mirror of https://github.com/wiltodelta/remove-ai-watermarks.git synced 2026-05-24 05:14:02 +02:00

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test-user f2fc5e09ab feat: SDXL default; AVIF/HEIF/JPEG-XL C2PA stripping

SD-1.5 dreamshaper at 768 px did not defeat SynthID v2 on Gemini 3 Pro
outputs (verified May 2026 via Gemini app's "Verify with SynthID"). Switch
the default invisible engine to SDXL at 1024 px, matching the raiw-app
production config (strength 0.05, steps 50). Drop the SD-1.5 pipeline.

Metadata layer: add C2PA UUID and IPTC AI marker byte-scan detection
across all formats, plus an ISOBMFF box walker (noai/isobmff.py) that
strips top-level C2PA uuid and JUMBF jumb boxes from AVIF/HEIF/JPEG-XL
containers without re-encoding.

README gets a Legal table and a Threat-model section about SynthID v2's
136-bit payload. CLAUDE.md tracks the SD-1.5 regression as historical
context.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-17 12:54:37 -07:00

2.4 KiB

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Remove-AI-Watermarks

You are a principal Python engineer maintaining a CLI tool and library for removing visible and invisible AI watermarks from images.

How to run

uv run remove-ai-watermarks all <image.png> -o <output.png>
uv run remove-ai-watermarks metadata <image.png> --check — inspect AI metadata (C2PA, EXIF, PNG chunks)
uv run remove-ai-watermarks metadata <image.png> --remove -o <out.png> — strip all AI metadata

Configuration

GPU/ML modules (invisible_engine, ctrlregen, watermark_remover) are optional — guard imports with is_available() checks
Tests for ML modules are limited to availability checks (require multi-GB downloads)

Key modules

noai/c2pa.py — PNG chunk parser; use extract_c2pa_chunk(path) to get raw caBX payload, has_c2pa_metadata(path) to detect. Do not reimplement chunk parsing.
noai/constants.py — PNG_SIGNATURE, C2PA_CHUNK_TYPE, C2PA_SIGNATURES constants
face_protector.py — YOLO detect + soft-blend pattern; mirror this for any "protect region during diffusion" features

Known limitations

invisible pipeline downscales to model-native resolution (1024 px for SDXL) before diffusion. Degrades fine text in infographics. Tracked; fix is tile-based diffusion.
Pyright first run is slow (2-3 min) due to ML deps (torch/diffusers/transformers stubs)
ultralytics monkey-patches PIL.Image.open and tries to autoload pi_heif. When pi_heif is missing, opening files raises ModuleNotFoundError, not UnidentifiedImageError. Code that opens user-supplied or unknown-format files should except Exception, not just OSError/UnidentifiedImageError.
Metadata detection for AVIF/HEIF/JPEG-XL relies on a binary scan for C2PA_UUID + IPTC_AI_MARKERS. C2PA removal in those containers is implemented via noai/isobmff.py (top-level uuid / jumb box stripper, no re-encoding). EXIF/XMP boxes inside those containers are not yet scrubbed.
SynthID v2 vs default pipeline: the SDXL-based default profile (since May 2026) defeats SynthID v2 per the deployment in raiw-app (fal-ai/fast-sdxl at native ~1024 px, strength 0.05, steps 50). SD-1.5 dreamshaper at 768 px was previously the default and does NOT defeat v2 — verified empirically against Gemini app's "Verify with SynthID" feature (strength 0.04, 0.10, and elastic warp α∈{5,8} all flagged positive). That SD-1.5 path was removed; only default (SDXL) and ctrlregen profiles remain.