mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-07-12 02:56:32 +02:00
Victor Kuznetsov
c858006e93
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>
275 lines
12 KiB
Python
275 lines
12 KiB
Python
"""Tests for the known-visible-watermark registry (localize -> fill)."""
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from __future__ import annotations
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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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from remove_ai_watermarks import watermark_registry as reg
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DOUBAO_SAMPLE = Path(__file__).resolve().parents[1] / "data" / "samples" / "doubao-1.png"
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class TestCatalog:
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def test_keys(self):
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assert reg.mark_keys() == ["gemini", "doubao", "jimeng", "samsung", "jimeng_pill"]
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def test_all_in_auto(self):
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assert all(m.in_auto for m in reg.known_marks())
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def test_marks_expose_detect_and_mask(self):
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# Every mark drives the uniform localize -> fill contract: a detect callable
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# (verdict + bbox, no mask) and a mask callable (full-frame footprint).
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for m in reg.known_marks():
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assert callable(m._detect)
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assert callable(m._mask)
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def test_locations(self):
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by_key = {m.key: m for m in reg.known_marks()}
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assert by_key["gemini"].location == "bottom-right"
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assert by_key["doubao"].location == "bottom-right"
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assert by_key["jimeng"].location == "bottom-right"
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assert by_key["samsung"].location == "bottom-left"
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assert by_key["jimeng_pill"].location == "top-left"
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def test_get_mark_unknown_raises(self):
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with pytest.raises(KeyError):
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reg.get_mark("nope")
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class TestScan:
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def test_detect_marks_scans_all(self):
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img = np.zeros((256, 256, 3), np.uint8)
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keys = {d.key for d in reg.detect_marks(img)}
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assert keys == {"gemini", "doubao", "jimeng", "samsung", "jimeng_pill"}
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def test_blank_image_no_auto_mark(self):
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dets = reg.detect_marks(np.zeros((256, 256, 3), np.uint8), include_explicit=False)
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assert not any(d.detected for d in dets)
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class TestBackendResolution:
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def test_auto_resolves_to_available_backend(self):
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assert reg.resolve_backend("auto") in ("cv2", "migan", "lama")
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def test_explicit_backend_passes_through(self):
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assert reg.resolve_backend("cv2") == "cv2"
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assert reg.resolve_backend("lama") == "lama"
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def test_cv2_fallback_warns_once(self, monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture):
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import logging
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from remove_ai_watermarks import region_eraser
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monkeypatch.setattr(region_eraser, "lama_available", lambda: False)
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monkeypatch.setattr(region_eraser, "migan_available", lambda: False)
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monkeypatch.setattr(reg, "_warned_cv2_fallback", False)
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with caplog.at_level(logging.WARNING):
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assert reg.preferred_inpaint_backend() == "cv2"
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assert reg.preferred_inpaint_backend() == "cv2"
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assert sum("cv2 classical inpaint" in r.message for r in caplog.records) == 1
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class TestFill:
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def test_fill_erases_masked_region(self):
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# A bright square on a flat field, masked, is inpainted away (cv2 backend).
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img = np.full((128, 128, 3), 60, np.uint8)
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img[40:70, 40:70] = 240
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mask = np.zeros((128, 128), np.uint8)
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mask[36:74, 36:74] = 255
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out = reg.fill(img, mask, backend="cv2")
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assert out.shape == img.shape
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# the masked bright square is pulled toward the surrounding field
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assert int(out[55, 55].mean()) < 160
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def test_fill_empty_mask_is_noop(self):
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img = np.full((64, 64, 3), 100, np.uint8)
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out = reg.fill(img, np.zeros((64, 64), np.uint8), backend="cv2")
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assert np.array_equal(out, img)
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class TestProvenanceGate:
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"""The Gemini trust gate relaxes from 0.5 to 0.35 when provenance confirms Google;
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tested deterministically by stubbing the engine's raw detection confidence."""
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def _stub(self, monkeypatch: pytest.MonkeyPatch, conf: float) -> None:
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from remove_ai_watermarks.gemini_engine import DetectionResult
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def fake_detect(image, force_size=None, *, trust_provenance=False):
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return DetectionResult(detected=conf >= 0.35, confidence=conf, region=(10, 10, 48, 48))
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monkeypatch.setattr(reg._engine("gemini"), "detect_watermark", fake_detect)
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def test_midband_conf_needs_provenance(self, monkeypatch: pytest.MonkeyPatch):
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# conf 0.42 sits in [0.35, 0.5): demoted without provenance, trusted with it.
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self._stub(monkeypatch, 0.42)
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img = np.zeros((256, 256, 3), np.uint8)
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assert reg.get_mark("gemini").detect(img).detected is False
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assert reg.get_mark("gemini").detect(img, provenance=True).detected is True
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def test_high_conf_detected_either_way(self, monkeypatch: pytest.MonkeyPatch):
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self._stub(monkeypatch, 0.72)
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img = np.zeros((256, 256, 3), np.uint8)
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assert reg.get_mark("gemini").detect(img).detected is True
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assert reg.get_mark("gemini").detect(img, provenance=True).detected is True
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@pytest.mark.skipif(not DOUBAO_SAMPLE.exists(), reason="doubao sample not present")
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class TestRealSample:
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def test_doubao_sample_detected(self):
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from remove_ai_watermarks.image_io import imread
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fired = [d.key for d in reg.detect_marks(imread(DOUBAO_SAMPLE), include_explicit=False) if d.detected]
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assert "doubao" in fired
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def test_doubao_remove_returns_region(self):
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from remove_ai_watermarks.image_io import imread
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img = imread(DOUBAO_SAMPLE)
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result, region = reg.get_mark("doubao").remove(img, backend="cv2")
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assert region is not None
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assert result.shape == img.shape
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class TestLocalizeFill:
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def test_clean_corner_is_untouched(self):
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# No glyph in the corner -> no mask -> remove is a no-op copy.
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img = np.zeros((512, 512, 3), np.uint8)
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result, region = reg.get_mark("doubao").remove(img, backend="cv2")
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assert region is None
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assert np.array_equal(result, img)
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class TestSensitivity:
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"""``resolve_relax`` turns the sensitivity policy + evidence into the per-mark
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relaxation boolean the engines consume."""
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def test_strict_never_relaxes(self):
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# even with metadata provenance, strict keeps the conservative gate
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assert (
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reg.resolve_relax("gemini", sensitivity="strict", provenance=frozenset({"gemini"}), strict_keys=set())
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is False
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)
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def test_assume_ai_always_relaxes(self):
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assert reg.resolve_relax("gemini", sensitivity="assume_ai", provenance=frozenset(), strict_keys=set()) is True
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def test_auto_relaxes_on_own_metadata(self):
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assert (
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reg.resolve_relax("gemini", sensitivity="auto", provenance=frozenset({"gemini"}), strict_keys=set()) is True
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)
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def test_auto_strict_without_evidence(self):
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assert reg.resolve_relax("gemini", sensitivity="auto", provenance=frozenset(), strict_keys=set()) is False
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def test_auto_cross_mark_same_product(self):
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# a detected Jimeng wordmark relaxes the Jimeng pill (same product, other corner)
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assert (
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reg.resolve_relax("jimeng_pill", sensitivity="auto", provenance=frozenset(), strict_keys={"jimeng"}) is True
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)
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def test_auto_no_cross_mark_across_products(self):
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# a detected Jimeng wordmark must NOT relax Doubao (distinct products, same corner)
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assert reg.resolve_relax("doubao", sensitivity="auto", provenance=frozenset(), strict_keys={"jimeng"}) is False
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def test_remove_auto_marks_accepts_all_sensitivities(self):
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blank = np.zeros((256, 256, 3), np.uint8)
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for s in ("auto", "strict", "assume_ai"):
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_, removed = reg.remove_auto_marks(blank, sensitivity=s, backend="cv2")
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assert removed == []
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class TestArbiter:
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"""``decide`` is the PURE removal arbiter: (candidates, context) -> ordered
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winners, no image / no I/O. Tested in isolation by handing it fabricated
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Candidates -- this is the payoff of separating decision from perception."""
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@staticmethod
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def _c(key, *, strict=False, relaxed=False, flat=False):
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feats = {"footprint_flat": 1.0} if flat else {}
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return reg.Candidate(key, f"L:{key}", strict, relaxed, feats)
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def _keys(self, cands, ctx):
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return {d.candidate.key for d in reg.decide(cands, ctx)}
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def test_empty(self):
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assert reg.decide([], reg.Context()) == []
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def test_strict_uses_strict_verdict(self):
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# relaxed-only detection must NOT fire under strict
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assert self._keys([self._c("gemini", relaxed=True)], reg.Context(sensitivity="strict")) == set()
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def test_assume_ai_uses_relaxed(self):
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fired = reg.decide([self._c("gemini", relaxed=True)], reg.Context(sensitivity="assume_ai"))
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assert [d.candidate.key for d in fired] == ["gemini"]
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assert fired[0].relax is True
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def test_auto_relaxes_on_provenance(self):
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c = [self._c("gemini", relaxed=True)]
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assert self._keys(c, reg.Context(provenance=frozenset({"gemini"}))) == {"gemini"}
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assert self._keys(c, reg.Context()) == set() # no evidence -> strict verdict (not fired)
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def test_cross_mark_relaxes_pill_via_jimeng(self):
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cands = [self._c("jimeng", strict=True, relaxed=True), self._c("jimeng_pill", relaxed=True, flat=True)]
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assert self._keys(cands, reg.Context()) == {"jimeng", "jimeng_pill"}
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def test_pill_dropped_on_doubao(self):
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cands = [
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self._c("doubao", strict=True, relaxed=True),
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self._c("jimeng_pill", strict=True, relaxed=True, flat=True),
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]
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keys = self._keys(cands, reg.Context(provenance=frozenset({"jimeng"})))
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assert "doubao" in keys
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assert "jimeng_pill" not in keys
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def test_pill_metadata_arm_gated_on_flatness(self):
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ctx = reg.Context(provenance=frozenset({"jimeng"}))
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assert self._keys([self._c("jimeng_pill", strict=True, relaxed=True, flat=True)], ctx) == {"jimeng_pill"}
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assert self._keys([self._c("jimeng_pill", strict=True, relaxed=True, flat=False)], ctx) == set()
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def test_pill_wordmark_arm_ignores_flatness(self):
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# wordmark present -> pill removed even on a textured (non-flat) footprint
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cands = [
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self._c("jimeng", strict=True, relaxed=True),
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self._c("jimeng_pill", strict=True, relaxed=True, flat=False),
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]
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assert "jimeng_pill" in self._keys(cands, reg.Context())
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class TestProvenanceMaskThreading:
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"""Regression for the provenance-relaxed Gemini no-op (#1) and the false 'removed'
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label (#2). Before the fix, footprint_mask re-detected WITHOUT trust_provenance, the
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FP gate demoted the sparkle to detected=False, the mask came back None, yet
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remove_auto_marks still reported the mark as removed."""
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def test_relaxed_sparkle_yields_mask(self, monkeypatch: pytest.MonkeyPatch):
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# A sparkle a strict re-detect would demote (detected False) but a
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# provenance-relaxed detect accepts must still produce a removal mask.
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from remove_ai_watermarks.gemini_engine import DetectionResult
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def fake(image, force_size=None, *, trust_provenance=False):
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return DetectionResult(
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detected=trust_provenance, confidence=0.42 if trust_provenance else 0.30, region=(400, 400, 60)
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)
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monkeypatch.setattr(reg._engine("gemini"), "detect_watermark", fake)
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img = np.full((512, 512, 3), 90, np.uint8)
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assert reg.get_mark("gemini").localize(img, provenance=True).mask is not None
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assert reg.get_mark("gemini").localize(img, provenance=False).mask is None
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def test_no_label_when_mask_none(self, monkeypatch: pytest.MonkeyPatch):
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# A decided mark whose mask comes back None must NOT be reported as removed.
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from remove_ai_watermarks.gemini_engine import DetectionResult
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eng = reg._engine("gemini")
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monkeypatch.setattr(
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eng,
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"detect_watermark",
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lambda image, force_size=None, *, trust_provenance=False: DetectionResult(True, 0.9, (10, 10, 40)),
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)
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monkeypatch.setattr(eng, "footprint_mask", lambda image, *, force=False, region=None, dilate=None: None)
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_, removed = reg.remove_auto_marks(np.zeros((256, 256, 3), np.uint8), sensitivity="strict", backend="cv2")
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assert "Google Gemini sparkle" not in removed
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