Remove AI Watermarks

Remove AI provenance marks from images and video you generated yourself:

• known visible labels such as the Google Gemini sparkle watermark and vendor text marks;
• invisible pixel watermarks through direct local-format disruption or diffusion regeneration;
• C2PA, EXIF, XMP, IPTC, and related AI metadata.

Before	After

uv tool install "remove-ai-watermarks[visible]"   # CLI for visible marks and metadata
npx skills add wiltodelta/remove-ai-watermarks    # agent skill for Claude Code, Codex, Cursor, and other agents

Video support covers provenance identification, complete visible-plus-metadata cleaning, directory batches, visible Sora, Veo, Seedance, Doubao, Dola, Hailuo AI, and Kling AI mark removal, and oracle-certified VAE regeneration for video SynthID removal.

raiw.cc runs this library as a hosted service, with the GPU included and nothing to install. Visible mark and metadata removal at Standard output up to 12 MP are free there; original resolution above 12 MP and invisible watermark removal are paid.

This is a research project on AI provenance signals, for lawful use on content you own. It is provided as is, without warranty or liability, and each user is responsible for how they use it. It does not target stock agency previews or other watermarks that protect third party paid content. Some jurisdictions, including China, restrict removing AI labels or supplying tools that do. See scope, safety, and legal notes.

Choose what you want to do

Goal	Command	GPU
Find provenance signals and watermarks	identify	No
Classify a photograph from pixels (opt-in, not provenance)	classify	No
Remove known visible AI marks	visible	No
Erase a region you select	erase	No
Strip AI metadata	metadata	No
Identify supported video provenance	video identify	No
Remove visible marks and AI metadata from video	video all	No
Strip AI metadata from video	video metadata	No
Remove a registered visible AI mark from video	video visible	No
Process a directory of videos	video batch	Depends on mode
Apply the calibrated video-pixel SynthID-removal profile	video invisible	Recommended
Identify C2PA text/HTML wrappers, VSRMark, and Unicode steganography	text identify	No
Strip C2PA text/HTML wrappers, VSRMark, and Unicode steganography	text metadata	No
Regenerate unlocked prose spans to remove token-level LLM watermarks	text invisible	Recommended
Strip text provenance and optionally regenerate prose spans	text all	Depends on --invisible
Regenerate an image to disrupt invisible watermarks	invisible	Required (CUDA)
Run visible, invisible, and metadata removal	all	Recommended
Process a directory	batch	Depends on mode

Microsoft Paint and Photos InvisMark declarations are routed automatically to pixel regeneration. The all command removes both the hidden pixel watermark and its linked C2PA manifest; metadata stripping alone removes only the manifest. A specialized Python API can inspect and disrupt the validated local Watermarker.dll payload without diffusion.

Installation modes

Need	Install
Metadata inspection and stripping	remove-ai-watermarks
Photograph AI-versus-camera classification	remove-ai-watermarks[classify]
10-provider source-export & model-version classification	remove-ai-watermarks[source-classify]
Visible detection and removal	remove-ai-watermarks[visible]
Visible video processing	remove-ai-watermarks[video]
Video SynthID removal	remove-ai-watermarks[video,diffusion]
Torch-free DWT-DCT detection	remove-ai-watermarks[detect]
Direct local Paint InvisMark disruption	remove-ai-watermarks[pixels]
Invisible image removal (needs CUDA)	remove-ai-watermarks[qwen-zimage]
Production removal and default classifier backends	remove-ai-watermarks[all]
Optional OCR proposals for operator verification	remove-ai-watermarks[text-draft]

Lower-level and specialized extras include pixels, heif, trustmark, migan, lama, diffusion, classify-onnx, and source-classify. The installation guide documents their exact dependency composition, Python compatibility, and model requirements.

Quick start

Install the metadata-focused default CLI:

uv tool install remove-ai-watermarks

Inspect an image:

remove-ai-watermarks identify image.png

To classify a photograph from pixels (AI versus camera, optional provider), install the extra and call classify. identify never starts it:

uv tool install --force "remove-ai-watermarks[classify]"
remove-ai-watermarks classify image.png

Guide: photo pixel classification.

For a lightweight, abstaining 10-provider source-export and model-version signal after metadata removal (classify_source and CLI classify-source), use the separate API or CLI command. It is not a SynthID detector:

remove-ai-watermarks classify-source image.png

import remove_ai_watermarks as raiw

result = raiw.classify_source("image.png")
print(result.label, result.version_subfamily, result.reason)

Install remove-ai-watermarks[source-classify]. Guide: source-pipeline classification.

Signed provenance is the supported route for SynthID and identify reads it. There is no local SynthID pixel detector in the package. Research on a periodic lattice expert is in scripts/synthid_runtime/ and synthid-detector-research.md.

For visible watermark removal, install the pixel dependencies:

uv tool install --force "remove-ai-watermarks[visible]"

Then remove a known visible mark and AI metadata: