CalvinBackup/remove-ai-watermarks
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Align watermark content with detector coverage

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Victor Kuznetsov
2026-08-27 20:05:03 -07:00
parent 17408b958e
commit a8d4bce14a
61 changed files with 933 additions and 198 deletions
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scripts/vendor_mark_calibrate.py
+4 -4
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@@ -14,7 +14,7 @@ WHERE THE POSITIVES COME FROM
absence) visible directly.
NEGATIVES
The 432 frames hand-labelled `present: []` in the 2026-07-18 round -- already-adjudicated
The 432 frames hand-labeled `present: []` in the 2026-07-18 round -- already-adjudicated
no-visible-mark images, so the false-fire arm rests on human labels rather than on the
absence of a detection.
@@ -115,7 +115,7 @@ def load_sets(cohort: str) -> tuple[list[str], list[str]]:
# The 2026-07-18 labels are in the vocabulary of the REGISTERED marks only
# (gemini/doubao/jimeng/jimeng_pill): `present: []` means "no registered mark", NOT
# "no mark at all" -- 146 of the 432 sit in a TC260 cohort, and qwen-cohort frames
# visibly carrying 千问AI生成 are labelled `present: []` there (measured 2026-07-21:
# visibly carrying 千问AI生成 are labeled `present: []` there (measured 2026-07-21:
# they made up the clean arm's whole top tail, clean p99 0.37 -> 0.69). A gate read
# off that arm is meaningless, so the clean arm excludes every frame in ANY TC260
# cohort -- cohort membership is the cheap proxy for "may carry a CJK AI label".
@@ -168,7 +168,7 @@ def report(pos: list[dict[str, Any]], neg: list[dict[str, Any]], name: str) -> N
p = np.array([r["score"] for r in pos])
n = np.array([r["score"] for r in neg])
print(f"\n{'=' * 78}\n{name}: candidate-cohort vs hand-labelled clean\n{'=' * 78}")
print(f"\n{'=' * 78}\n{name}: candidate-cohort vs hand-labeled clean\n{'=' * 78}")
print(f"\n{'arm':10s} {'n':>5s} {'p10':>7s} {'p50':>7s} {'p90':>7s} {'p95':>7s} {'p99':>7s} {'max':>7s}")
for label, arr in (("cohort", p), ("clean", n)):
if not len(arr):
@@ -602,7 +602,7 @@ def main() -> None:
crossfire(pools, cand, a.workers, a.gate)
return
print(f"cohort {a.cohort}: {len(pos_paths)} candidates | clean: {len(neg_paths)} hand-labelled")
print(f"cohort {a.cohort}: {len(pos_paths)} candidates | clean: {len(neg_paths)} hand-labeled")
print(f"scale_basis={a.scale_basis} overrides={overrides}")
pos = run(pos_paths, a.asset, name, a.workers, a.scale_basis, overrides)
neg = run(neg_paths, a.asset, name, a.workers, a.scale_basis, overrides)