"""Build non-generative spatial-fragmentation SynthID attack candidates. The candidates combine deterministic smooth local warps with mild global resampling, color changes, and codec round-trips. These are pixel transforms, not semantic reconstruction or generative inpainting. Provider-oracle results must be evaluated in a frozen batch with a source-positive control. """ from __future__ import annotations import json import logging from dataclasses import asdict from pathlib import Path import click import cv2 import numpy as np from PIL import Image from synthid_pixel_attack import ( jpeg_round_trip, load_rgb, measure, norm_matched_noise, resize_squeeze, smooth_warp, ) from synthid_v3_codebook_probe import load_v3_model, score_image log = logging.getLogger(__name__) def bounded_smooth_warp( pixels: np.ndarray, *, max_displacement: float, sigma: float, seed: int, ) -> np.ndarray: """Apply a smooth warp whose per-axis displacement is absolutely bounded.""" if max_displacement < 0.0 or sigma <= 0.0: raise ValueError("max_displacement must be nonnegative and sigma positive") height, width = pixels.shape[:2] rng = np.random.default_rng(seed) fields: list[np.ndarray] = [] for _ in range(2): noise = rng.normal(size=(height, width)).astype(np.float32) field = cv2.GaussianBlur(noise, (0, 0), sigmaX=sigma, sigmaY=sigma) maximum = float(np.max(np.abs(field))) fields.append(np.zeros_like(field) if maximum == 0.0 else field * (max_displacement / maximum)) yy, xx = np.mgrid[:height, :width].astype(np.float32) return cv2.remap( pixels, xx + fields[0], yy + fields[1], interpolation=cv2.INTER_LANCZOS4, borderMode=cv2.BORDER_REFLECT_101, ) def affine_combo(pixels: np.ndarray, *, rotation_degrees: float, zoom: float) -> np.ndarray: """Apply one centered rotation-and-zoom resampling operation.""" if zoom < 0.0: raise ValueError("zoom must be nonnegative") height, width = pixels.shape[:2] matrix = cv2.getRotationMatrix2D( center=((width - 1) / 2.0, (height - 1) / 2.0), angle=rotation_degrees, scale=1.0 + zoom, ) return cv2.warpAffine( pixels, matrix, (width, height), flags=cv2.INTER_LANCZOS4, borderMode=cv2.BORDER_REFLECT_101, ) def color_nudge( pixels: np.ndarray, *, brightness: float, contrast: float, saturation: float, hue_degrees: float, ) -> np.ndarray: """Apply bounded global RGB contrast and HSV saturation/hue changes.""" rgb = pixels.astype(np.float32) / 255.0 rgb = np.clip((rgb - 0.5) * (1.0 + contrast) + 0.5 + brightness, 0.0, 1.0) hsv = cv2.cvtColor(rgb, cv2.COLOR_RGB2HSV) hsv[:, :, 0] = np.mod(hsv[:, :, 0] + hue_degrees, 360.0) hsv[:, :, 1] = np.clip(hsv[:, :, 1] * (1.0 + saturation), 0.0, 1.0) result = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB) return np.clip(np.rint(result * 255.0), 0, 255).astype(np.uint8) def jpeg_chain(pixels: np.ndarray, qualities: tuple[int, ...]) -> np.ndarray: """Apply sequential JPEG round-trips at QUALITIES.""" result = pixels for quality in qualities: result = jpeg_round_trip(result, quality) return result def build_candidates(source: np.ndarray) -> dict[str, np.ndarray]: """Build the frozen spatial-fragmentation ladder for SOURCE.""" candidates: dict[str, np.ndarray] = { "control": source.copy(), "elastic-075": smooth_warp(source, amplitude=0.75, sigma=56.0, seed=20260812), "elastic-125": smooth_warp(source, amplitude=1.25, sigma=52.0, seed=20260813), "bounded-100": bounded_smooth_warp( source, max_displacement=1.0, sigma=56.0, seed=20260817, ), "bounded-180": bounded_smooth_warp( source, max_displacement=1.8, sigma=56.0, seed=20260818, ), "bounded-280": bounded_smooth_warp( source, max_displacement=2.8, sigma=44.0, seed=20260819, ), } balanced = smooth_warp(source, amplitude=0.75, sigma=56.0, seed=20260814) balanced = affine_combo(balanced, rotation_degrees=0.2, zoom=0.004) balanced = resize_squeeze(balanced, 0.94) balanced = color_nudge( balanced, brightness=0.004, contrast=0.006, saturation=-0.005, hue_degrees=0.15, ) balanced = jpeg_chain(balanced, (94, 90)) candidates["fragment-balanced"] = balanced strong = smooth_warp(source, amplitude=1.5, sigma=48.0, seed=20260815) strong = affine_combo(strong, rotation_degrees=0.4, zoom=0.01) strong = resize_squeeze(strong, 0.88) strong = color_nudge( strong, brightness=0.008, contrast=0.012, saturation=-0.01, hue_degrees=0.3, ) strong = jpeg_chain(strong, (92, 88)) candidates["fragment-strong"] = strong candidates["sham-strong-rms"] = norm_matched_noise(source, strong, seed=20260816) bounded_balanced = bounded_smooth_warp( source, max_displacement=1.8, sigma=56.0, seed=20260820, ) bounded_balanced = resize_squeeze(bounded_balanced, 0.98) bounded_balanced = color_nudge( bounded_balanced, brightness=0.002, contrast=0.003, saturation=-0.003, hue_degrees=0.1, ) bounded_balanced = jpeg_chain(bounded_balanced, (96,)) candidates["bounded-fragment-balanced"] = bounded_balanced bounded_strong = bounded_smooth_warp( source, max_displacement=2.8, sigma=44.0, seed=20260821, ) bounded_strong = affine_combo(bounded_strong, rotation_degrees=0.2, zoom=0.004) bounded_strong = resize_squeeze(bounded_strong, 0.94) bounded_strong = color_nudge( bounded_strong, brightness=0.004, contrast=0.006, saturation=-0.005, hue_degrees=0.15, ) bounded_strong = jpeg_chain(bounded_strong, (94, 90)) candidates["bounded-fragment-strong"] = bounded_strong candidates["sham-bounded-strong-rms"] = norm_matched_noise(source, bounded_strong, seed=20260822) return candidates @click.command() @click.argument("codebook", type=click.Path(exists=True, dir_okay=False, path_type=Path)) @click.argument("source", type=click.Path(exists=True, dir_okay=False, path_type=Path)) @click.argument("output_dir", type=click.Path(file_okay=False, path_type=Path)) @click.option("--height", type=click.IntRange(min=64), required=True) @click.option("--width", type=click.IntRange(min=64), required=True) @click.option("--peak-count", type=click.IntRange(min=1), default=256, show_default=True) def main(codebook: Path, source: Path, output_dir: Path, height: int, width: int, peak_count: int) -> None: """Write a frozen spatial-fragmentation batch for SOURCE.""" reference = load_rgb(source) if reference.shape != (height, width, 3): raise click.BadParameter("source geometry does not match --height and --width") model = load_v3_model(codebook, height=height, width=width, peak_count=peak_count) output_dir.mkdir(parents=True, exist_ok=True) rows: list[dict[str, object]] = [] for name, pixels in build_candidates(reference).items(): path = output_dir / f"{name}.png" Image.fromarray(pixels, mode="RGB").save(path) rows.append( { **asdict(measure(reference, pixels, name=name, path=path)), **asdict(score_image(path, model)), } ) report_path = output_dir / "report.json" report_path.write_text( json.dumps( { "source": str(source), "codebook": str(codebook), "height": height, "width": width, "peak_count": peak_count, "variants": rows, }, indent=2, ) + "\n", encoding="utf-8", ) log.info("Wrote %d frozen fragmentation candidates: %s", len(rows), report_path) if __name__ == "__main__": logging.basicConfig(level=logging.INFO, format="%(message)s") main()