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

tests/test_synthid_ensemble_attack.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+from PIL import Image
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "scripts"))
+
+import synthid_color_space_probe as probe
+import synthid_ensemble_attack as attack
+
+
+def _write_image(path: Path, *, phase: float, seed: int) -> None:
+    height = width = 64
+    rng = np.random.default_rng(seed)
+    pixels = 100.0 + rng.normal(0.0, 2.0, size=(height, width, 3))
+    yy, xx = np.mgrid[:height, :width]
+    wave = np.cos(2.0 * np.pi * (7.0 * yy / height + 5.0 * xx / width) + phase)
+    pixels[:, :, 0] += 14.0 * wave
+    pixels[:, :, 1] -= 10.0 * wave
+    Image.fromarray(np.clip(np.rint(pixels), 0, 255).astype(np.uint8), mode="RGB").save(path)
+
+
+def test_projection_removes_only_positive_phase_component() -> None:
+    height = width = 64
+    yy, xx = np.mgrid[:height, :width]
+    channel = np.cos(2.0 * np.pi * (7.0 * yy / height + 5.0 * xx / width) + 0.4)
+    before = np.fft.fft2(channel)[7, 5]
+    phase = np.angle(before)
+
+    projected = attack._remove_positive_projection(
+        channel,
+        rows=np.asarray([7]),
+        columns=np.asarray([5]),
+        phases=np.asarray([phase]),
+        strength=1.0,
+    )
+    after = np.fft.fft2(projected)[7, 5]
+
+    assert np.real(after * np.exp(-1j * phase)) == pytest.approx(0.0, abs=1e-10)
+    assert np.max(np.abs(np.imag(np.fft.ifft2(np.fft.fft2(projected))))) < 1e-12
+
+
+def test_alternating_projection_reduces_rgb_and_sv_evidence(tmp_path: Path) -> None:
+    positives: list[Path] = []
+    for index in range(3):
+        path = tmp_path / f"positive-{index}.png"
+        _write_image(path, phase=0.4, seed=index)
+        positives.append(path)
+    source = tmp_path / "source.png"
+    _write_image(source, phase=0.4, seed=10)
+    bins = np.asarray([(7, 5, channel) for channel in range(3)], dtype=np.int32)
+    rgb_model = probe.discover_model(positives, color_space="rgb", candidate_bins=bins, peak_count=3)
+    hsv_model = probe.discover_model(positives, color_space="hsv", candidate_bins=bins, peak_count=3)
+    before_rgb = probe.score_image(source, rgb_model)
+    before_hsv = probe.score_image(source, hsv_model)
+
+    with Image.open(source) as image:
+        source_pixels = np.asarray(image.convert("RGB"), dtype=np.uint8)
+    projected = attack.alternating_projection(
+        source_pixels,
+        rgb_model,
+        hsv_model,
+        strength=1.0,
+        iterations=2,
+    )
+    output = tmp_path / "projected.png"
+    Image.fromarray(projected, mode="RGB").save(output)
+    after_rgb = probe.score_image(output, rgb_model)
+    after_hsv = probe.score_image(output, hsv_model)
+
+    assert after_rgb.evidence_score < before_rgb.evidence_score
+    assert sum(after_hsv.channel_evidence[1:]) < sum(before_hsv.channel_evidence[1:])
+
+
+@pytest.mark.parametrize("strength", [-0.1, 1.1])
+def test_projection_rejects_out_of_range_strength(strength: float) -> None:
+    channel = np.zeros((64, 64))
+
+    with pytest.raises(ValueError, match="between zero and one"):
+        attack._remove_positive_projection(
+            channel,
+            rows=np.asarray([7]),
+            columns=np.asarray([5]),
+            phases=np.asarray([0.0]),
+            strength=strength,
+        )