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.
66 lines
1.7 KiB
Python
66 lines
1.7 KiB
Python
from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "scripts"))
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import synthid_cyclostationary_probe as probe
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def _template() -> np.ndarray:
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_y, x = np.indices((16, 16))
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carrier = np.cos(2.0 * np.pi * 4.0 * x / 16.0)
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template = np.stack((carrier, 0.8 * carrier, 0.6 * carrier), axis=2)
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template -= np.mean(template, axis=(0, 1), keepdims=True)
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return template / np.linalg.norm(template)
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def test_detects_complex_spectral_coupling() -> None:
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rng = np.random.default_rng(20260814)
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base = rng.normal(0.0, 1.0, (1024, 1024, 3))
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_y, x = np.indices(base.shape[:2])
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modulation = 1.0 + 0.8 * np.cos(2.0 * np.pi * 4.0 * x / 16.0)
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result = probe.score_cyclostationary(
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base * modulation[:, :, None],
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_template(),
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period=16.0,
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harmonic_count=1,
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)
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assert result.selection_contrast > 0.1
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assert result.confirmation_contrast > 0.1
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assert result.joint_contrast > 0.1
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def test_rejects_independent_equal_power_noise() -> None:
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rng = np.random.default_rng(20260815)
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noise = rng.normal(0.0, 1.0, (1024, 1024, 3))
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result = probe.score_cyclostationary(
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noise,
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_template(),
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period=16.0,
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harmonic_count=1,
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)
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assert result.joint_contrast < 0.01
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def test_does_not_confuse_additive_carrier_with_modulation() -> None:
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rng = np.random.default_rng(20260816)
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noise = rng.normal(0.0, 1.0, (1024, 1024, 3))
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additive = np.tile(_template(), (64, 64, 1)) * 2.0
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result = probe.score_cyclostationary(
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noise + additive,
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_template(),
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period=16.0,
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harmonic_count=1,
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)
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assert result.joint_contrast < 0.01
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