Files
remove-ai-watermarks/scripts/synthid_research_manifest.py
T
Victor Kuznetsov 07b07634cf Merge the docs/arxiv-paper-review research line into main (#95)
* Add rigorous SynthID research and evaluation harness

* Record SynthID cross-epoch validation results

* Reset SynthID detector hypotheses to paired training

* Add paired wavelet and spectral SynthID probes

* Record closed-corpus SynthID detector ablations

* Add registered SynthID phase probing

* Add periodic SynthID tile probing

* Add calibrated SynthID pixel detector

* Harden scale-registered SynthID detection

* Record SynthID phase and channel rescue findings

* Add production SynthID routing and OpenAI verification

* Reframe periodic pixel route as pipeline lattice; confirm and harden detection

The frozen periodic experts read an origin-anchored generation-pipeline lattice destroyed by a crop off the tile grid, not the crop-robust SynthID mark. Route the pixel result as an experimental pipeline_lattice signal kept out of the watermark inventory, and carry the crop sensitivity in every verdict envelope.

Add split-patch phase/amplitude/codeword confirmation for registered-v3, affine-lattice and cyclostationary research probes, and timeout/retry/error-taxonomy hardening for the official OpenAI verification path.

* Record pipeline-lattice reframing and registered-v3 phase lock

Document that the pixel route detects an origin-anchored lattice rather than the watermark, and record the registered-v3 two-pixel phase-lock measurement: a 2px diagonal crop killed all 36 tested detections (28 foreign-generator, 8 Google) with recovery only at offsets that are multiples of four. Update README, CLI, Python API, supported-signals, known-limitations, module-internals, the SynthID reference, and the detector research plan.

* Split SynthID campaign docs by detector, classifiers, and removal

Keep Model 1 as a research AI-versus-camera result outside the library.
Retire the mixed oracle-ladders page to a routing hub and mark the plan
file as a chronological archive.

* Keep 2026-08-22 SynthID campaign results in research docs only

Drop Bayer, Model 1, and lattice re-check numbers from user guides and
module internals. The library surface is unchanged.

* Unplug lattice detector from package, expand research docs

* Remove lattice detector sources from the public package

* Document SynthID GitHub sweep findings

* Document general AI classifier sweep

* Document modern-negative taxonomy audit and classifier doc split

Split general AI-classifier research into ai-generated-image-classifiers.md
and record the modern-negative campaign: Unsplash/Openverse date-clean cells,
contamination controls, quarter-hard paired result, closed linear veto and
taxonomy-continuation arms.

pre-commit: 1) maintain.sh - exit 1, known uv-secure lightning advisory with no upstream fix (same triage as main); core checks separately green (ruff, format, pyright, 1564 tests); 2) /simplify - docs-only single pass, no findings; 3) docs sync - split references updated everywhere, none stale; 4) CLAUDE.md - routing line already updated, compliant

* Record wild-AI audit, Meta provider class, and stock-negative expansion

The wild vendor-flagged AI cell (300 stratified rows) puts Model 1 recall at
69.7% on unknown-renderer stock AI; the stock-negative harvest triples the
modern fashion/product cells and confirms the combined-pool veto control;
the Meta muse-image corpus doubles to 132 rows with its margin sweep; a
per-channel cv2 reference fixes the latent fold test under cv2 4.10.0.

pre-commit: 1) maintain.sh - exit 1, known uv-secure lightning advisory with no upstream fix; core checks separately green (ruff, format, pyright, 1665 tests); 2) /simplify - docs-only single pass, no findings; 3) docs sync - new run references point at the gitignored research store, none stale; 4) CLAUDE.md - compact, no changes needed

* Close representation-change campaign: both candidates falsified

Pre-registered gates (AI-test >=1772, fresh OI <=50, Kodak 0, FLUX >=288,
EvalGEN >=95) rejected DINOv2-L ridge head (1427/1847, 162/3000, 1/24, 29/300,
5/100) and the paired-reconstruction FFT tower (14/1847, 12/3000, 0/24, 0/300,
0/100; contract learned at 0.949 pairwise separation but no transfer to
production generators). Campaign verdict: general classifier beyond local
scale as pursued; product stays on documented remainder.

pre-commit: 1) maintain.sh - exit 1, known uv-secure lightning advisory with no upstream fix; core checks green minutes ago (ruff, format, pyright, 1665 tests); 2) /simplify - docs-only single pass, no findings; 3) docs sync - verdict mirrored in data/research/REPRESENTATION-CAMPAIGN.md, no stale refs; 4) CLAUDE.md - no changes

* Record full failure map, H2 router falsification, and H3 process detector closure

20 measured negative domains with batch-2 (memes, whiteboards, tickets, book
pages). H2 domain router falsified: 97.9% of AI-test looks structured, every
family 94-100%. H3 process detector on 369 contrastive pairs from three
generators (OpenAI gpt-image-1-mini, Meta muse-image-1.0, Gemini flash-image)
achieves 0.909 pairwise AUC on training pairs but transfers at AUC 0.55-0.60
to production AI; human receipts score higher than any AI family. The
generator-specific spectral trace does not generalize across three generators,
closing the process-trace family in its current form.

pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1665 tests); 2) /simplify - docs + research scripts, single pass, no findings; 3) docs sync - all artifacts in data/research/, no stale refs; 4) CLAUDE.md - no changes

* Record H4-H6 research hypotheses: MLP probe, spectral invariant, TTA voting

H4 (MLP on frozen CLIP): 98.6% AI-test recall but 15.8% photo FPR — same
trade-off curve as quarter-hard. H5 (spectral invariant from CVPR 2025):
2.4% recall, the 1/f^alpha profile does not discriminate current generators.
H6 (TTA voting): fixes museum FP but not documents/UI/fashion, loses 9% recall.
All three falsified against frozen gates; ten total closed hypotheses now
document the CLIP-L representation limit from every angle.

Also records the provider cascade across all 20 domains (69.8% stray on
documents mirrors the detector failure map) and the full Y-phase campaign
PSNR data awaiting oracle rate-limit reset.

pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1665 tests); 2) /simplify - docs-only single pass, no findings; 3) docs sync - all artifacts in data/research/, no stale refs; 4) CLAUDE.md - no changes

* Fix Y-phase frequency convention, record H4-H6 and campaign results

The initial Y-phase campaign targeted FFT bins 16-32 instead of spatial
periods 16-32 px (1/32 to 1/16 cycles/px). After the fix, the full-octave
Y-phase scramble reproduces the known kill (not_detected at 20.5 dB on s1
at native 1122x1402). Partial sub-bands are visually invisible (42.5 dB)
but stay detected: the mark is distributed across the full octave, not
concentrated. Point intervention is not viable within this frequency band.

H4 MLP probe: 98.6% recall, 15.8% FPR (same trade-off wall).
H5 spectral invariant: 2.4% recall (1/f^alpha does not discriminate).
H6 TTA voting: fixes museum FP but not documents/UI/fashion.

pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (1665 tests, verified minutes ago); 2) /simplify - docs-only; 3) docs sync - all artifacts synced; 4) CLAUDE.md - no changes

* Add TC260 fifth class and per-class margin calibration to provider cascade

Five-head cascade (openai/google/tc260/meta/no_ai): openai 68.8%, google
76.6%, tc260 63.8%, meta 71.8% mean recall at uniform margin 0.50.
Per-class margins calibrated against 1.67% photo budget: openai 0.31,
google 0.29, tc260 0.39, meta 0.55. TC260 test recall 71.4% at calibrated
margins on 196 held-out images. Chinese-AIGC is a viable pixel class.

pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (verified within the hour); 2) /simplify - research script, single pass; 3) docs sync - artifacts in data/research/; 4) CLAUDE.md - no changes

* Record universal detector v3-v8: diverse training shifts the curve

Fresh MLP on frozen CLIP embeddings with 49 negative domains (12,258 images)
and 5,043 AI positives achieves 93.0% AI-test recall with 39/49 domains at
zero false positives. Digital art FP dropped from 99.3% (Model 1) to 24.8%,
fashion from 62-71% to 12%, UI from 99.3% to 12%. Photo FPR 0.7%. The
improvement comes from training data diversity on frozen embeddings, not
representation change (v5 fine-tune proved this by undoing the gains).
Also adds .local-eval to ruff exclude so the gate covers only tracked files.

pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1731 tests); 2) /simplify - docs + research; 3) docs sync - all artifacts in data/research/; 4) CLAUDE.md - no changes

* Add three-tier ensemble detector: DEFINITELY/POSSIBLY/LIKELY_HUMAN

Combines Model 1 and v8 into calibrated confidence tiers. DEFINITELY_AI
(both models agree) achieves 79.6% recall at 0.50% photo FPR with 46/49
domains at zero FP. The union reaches 93.3% recall at 1.80% photo FPR
with Kodak 0/24. First detector working across ALL content types with
per-tier calibrated error rates.

pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1731 tests); 2) /simplify - docs + ensemble; 3) docs sync - artifacts in data/research/; 4) CLAUDE.md - no changes

* Add provider v2: diversity training eliminates document leakage

Same diversity-training insight applied to the provider cascade. Adding
3,769 structured negative features to training drops document/receipt
leakage from 69.8% to 5.1%, UI from 10% to 4.3%, fashion from 5-15% to
0-3%. AI recall improves: OpenAI 84% (was 75%), Google 88.8% (was 79%).
Photo leakage 1.4%. Worst regression: memes 47.9% (was 27%).

pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1731 tests); 2) /simplify - provider v2; 3) docs sync - artifacts in data/research/; 4) CLAUDE.md - no changes

* Add universal v11: full taxonomy, 136 domains, 124 at zero FP

Master data plan mapped 148 visual domains. Massive collection (3,343
images, 73 domains from Openverse) plus gap-filler (724 images from
Safebooru anime/manga) expanded training to 19,768 images across 136
negative domains. Universal v11 (MLP-512 + Model 1 ensemble):
DEFINITELY 90.3% recall at 0.50% photo FPR, ANY 95.6% recall,
124/134 domains at exactly 0% FP, worst FP 13.8% (digital art).

New domains opened: anime, manga, chibi, mecha, vintage photos,
banknotes, newspapers, magazines, movie posters, antique maps,
watercolor/oil originals, patterns, cosmetics, real estate, 3D
renders, and 60+ more.

pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1731 tests); 2) /simplify - docs + research; 3) docs sync - artifacts in data/research/; 4) CLAUDE.md - no changes

* Confirm representation limit: v12 fine-tune destroys structured FP despite 6x data

Fine-tuning CLIP tower on 19,518 images achieves 95.1% standalone recall
but returns to 100% FP on art/UI and 14/59 zero-FP domains (vs 124/134
for frozen v11). This is a representation property, not a data issue.
Frozen CLIP + MLP (v11) is the only working architecture. Also records
provider v3 trained on all negative domains.

pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1731 tests); 2) /simplify - docs + research; 3) docs sync - artifacts in data/research/; 4) CLAUDE.md - no changes

* Document v13 plateau: 128/136 zero FP, frozen-CLIP ceiling confirmed

v13 with top-plan expansion (1,169 new images) improves domain coverage
to 128/136 at zero FP and reduces fashion FP to 5.3%, but DEFINITELY
recall plateaus at 89.6% and art FP at 15.9%. The frozen CLIP + MLP
architecture has reached its ceiling.

pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (verified minutes ago, 1731 tests); 2) /simplify - docs-only; 3) docs sync - v13 recorded; 4) CLAUDE.md - no changes

* Document the 2026-08-31 photo-classifier freeze.

Record the AI-versus-camera contract, Meta v3 hold-out, Flickr UI
replacement, and that receipts stay out of the supported domain.

* Scrub Spaces catalog details from classifier research docs

* Record receipt layout-gate probe for the universal detector
2026-09-01 11:37:32 -07:00

348 lines
14 KiB
Python

"""Audit a private SynthID research manifest before training or evaluation.
The research manifest is intentionally separate from ``data/synthid/manifest.csv``.
The latter records a small public regression corpus, while this schema tracks
private experiment lineage, provider-specific oracle evidence, and split groups.
Usage:
uv run python scripts/synthid_research_manifest.py MANIFEST.csv
uv run python scripts/synthid_research_manifest.py MANIFEST.csv --verify-files
"""
from __future__ import annotations
import csv
import hashlib
import logging
import re
from collections import defaultdict
from datetime import datetime
from pathlib import Path
import click
from PIL import Image
log = logging.getLogger(__name__)
FIELDNAMES = (
"artifact_sha256",
"pixel_sha256",
"artifact_path",
"parent_sha256",
"group_id",
"target_provider",
"source_provider",
"surface",
"model_epoch",
"generation_session",
"content_stratum",
"width",
"height",
"format",
"transform",
"split",
"c2pa_outcome",
"synthid_outcome",
"verified_via",
"evidence_reference",
"oracle_session",
"oracle_role",
"captured_at",
"oracle_checked_at",
"notes",
)
_SHA256 = re.compile(r"^[0-9a-f]{64}$")
_TARGET_PROVIDERS = {"openai", "google"}
_SOURCE_PROVIDERS = {"openai", "google", "camera", "other_ai", "synthetic", "editor"}
_SPLITS = {"discovery", "train", "validation", "test", "temporal"}
_C2PA_OUTCOMES = {"detected", "not_detected", "invalid", "not_present", "not_checked"}
_SYNTHID_OUTCOMES = {"detected", "not_detected", "indeterminate", "refused", "not_checked"}
_VERIFIERS = {"openai-api", "openai-web", "gemini-app", "synthid-portal", "source-evidence", "none"}
_FORMATS = {"png", "jpeg", "webp"}
_ORACLE_ROLES = {"ordinary", "source_control", "candidate", "sham"}
_FINAL_SPLITS = {"train", "validation", "test", "temporal"}
_MATCHING_VERIFIERS = {
"openai": {"openai-api", "openai-web"},
"google": {"gemini-app", "synthid-portal"},
}
def _read_rows(path: Path) -> tuple[list[dict[str, str]], list[str]]:
"""Read a CSV and return rows plus header errors."""
with path.open(newline="", encoding="utf-8") as stream:
reader = csv.DictReader(stream)
actual = tuple(reader.fieldnames or ())
missing = [field for field in FIELDNAMES if field not in actual]
errors = [f"header: missing required field {field!r}" for field in missing]
return list(reader), errors
def _is_iso8601(value: str) -> bool:
"""Return whether VALUE is a timezone-aware ISO-8601 timestamp."""
if not value:
return False
try:
parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
except ValueError:
return False
return parsed.tzinfo is not None
def _file_sha256(path: Path) -> str:
"""Hash a file without loading it entirely into memory."""
digest = hashlib.sha256()
with path.open("rb") as stream:
for chunk in iter(lambda: stream.read(1 << 20), b""):
digest.update(chunk)
return digest.hexdigest()
def decoded_image_fingerprint(path: Path) -> tuple[str, int, int, str]:
"""Return the decoded-RGB digest, geometry, and source format."""
with Image.open(path) as image:
image_format = (image.format or path.suffix.lstrip(".")).lower()
rgb = image.convert("RGB")
digest = hashlib.sha256(rgb.tobytes()).hexdigest()
return digest, rgb.width, rgb.height, "jpeg" if image_format == "jpg" else image_format
def _pixel_sha256(path: Path) -> tuple[str, int, int]:
"""Hash canonical decoded RGB pixels and return hash, width, and height."""
digest, width, height, _ = decoded_image_fingerprint(path)
return digest, width, height
def artifact_sha256(path: Path) -> str:
"""Return the artifact digest used by research manifests and inventories."""
return _file_sha256(path)
def pixel_fingerprint(path: Path) -> tuple[str, int, int]:
"""Return the decoded-RGB digest and geometry used by manifest verification."""
return _pixel_sha256(path)
def resolve_artifact_path(root: Path, value: str) -> Path | None:
"""Resolve a manifest-relative artifact path without allowing traversal."""
relative = Path(value)
if not value or relative.is_absolute() or ".." in relative.parts:
return None
candidate = (root / relative).resolve()
try:
candidate.relative_to(root.resolve())
except ValueError:
return None
return candidate
def _row_errors(row: dict[str, str], index: int) -> list[str]:
"""Validate one row without consulting other rows."""
prefix = f"row {index}"
errors: list[str] = []
artifact_sha = row.get("artifact_sha256", "")
pixel_sha = row.get("pixel_sha256", "")
parent_sha = row.get("parent_sha256", "")
if not _SHA256.fullmatch(artifact_sha):
errors.append(f"{prefix}: invalid artifact_sha256")
if not _SHA256.fullmatch(pixel_sha):
errors.append(f"{prefix}: invalid pixel_sha256")
if parent_sha and not _SHA256.fullmatch(parent_sha):
errors.append(f"{prefix}: invalid parent_sha256")
for field in ("group_id", "surface", "model_epoch", "generation_session", "content_stratum", "transform"):
if not row.get(field, "").strip():
errors.append(f"{prefix}: {field} must not be empty")
target = row.get("target_provider", "")
source = row.get("source_provider", "")
split = row.get("split", "")
c2pa = row.get("c2pa_outcome", "")
synthid = row.get("synthid_outcome", "")
verifier = row.get("verified_via", "")
oracle_role = row.get("oracle_role", "")
image_format = row.get("format", "").lower()
if target not in _TARGET_PROVIDERS:
errors.append(f"{prefix}: unsupported target_provider {target!r}")
if source not in _SOURCE_PROVIDERS:
errors.append(f"{prefix}: unsupported source_provider {source!r}")
if split not in _SPLITS:
errors.append(f"{prefix}: unsupported split {split!r}")
if c2pa not in _C2PA_OUTCOMES:
errors.append(f"{prefix}: unsupported c2pa_outcome {c2pa!r}")
if synthid not in _SYNTHID_OUTCOMES:
errors.append(f"{prefix}: unsupported synthid_outcome {synthid!r}")
if verifier not in _VERIFIERS:
errors.append(f"{prefix}: unsupported verified_via {verifier!r}")
if image_format not in _FORMATS:
errors.append(f"{prefix}: unsupported format {image_format!r}")
if oracle_role not in _ORACLE_ROLES:
errors.append(f"{prefix}: unsupported oracle_role {oracle_role!r}")
for dimension in ("width", "height"):
try:
if int(row.get(dimension, "")) <= 0:
raise ValueError
except ValueError:
errors.append(f"{prefix}: {dimension} must be a positive integer")
if not _is_iso8601(row.get("captured_at", "")):
errors.append(f"{prefix}: captured_at must be timezone-aware ISO-8601")
final_outcome = synthid in {"detected", "not_detected"}
if split in _FINAL_SPLITS and not final_outcome:
errors.append(f"{prefix}: split {split!r} requires a detected or not_detected SynthID outcome")
if final_outcome and not _is_iso8601(row.get("oracle_checked_at", "")):
errors.append(f"{prefix}: a final SynthID outcome requires oracle_checked_at")
matching = _MATCHING_VERIFIERS.get(target, set())
if synthid == "detected" and verifier not in matching:
errors.append(f"{prefix}: a detected {target!r} signal requires a matching provider verifier")
if synthid == "not_detected" and source == target and verifier not in matching:
errors.append(f"{prefix}: a same-provider negative requires a matching provider verifier")
if verifier in {"source-evidence", "none"} and synthid == "detected":
errors.append(f"{prefix}: {verifier!r} cannot establish a positive SynthID label")
if verifier == "source-evidence" and source == target:
errors.append(f"{prefix}: source-evidence cannot establish a same-provider negative")
if verifier == "source-evidence" and not row.get("evidence_reference", "").strip():
errors.append(f"{prefix}: source-evidence requires evidence_reference")
if verifier in matching and not row.get("oracle_session", "").strip():
errors.append(f"{prefix}: provider verification requires oracle_session")
if oracle_role == "source_control" and synthid != "detected":
errors.append(f"{prefix}: a source_control must have a detected SynthID outcome")
transform = row.get("transform", "")
if transform == "original" and parent_sha:
errors.append(f"{prefix}: an original must not have parent_sha256")
if transform != "original" and not parent_sha:
errors.append(f"{prefix}: a derivative requires parent_sha256")
return errors
def _lineage_errors(rows: list[dict[str, str]]) -> list[str]:
"""Validate uniqueness, parent links, group splits, and lineage cycles."""
errors: list[str] = []
by_sha: dict[str, dict[str, str]] = {}
group_splits: defaultdict[str, set[str]] = defaultdict(set)
pixel_groups: defaultdict[str, set[str]] = defaultdict(set)
for index, row in enumerate(rows, start=2):
artifact_sha = row.get("artifact_sha256", "")
if artifact_sha in by_sha:
errors.append(f"row {index}: duplicate artifact_sha256 {artifact_sha}")
else:
by_sha[artifact_sha] = row
group_splits[row.get("group_id", "")].add(row.get("split", ""))
pixel_groups[row.get("pixel_sha256", "")].add(row.get("group_id", ""))
for index, row in enumerate(rows, start=2):
parent_sha = row.get("parent_sha256", "")
if not parent_sha:
continue
parent = by_sha.get(parent_sha)
if parent is None:
errors.append(f"row {index}: parent_sha256 is not present in the manifest")
continue
if parent.get("group_id") != row.get("group_id"):
errors.append(f"row {index}: derivative and parent must share group_id")
if parent.get("target_provider") != row.get("target_provider"):
errors.append(f"row {index}: derivative and parent must share target_provider")
for group_id, splits in sorted(group_splits.items()):
if group_id and len(splits) > 1:
errors.append(f"group {group_id!r}: leaks across splits {sorted(splits)}")
for pixel_sha, groups in sorted(pixel_groups.items()):
if pixel_sha and len(groups) > 1:
errors.append(f"pixel_sha256 {pixel_sha}: appears in multiple groups {sorted(groups)}")
for artifact_sha in by_sha:
seen: set[str] = set()
current_sha = artifact_sha
while current_sha:
if current_sha in seen:
errors.append(f"artifact_sha256 {artifact_sha}: lineage cycle detected")
break
seen.add(current_sha)
current = by_sha.get(current_sha)
if current is None:
break
current_sha = current.get("parent_sha256", "")
return errors
def _oracle_session_errors(rows: list[dict[str, str]]) -> list[str]:
"""Require a healthy source control before accepting removal outcomes."""
errors: list[str] = []
positive_controls = {
(row.get("oracle_session", ""), row.get("group_id", ""), row.get("target_provider", ""))
for row in rows
if row.get("oracle_role") == "source_control" and row.get("synthid_outcome") == "detected"
}
for index, row in enumerate(rows, start=2):
if row.get("oracle_role") not in {"candidate", "sham"} or row.get("synthid_outcome") != "not_detected":
continue
key = (row.get("oracle_session", ""), row.get("group_id", ""), row.get("target_provider", ""))
if key not in positive_controls:
errors.append(
f"row {index}: a not_detected {row.get('oracle_role')} requires a detected "
"source_control in the same oracle session, group, and provider"
)
return errors
def audit_manifest(path: Path, *, verify_files: bool = False) -> list[str]:
"""Return all manifest errors, including optional on-disk hash checks."""
rows, errors = _read_rows(path)
if errors:
return errors
for index, row in enumerate(rows, start=2):
errors.extend(_row_errors(row, index))
errors.extend(_lineage_errors(rows))
errors.extend(_oracle_session_errors(rows))
if verify_files:
root = path.parent
for index, row in enumerate(rows, start=2):
artifact = resolve_artifact_path(root, row.get("artifact_path", ""))
if artifact is None:
errors.append(f"row {index}: artifact_path must be a safe manifest-relative path")
continue
if not artifact.is_file():
errors.append(f"row {index}: artifact_path does not exist: {row.get('artifact_path', '')}")
continue
if _file_sha256(artifact) != row.get("artifact_sha256"):
errors.append(f"row {index}: artifact_sha256 does not match the file")
try:
pixel_sha, width, height = _pixel_sha256(artifact)
except Exception as exc: # Pillow intentionally accepts many user-controlled formats.
errors.append(f"row {index}: could not decode artifact: {exc}")
continue
if pixel_sha != row.get("pixel_sha256"):
errors.append(f"row {index}: pixel_sha256 does not match decoded RGB pixels")
if str(width) != row.get("width") or str(height) != row.get("height"):
errors.append(f"row {index}: dimensions do not match decoded pixels")
return errors
@click.command()
@click.argument("manifest", type=click.Path(exists=True, dir_okay=False, path_type=Path))
@click.option("--verify-files", is_flag=True, help="Verify artifact bytes, decoded pixels, and dimensions.")
def main(manifest: Path, verify_files: bool) -> None:
"""Audit MANIFEST for evidence, lineage, and split integrity."""
logging.basicConfig(level=logging.INFO, format="%(message)s")
errors = audit_manifest(manifest, verify_files=verify_files)
if errors:
for error in errors:
log.error("ERROR: %s", error)
raise click.ClickException(f"manifest audit failed with {len(errors)} error(s)")
log.info("Manifest audit passed: %s", manifest)
if __name__ == "__main__":
main()