remove-ai-watermarks/data/samsung_capture/README.md

Samsung Galaxy AI visible watermark capture

Status (built 2026-06-05): flat black/gray/white Samsung Galaxy AI captures were obtained (issue #37, from @f-liva) and the alpha map was solved. Removal is reverse-alpha plus a thin residual inpaint over the glyph footprint; see the samsung_engine.py notes in the root CLAUDE.md. The text below is the capture plan and the open quality follow-up.

Goal: capture the Samsung Galaxy AI "✦ Contenuti generati dall'AI" visible wordmark over known flat backgrounds so we can build a per-pixel alpha map and a reverse-alpha remover, the same way the Gemini sparkle and the Doubao / Jimeng strips work (src/remove_ai_watermarks/gemini_engine.py, doubao_engine.py, jimeng_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):

1. 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).
2. Repeat over solid white and solid gray (those pin the exact glyph color).
3. 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.
4. Plus 3-5 real outputs with the visible mark over normal content for validation.

Files

• captures/samsung_black_1.png, samsung_gray_1.png, samsung_white_1.png -- portrait flat edits (1086 wide), the primary calibration set.
• captures/samsung_black_2.png, samsung_gray_2.png, samsung_white_2.png -- a second (landscape 1920) set.
• captures/samsung_content_* -- real-photo validation downloads, gitignored (user content, repo is public).
• seeds/ -- synthetic solid-color inputs, gitignored (regenerable).

Rebuild the alpha asset with:

uv run python scripts/visible_alpha_solve.py samsung