mirror of
https://github.com/aloshdenny/reverse-SynthID.git
synced 2026-07-19 13:47:20 +02:00
feat(scripts): add V4 codebook build, batch dissolve, and calibration scripts
build_codebook_v4.py — builds SpectralCodebookV4 from the hierarchical reverse-synthid-dataset (model × color × resolution). dissolve_batch.py — runs all bypass presets (gentle … nuke) over an input directory. Supports Round-06 'final' and 'nuke' strengths. calibrate_from_feedback.py — updates carrier_weights from detection feedback, closing the human-in-the-loop calibration loop. Made-with: Cursor
This commit is contained in:
Alosh Denny
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#!/usr/bin/env python3
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"""
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Close the manual-validation loop for reverse-SynthID V4.
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Reads the ``manifest.csv`` from ``dissolve_batch.py`` plus a ``tally.csv``
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you filled by hand after checking each variant in the Gemini app. Updates
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``carrier_weights`` in the V4 codebook in place:
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- Bins that the **failed** variants (``still_watermarked=y``) tried to subtract
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get their weights **bumped up**, so subsequent dissolves attack those bins
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harder.
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- Bins that the **succeeded** variants (``still_watermarked=n``) already
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subtracted get their weights **damped slightly**, to recover fidelity
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without giving up detector immunity.
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The tally CSV accepts ``y``/``n``/``yes``/``no``/``1``/``0`` (case-insensitive)
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in ``still_watermarked``. Rows with a blank value are ignored.
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Usage::
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python scripts/calibrate_from_feedback.py \\
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--manifest runs/round_01/manifest.csv \\
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--tally runs/round_01/tally.csv \\
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--codebook artifacts/spectral_codebook_v4.npz \\
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--step 0.25
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The codebook is rewritten in place; a timestamped backup is made next to it
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unless ``--no-backup`` is passed.
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"""
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from __future__ import annotations
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import argparse
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import csv
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import datetime
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import os
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import shutil
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import sys
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from typing import Dict, List, Optional, Tuple
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REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, os.path.join(REPO_ROOT, "src", "extraction"))
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import numpy as np # noqa: E402
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from synthid_bypass_v4 import SpectralCodebookV4 # noqa: E402
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TRUE_TOKENS = {"y", "yes", "1", "true", "t"}
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FALSE_TOKENS = {"n", "no", "0", "false", "f"}
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# ---------------------------------------------------------------------------
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# CSV loading
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# ---------------------------------------------------------------------------
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def _read_csv_dicts(path: str) -> List[Dict[str, str]]:
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with open(path, newline="") as f:
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return list(csv.DictReader(f))
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def _parse_still_watermarked(value: str) -> Optional[bool]:
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"""``y/n`` → ``True/False``; empty/unknown → ``None``."""
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if value is None:
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return None
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v = value.strip().lower()
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if v == "":
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return None
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if v in TRUE_TOKENS:
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return True
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if v in FALSE_TOKENS:
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return False
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return None
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def load_feedback(
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manifest_path: str, tally_path: str,
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) -> List[Dict]:
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"""Join manifest + tally on ``(source, variant)``; return labelled rows.
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Only rows whose tally has a parseable ``still_watermarked`` are returned.
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"""
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manifest = _read_csv_dicts(manifest_path)
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# Tally may be the same file as the manifest (user filled in place) or a
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# separate file with at least (source, variant, still_watermarked).
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tally_raw = _read_csv_dicts(tally_path)
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tally: Dict[Tuple[str, str], bool] = {}
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for row in tally_raw:
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still = _parse_still_watermarked(row.get("still_watermarked", ""))
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if still is None:
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continue
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key = (row["source"], row["variant"])
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tally[key] = still
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joined: List[Dict] = []
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for row in manifest:
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key = (row["source"], row["variant"])
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if key not in tally:
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continue
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merged = dict(row)
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merged["still_watermarked"] = tally[key]
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joined.append(merged)
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return joined
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# ---------------------------------------------------------------------------
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# Calibration logic
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# ---------------------------------------------------------------------------
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def _parse_profile_key(profile_key: str) -> Optional[Tuple[str, int, int]]:
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"""Parse ``'model_name/HxW'`` → ``(model, H, W)``."""
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if not profile_key or "/" not in profile_key:
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return None
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model, res = profile_key.rsplit("/", 1)
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if "x" not in res:
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return None
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try:
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h, w = (int(p) for p in res.lower().split("x"))
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except ValueError:
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return None
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return (model, h, w)
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def calibrate(
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codebook: SpectralCodebookV4,
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feedback: List[Dict],
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step: float,
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damp_factor: float,
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consensus_floor: float,
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verbose: bool,
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) -> Dict[Tuple[str, int, int], Dict[str, float]]:
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"""Update ``carrier_weights`` in-place. Returns per-profile summary stats.
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The update rule, per profile ``P``:
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Let ``F`` = number of feedback rows against ``P`` with
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``still_watermarked=True`` (failed dissolves).
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Let ``S`` = number with ``still_watermarked=False`` (cleared dissolves).
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If ``F > 0``: scale ``carrier_weights`` by ``1 + step * (F / (F + S))``
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but only on bins with ``consensus_coherence >= consensus_floor``. Non-
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carrier bins are never touched — we don't want to amplify noise.
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If ``F == 0 and S > 0``: scale ``carrier_weights`` by
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``1 - damp_factor * step`` on carrier bins (gentle fidelity recovery
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once we're clearing the detector).
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"""
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groups: Dict[Tuple[str, int, int], Dict[str, List[Dict]]] = {}
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for row in feedback:
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pkey = _parse_profile_key(row.get("profile_key", ""))
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if pkey is None:
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continue
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bucket = groups.setdefault(pkey, {"fail": [], "pass": []})
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target = "fail" if row["still_watermarked"] else "pass"
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bucket[target].append(row)
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summary: Dict[Tuple[str, int, int], Dict[str, float]] = {}
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for pkey, bucket in groups.items():
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if pkey not in codebook.profiles:
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if verbose:
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print(f" skip {pkey}: no matching profile in codebook")
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continue
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prof = codebook.profiles[pkey]
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F = len(bucket["fail"])
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S = len(bucket["pass"])
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carrier_mask = (prof.consensus_coherence >= consensus_floor).astype(np.float32)
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if F > 0:
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fail_ratio = F / max(F + S, 1)
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scale = 1.0 + step * fail_ratio
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delta = 1.0 + (scale - 1.0) * carrier_mask
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action = f"bump ×{scale:.3f}"
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elif S > 0:
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scale = max(1.0 - damp_factor * step, 0.2)
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delta = 1.0 + (scale - 1.0) * carrier_mask
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action = f"damp ×{scale:.3f}"
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else:
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continue
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before_mean = float(np.mean(prof.carrier_weights[..., 1]))
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codebook.update_carrier_weights(pkey, delta)
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after_mean = float(np.mean(prof.carrier_weights[..., 1]))
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summary[pkey] = {
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"fail": F,
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"pass": S,
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"before_mean_g": before_mean,
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"after_mean_g": after_mean,
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"action": action,
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}
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if verbose:
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print(f" {pkey[0]}/{pkey[1]}x{pkey[2]}: {action} "
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f"fail={F} pass={S} "
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f"mean(G) {before_mean:.4f} → {after_mean:.4f}")
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return summary
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# ---------------------------------------------------------------------------
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# Entry point
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# ---------------------------------------------------------------------------
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def run(
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manifest_path: str,
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tally_path: str,
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codebook_path: str,
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step: float,
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damp_factor: float,
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consensus_floor: float,
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backup: bool,
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) -> None:
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if not os.path.isfile(manifest_path):
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raise FileNotFoundError(f"Manifest not found: {manifest_path}")
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if not os.path.isfile(tally_path):
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raise FileNotFoundError(f"Tally not found: {tally_path}")
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if not os.path.isfile(codebook_path):
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raise FileNotFoundError(f"Codebook not found: {codebook_path}")
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feedback = load_feedback(manifest_path, tally_path)
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if not feedback:
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print("No usable feedback rows (empty still_watermarked?). Nothing "
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"to do.")
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return
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print(f"Loaded {len(feedback)} labelled rows from tally.")
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codebook = SpectralCodebookV4()
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codebook.load(codebook_path)
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if backup:
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ts = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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backup_path = codebook_path + f".bak-{ts}.npz"
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shutil.copyfile(codebook_path, backup_path)
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print(f"Backup → {backup_path}")
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summary = calibrate(
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codebook=codebook,
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feedback=feedback,
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step=step,
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damp_factor=damp_factor,
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consensus_floor=consensus_floor,
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verbose=True,
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)
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if not summary:
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print("No profiles updated.")
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return
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codebook.save(codebook_path)
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n_fail = sum(s["fail"] for s in summary.values())
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n_pass = sum(s["pass"] for s in summary.values())
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print(f"\nCalibration complete. Profiles updated: {len(summary)}")
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print(f"Feedback: {n_pass} cleared / {n_fail} still watermarked "
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f"({n_pass * 100.0 / max(n_pass + n_fail, 1):.1f}% success).")
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if n_fail > 0:
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print("Next: re-run dissolve_batch.py on a fresh batch; weights "
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"are now stronger at persistent carriers.")
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else:
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print("100% cleared — consider lowering strength for better "
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"fidelity on the next batch.")
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def main() -> None:
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p = argparse.ArgumentParser(
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description=(
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"Update V4 carrier_weights from manual Gemini detection tallies."
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),
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)
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p.add_argument("--manifest", required=True,
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help="Path to manifest.csv produced by dissolve_batch.py.")
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p.add_argument("--tally", required=True,
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help=(
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"Path to tally.csv with (source, variant, "
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"still_watermarked) columns. May be the manifest file "
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"itself if you filled it in place."
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))
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p.add_argument("--codebook", required=True,
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help="V4 codebook .npz to update (in place).")
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p.add_argument("--step", type=float, default=0.25,
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help="Base scale step; 0.25 = up to +25%% per round.")
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p.add_argument("--damp-factor", type=float, default=0.15,
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help="Damping multiplier applied when all variants "
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"cleared (fidelity recovery).")
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p.add_argument("--consensus-floor", type=float, default=0.50,
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help="Only update bins with consensus_coherence >= this.")
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p.add_argument("--no-backup", dest="backup", action="store_false",
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help="Skip the timestamped backup of the codebook.")
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p.set_defaults(backup=True)
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args = p.parse_args()
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run(
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manifest_path=args.manifest,
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tally_path=args.tally,
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codebook_path=args.codebook,
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step=args.step,
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damp_factor=args.damp_factor,
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consensus_floor=args.consensus_floor,
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backup=args.backup,
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)
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if __name__ == "__main__":
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main()
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