Status (built 2026-06-05): flat black and gray Samsung Galaxy AI captures were obtained and the detection asset was solved. The asset now supports detection and mask geometry only. Current removal localizes the wordmark and uses the shared fill backend. The text below records the capture plan and its locale and resolution limits.

Goal: capture the Samsung Galaxy AI "✦ Contenuti generati dall'AI" visible wordmark over known flat backgrounds so we can rebuild and validate the silhouette used by src/remove_ai_watermarks/samsung_engine.py.

What we learned (verified from the captures, 2026-06-05)

Mark: a sparkle icon followed by the locale string "Contenuti generati dall'AI" (Italian), a light low-opacity (peak alpha ~0.38) semi-transparent white overlay, anchored bottom-LEFT (Doubao/Jimeng are bottom-right). The string is locale-specific, so the alpha template is per-locale; this build is the Italian variant. Other locales need their own captured template.
Blend model: alpha compositing with a pure-white logo, watermarked = a*255 + (1-a)*original, solved from the GRAY capture (same careful recipe as Doubao/Jimeng: cubic-background fit, mean over channels, full halo extent, unblurred). The white capture confirms the logo is white; on white the mark is white-on-white and not detectable (no contrast), which is fine -- there is nothing to recover there.
Geometry (fraction of image WIDTH): asset width ~0.32, height ~0.038, left margin ~0.011, bottom margin ~0.006. The mark scales with width: a 1086-wide flat capture and a 2958-wide real photo both measure width_frac ~0.31.
Resolution caveat (open quality follow-up): the flat black/gray/white captures arrived at the phone's flat-edit size (1086 wide and a landscape 1920 set), while the real photos are ~3000 wide, so the captured glyph (~334 px) is ~2.7x smaller than on a real photo (~900 px). The alpha is solved at the capture size and width-scaled + NCC-aligned per image, which removes the mark cleanly (verified on a real 2958-wide photo: re-detect 0.79 -> 0.00, no readable text or outline), but a flat capture taken at the real photo resolution (~3000 wide) would let the alpha be pixel-sharp instead of upscaled. Not a blocker; a quality upgrade if a full-res flat capture is provided.

Capture protocol (to re-capture or add a locale)

On a Samsung Galaxy AI device (set the UI language to the target locale):

Run the AI edit (Generative Edit / Sketch to Image) on a solid black image, so the overlay lands on a flat black background. Download the ORIGINAL output file (not a screenshot, no crop or re-save).
Repeat over solid white and solid gray (those pin the exact glyph color).
Ideally run all three flat edits at the same resolution as real photos (~3000 wide) so the alpha map is pixel-sharp rather than upscaled.
Plus 3-5 real outputs with the visible mark over normal content for validation.

Files

samsung_black_1.png and samsung_gray_1.png are the retained solver inputs.
White, secondary-resolution, and real-content captures were validation material and are not required to rebuild the asset.

Rebuild the detection asset with:

uv run python scripts/visible_alpha_solve.py samsung