mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-08-09 23:50:40 +02:00
Keep the pixel forensics in the library, drop the ai-score tooling
`scripts/ai_score.py` and the dataset scanner that fed it are gone: the detector they trained is not something this project runs, and the corpus lived outside the repository anyway. Nothing else referenced them. The scanner's pixel layer was worth keeping, so it moves into the package as `pixel_evidence.py` -- six families of scale-robust statistics (block-DCT histograms and Benford deviation, FFT band energies and CFA peaks, high-pass residual, error level, gradient, colour) measured in a single shared decode. The arithmetic was verified against the scanner over 60 corpus images, families and artifacts alike, before the scanner was removed; that comparison is no longer possible, which is why the tests now pin behavior instead: determinism, empty-not-wrong on images too small for a family, and one failing family not taking the others with it. It has no consumer. Nothing in the package reads it, and the module says so. `artifacts=True` returns the spatial layer -- perceptual hash, 128px thumbnail, coarse ELA/residual/phase maps. Those identify the source image rather than describe it, so they are opt-in and separate: everything else is a scalar or a fixed-length histogram nothing can be reconstructed from. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
Victor Kuznetsov
co-authored by
Claude Opus 5
Claude Opus 5
parent
b5da0510c9
commit
9a29dcac8a
@@ -1,436 +0,0 @@
|
||||
"""AI-generation scorer for scanned datasets.
|
||||
|
||||
Trains a gradient-boosted classifier on the metadata-derived labels of a
|
||||
scan_dataset.py output and scores every file, WITHOUT needing the
|
||||
metadata to be present at scoring time: the features are pixel and
|
||||
container statistics, so a metadata-stripped file still gets a score.
|
||||
The model is distribution-specific by design; evaluate it again before
|
||||
using it on an unrelated target distribution.
|
||||
|
||||
Modes:
|
||||
uv run --with scikit-learn python scripts/ai_score.py train <scan_glob> <model.pkl> [v1|v2]
|
||||
uv run --with scikit-learn python scripts/ai_score.py score <scan_glob> <model.pkl> <out.jsonl>
|
||||
|
||||
<scan_glob> is a glob of scan_dataset shards, e.g. 'data/scan/part_*.jsonl'.
|
||||
score output: one JSON line per file with file, sha256, ai_score, and the
|
||||
label evidence when a metadata label existed (for monitoring drift).
|
||||
"""
|
||||
|
||||
import glob
|
||||
import json
|
||||
import math
|
||||
import pickle
|
||||
import sys
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
from typing import Any, Literal
|
||||
|
||||
import numpy as np
|
||||
|
||||
# AI-generator names in C2PA claim_generator_info (metadata-level truth).
|
||||
AI_GENERATORS = (
|
||||
"openai",
|
||||
"adobe firefly",
|
||||
"adobe_firefly",
|
||||
"microsoft responsible ai",
|
||||
"microsoft_designer",
|
||||
"black forest labs",
|
||||
"fal-ai",
|
||||
"bria",
|
||||
"chatgpt",
|
||||
"stability",
|
||||
"dreamina",
|
||||
"canva",
|
||||
)
|
||||
# Human-origin software (negative evidence, NOT proof by itself).
|
||||
HUMAN_SOFTWARE = (
|
||||
"photoshop",
|
||||
"lightroom",
|
||||
"picsart",
|
||||
"snapseed",
|
||||
"paint.net",
|
||||
"gimp",
|
||||
"capture one",
|
||||
"meitu",
|
||||
"xingtu",
|
||||
"snow",
|
||||
)
|
||||
|
||||
_FEAT_SCALAR = [
|
||||
("noise", "noise_std"),
|
||||
("noise", "noise_kurtosis"),
|
||||
("fft", "cfa_peak"),
|
||||
("ela", "ela_mean"),
|
||||
("ela", "ela_p95"),
|
||||
("gradient", "laplacian_var"),
|
||||
("color", "saturation_mean"),
|
||||
("color", "value_mean"),
|
||||
("dct", "benford_mad"),
|
||||
]
|
||||
FeatureSchema = Literal["v1", "v2"]
|
||||
_FFT_BANDS = 8
|
||||
_GRADIENT_BINS = 10
|
||||
_COLOR_BINS = 64
|
||||
_DCT_AC_POSITIONS = 8
|
||||
_DCT_AC_BINS = 21
|
||||
_JPEG_QUANT_TABLES = 2
|
||||
_JPEG_QUANT_VALUES = 64
|
||||
_SCORE_BATCH = 4096
|
||||
|
||||
_V1_FEATURE_NAMES = (
|
||||
*(key for _, key in _FEAT_SCALAR),
|
||||
*(f"fft_band_energy_{index}" for index in range(_FFT_BANDS)),
|
||||
*(f"gradient_hist_{index}" for index in range(_GRADIENT_BINS)),
|
||||
*(f"color_hist_4x4x4_{index}" for index in range(_COLOR_BINS)),
|
||||
"jpeg_444",
|
||||
"jpeg_progressive",
|
||||
"megapixels",
|
||||
"aspect_ratio",
|
||||
"format_jpeg",
|
||||
"format_png",
|
||||
)
|
||||
_V2_EXTRA_FEATURE_NAMES = (
|
||||
"cfa_peak_0",
|
||||
"cfa_peak_1",
|
||||
*(f"dct_ac_{position}_{bin_index}" for position in range(_DCT_AC_POSITIONS) for bin_index in range(_DCT_AC_BINS)),
|
||||
*(f"jpeg_quant_{table}_{index}" for table in range(_JPEG_QUANT_TABLES) for index in range(_JPEG_QUANT_VALUES)),
|
||||
"jpeg_quant_table_count",
|
||||
"jpeg_huffman_table_count",
|
||||
"jpeg_huffman_total_bytes",
|
||||
"jpeg_scan_count",
|
||||
"jpeg_restart_interval",
|
||||
"jpeg_precision_bits",
|
||||
"jpeg_adobe_transform",
|
||||
"jpeg_jfif_present",
|
||||
"format_webp",
|
||||
"format_isobmff",
|
||||
"format_other",
|
||||
)
|
||||
_SCHEMA_FEATURE_NAMES: dict[str, tuple[str, ...]] = {
|
||||
"v1": _V1_FEATURE_NAMES,
|
||||
"v2": _V1_FEATURE_NAMES + _V2_EXTRA_FEATURE_NAMES,
|
||||
}
|
||||
|
||||
|
||||
def parse_schema(value: Any) -> FeatureSchema:
|
||||
"""Validate a feature-schema token (CLI argument or model-bundle field)."""
|
||||
if value == "v1" or value == "v2":
|
||||
return value
|
||||
raise ValueError(f"Unknown feature schema: {value!r}")
|
||||
|
||||
|
||||
def feature_names(schema: FeatureSchema = "v1") -> tuple[str, ...]:
|
||||
"""Return the stable ordered feature names for a model schema."""
|
||||
return _SCHEMA_FEATURE_NAMES[parse_schema(schema)]
|
||||
|
||||
|
||||
def _fixed_values(values: Any, count: int, *, normalize: bool = False) -> list[float]:
|
||||
"""Return a fixed-width float vector, padding absent values with NaN."""
|
||||
if not isinstance(values, (list, tuple)):
|
||||
return [float("nan")] * count
|
||||
result = [float(value) for value in values[:count]]
|
||||
if normalize and result:
|
||||
total = sum(value for value in result if math.isfinite(value))
|
||||
if total > 0:
|
||||
result = [value / total for value in result]
|
||||
return result + [float("nan")] * (count - len(result))
|
||||
|
||||
|
||||
def _optional_float(mapping: dict[str, Any], key: str) -> float:
|
||||
value = mapping.get(key)
|
||||
return float(value) if isinstance(value, (int, float)) else float("nan")
|
||||
|
||||
|
||||
def label_of(record: dict[str, Any]) -> int | None:
|
||||
"""1 = strong metadata AI, 0 = human-origin, None = unlabeled."""
|
||||
store = record.get("c2pa_store") or {}
|
||||
for m in (store.get("manifests") or {}).values():
|
||||
for cgi in m.get("claim_generator_info") or []:
|
||||
if any(g in str(cgi.get("name", "")).lower() for g in AI_GENERATORS):
|
||||
return 1
|
||||
for a in m.get("assertions") or []:
|
||||
d = a.get("data") if isinstance(a.get("data"), dict) else {}
|
||||
if "trainedAlgorithmicMedia" in str(d.get("digitalSourceType", "")):
|
||||
return 1
|
||||
for c in record.get("png_chunks", []):
|
||||
t = c.get("text", "")
|
||||
kw = t.split("\x00")[0]
|
||||
if kw in ("prompt", "workflow", "parameters") or "AIGC" in t[:80]:
|
||||
return 1
|
||||
exif = record.get("exif", {})
|
||||
z = exif.get("0th") or {}
|
||||
e = exif.get("Exif") or {}
|
||||
if z.get("Make") and z.get("Model") and (e.get("MakerNote") or e.get("LensModel") or e.get("LensMake")):
|
||||
return 0
|
||||
for c in record.get("png_chunks", []):
|
||||
if c.get("apple_screenshot_marker"):
|
||||
return 0
|
||||
for v in (record.get("iptc") or {}).values():
|
||||
if str(v).strip() == "Screenshot":
|
||||
return 0
|
||||
if any(t in str(z.get("Software", "")).lower() for t in HUMAN_SOFTWARE):
|
||||
return 0
|
||||
return None
|
||||
|
||||
|
||||
def features_of(record: dict[str, Any], *, schema: FeatureSchema = "v1") -> list[float]:
|
||||
"""The structural feature vector (pixel + container stats, no metadata)."""
|
||||
v = [float((record.get(s) or {}).get(k, np.nan)) for s, k in _FEAT_SCALAR]
|
||||
fft = record.get("fft") or {}
|
||||
gradient = record.get("gradient") or {}
|
||||
color = record.get("color") or {}
|
||||
v.extend(_fixed_values(fft.get("fft_band_energy"), _FFT_BANDS))
|
||||
v.extend(_fixed_values(gradient.get("gradient_hist"), _GRADIENT_BINS))
|
||||
v.extend(_fixed_values(color.get("color_hist_4x4x4"), _COLOR_BINS))
|
||||
jf = record.get("jpeg_forensics") or {}
|
||||
v.append(1.0 if jf.get("subsampling") == "4:4:4" else 0.0)
|
||||
v.append(1.0 if jf.get("progressive") else 0.0)
|
||||
pil = record.get("pil") or {}
|
||||
w, h = pil.get("width") or 0, pil.get("height") or 0
|
||||
v += [float(w * h) / 1e6, float(w) / max(h, 1)]
|
||||
fmt = record.get("content_format")
|
||||
v += [1.0 if fmt == "jpeg" else 0.0, 1.0 if fmt == "png" else 0.0]
|
||||
if schema == "v2":
|
||||
v.extend(_fixed_values(fft.get("cfa_peaks"), 2))
|
||||
dct_hist = (record.get("dct") or {}).get("dct_ac_hist")
|
||||
for position in range(_DCT_AC_POSITIONS):
|
||||
values = dct_hist[position] if isinstance(dct_hist, list) and position < len(dct_hist) else None
|
||||
v.extend(_fixed_values(values, _DCT_AC_BINS, normalize=True))
|
||||
quant_tables = jf.get("quant_tables")
|
||||
for table in range(_JPEG_QUANT_TABLES):
|
||||
values = quant_tables.get(str(table)) if isinstance(quant_tables, dict) else None
|
||||
v.extend(_fixed_values(values, _JPEG_QUANT_VALUES))
|
||||
is_jpeg = fmt == "jpeg"
|
||||
huffman_tables = jf.get("huffman_tables_hex")
|
||||
huffman_values = huffman_tables if isinstance(huffman_tables, list) else []
|
||||
v.extend(
|
||||
[
|
||||
float(len(quant_tables)) if isinstance(quant_tables, dict) else (0.0 if is_jpeg else float("nan")),
|
||||
float(len(huffman_values)) if is_jpeg else float("nan"),
|
||||
(
|
||||
float(sum(len(value) // 2 for value in huffman_values if isinstance(value, str)))
|
||||
if is_jpeg
|
||||
else float("nan")
|
||||
),
|
||||
_optional_float(jf, "scan_count"),
|
||||
_optional_float(jf, "restart_interval"),
|
||||
_optional_float(jf, "precision_bits"),
|
||||
_optional_float(jf, "adobe_transform"),
|
||||
1.0 if jf.get("jfif") else (0.0 if is_jpeg else float("nan")),
|
||||
1.0 if fmt == "webp" else 0.0,
|
||||
1.0 if isinstance(fmt, str) and fmt.startswith("isobmff:") else 0.0,
|
||||
(
|
||||
1.0
|
||||
if fmt is not None and fmt not in {"jpeg", "png", "webp"} and not str(fmt).startswith("isobmff:")
|
||||
else 0.0
|
||||
),
|
||||
]
|
||||
)
|
||||
elif schema != "v1":
|
||||
raise ValueError(f"Unknown feature schema: {schema}")
|
||||
expected = len(feature_names(schema))
|
||||
if len(v) != expected:
|
||||
raise ValueError(f"Feature schema {schema} produced {len(v)} values; expected {expected}")
|
||||
return v
|
||||
|
||||
|
||||
def grouped_stratified_split(
|
||||
labels: np.ndarray,
|
||||
hashes: np.ndarray,
|
||||
*,
|
||||
test_size: float = 0.3,
|
||||
random_state: int = 0,
|
||||
) -> tuple[np.ndarray, np.ndarray]:
|
||||
"""Split records by hash while preserving label balance across hash groups."""
|
||||
if len(labels) != len(hashes):
|
||||
raise ValueError("labels and hashes must have equal length")
|
||||
if not 0 < test_size < 1:
|
||||
raise ValueError("test_size must be between 0 and 1")
|
||||
groups: dict[str, list[int]] = defaultdict(list)
|
||||
group_labels: dict[str, int] = {}
|
||||
for index, (label, digest) in enumerate(zip(labels, hashes, strict=True)):
|
||||
key = str(digest) if digest else f"__row_{index}"
|
||||
int_label = int(label)
|
||||
if key in group_labels and group_labels[key] != int_label:
|
||||
raise ValueError(f"Hash {key!r} has conflicting labels")
|
||||
group_labels[key] = int_label
|
||||
groups[key].append(index)
|
||||
keys_by_label: dict[int, list[str]] = defaultdict(list)
|
||||
for key, label in group_labels.items():
|
||||
keys_by_label[label].append(key)
|
||||
rng = np.random.default_rng(random_state)
|
||||
test_set: set[str] = set()
|
||||
for class_keys in keys_by_label.values():
|
||||
if len(class_keys) < 2:
|
||||
raise ValueError("Each label needs at least two distinct hash groups")
|
||||
shuffled = np.asarray(class_keys)
|
||||
rng.shuffle(shuffled)
|
||||
test_count = min(max(round(len(shuffled) * test_size), 1), len(shuffled) - 1)
|
||||
test_set.update(shuffled[:test_count].tolist())
|
||||
train_set = set(groups) - test_set
|
||||
train = np.asarray([index for key, indices in groups.items() if key in train_set for index in indices])
|
||||
test = np.asarray([index for key, indices in groups.items() if key in test_set for index in indices])
|
||||
return np.sort(train), np.sort(test)
|
||||
|
||||
|
||||
def temporal_holdout_split(
|
||||
dates: np.ndarray,
|
||||
hashes: np.ndarray,
|
||||
*,
|
||||
train_fraction: float = 0.7,
|
||||
) -> tuple[np.ndarray, np.ndarray, str]:
|
||||
"""Split chronologically and remove later copies of training hashes."""
|
||||
if len(dates) != len(hashes):
|
||||
raise ValueError("dates and hashes must have equal length")
|
||||
if not 0 < train_fraction < 1:
|
||||
raise ValueError("train_fraction must be between 0 and 1")
|
||||
unique_dates = sorted({str(date) for date in dates})
|
||||
if len(unique_dates) < 2:
|
||||
raise ValueError("Temporal holdout needs at least two distinct dates")
|
||||
cutoff_index = min(int(len(unique_dates) * train_fraction), len(unique_dates) - 1)
|
||||
cutoff = unique_dates[cutoff_index]
|
||||
train = np.flatnonzero(dates < cutoff)
|
||||
test = np.flatnonzero((dates >= cutoff) & ~np.isin(hashes, hashes[train]))
|
||||
return train, test, cutoff
|
||||
|
||||
|
||||
def model_schema(bundle: dict[str, Any]) -> FeatureSchema:
|
||||
"""Read a model's feature schema, defaulting legacy bundles to v1."""
|
||||
return parse_schema(bundle.get("feature_schema", "v1"))
|
||||
|
||||
|
||||
def iter_records(pattern: str) -> Any:
|
||||
for path in sorted(glob.glob(pattern)):
|
||||
with open(path) as fh:
|
||||
for line in fh:
|
||||
yield json.loads(line)
|
||||
|
||||
|
||||
def cmd_train(pattern: str, model_path: str, schema: FeatureSchema = "v2") -> None:
|
||||
from sklearn.ensemble import HistGradientBoostingClassifier
|
||||
from sklearn.impute import SimpleImputer
|
||||
from sklearn.metrics import average_precision_score, roc_auc_score
|
||||
|
||||
# keep the feature vector, not the record: a parsed scan record is an order of
|
||||
# magnitude larger than the row it collapses to.
|
||||
labeled: dict[str, tuple[list[float], int, str]] = {}
|
||||
missing_hash_index = 0
|
||||
for r in iter_records(pattern):
|
||||
if "noise" not in r:
|
||||
continue
|
||||
lab = label_of(r)
|
||||
if lab is None:
|
||||
continue
|
||||
digest = r.get("sha256")
|
||||
if not digest:
|
||||
digest = f"__missing_{missing_hash_index}"
|
||||
missing_hash_index += 1
|
||||
date = Path(r["file"]).parent.name
|
||||
previous = labeled.get(str(digest))
|
||||
if previous is not None and previous[1] != lab:
|
||||
raise ValueError(f"Hash {digest!r} has conflicting labels")
|
||||
if previous is None or date < previous[2]:
|
||||
labeled[str(digest)] = (features_of(r, schema=schema), lab, date)
|
||||
|
||||
rows, labels, row_dates = zip(*labeled.values(), strict=True)
|
||||
X = np.asarray(rows)
|
||||
y = np.asarray(labels)
|
||||
dates = np.asarray(row_dates)
|
||||
hashes = np.asarray(list(labeled))
|
||||
print(
|
||||
f"unique labeled: {len(y)} "
|
||||
f"(pos {int(y.sum())}, neg {int((1 - y).sum())}, features {X.shape[1]}, schema {schema})"
|
||||
)
|
||||
|
||||
def fit(train: np.ndarray) -> tuple[Any, Any]:
|
||||
rows = X[train]
|
||||
imputer = SimpleImputer(strategy="median")
|
||||
classifier = HistGradientBoostingClassifier(
|
||||
random_state=0,
|
||||
max_iter=300,
|
||||
l2_regularization=5.0,
|
||||
early_stopping=False,
|
||||
)
|
||||
classifier.fit(imputer.fit_transform(rows), y[train])
|
||||
return imputer, classifier
|
||||
|
||||
train, test = grouped_stratified_split(y, hashes, test_size=0.3, random_state=0)
|
||||
imp, clf = fit(train)
|
||||
p = clf.predict_proba(imp.transform(X[test]))[:, 1]
|
||||
print(f"SHA-grouped holdout: AUC {roc_auc_score(y[test], p):.4f} | AP {average_precision_score(y[test], p):.4f}")
|
||||
|
||||
temporal_train, temporal_test, cutoff = temporal_holdout_split(dates, hashes)
|
||||
if len(temporal_train) > 100 and len(temporal_test) > 100:
|
||||
imp_t, clf_t = fit(temporal_train)
|
||||
pt = clf_t.predict_proba(imp_t.transform(X[temporal_test]))[:, 1]
|
||||
auc_t = roc_auc_score(y[temporal_test], pt)
|
||||
ap_t = average_precision_score(y[temporal_test], pt)
|
||||
print(f"temporal (<{cutoff}): AUC {auc_t:.4f} | AP {ap_t:.4f}")
|
||||
|
||||
all_indices = np.arange(len(y))
|
||||
imp, clf = fit(all_indices)
|
||||
bundle = {
|
||||
"imputer": imp,
|
||||
"clf": clf,
|
||||
"feature_schema": schema,
|
||||
"feature_names": feature_names(schema),
|
||||
}
|
||||
with open(model_path, "wb") as f:
|
||||
pickle.dump(bundle, f)
|
||||
print(f"model written: {model_path}")
|
||||
|
||||
|
||||
def cmd_score(pattern: str, model_path: str, out_path: str) -> None:
|
||||
# the model file is produced locally by `train`; do not load third-party pickles
|
||||
with open(model_path, "rb") as f:
|
||||
bundle = pickle.load(f) # noqa: S301
|
||||
imp, clf = bundle["imputer"], bundle["clf"]
|
||||
schema = model_schema(bundle)
|
||||
stored_names = bundle.get("feature_names")
|
||||
if stored_names is not None and tuple(stored_names) != feature_names(schema):
|
||||
raise ValueError(f"Model feature names do not match schema {schema}")
|
||||
n = 0
|
||||
batch_rows: list[list[float]] = []
|
||||
batch_meta: list[dict[str, Any]] = []
|
||||
|
||||
def flush(out: Any) -> None:
|
||||
"""Score one batch: per-record predict_proba is dominated by call overhead."""
|
||||
nonlocal n
|
||||
if not batch_rows:
|
||||
return
|
||||
scores = clf.predict_proba(imp.transform(np.asarray(batch_rows)))[:, 1]
|
||||
for meta, score in zip(batch_meta, scores, strict=True):
|
||||
out.write(json.dumps({**meta, "ai_score": round(float(score), 4)}) + "\n")
|
||||
n += len(batch_rows)
|
||||
batch_rows.clear()
|
||||
batch_meta.clear()
|
||||
print(f" {n}", flush=True)
|
||||
|
||||
with open(out_path, "w") as out:
|
||||
for r in iter_records(pattern):
|
||||
if "noise" not in r:
|
||||
continue
|
||||
batch_rows.append(features_of(r, schema=schema))
|
||||
batch_meta.append({"file": r["file"], "sha256": r.get("sha256"), "metadata_label": label_of(r)})
|
||||
if len(batch_rows) >= _SCORE_BATCH:
|
||||
flush(out)
|
||||
flush(out)
|
||||
print(f"scored {n} -> {out_path}")
|
||||
|
||||
|
||||
def main() -> None:
|
||||
if len(sys.argv) < 2 or sys.argv[1] not in ("train", "score"):
|
||||
print(__doc__)
|
||||
sys.exit(2)
|
||||
if sys.argv[1] == "train" and len(sys.argv) in (4, 5):
|
||||
schema = parse_schema(sys.argv[4]) if len(sys.argv) == 5 else "v2"
|
||||
cmd_train(sys.argv[2], sys.argv[3], schema)
|
||||
elif sys.argv[1] == "score" and len(sys.argv) == 5:
|
||||
cmd_score(sys.argv[2], sys.argv[3], sys.argv[4])
|
||||
else:
|
||||
print(__doc__)
|
||||
sys.exit(2)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user