mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-08-31 09:40:38 +02:00
Victor Kuznetsov
8eb9c06265
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.
85 lines
3.2 KiB
Python
85 lines
3.2 KiB
Python
from __future__ import annotations
|
|
|
|
import sys
|
|
from dataclasses import replace
|
|
from pathlib import Path
|
|
|
|
import cv2
|
|
import numpy as np
|
|
import pytest
|
|
|
|
sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "scripts"))
|
|
|
|
import synthid_affine_lattice_probe as research_probe
|
|
|
|
from remove_ai_watermarks._synthid_confirmation import (
|
|
RegisteredConfirmationComponents,
|
|
registered_confirmation_components,
|
|
)
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def periodic_fixture() -> tuple[np.ndarray, np.ndarray]:
|
|
rng = np.random.default_rng(20260818)
|
|
template = rng.normal(0.0, 1.0, (16, 16, 3))
|
|
template -= np.mean(template, axis=(0, 1), keepdims=True)
|
|
template /= np.linalg.norm(template)
|
|
coarse = rng.normal(0.0, 8.0, (16, 16, 3)).astype(np.float32)
|
|
background = cv2.resize(coarse, (1024, 1024), interpolation=cv2.INTER_CUBIC) + 128.0
|
|
carrier = np.tile(template, (64, 64, 1)) * 3.0
|
|
pixels = np.clip(np.rint(background + carrier), 0, 255).astype(np.uint8)
|
|
return pixels, template
|
|
|
|
|
|
def test_runtime_components_match_frozen_research_seam(
|
|
periodic_fixture: tuple[np.ndarray, np.ndarray],
|
|
) -> None:
|
|
pixels, template = periodic_fixture
|
|
|
|
runtime = registered_confirmation_components(pixels, template, 16.0, 1.0)
|
|
research = research_probe.score_lattice(
|
|
pixels,
|
|
template,
|
|
periods=np.asarray([16.0]),
|
|
rotations_degrees=np.asarray([0.0]),
|
|
)
|
|
|
|
assert runtime.period == research.selected_period
|
|
assert runtime.joint_coherence == pytest.approx(research.joint_coherence)
|
|
assert runtime.joint_amplitude == pytest.approx(research.joint_amplitude)
|
|
assert runtime.unknown_codeword_fixed_confirmation == pytest.approx(research.unknown_codeword_fixed_confirmation)
|
|
assert runtime.selection_patches == research.selection_patches
|
|
assert runtime.confirmation_patches == research.confirmation_patches
|
|
assert runtime.passes
|
|
|
|
|
|
def test_confirmation_rejects_independent_noise(periodic_fixture: tuple[np.ndarray, np.ndarray]) -> None:
|
|
_pixels, template = periodic_fixture
|
|
pixels = np.random.default_rng(20260819).integers(0, 256, (1024, 1024, 3), dtype=np.uint8)
|
|
|
|
result = registered_confirmation_components(pixels, template, 16.0, 1.0)
|
|
|
|
assert not result.passes
|
|
|
|
|
|
def test_period_aware_confirmation_boundaries() -> None:
|
|
baseline = RegisteredConfirmationComponents(
|
|
period=16.0,
|
|
joint_coherence=0.30,
|
|
joint_amplitude=0.0,
|
|
unknown_codeword_fixed_confirmation=0.5,
|
|
selection_patches=8,
|
|
confirmation_patches=8,
|
|
)
|
|
|
|
assert baseline.passes
|
|
assert not replace(baseline, period=9.99).passes
|
|
assert not replace(baseline, joint_coherence=0.299).passes
|
|
assert not replace(baseline, joint_amplitude=-0.001).passes
|
|
assert not replace(baseline, period=18.28, unknown_codeword_fixed_confirmation=0.129).passes
|
|
assert replace(baseline, period=18.28, unknown_codeword_fixed_confirmation=0.13).passes
|
|
assert not replace(baseline, period=19.14, joint_coherence=0.399).passes
|
|
assert replace(baseline, period=19.14, joint_coherence=0.40).passes
|
|
assert not replace(baseline, period=21.31, unknown_codeword_fixed_confirmation=0.019).passes
|
|
assert replace(baseline, period=21.31, unknown_codeword_fixed_confirmation=0.02).passes
|