Remove AI Watermarks

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.

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

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

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:

remove-ai-watermarks visible image.png -o clean.png

Strip metadata without running visible inpainting or diffusion:

remove-ai-watermarks metadata image.png --remove -o clean.png

Without -o this command overwrites the source in place.

Inspect or remove AI metadata from an MP4, MOV, M4V, WebM, MKV, AVI, or FLV file:

remove-ai-watermarks video metadata input.mp4 --check

remove-ai-watermarks video metadata input.mp4 --remove -o clean.mp4

The video metadata command does not transcode video or audio streams. Unlike the image command above, when -o is omitted it writes _clean and preserves the original. MP4 and MOV inspection includes the native TC260 AIGC tag in moov.udta.meta.keys/ilst, including a moov placed after the media payload, plus the QuickTime-form meta variants Doubao's iOS export writes (a bare meta box as a direct moov child, and a keyless hdlr=mdir metadata list). Keyed workflow and prompt entries in the same metadata-list structure are also detected and removed, including ComfyUI exports. MKV and WebM inspection reads the normative Segment.Tags.Tag.SimpleTag placement. AVI uses LIST/INFO/AIGC, while FLV uses script.onMetaData.AIGC. The non-ISOBMFF formats are remuxed with stream copy for removal.

Use the product-oriented video path to identify or clean a file:

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

remove-ai-watermarks video identify input.mp4 remove-ai-watermarks video all input.mp4 -o clean.mp4

video all removes a stable registered visible mark when present and always strips verified AI metadata. If neither signal is found, it still writes a same-container passthrough, so application callers get one predictable output contract. Proprietary invisible-video removal is excluded by default. --invisible opts into the lossy, oracle-certified video SynthID profile.

Process a directory with the same contract:

remove-ai-watermarks video batch ./videos --mode all

Remove a supported visible video mark:

remove-ai-watermarks video visible input.mp4 -o clean.mp4

remove-ai-watermarks video visible veo.mp4 --mark veo -o veo_clean.mp4 remove-ai-watermarks video visible seedance.mp4 --mark seedance -o seedance_clean.mp4 remove-ai-watermarks video visible dola.mp4 --mark dola -o dola_clean.mp4 remove-ai-watermarks video visible hailuo.mp4 --mark hailuo -o hailuo_clean.mp4 remove-ai-watermarks video visible kling.mp4 --mark kling -o kling_clean.mp4

This path scans the complete sequence before changing pixels. It accepts only a mark that repeats at a stable position across adjacent frames, then reuses the same OpenCV, MI-GAN, or LaMa fill backends as image removal. Audio is copied without re-encoding and is allowed to reach its natural end; the video stream is transcoded because its pixels change. By default, a guarded optical-flow pass motion-aligns the preceding accepted fill and blends it only when the nearby source context agrees; use --no-temporal-consistency to disable it. The encoder preserves supported 8-bit source chroma sampling, color tags, and MP4/MOV track timescale instead of relying on ffmpeg's implicit raw-BGR defaults. Variable frame intervals are preserved through a timestamped in-memory NUT bridge instead of being flattened to the average frame rate. Non-zero source start timestamps are retained together with the copied audio offset. The default --mark auto scans all providers in one decode pass and selects the first stable match in the specificity order shown below. Valid Google AI-video provenance breaks a Sora/Veo cross-match in favor of Veo when the diamond is nested in the Sora region or Veo persists through the complete clip while Sora does not. Pass an explicit mark to restrict detection to one provider. Sora covers the moving Sora 2 mascot, with or without the wordmark; mascot-only matches also require the complete two-eye shape. Veo covers both the current four-point diamond and the legacy Veo text. Seedance covers the fixed boxed AI label, Dola covers the fixed Dola AI text, Hailuo AI covers the composite MINIMAX | hailuo AI label, and Kling AI covers the bottom-right KLING AI or KlingAI label with its version suffix. A completed encode is published atomically. No output is written when no stable mark is found. HDR, PQ/HLG, and greater-than-8-bit inputs are rejected before encoding rather than silently reduced through OpenCV's 8-bit BGR boundary.

Apply the calibrated video-pixel SynthID-removal profile:

uv tool install --force "remove-ai-watermarks[video,diffusion]"

remove-ai-watermarks video invisible input.mp4 -o clean.mp4

This path regenerates the complete sequence with one latent-noise field shared across time, copies complete audio, strips source metadata, and publishes the completed encode atomically. The default noise_std=0.15 profile passed both the two-carrier calibration and a complete public eight-second Veo oracle check. Google does not publish a local decoder, so a fresh provider check remains useful for unusually important files or after provider changes, but it is not a product result state. The CLI therefore describes