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test-user
7dcc922617
scripts/synthid_pixel_probe.py is an experimental/diagnostic tool for the one pixel-domain question that isn't a dead-end: on solid-color fills the zero-mean residual IS essentially the watermark carrier. Two modes: 'consistency' (mean pairwise NCC of carriers across fills vs random baseline) and 'removal' (does the pipeline drop the carrier toward baseline?). Logic validated synthetically (injected carrier correlates, random noise doesn't, simulated removal collapses it) -- no real fills or GPU needed. Running its metric on the corpus independently re-confirms the documented dead-end for real content: at matched resolution SynthID positives do not cluster apart from negatives (within-Gemini 0.07; at 1024 px pos-vs-neg >= pos-vs-pos). An apparent 0.62 among 1254px ChatGPT positives turned out to be near-duplicate content (5 renders of one prompt at ~0.92; a distinct ChatGPT image scored ~0 against them), not a shared carrier. The probe is solid-fills-only; do not use on real content. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
105 lines
4.1 KiB
Python
105 lines
4.1 KiB
Python
"""Tests for the SynthID pixel-carrier probe.
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The probe's logic is validated synthetically: a fixed carrier injected into
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uniform fills must correlate strongly, random noise must not, and simulated
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removal (dropping the carrier) must collapse the correlation. No real SynthID
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fills or GPU are needed.
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"""
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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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from click.testing import CliRunner
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from PIL import Image
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# scripts/ is not an installed package; add it to the path for import.
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "scripts"))
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import synthid_pixel_probe as probe
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_SHAPE = (64, 64)
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def _fixed_carrier(seed: int = 7) -> np.ndarray:
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"""A reproducible 2-D carrier pattern (the stand-in for the SynthID signal)."""
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rng = np.random.default_rng(seed)
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return rng.standard_normal(_SHAPE)
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def _fill_with_carrier(base: float, pattern: np.ndarray, noise_seed: int, noise: float = 0.05) -> np.ndarray:
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"""A solid fill at brightness ``base`` carrying ``pattern`` plus light noise."""
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rng = np.random.default_rng(noise_seed)
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return base + pattern + noise * rng.standard_normal(_SHAPE)
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class TestCarrier:
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def test_flat_image_has_zero_carrier(self):
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# A perfectly uniform fill (std 0, like the synthetic refs) carries nothing.
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flat = np.zeros(_SHAPE)
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assert not np.any(probe.carrier(flat))
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def test_carrier_is_unit_norm(self):
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c = probe.carrier(_fixed_carrier() + 100.0)
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assert np.isclose(np.linalg.norm(c), 1.0)
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def test_ncc_self_is_one(self):
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c = probe.carrier(_fixed_carrier())
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assert np.isclose(probe.ncc(c, c), 1.0)
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def test_ncc_mismatched_shape_is_zero(self):
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a = probe.carrier(np.random.default_rng(1).standard_normal((8, 8)))
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b = probe.carrier(np.random.default_rng(2).standard_normal((16, 16)))
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assert probe.ncc(a, b) == 0.0
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class TestConsistency:
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def test_shared_carrier_correlates_high(self):
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pattern = _fixed_carrier()
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carriers = [probe.carrier(_fill_with_carrier(b, pattern, s)) for s, b in enumerate((10, 60, 120, 200))]
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assert probe.mean_pairwise_ncc(carriers) > 0.8
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def test_random_fills_near_zero(self):
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rng = np.random.default_rng(0)
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carriers = [probe.carrier(rng.standard_normal(_SHAPE)) for _ in range(5)]
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assert abs(probe.mean_pairwise_ncc(carriers)) < 0.2
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def test_random_baseline_near_zero(self):
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assert abs(probe.random_baseline(_SHAPE, 6)) < 0.2
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class TestRemoval:
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def test_removed_carrier_collapses_correlation(self):
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pattern = _fixed_carrier()
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pos = [probe.carrier(_fill_with_carrier(b, pattern, s)) for s, b in enumerate((20, 90, 160))]
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tmpl = probe.template(pos)
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# "Cleaned" fills keep the base + noise but lose the shared pattern.
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rng = np.random.default_rng(99)
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cleaned = [probe.carrier(b + 0.05 * rng.standard_normal(_SHAPE)) for b in (20, 90, 160)]
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pos_corr = float(np.mean([probe.ncc(c, tmpl) for c in pos]))
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cleaned_corr = float(np.mean([probe.ncc(c, tmpl) for c in cleaned]))
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assert pos_corr > 0.8
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assert cleaned_corr < 0.2
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class TestCli:
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def _solid_fill_png(self, tmp_path: Path, name: str, base: int, pattern: np.ndarray, seed: int) -> Path:
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arr = np.clip(_fill_with_carrier(base, pattern, seed), 0, 255).astype(np.uint8)
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path = tmp_path / name
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Image.fromarray(np.stack([arr] * 3, axis=2)).save(path)
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return path
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def test_consistency_command_runs(self, tmp_path: Path):
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pattern = _fixed_carrier()
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paths = [str(self._solid_fill_png(tmp_path, f"f{i}.png", b, pattern, i)) for i, b in enumerate((40, 120, 200))]
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result = CliRunner().invoke(probe.cli, ["consistency", *paths])
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assert result.exit_code == 0, result.output
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assert "mean pairwise NCC" in result.output
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def test_consistency_needs_two_images(self, tmp_path: Path):
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path = str(self._solid_fill_png(tmp_path, "only.png", 100, _fixed_carrier(), 0))
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result = CliRunner().invoke(probe.cli, ["consistency", path])
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assert result.exit_code != 0
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