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https://github.com/wiltodelta/remove-ai-watermarks.git
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Victor Kuznetsov
e7fb64dca1
The captured sparkle alpha peaks ~0.51, but some real Gemini sparkles are rendered more opaque. The fixed-alpha reverse blend then UNDER-subtracts and leaves a bright residual the detector still fires on. A visible-removal audit through the registry path on the spaces corpus showed this as a meaningful fraction of marks -- all under-removals, not a background-brightness class (failures and successes had the same input confidence and background luma; the discriminator was the removal delta itself). remove_watermark now estimates a per-image alpha gain (_estimate_alpha_gain: effective sparkle opacity at the bright core vs the local background ring, a_eff/a_cap, clamped [1.0, 1.94]) and scales the alpha to match before the over-sub/blend branch. A 1.05 deadband keeps a sparkle that already matches the capture byte-identical to the pre-fix output, so the fix is purely additive (0 regressions on the audit set; failures dropped substantially). The over-sub guard still runs on the scaled alpha as the safety net for an over-shoot. - _estimate_alpha_gain + _ALPHA_GAIN_MAX/_DEADBAND/_CORE_FRAC in gemini_engine. - TestUnderSubtractionGain asserts on footprint pixels, NOT the detector (its NCC is degenerate on a flat synthetic bg; the real corpus removal drops the detector ~0.80 -> ~0.27). - scripts/visible_removal_audit.py: the detect -> remove -> re-detect audit tool that found and validated this (operates on gitignored data/spaces only). - CLAUDE.md + README: document the under-subtraction gain. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>