.0)
|
|
assert unconfirmed.decision_score == pytest.approx(0.0)
|
|
|
|
|
|
def test_registered_high_band_mutation_changes_the_real_verdict(
|
|
monkeypatch: pytest.MonkeyPatch,
|
|
registered_scale_positive: Path,
|
|
) -> None:
|
|
import synthid_runtime._synthid_registered as registered_detector
|
|
|
|
components = registered_detector.registered_components(
|
|
np.asarray(Image.open(registered_scale_positive).convert("RGB"), dtype=np.uint8),
|
|
detector._load_template()[0],
|
|
detector._load_template()[1],
|
|
)
|
|
assert components.decision_score >= detector.REGISTERED_THRESHOLD
|
|
monkeypatch.setattr(
|
|
registered_detector,
|
|
"REGISTERED_HIGH_BAND_THRESHOLD",
|
|
float(np.nextafter(components.high_band_score, np.inf)),
|
|
)
|
|
|
|
mutated = detector.detect_synthid(registered_scale_positive, register_scale=True)
|
|
|
|
assert mutated.status == "indeterminate"
|
|
|
|
|
|
def test_registered_confirmation_mutation_changes_the_real_verdict(
|
|
monkeypatch: pytest.MonkeyPatch,
|
|
registered_scale_positive: Path,
|
|
) -> None:
|
|
import synthid_runtime._synthid_confirmation as confirmation_detector
|
|
import synthid_runtime._synthid_registered as registered_detector
|
|
|
|
components = registered_detector.registered_components(
|
|
np.asarray(Image.open(registered_scale_positive).convert("RGB"), dtype=np.uint8),
|
|
detector._load_template()[0],
|
|
detector._load_template()[1],
|
|
)
|
|
assert components.confirmation is not None
|
|
assert components.decision_score >= detector.REGISTERED_THRESHOLD
|
|
monkeypatch.setattr(
|
|
confirmation_detector,
|
|
"MIN_COHERENCE",
|
|
float(np.nextafter(components.confirmation.joint_coherence, np.inf)),
|
|
)
|
|
|
|
mutated = detector.detect_synthid(registered_scale_positive, register_scale=True)
|
|
|
|
assert mutated.status == "indeterminate"
|
|
|
|
|
|
def test_supported_negative_does_not_claim_clean(supported_images: tuple[Path, Path]) -> None:
|
|
_positive, negative = supported_images
|
|
|
|
result = detector.detect_synthid(negative, register_scale=False)
|
|
|
|
assert result.status == "indeterminate"
|
|
assert result.detected is False
|
|
assert result.score == pytest.approx(0.0)
|
|
|
|
|
|
def test_threshold_mutation_changes_the_real_verdict(
|
|
monkeypatch: pytest.MonkeyPatch,
|
|
supported_images: tuple[Path, Path],
|
|
) -> None:
|
|
positive, _negative = supported_images
|
|
baseline = detector.detect_synthid(positive, register_scale=False)
|
|
assert baseline.score is not None
|
|
assert baseline.status == "detected"
|
|
mutated_threshold = float(np.nextafter(baseline.score, np.inf))
|
|
assert mutated_threshold > baseline.score
|
|
|
|
monkeypatch.setattr(detector, "TILE_THRESHOLD", mutated_threshold)
|
|
mutated = detector.detect_synthid(positive, register_scale=False)
|
|
|
|
assert mutated.status == "indeterminate"
|
|
assert mutated.threshold == mutated_threshold
|
|
|
|
|
|
def test_unsupported_geometry_is_distinct_from_negative(tmp_path: Path) -> None:
|
|
path = tmp_path / "small.png"
|
|
Image.new("RGB", (64, 32), "white").save(path)
|
|
|
|
result = detector.detect_synthid(path, register_scale=False)
|
|
|
|
assert result.status == "unsupported"
|
|
assert result.score is None
|
|
assert (result.width, result.height) == (64, 32)
|
|
assert result.reason is not None
|
|
assert result.to_dict()["metadata_used_for_verdict"] is False
|
|
assert result.to_dict()["provider_scope"] == "provider-neutral"
|
|
|
|
|
|
def test_shared_bgr_decode_matches_file_decode(supported_images: tuple[Path, Path]) -> None:
|
|
import cv2
|
|
|
|
positive, _negative = supported_images
|
|
bgr = cv2.imread(str(positive))
|
|
assert bgr is not None
|
|
|
|
from_file = detector.detect_synthid(positive, register_scale=False)
|
|
from_array = detector.detect_synthid(positive, image=bgr, register_scale=False)
|
|
|
|
assert from_array == from_file
|
|
|
|
|
|
def test_supported_geometry_requires_pixel_dependencies(
|
|
monkeypatch: pytest.MonkeyPatch,
|
|
supported_images: tuple[Path, Path],
|
|
) -> None:
|
|
_positive, negative = supported_images
|
|
monkeypatch.setattr(detector, "is_available", lambda: False)
|
|
|
|
with pytest.raises(RuntimeError, match="needs numpy and OpenCV"):
|
|
detector.detect_synthid(negative, register_scale=False)
|
|
|
|
|
|
def test_fold_accepts_non_divisible_geometry_without_resampling() -> None:
|
|
rng = np.random.default_rng(20260810)
|
|
tile = rng.normal(0.0, 8.0, size=(16, 16, 3))
|
|
repeated = np.tile(tile, (19, 20, 1)) + 128.0
|
|
|
|
divisible = detector.fold_residual_template(
|
|
repeated,
|
|
tile_height=16,
|
|
tile_width=16,
|
|
denoise_sigma=1.0,
|
|
)
|
|
non_divisible = detector.fold_residual_template(
|
|
repeated[:299, :317],
|
|
tile_height=16,
|
|
tile_width=16,
|
|
denoise_sigma=1.0,
|
|
)
|
|
divisible_unit, _ = detector.unit_tile(divisible)
|
|
non_divisible_unit, _ = detector.unit_tile(non_divisible)
|
|
|
|
assert non_divisible.shape == (16, 16, 3)
|
|
assert float(np.sum(divisible_unit * non_divisible_unit)) > 0.999
|
|
|
|
|
|
def test_non_divisible_fold_matches_modulo_cell_means() -> None:
|
|
import cv2
|
|
|
|
rng = np.random.default_rng(44041)
|
|
pixels = rng.integers(0, 256, size=(53, 71, 3), dtype=np.uint8)
|
|
source = pixels.astype(np.float32)
|
|
# Mirror the module's documented per-channel blur: OpenCV's multi-channel
|
|
# GaussianBlur is not bit-identical to per-channel calls (observed 3e-5 on
|
|
# cv2 4.10.0), so a three-channel reference cannot satisfy atol=0.
|
|
residual = np.stack(
|
|
[
|
|
source[:, :, channel]
|
|
- cv2.GaussianBlur(
|
|
source[:, :, channel].copy(),
|
|
(0, 0),
|
|
sigmaX=1.25,
|
|
sigmaY=1.25,
|
|
borderType=cv2.BORDER_REFLECT_101,
|
|
)
|
|
for channel in range(3)
|
|
],
|
|
axis=2,
|
|
)
|
|
expected = np.empty((16, 16, 3), dtype=np.float64)
|
|
for tile_y in range(16):
|
|
for tile_x in range(16):
|
|
expected[tile_y, tile_x] = residual[tile_y::16, tile_x::16].mean(
|
|
axis=(0, 1),
|
|
dtype=np.float64,
|
|
)
|
|
expected -= np.mean(expected, axis=(0, 1), keepdims=True)
|
|
|
|
actual = detector.fold_residual_template(
|
|
pixels,
|
|
tile_height=16,
|
|
tile_width=16,
|
|
denoise_sigma=1.25,
|
|
)
|
|
|
|
np.testing.assert_allclose(actual, expected, rtol=0.0, atol=0.0)
|
|
|
|
|
|
def test_fold_rejects_tile_larger_than_image() -> None:
|
|
pixels = np.zeros((15, 16, 3), dtype=np.uint8)
|
|
|
|
with pytest.raises(ValueError, match="at least as large"):
|
|
detector.fold_residual_template(
|
|
pixels,
|
|
tile_height=16,
|
|
tile_width=16,
|
|
denoise_sigma=1.0,
|
|
)
|
|
|
|
|
|
def test_verdict_does_not_claim_the_watermark() -> None:
|
|
"""The result must not assert SynthID, because the statistic is not SynthID.
|
|
|
|
This was unguarded until 2026-08-16, and the claim had been wrong for months
|
|
without a single test noticing. The fields are pinned by value rather than by
|
|
presence so that a rename back to a watermark claim fails here.
|
|
"""
|
|
result = detector.SynthIDDetection(
|
|
status="detected",
|
|
width=4096,
|
|
height=2560,
|
|
score=1.0,
|
|
threshold=1.0,
|
|
)
|
|
|
|
payload = result.to_dict()
|
|
|
|
assert payload["signal_family"] == "generation-pipeline-lattice"
|
|
assert payload["identifies_watermark"] is False
|
|
assert payload["tile_aligned_crop_required"] is True
|
|
assert "synthid" not in str(payload["signal_family"]).lower()
|
|
|
|
|
|
def test_the_statistic_is_locked_to_the_image_origin() -> None:
|
|
"""A crop off the tile grid must destroy the score, and that must stay visible.
|
|
|
|
SynthID's published evaluation retains 99.97% TPR under aggressive crop and
|
|
resize. This statistic loses everything to a seven-pixel shift, measured on
|
|
the real runtime at 4096x2560 where aligned crops scored up to 1.069 and
|
|
shifted ones reached -0.438. The property is asserted here so that any future
|
|
expert claiming to read the watermark has to survive the same shift first.
|
|
"""
|
|
template, sigma, *_model = detector._load_template()
|
|
tile = template / np.max(np.abs(template))
|
|
pixels = np.full((1024, 1024, 3), 128.0)
|
|
pixels += 6.0 * np.tile(tile, (64, 64, 1))
|
|
aligned = np.clip(np.rint(pixels), 0, 255).astype(np.uint8)
|
|
|
|
aligned_score, _folded = detector.folded_template_score(aligned, template, sigma)
|
|
# Seven is deliberately coprime with the 16-pixel tile, so no residual phase survives.
|
|
shifted_score, _shifted_folded = detector.folded_template_score(
|
|
aligned[7:, 7:],
|
|
template,
|
|
sigma,
|
|
)
|
|
|
|
assert aligned_score > 0.5
|
|
assert shifted_score < 0.1 * aligned_score
|