mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-08-19 12:07:13 +02:00
260 lines
10 KiB
Python
260 lines
10 KiB
Python
"""Discover and evaluate an exact-geometry periodic residual carrier.
|
|
|
|
The model folds a high-pass residual modulo a fixed tile, averaging thousands
|
|
of spatial repetitions before normalized correlation. It is a positive-only
|
|
research signal, not a universal or certified SynthID decoder.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
import logging
|
|
from dataclasses import asdict, dataclass
|
|
from pathlib import Path
|
|
|
|
import click
|
|
import numpy as np
|
|
from synthid_periodic_tile import cyclic_tile_correlations, fold_residual_template, unit_tile
|
|
from synthid_pixel_attack import load_rgb
|
|
from synthid_research_manifest import artifact_sha256
|
|
|
|
log = logging.getLogger(__name__)
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class PeriodicTileModel:
|
|
"""One exact-geometry normalized periodic-residual template."""
|
|
|
|
height: int
|
|
width: int
|
|
tile_height: int
|
|
tile_width: int
|
|
denoise_sigma: float
|
|
template: np.ndarray
|
|
expected_norm: float
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class PeriodicTileScore:
|
|
"""Normalized tile correlation and support for one image."""
|
|
|
|
path: str
|
|
score: float
|
|
active_support: float
|
|
row_shift: int
|
|
column_shift: int
|
|
repeat_count: int
|
|
|
|
|
|
def _load_rgb(path: Path, *, height: int, width: int) -> np.ndarray:
|
|
"""Load PATH as exact-geometry uint8 RGB."""
|
|
pixels = load_rgb(path)
|
|
if pixels.shape != (height, width, 3):
|
|
raise ValueError(f"{path}: geometry {pixels.shape[1]}x{pixels.shape[0]} does not match {width}x{height}")
|
|
return pixels
|
|
|
|
|
|
def discover_model(
|
|
paths: list[Path],
|
|
*,
|
|
tile_height: int,
|
|
tile_width: int,
|
|
denoise_sigma: float = 1.0,
|
|
) -> PeriodicTileModel:
|
|
"""Learn a normalized periodic template from positive PATHS."""
|
|
if len(paths) < 3:
|
|
raise ValueError("at least three positive images are required")
|
|
first_pixels = load_rgb(paths[0])
|
|
height, width = first_pixels.shape[:2]
|
|
unit_sum = np.zeros((tile_height, tile_width, 3), dtype=np.float64)
|
|
norms: list[float] = []
|
|
for index, path in enumerate(paths):
|
|
folded = fold_residual_template(
|
|
first_pixels if index == 0 else _load_rgb(path, height=height, width=width),
|
|
tile_height=tile_height,
|
|
tile_width=tile_width,
|
|
denoise_sigma=denoise_sigma,
|
|
)
|
|
unit, norm = unit_tile(folded)
|
|
unit_sum += unit
|
|
norms.append(norm)
|
|
template, template_norm = unit_tile(unit_sum / len(paths))
|
|
if template_norm == 0.0:
|
|
raise ValueError("positive images expose no periodic residual template")
|
|
return PeriodicTileModel(
|
|
height=height,
|
|
width=width,
|
|
tile_height=tile_height,
|
|
tile_width=tile_width,
|
|
denoise_sigma=denoise_sigma,
|
|
template=template,
|
|
expected_norm=float(np.median(norms)),
|
|
)
|
|
|
|
|
|
def score_image(path: Path, model: PeriodicTileModel, *, register: bool = False) -> PeriodicTileScore:
|
|
"""Score PATH against MODEL, optionally searching cyclic tile shifts."""
|
|
folded = fold_residual_template(
|
|
_load_rgb(path, height=model.height, width=model.width),
|
|
tile_height=model.tile_height,
|
|
tile_width=model.tile_width,
|
|
denoise_sigma=model.denoise_sigma,
|
|
)
|
|
unit, norm = unit_tile(folded)
|
|
if register:
|
|
correlations = cyclic_tile_correlations(model.template, unit)
|
|
row_shift, column_shift = np.unravel_index(int(np.argmax(correlations)), correlations.shape)
|
|
score = float(correlations[row_shift, column_shift])
|
|
else:
|
|
score = float(np.sum(model.template * unit))
|
|
row_shift = column_shift = 0
|
|
return PeriodicTileScore(
|
|
path=str(path),
|
|
score=score,
|
|
active_support=min(norm / (model.expected_norm + 1e-12), 1.0),
|
|
row_shift=row_shift,
|
|
column_shift=column_shift,
|
|
repeat_count=(model.height // model.tile_height) * (model.width // model.tile_width),
|
|
)
|
|
|
|
|
|
def calibrate_threshold(paths: list[Path], model: PeriodicTileModel, *, register: bool = False) -> float:
|
|
"""Return the first float above every negative score in PATHS."""
|
|
if not paths:
|
|
raise ValueError("at least one calibration negative is required")
|
|
maximum = max(score_image(path, model, register=register).score for path in paths)
|
|
return float(np.nextafter(maximum, np.inf))
|
|
|
|
|
|
def save_model(path: Path, model: PeriodicTileModel) -> None:
|
|
"""Save MODEL as a pickle-free numeric artifact without precision loss."""
|
|
path.parent.mkdir(parents=True, exist_ok=True)
|
|
np.savez_compressed(
|
|
path,
|
|
format_version=np.asarray(1, dtype=np.int32),
|
|
height=np.asarray(model.height, dtype=np.int32),
|
|
width=np.asarray(model.width, dtype=np.int32),
|
|
tile_height=np.asarray(model.tile_height, dtype=np.int32),
|
|
tile_width=np.asarray(model.tile_width, dtype=np.int32),
|
|
denoise_sigma=np.asarray(model.denoise_sigma, dtype=np.float64),
|
|
template=model.template.astype(np.float64),
|
|
expected_norm=np.asarray(model.expected_norm, dtype=np.float64),
|
|
)
|
|
|
|
|
|
def load_model(path: Path) -> PeriodicTileModel:
|
|
"""Load and validate one numeric periodic-tile model."""
|
|
with np.load(path, allow_pickle=False) as artifact:
|
|
if int(artifact["format_version"]) != 1:
|
|
raise ValueError("unsupported periodic-tile model format version")
|
|
model = PeriodicTileModel(
|
|
height=int(artifact["height"]),
|
|
width=int(artifact["width"]),
|
|
tile_height=int(artifact["tile_height"]),
|
|
tile_width=int(artifact["tile_width"]),
|
|
denoise_sigma=float(artifact["denoise_sigma"]),
|
|
template=np.asarray(artifact["template"], dtype=np.float64),
|
|
expected_norm=float(artifact["expected_norm"]),
|
|
)
|
|
if model.height < 1 or model.width < 1 or model.tile_height < 1 or model.tile_width < 1:
|
|
raise ValueError("invalid periodic-tile geometry")
|
|
if model.height % model.tile_height or model.width % model.tile_width:
|
|
raise ValueError("image geometry is not divisible by periodic-tile geometry")
|
|
if model.template.shape != (model.tile_height, model.tile_width, 3):
|
|
raise ValueError("invalid periodic-tile template shape")
|
|
if not np.all(np.isfinite(model.template)) or not np.isclose(np.linalg.norm(model.template), 1.0):
|
|
raise ValueError("invalid periodic-tile template")
|
|
if not np.isfinite(model.denoise_sigma) or model.denoise_sigma <= 0.0:
|
|
raise ValueError("invalid periodic-tile denoise sigma")
|
|
if not np.isfinite(model.expected_norm) or model.expected_norm <= 0.0:
|
|
raise ValueError("invalid periodic-tile expected norm")
|
|
return model
|
|
|
|
|
|
@click.group()
|
|
def main() -> None:
|
|
"""Discover and evaluate an exact-geometry periodic carrier."""
|
|
logging.basicConfig(level=logging.INFO, format="%(message)s")
|
|
|
|
|
|
@main.command()
|
|
@click.argument("positives", nargs=-1, required=True, type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
|
@click.option("--tile-height", type=click.IntRange(min=1), required=True)
|
|
@click.option("--tile-width", type=click.IntRange(min=1), required=True)
|
|
@click.option("--denoise-sigma", type=click.FloatRange(min=0.0, min_open=True), default=1.0, show_default=True)
|
|
@click.option("--model-out", type=click.Path(dir_okay=False, path_type=Path), required=True)
|
|
def discover(
|
|
positives: tuple[Path, ...],
|
|
tile_height: int,
|
|
tile_width: int,
|
|
denoise_sigma: float,
|
|
model_out: Path,
|
|
) -> None:
|
|
"""Learn a periodic tile from exact-geometry POSITIVES."""
|
|
save_model(
|
|
model_out,
|
|
discover_model(
|
|
list(positives),
|
|
tile_height=tile_height,
|
|
tile_width=tile_width,
|
|
denoise_sigma=denoise_sigma,
|
|
),
|
|
)
|
|
log.info("Wrote periodic-tile model: %s", model_out)
|
|
|
|
|
|
@main.command()
|
|
@click.argument("model_path", type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
|
@click.argument("negatives", nargs=-1, required=True, type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
|
@click.option("--register", is_flag=True, help="Search every cyclic shift within the learned tile.")
|
|
@click.option("--report-out", type=click.Path(dir_okay=False, path_type=Path), required=True)
|
|
def calibrate(model_path: Path, negatives: tuple[Path, ...], register: bool, report_out: Path) -> None:
|
|
"""Calibrate a zero-observed-error threshold on NEGATIVES."""
|
|
model = load_model(model_path)
|
|
threshold = calibrate_threshold(list(negatives), model, register=register)
|
|
report_out.parent.mkdir(parents=True, exist_ok=True)
|
|
report_out.write_text(
|
|
json.dumps(
|
|
{
|
|
"model": str(model_path),
|
|
"model_sha256": artifact_sha256(model_path),
|
|
"register": register,
|
|
"negative_count": len(negatives),
|
|
"threshold": threshold,
|
|
},
|
|
indent=2,
|
|
)
|
|
+ "\n",
|
|
encoding="utf-8",
|
|
)
|
|
log.info("Wrote periodic-tile calibration report: %s", report_out)
|
|
|
|
|
|
@main.command()
|
|
@click.argument("model_path", type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
|
@click.argument("images", nargs=-1, required=True, type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
|
@click.option("--register", is_flag=True, help="Search every cyclic shift within the learned tile.")
|
|
@click.option("--report-out", type=click.Path(dir_okay=False, path_type=Path), required=True)
|
|
def score(model_path: Path, images: tuple[Path, ...], register: bool, report_out: Path) -> None:
|
|
"""Score exact-geometry IMAGES with MODEL_PATH."""
|
|
model = load_model(model_path)
|
|
report_out.parent.mkdir(parents=True, exist_ok=True)
|
|
report_out.write_text(
|
|
json.dumps(
|
|
{
|
|
"model": str(model_path),
|
|
"model_sha256": artifact_sha256(model_path),
|
|
"register": register,
|
|
"scores": [asdict(score_image(image, model, register=register)) for image in images],
|
|
},
|
|
indent=2,
|
|
)
|
|
+ "\n",
|
|
encoding="utf-8",
|
|
)
|
|
log.info("Wrote periodic-tile score report: %s", report_out)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|