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https://github.com/wiltodelta/remove-ai-watermarks.git
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62 lines
3.3 KiB
Markdown
62 lines
3.3 KiB
Markdown
# Samsung Galaxy AI visible watermark capture
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> **Status (built 2026-06-05):** flat black and gray Samsung Galaxy AI captures
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> were obtained and the detection asset was solved. The asset now supports
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> detection and mask geometry only. Current removal localizes the wordmark and
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> uses the shared fill backend. The text below records the capture plan and its
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> locale and resolution limits.
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Goal: capture the Samsung Galaxy AI "✦ Contenuti generati dall'AI" visible wordmark
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over known flat backgrounds so we can rebuild and validate the silhouette used
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by `src/remove_ai_watermarks/samsung_engine.py`.
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## What we learned (verified from the captures, 2026-06-05)
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- Mark: a sparkle icon followed by the locale string "Contenuti generati dall'AI"
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(Italian), a light low-opacity (peak alpha ~0.38) semi-transparent **white**
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overlay, anchored **bottom-LEFT** (Doubao/Jimeng are bottom-right). The string is
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locale-specific, so the alpha template is per-locale; this build is the Italian
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variant. Other locales need their own captured template.
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- Blend model: alpha compositing with a pure-white logo, `watermarked =
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a*255 + (1-a)*original`, solved from the GRAY capture (same careful recipe as
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Doubao/Jimeng: cubic-background fit, mean over channels, full halo extent,
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unblurred). The white capture confirms the logo is white; on white the mark is
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white-on-white and not detectable (no contrast), which is fine -- there is nothing
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to recover there.
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- Geometry (fraction of image WIDTH): asset width ~0.32, height ~0.038, left margin
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~0.011, bottom margin ~0.006. The mark scales with width: a 1086-wide flat capture
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and a 2958-wide real photo both measure width_frac ~0.31.
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- **Resolution caveat (open quality follow-up):** the flat black/gray/white captures
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arrived at the phone's flat-edit size (1086 wide and a landscape 1920 set), while
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the real photos are ~3000 wide, so the captured glyph (~334 px) is ~2.7x smaller
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than on a real photo (~900 px). The alpha is solved at the capture size and
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width-scaled + NCC-aligned per image, which removes the mark cleanly (verified on a
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real 2958-wide photo: re-detect 0.79 -> 0.00, no readable text or outline), but a
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flat capture taken at the real photo resolution (~3000 wide) would let the alpha be
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pixel-sharp instead of upscaled. Not a blocker; a quality upgrade if a full-res
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flat capture is provided.
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## Capture protocol (to re-capture or add a locale)
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On a Samsung Galaxy AI device (set the UI language to the target locale):
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1. Run the AI edit (Generative Edit / Sketch to Image) on a solid black image, so
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the overlay lands on a flat black background. Download the ORIGINAL output file
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(not a screenshot, no crop or re-save).
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2. Repeat over solid white and solid gray (those pin the exact glyph color).
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3. Ideally run all three flat edits at the same resolution as real photos (~3000
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wide) so the alpha map is pixel-sharp rather than upscaled.
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4. Plus 3-5 real outputs with the visible mark over normal content for validation.
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## Files
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- `samsung_black_1.png` and `samsung_gray_1.png` are the retained solver inputs.
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- White, secondary-resolution, and real-content captures were validation
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material and are not required to rebuild the asset.
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Rebuild the detection asset with:
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```
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uv run python scripts/visible_alpha_solve.py samsung
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```
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