Files
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

468 lines
19 KiB
Python

"""Build and verify an immutable confirmatory batch for a SynthID oracle.
The batch contains a referenced untouched source plus four lossless PNG views:
an exact-pixel re-encode, aligned periodic-tile subtraction, a cyclically
shifted tile, and an orthogonal random tile. Building the batch performs no
network requests. Oracle results remain empty until a separately authorized
submission records them.
"""
from __future__ import annotations
import json
import logging
import math
from datetime import datetime
from pathlib import Path
from typing import TYPE_CHECKING
import click
from PIL import Image
from synthid_periodic_tile_ablation import (
candidate_quality,
control_templates,
phase_score_pixels,
tile_score_pixels,
)
from synthid_periodic_tile_probe import PeriodicTileModel
from synthid_periodic_tile_probe import load_model as load_tile_model
from synthid_phase_carrier import PhaseCarrierModel
from synthid_phase_carrier import load_model as load_phase_model
from synthid_pixel_attack import load_rgb
from synthid_research_manifest import artifact_sha256, pixel_fingerprint
from synthid_tile_attack import subtract_tiled_template
if TYPE_CHECKING:
import numpy as np
log = logging.getLogger(__name__)
ROLE_ORDER = ("source", "reencode_control", "aligned", "shifted", "orthogonal_random")
DERIVATIVE_ROLES = ROLE_ORDER[1:]
FORMAT_VERSION = 1
SYNTHID_RESULTS = {"detected", "not_detected", "indeterminate", "refused"}
C2PA_RESULTS = {"present", "absent", "indeterminate", "unavailable"}
def _inside(path: Path, parent: Path) -> bool:
"""Return whether resolved PATH is inside resolved PARENT."""
try:
path.resolve().relative_to(parent.resolve())
except ValueError:
return False
return True
def _json_float(value: float) -> float | None:
"""Return finite VALUE or None for JSON interoperability."""
return value if math.isfinite(value) else None
def _write_png(path: Path, pixels: np.ndarray) -> None:
"""Write exact RGB PIXELS as a deterministic lossless PNG."""
path.parent.mkdir(parents=True, exist_ok=True)
if path.exists():
raise ValueError(f"refusing to overwrite oracle artifact: {path}")
Image.fromarray(pixels, mode="RGB").save(path, format="PNG", compress_level=9)
def _score_row(
pixels: np.ndarray,
*,
source: np.ndarray,
tile_model: PeriodicTileModel,
phase_model: PhaseCarrierModel,
tile_threshold: float,
phase_threshold: float,
active_threshold: float,
) -> dict[str, object]:
"""Return local frozen scores and paired fidelity for PIXELS."""
tile_score = tile_score_pixels(pixels, tile_model)
phase_score, active_support = phase_score_pixels(pixels, phase_model)
quality = candidate_quality(source, pixels)
return {
"tile_score": tile_score,
"tile_accepted": tile_score >= tile_threshold,
"phase_score": phase_score,
"active_support": active_support,
"phase_accepted": phase_score >= phase_threshold and active_support >= active_threshold,
"residual_rms": quality["residual_rms"],
"psnr_db": _json_float(quality["psnr_db"]),
"ssim": quality["ssim"],
"changed_pixel_fraction": quality["changed_pixel_fraction"],
}
def _artifact_row(
path: Path,
*,
role: str,
source_id: str,
in_batch: bool,
batch_root: Path,
scores: dict[str, object],
) -> dict[str, object]:
"""Return one hash-frozen manifest row for PATH."""
pixel_sha256, width, height = pixel_fingerprint(path)
return {
"source_id": source_id,
"role": role,
"path": str(path.relative_to(batch_root) if in_batch else path.resolve()),
"in_batch": in_batch,
"artifact_sha256": artifact_sha256(path),
"pixel_sha256": pixel_sha256,
"width": width,
"height": height,
"synthid_result": None,
"c2pa_result": None,
"submitted_at": None,
**scores,
}
def build_batch(
sources: list[Path],
*,
output_dir: Path,
tile_model_path: Path,
phase_model_path: Path,
tile_threshold: float,
phase_threshold: float,
active_threshold: float,
strength: float,
seed: int,
provider: str,
repository_root: Path,
) -> Path:
"""Build a preregistered, unsubmitted oracle batch and return its manifest."""
if not sources:
raise ValueError("at least one source is required")
if strength <= 0.0 or not math.isfinite(strength):
raise ValueError("strength must be finite and positive")
if provider not in {"google", "openai"}:
raise ValueError("provider must be google or openai")
if _inside(output_dir, repository_root):
raise ValueError("oracle batches must be written outside the repository")
if output_dir.exists() and any(output_dir.iterdir()):
raise ValueError("output directory must not already contain files")
output_dir.mkdir(parents=True, exist_ok=True)
tile_model = load_tile_model(tile_model_path)
phase_model = load_phase_model(phase_model_path)
if (tile_model.height, tile_model.width) != (phase_model.height, phase_model.width):
raise ValueError("tile and phase model geometries differ")
templates = control_templates(tile_model.template, seed=seed)
rows: list[dict[str, object]] = []
seen_source_hashes: set[str] = set()
for source_path in sources:
source_hash = artifact_sha256(source_path)
if source_hash in seen_source_hashes:
raise ValueError("duplicate source artifact")
seen_source_hashes.add(source_hash)
source = load_rgb(source_path)
if source.shape != (tile_model.height, tile_model.width, 3):
raise ValueError(f"{source_path}: geometry does not match the frozen models")
source_id = source_hash[:16]
score_args = {
"source": source,
"tile_model": tile_model,
"phase_model": phase_model,
"tile_threshold": tile_threshold,
"phase_threshold": phase_threshold,
"active_threshold": active_threshold,
}
source_scores = _score_row(source, **score_args)
rows.append(
_artifact_row(
source_path,
role="source",
source_id=source_id,
in_batch=False,
batch_root=output_dir,
scores=source_scores,
)
)
for role in DERIVATIVE_ROLES:
variant = (
source
if role == "reencode_control"
else subtract_tiled_template(
source,
templates[role] * tile_model.expected_norm,
strength=strength,
)
)
output_path = output_dir / source_id / f"{role}.png"
_write_png(output_path, variant)
rows.append(
_artifact_row(
output_path,
role=role,
source_id=source_id,
in_batch=True,
batch_root=output_dir,
scores=(source_scores if role == "reencode_control" else _score_row(variant, **score_args)),
)
)
manifest = {
"format_version": FORMAT_VERSION,
"status": "preregistered_unsubmitted",
"source_count": len(sources),
"request_count": len(rows),
"request_order": ROLE_ORDER,
"provider": provider,
"decision_rule": (
"Count causal success only when source, reencode_control, shifted, and "
"orthogonal_random are detected in the matching provider SynthID oracle, "
"aligned is not_detected, and indeterminate is never treated as negative."
),
"strength": strength,
"seed": seed,
"tile_threshold": tile_threshold,
"phase_threshold": phase_threshold,
"active_threshold": active_threshold,
"tile_model": str(tile_model_path.resolve()),
"tile_model_sha256": artifact_sha256(tile_model_path),
"phase_model": str(phase_model_path.resolve()),
"phase_model_sha256": artifact_sha256(phase_model_path),
"rows": rows,
}
manifest_path = output_dir / "manifest.json"
manifest_path.write_text(json.dumps(manifest, indent=2, allow_nan=False) + "\n", encoding="utf-8")
manifest_hash = artifact_sha256(manifest_path)
(output_dir / "manifest.sha256").write_text(manifest_hash + " manifest.json\n", encoding="utf-8")
results_template = {
"format_version": FORMAT_VERSION,
"manifest_sha256": manifest_hash,
"provider": provider,
"rows": [
{
"source_id": row["source_id"],
"role": row["role"],
"artifact_sha256": row["artifact_sha256"],
"synthid_result": None,
"c2pa_result": None,
"raw_response": None,
"submitted_at": None,
}
for row in rows
],
}
(output_dir / "results-template.json").write_text(
json.dumps(results_template, indent=2) + "\n",
encoding="utf-8",
)
verify_batch(manifest_path, repository_root=repository_root)
return manifest_path
def verify_batch(manifest_path: Path, *, repository_root: Path) -> dict[str, object]:
"""Verify manifest, model, source, and derivative hashes without mutation."""
batch_root = manifest_path.parent
if _inside(batch_root, repository_root):
raise ValueError("oracle batches must remain outside the repository")
digest_path = batch_root / "manifest.sha256"
expected_manifest_hash = digest_path.read_text(encoding="utf-8").split()[0]
if artifact_sha256(manifest_path) != expected_manifest_hash:
raise ValueError("manifest hash mismatch")
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
if manifest.get("format_version") != FORMAT_VERSION:
raise ValueError("unsupported oracle-batch manifest version")
rows = manifest.get("rows")
if not isinstance(rows, list) or len(rows) != manifest.get("request_count"):
raise ValueError("manifest request count mismatch")
if artifact_sha256(Path(manifest["tile_model"])) != manifest["tile_model_sha256"]:
raise ValueError("tile model hash mismatch")
if artifact_sha256(Path(manifest["phase_model"])) != manifest["phase_model_sha256"]:
raise ValueError("phase model hash mismatch")
groups: dict[str, list[str]] = {}
artifact_hashes: set[str] = set()
for row in rows:
source_id = str(row["source_id"])
groups.setdefault(source_id, []).append(str(row["role"]))
path = batch_root / str(row["path"]) if row["in_batch"] else Path(str(row["path"]))
if artifact_sha256(path) != row["artifact_sha256"]:
raise ValueError(f"artifact hash mismatch for {source_id}/{row['role']}")
pixel_sha256, width, height = pixel_fingerprint(path)
if (pixel_sha256, width, height) != (row["pixel_sha256"], row["width"], row["height"]):
raise ValueError(f"pixel fingerprint mismatch for {source_id}/{row['role']}")
artifact_hashes.add(str(row["artifact_sha256"]))
if any(tuple(roles) != ROLE_ORDER for roles in groups.values()):
raise ValueError("each source must have the fixed request-role order")
if len(groups) != manifest.get("source_count"):
raise ValueError("manifest source count mismatch")
if len(artifact_hashes) < len(rows) - len(groups):
raise ValueError("unexpected duplicate derivative artifacts")
template = json.loads((batch_root / "results-template.json").read_text(encoding="utf-8"))
expected_identities = [(row["source_id"], row["role"], row["artifact_sha256"]) for row in rows]
template_identities = [(row["source_id"], row["role"], row["artifact_sha256"]) for row in template.get("rows", [])]
if template.get("manifest_sha256") != expected_manifest_hash or template.get("provider") != manifest["provider"]:
raise ValueError("results template does not identify the preregistered batch")
if template_identities != expected_identities:
raise ValueError("results template row identities differ from the manifest")
return manifest
def _parse_submitted_at(value: object) -> None:
"""Reject timestamps that are absent or not timezone-aware ISO-8601."""
if not isinstance(value, str):
raise ValueError("submitted_at must be a timezone-aware ISO-8601 timestamp")
try:
parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
except ValueError as error:
raise ValueError("submitted_at must be a timezone-aware ISO-8601 timestamp") from error
if parsed.tzinfo is None:
raise ValueError("submitted_at must be a timezone-aware ISO-8601 timestamp")
def evaluate_results(
manifest_path: Path,
results_path: Path,
*,
repository_root: Path,
) -> dict[str, object]:
"""Validate a complete result file and apply the preregistered decision rule."""
manifest = verify_batch(manifest_path, repository_root=repository_root)
results = json.loads(results_path.read_text(encoding="utf-8"))
manifest_hash = artifact_sha256(manifest_path)
if results.get("format_version") != FORMAT_VERSION:
raise ValueError("unsupported oracle-results version")
if results.get("manifest_sha256") != manifest_hash or results.get("provider") != manifest["provider"]:
raise ValueError("results do not identify the preregistered batch")
manifest_rows = manifest["rows"]
result_rows = results.get("rows")
if not isinstance(result_rows, list) or len(result_rows) != len(manifest_rows):
raise ValueError("oracle results must cover every preregistered request")
grouped: dict[str, dict[str, str]] = {}
for expected, result in zip(manifest_rows, result_rows, strict=True):
identity = (result.get("source_id"), result.get("role"), result.get("artifact_sha256"))
expected_identity = (expected["source_id"], expected["role"], expected["artifact_sha256"])
if identity != expected_identity:
raise ValueError("oracle result order or artifact identity differs from the manifest")
synthid_result = result.get("synthid_result")
c2pa_result = result.get("c2pa_result")
if synthid_result not in SYNTHID_RESULTS:
raise ValueError("invalid or missing SynthID result")
if c2pa_result not in C2PA_RESULTS:
raise ValueError("invalid or missing C2PA result")
if not isinstance(result.get("raw_response"), str) or not result["raw_response"].strip():
raise ValueError("raw_response must preserve the nonempty verbatim oracle result")
_parse_submitted_at(result.get("submitted_at"))
grouped.setdefault(str(expected["source_id"]), {})[str(expected["role"])] = str(synthid_result)
source_results: list[dict[str, str]] = []
for source_id, roles in grouped.items():
if tuple(roles) != ROLE_ORDER:
raise ValueError("oracle results do not preserve the fixed role order")
values = set(roles.values())
if values & {"indeterminate", "refused"}:
verdict = "indeterminate"
elif (
roles["source"] == "detected"
and roles["reencode_control"] == "detected"
and roles["shifted"] == "detected"
and roles["orthogonal_random"] == "detected"
and roles["aligned"] == "not_detected"
):
verdict = "causal_success"
elif any(roles[role] != "detected" for role in ("source", "reencode_control", "shifted", "orthogonal_random")):
verdict = "control_failed"
else:
verdict = "aligned_still_detected"
source_results.append({"source_id": source_id, "verdict": verdict})
counts = {
verdict: sum(row["verdict"] == verdict for row in source_results)
for verdict in ("causal_success", "aligned_still_detected", "control_failed", "indeterminate")
}
return {
"format_version": FORMAT_VERSION,
"manifest_sha256": manifest_hash,
"provider": manifest["provider"],
"source_count": manifest["source_count"],
"counts": counts,
"sources": source_results,
}
@click.group()
def main() -> None:
"""Build and verify an immutable confirmatory oracle batch."""
logging.basicConfig(level=logging.INFO, format="%(message)s")
@main.command("build")
@click.argument("tile_model_path", type=click.Path(exists=True, dir_okay=False, path_type=Path))
@click.argument("phase_model_path", type=click.Path(exists=True, dir_okay=False, path_type=Path))
@click.argument("sources", nargs=-1, required=True, type=click.Path(exists=True, dir_okay=False, path_type=Path))
@click.option("--output-dir", type=click.Path(file_okay=False, path_type=Path), required=True)
@click.option("--tile-threshold", type=float, required=True)
@click.option("--phase-threshold", type=float, required=True)
@click.option("--active-threshold", type=float, required=True)
@click.option("--strength", type=click.FloatRange(min=0.0, min_open=True), default=2.0, show_default=True)
@click.option("--seed", type=int, default=20260810, show_default=True)
@click.option("--provider", type=click.Choice(["google", "openai"]), required=True)
def build_command(
tile_model_path: Path,
phase_model_path: Path,
sources: tuple[Path, ...],
output_dir: Path,
tile_threshold: float,
phase_threshold: float,
active_threshold: float,
strength: float,
seed: int,
provider: str,
) -> None:
"""Build a frozen batch from exact-geometry SOURCES."""
manifest = build_batch(
list(sources),
output_dir=output_dir,
tile_model_path=tile_model_path,
phase_model_path=phase_model_path,
tile_threshold=tile_threshold,
phase_threshold=phase_threshold,
active_threshold=active_threshold,
strength=strength,
seed=seed,
provider=provider,
repository_root=Path(__file__).resolve().parent.parent,
)
log.info("Wrote preregistered oracle batch: %s", manifest)
@main.command("verify")
@click.argument("manifest_path", type=click.Path(exists=True, dir_okay=False, path_type=Path))
def verify_command(manifest_path: Path) -> None:
"""Verify an existing batch without changing it."""
manifest = verify_batch(manifest_path, repository_root=Path(__file__).resolve().parent.parent)
log.info("Verified %d immutable oracle requests", manifest["request_count"])
@main.command("evaluate")
@click.argument("manifest_path", type=click.Path(exists=True, dir_okay=False, path_type=Path))
@click.argument("results_path", type=click.Path(exists=True, dir_okay=False, path_type=Path))
@click.option("--report-out", type=click.Path(dir_okay=False, path_type=Path), required=True)
def evaluate_command(manifest_path: Path, results_path: Path, report_out: Path) -> None:
"""Validate RESULTS_PATH and write the preregistered batch verdict."""
repository_root = Path(__file__).resolve().parent.parent
if _inside(report_out, repository_root):
raise click.BadParameter("oracle result reports must be written outside the repository")
report = evaluate_results(
manifest_path,
results_path,
repository_root=repository_root,
)
report_out.parent.mkdir(parents=True, exist_ok=True)
if report_out.exists():
raise click.BadParameter("refusing to overwrite an oracle result report")
report_out.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
log.info("Wrote oracle batch verdict: %s", report_out)
if __name__ == "__main__":
main()