mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-10-14 13:30:55 +02:00
Victor Kuznetsov
5c6588b3ef
Repair image and metadata publication contracts, detection routing, research artifact validation, release verification, and the published agent skill. Refresh the dependency lock and reconcile documentation with verified behavior. Add regression coverage and record the completed audit and corrected VideoSeal measurements. Preserve the existing Accelerate and Lightning versions. Validation: Ruff and Pyright pass; 2358 tests pass with 3 skipped. The maintenance gate exits 2 on the documented Accelerate and Lightning security findings.
101 lines
4.1 KiB
Python
101 lines
4.1 KiB
Python
from __future__ import annotations
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import json
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import sys
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from pathlib import Path
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import numpy as np
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from click.testing import CliRunner
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from PIL import Image
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "scripts"))
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import synthid_runtime_expert_scores as scorer
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def test_unsupported_geometry_emits_no_synthetic_scores() -> None:
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observations = scorer.score_pixels(np.zeros((64, 64, 3), dtype=np.uint8))
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assert observations == [
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{"name": scorer.FIXED_EXPERT_NAME, "supported": False, "score": None},
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{"name": scorer.REGISTERED_EXPERT_NAME, "supported": False, "score": None},
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{"name": scorer.LARGE_EXPERT_NAME, "supported": False, "score": None},
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{"name": scorer.OPPONENT_EXPERT_NAME, "supported": False, "score": None},
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{"name": scorer.FINE_OPPONENT_EXPERT_NAME, "supported": False, "score": None},
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]
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def test_supported_image_scores_each_expert_once(monkeypatch) -> None:
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calls = {"fixed": 0, "registered": 0}
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def detect(path, *, image, register_scale=False):
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branch = "registered" if register_scale else "fixed"
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calls[branch] += 1
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return scorer.synthid_detector.SynthIDDetection(
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status="detected",
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width=1024,
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height=1024,
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score=1.5 if register_scale else 0.25,
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threshold=1.0 if register_scale else 0.17,
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detector=scorer.REGISTERED_EXPERT_NAME if register_scale else scorer.FIXED_EXPERT_NAME,
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)
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monkeypatch.setattr(scorer.synthid_detector, "detect_synthid", detect)
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observations = scorer.score_pixels(np.zeros((1024, 1024, 3), dtype=np.uint8))
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assert observations == [
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{"name": scorer.FIXED_EXPERT_NAME, "supported": True, "score": 0.25},
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{"name": scorer.REGISTERED_EXPERT_NAME, "supported": True, "score": 1.5},
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{"name": scorer.LARGE_EXPERT_NAME, "supported": False, "score": None},
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{"name": scorer.OPPONENT_EXPERT_NAME, "supported": False, "score": None},
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{"name": scorer.FINE_OPPONENT_EXPERT_NAME, "supported": False, "score": None},
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]
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assert calls == {"fixed": 1, "registered": 1}
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def test_pixels_must_be_rgb_uint8() -> None:
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with np.testing.assert_raises_regex(ValueError, "RGB uint8"):
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scorer.score_pixels(np.zeros((64, 64, 3), dtype=np.float32))
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def test_cli_writes_hash_pinned_observation_manifest(tmp_path: Path) -> None:
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image_path = tmp_path / "small.png"
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report_path = tmp_path / "scores.json"
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Image.new("RGB", (64, 64), (1, 2, 3)).save(image_path)
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result = CliRunner().invoke(scorer.main, [str(image_path), "--report-out", str(report_path)])
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assert result.exit_code == 0, result.output
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report = json.loads(report_path.read_text(encoding="utf-8"))
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assert report["schema_version"] == 2
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assert report["experts"] == list(scorer.EXPERT_NAMES)
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assert len(report["records"][0]["id"]) == 64
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assert report["records"][0]["width"] == 64
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assert all(not observation["supported"] for observation in report["records"][0]["observations"])
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def test_large_default_is_not_mislabeled_as_fixed(monkeypatch) -> None:
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def detect(path, *, image, register_scale=False):
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detector_id = scorer.REGISTERED_EXPERT_NAME if register_scale else scorer.LARGE_EXPERT_NAME
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return scorer.synthid_detector.SynthIDDetection(
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status="unsupported" if register_scale else "detected",
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width=4096,
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height=4096,
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score=None if register_scale else 1.2,
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threshold=1.0,
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detector=detector_id,
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)
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monkeypatch.setattr(scorer.synthid_detector, "detect_synthid", detect)
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observations = scorer.score_pixels(np.zeros((4096, 4096, 3), dtype=np.uint8))
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assert observations == [
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{"name": scorer.FIXED_EXPERT_NAME, "supported": False, "score": None},
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{"name": scorer.REGISTERED_EXPERT_NAME, "supported": False, "score": None},
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{"name": scorer.LARGE_EXPERT_NAME, "supported": True, "score": 1.2},
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{"name": scorer.OPPONENT_EXPERT_NAME, "supported": False, "score": None},
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{"name": scorer.FINE_OPPONENT_EXPERT_NAME, "supported": False, "score": None},
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]
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