mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-10-14 21:40:47 +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.
124 lines
4.1 KiB
Python
124 lines
4.1 KiB
Python
"""Export correctly named fixed, large, and routed registered observations.
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The output is an input manifest for ``synthid_conformal_cascade.py``. All
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experts consume decoded RGB pixels only. Unsupported geometry is recorded
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explicitly and never represented by a synthetic score.
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"""
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from __future__ import annotations
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import json
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import logging
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import sys
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from pathlib import Path
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from typing import TYPE_CHECKING, TypedDict
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import click
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import numpy as np
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if TYPE_CHECKING:
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from numpy.typing import NDArray
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(PROJECT_ROOT / "scripts"))
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sys.path.insert(0, str(PROJECT_ROOT / "src"))
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from synthid_pixel_attack import load_rgb # noqa: E402
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from synthid_research_manifest import artifact_sha256 # noqa: E402
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from synthid_runtime import synthid_detector # noqa: E402
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log = logging.getLogger(__name__)
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FIXED_EXPERT_NAME = synthid_detector.DETECTOR_ID
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REGISTERED_EXPERT_NAME = synthid_detector.REGISTERED_DETECTOR_ID
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LARGE_EXPERT_NAME = synthid_detector.LARGE_DETECTOR_ID
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OPPONENT_EXPERT_NAME = synthid_detector.OPPONENT_REGISTERED_DETECTOR_ID
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FINE_OPPONENT_EXPERT_NAME = synthid_detector.FINE_OPPONENT_REGISTERED_DETECTOR_ID
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EXPERT_NAMES = (
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FIXED_EXPERT_NAME,
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REGISTERED_EXPERT_NAME,
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LARGE_EXPERT_NAME,
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OPPONENT_EXPERT_NAME,
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FINE_OPPONENT_EXPERT_NAME,
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)
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class ExpertScore(TypedDict):
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"""One JSON-safe runtime expert observation."""
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name: str
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supported: bool
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score: float | None
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class ScoredImage(TypedDict):
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"""One hash-pinned image with every runtime expert observation."""
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id: str
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path: str
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width: int
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height: int
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observations: list[ExpertScore]
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def _observation(name: str, supported: bool, score: float | None) -> ExpertScore:
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return {"name": name, "supported": supported, "score": score}
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def score_pixels(pixels: NDArray[np.uint8]) -> list[ExpertScore]:
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"""Return five named slots; inactive registered fallbacks remain unsupported."""
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if pixels.ndim != 3 or pixels.shape[2] != 3 or pixels.dtype != np.uint8:
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raise ValueError("pixels must be an RGB uint8 array")
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bgr_pixels = np.ascontiguousarray(pixels[:, :, ::-1])
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native = synthid_detector.detect_synthid("decoded-image", image=bgr_pixels, register_scale=False)
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registered = synthid_detector.detect_synthid("decoded-image", image=bgr_pixels, register_scale=True)
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observations = {name: _observation(name, False, None) for name in EXPERT_NAMES}
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for result, expected in (
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(native, {FIXED_EXPERT_NAME, LARGE_EXPERT_NAME}),
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(registered, {REGISTERED_EXPERT_NAME, OPPONENT_EXPERT_NAME, FINE_OPPONENT_EXPERT_NAME}),
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):
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if result.detector not in expected:
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raise RuntimeError(f"unexpected SynthID expert: {result.detector}")
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observations[result.detector] = _observation(result.detector, result.status != "unsupported", result.score)
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return list(observations.values())
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def score_path(path: Path) -> ScoredImage:
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"""Decode PATH once and return one hash-pinned observation record."""
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pixels = load_rgb(path)
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height, width = pixels.shape[:2]
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return {
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"id": artifact_sha256(path),
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"path": str(path),
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"width": width,
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"height": height,
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"observations": score_pixels(pixels),
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}
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@click.command()
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@click.argument("images", nargs=-1, required=True, type=click.Path(exists=True, dir_okay=False, path_type=Path))
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@click.option("--report-out", type=click.Path(dir_okay=False, path_type=Path), required=True)
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def main(images: tuple[Path, ...], report_out: Path) -> None:
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"""Score IMAGES with every shipped pixel expert."""
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logging.basicConfig(level=logging.INFO, format="%(message)s")
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records = [score_path(path) for path in images]
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report_out.parent.mkdir(parents=True, exist_ok=True)
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report_out.write_text(
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json.dumps(
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{
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"schema_version": 2,
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"experts": list(EXPERT_NAMES),
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"records": records,
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},
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indent=2,
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)
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+ "\n",
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encoding="utf-8",
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)
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log.info("Wrote %d runtime expert score records: %s", len(records), report_out)
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if __name__ == "__main__":
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main()
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