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 diffusion regeneration;
• C2PA, EXIF, XMP, IPTC, and related AI metadata.

Video support covers provenance identification, complete visible-plus-metadata cleaning, directory batches, visible Sora, Veo, Seedance, 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 project is for lawful use on content you own. It does not target stock agency previews or other watermarks that protect third party paid content. 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
Verify supported OpenAI SynthID from pixels with the official remote API	verify-openai-synthid	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
Remove video SynthID with the certified VAE profile	video invisible	Recommended
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.

Installation modes

Need	Install
Metadata inspection and stripping	remove-ai-watermarks
Photograph AI-versus-camera classification	remove-ai-watermarks[classify]
Official remote OpenAI SynthID verification	remove-ai-watermarks[verify]
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]
Invisible image removal (needs CUDA)	remove-ai-watermarks[qwen-zimage]
Every production feature available on the active Python	remove-ai-watermarks[all]

Lower-level and specialized extras include pixels, heif, trustmark, migan, lama, and diffusion. 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.

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 supported OpenAI images, the optional official verifier provides a pixel watermark verdict:

uv tool install --force "remove-ai-watermarks[verify]"
remove-ai-watermarks verify-openai-synthid image.png --acknowledge-upload

The command removes AI provenance metadata from a temporary copy, verifies that the decoded pixels are unchanged, uploads only that copy to OpenAI, and consumes only the independent SynthID response. It never runs implicitly from identify. An API key, endpoint access, and explicit upload acknowledgement are required.

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 <source>_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). 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. Pass an explicit mark to restrict detection to one provider. Sora covers the moving Sora 2 mascot and wordmark. 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 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.

Remove video SynthID:

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.

For invisible watermark removal, install the qwen-zimage extra. An NVIDIA GPU is required: all profiles are CUDA-only, and there is no CPU or MPS fallback.

uv tool install --force "remove-ai-watermarks[qwen-zimage]"
remove-ai-watermarks invisible image.png -o clean.png

If the local detectors cannot confirm an invisible watermark but you know the image came from an AI generator, add --force:

remove-ai-watermarks invisible image.png -o clean.png --force

Typography-heavy images can opt into the experimental verified-text post-pass. It accepts manually reviewed strings and line boxes or an operator-verified geometry-only manifest; it never treats raw OCR output as ground truth or runs automatically:

uv tool install --force "remove-ai-watermarks[text-restoration]"
remove-ai-watermarks invisible image.png -o clean.png \
  --text-manifest verified-lines.json --force

See the CLI guide for the manifest schema, compatibility restrictions, and oracle caveats.

See the installation guide for Homebrew, uv, optional features, and development setup.

Examples

Visible Gemini mark

Before	After

The after raster is generated from the tracked before raster by the public path:

uv run remove-ai-watermarks visible demo_banana_before.png \
  --backend cv2 -o demo_banana_after.png

High quality invisible removal

qwen-zimage is the default profile: a Qwen-Image-2512 Lightning pass under Canny ControlNet, followed by SAM-masked Z-Image repair of any detected face. The alternative, sdxl-zimage, swaps the global stage for SDXL and keeps the same face stage. A third profile, chroma-zimage, uses the Apache-2.0 Chroma1 global pass with its own flat vendor floors; see docs/chroma1-engine-research.md for the calibration. --pipeline auto picks chroma-zimage for OpenAI and Microsoft provenance and qwen-zimage otherwise. All are CUDA only.

uv tool install --force "remove-ai-watermarks[qwen-zimage]"
remove-ai-watermarks invisible image.png -o clean.png --force

OpenAI example before	OpenAI example after

Gemini example before	Gemini example after

These exact output files were checked with the matching provider verifiers. That result applies to these files, not to every seed, image, or future watermark version.

Common recipes

Remove every detected visible mark

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

The default --mark auto checks all registered visible marks and removes every match. If the mark is visible to you but the detector misses it, select its region explicitly:

remove-ai-watermarks erase image.png \
  --region 1640,1930,400,100 \
  -o clean.png

--region uses x,y,width,height and may be repeated.

Use a learned fill backend

The visible extra uses OpenCV inpainting when no learned backend is installed. For more difficult backgrounds, the learned-backend extras include the same pixel dependencies automatically:

uv tool install --force "remove-ai-watermarks[migan]"
remove-ai-watermarks visible image.png -o clean.png --backend migan

uv tool install --force "remove-ai-watermarks[lama]"
remove-ai-watermarks visible image.png -o clean.png --backend lama

Reduce CUDA memory use

remove-ai-watermarks invisible image.png -o clean.png \
  --cpu-offload --force

CPU offload lowers CUDA memory pressure by moving model components between CPU and GPU, at the cost of speed.

Process a directory

remove-ai-watermarks batch ./images --mode visible
remove-ai-watermarks batch ./images --mode all

What the tool can recognize

Visible mark support includes:

• Google Gemini and Nano Banana visible sparkle watermark;
• Doubao, the Jimeng wordmark and top-left AI生成 pill, Qwen, Kling AI, Yuanbao, Baidu, LiblibAI, and RunningHub labels;
• one calibrated Microsoft top-right white AI-badge variant;
• one calibrated Samsung Galaxy AI label variant.

Metadata and provenance inspection covers C2PA, EXIF, XMP, IPTC, common generator parameters, China TC260 AIGC labels, and several vendor specific signals. Optional decoders add support for open DWT-DCT watermarks and Adobe TrustMark.

The exact support matrix, including important locale and detector limits, lives in supported signals. The synthetic example gallery carries one canonical sample for every registered image and video mark.

How it works

Visible removal follows three steps:

1. Detect a registered mark in its expected area.
2. Build a mask around the mark.
3. Fill only the masked region with OpenCV, MI-GAN, or LaMa.

Metadata removal uses format aware stripping. JPEG metadata removal preserves the encoded image scan instead of recompressing it. Native MP4/MOV TC260 values are blanked without changing box sizes or media offsets. Other supported containers use their corresponding metadata path.

Invisible removal is different. It regenerates the image through a diffusion pipeline to disrupt pixel and frequency domain watermarks. This changes the image and cannot guarantee that a proprietary verifier will reject every output.

See supported signals and known limitations for the full technical boundary.

Python API

The visible-removal API requires remove-ai-watermarks[visible].

import remove_ai_watermarks as raiw

result, removed = raiw.remove_visible("watermarked.png", "clean.png")
print(removed)

openai_synthid = raiw.verify_openai_synthid("image.png", acknowledge_upload=True)
print(openai_synthid.status)

provenance = raiw.identify_video("input.mp4")
report = raiw.inspect_video_metadata("input.mp4")
complete = raiw.remove_video_all("input.mp4", "clean.mp4")
batch = raiw.remove_video_batch("videos", "videos_clean")
cleaned = raiw.remove_video_metadata("input.mp4")
synthid_cleaned = raiw.remove_video_invisible("input.mp4", "synthid_clean.mp4")
visible = raiw.remove_video_visible("input.mp4", "clean.mp4")
print(visible.mark)
veo = raiw.remove_video_visible("veo.mp4", "veo_clean.mp4", mark="veo")
seedance = raiw.remove_video_visible(
    "seedance.mp4",
    "seedance_clean.mp4",
    mark="seedance",
)
dola = raiw.remove_video_visible("dola.mp4", "dola_clean.mp4", mark="dola")

The high level API accepts a file path or a BGR NumPy array. For path inputs it also reads provenance metadata, preserves alpha, and can strip AI metadata from the written result.

See the Python API guide for visible removal, the full remove_all and remove_batch pipeline, provenance inspection, metadata stripping, and diffusion usage.

ComfyUI

The separate ComfyUI Remove AI Watermarks package provides nodes for visible removal, detection, region erasing, and invisible removal.

Important limitations

• A missing local signal means unknown, not clean. Proprietary pixel watermarks may remain after metadata has been stripped.
• Visible removal reconstructs a small region. Results depend on the background and selected fill backend.
• Invisible removal changes the whole image and may alter faces, text, or fine detail.
• Visible video removal recognizes the moving Sora 2 wordmark, the current Veo diamond plus legacy Veo text, the Seedance boxed AI label, and the fixed Dola, Hailuo AI, and Kling AI labels. It does not recognize the older Sora Turbo corner swirl or unregistered layouts from those providers. The classical OpenCV backend can smear structured backgrounds; use MI-GAN or LaMa when recovery quality matters.
• Video SynthID regeneration changes resolution, frame rate, and image detail. The shipped profile is oracle-certified, but no public local decoder can certify an arbitrary output at runtime. Recheck unusually important outputs after provider changes.
• Invisible-watermark removal requires CUDA. All profiles refuse any other device at construction rather than falling back to one that cannot run them. Visible removal, metadata stripping and identify still run anywhere.
• Provider watermark systems can change. Validate important outputs with the provider's own verifier when one is available.

The shipped video invisible command uses the certified noise_std=0.15 profile. The companion scripts/video_synthid_sweep.py research harness builds a matched re-encode control plus VAE-regenerated candidates and leaves the verifier verdict blank:

uv run --extra video --extra diffusion python scripts/video_synthid_sweep.py input.mp4 -o sweep/

To score what an output actually cost, use scripts/video_fidelity_probe.py: the engine's own PSNR is measured before the resize and the encode, so only the probe sees the delivered picture.

uv run --extra video python scripts/video_fidelity_probe.py input.mp4 input_clean.mp4

The control must still be SynthID-positive before a negative candidate can count as removal evidence. In the 2026-07-29 two-clip calibration, both matched controls were positive in Gemini's built-in SynthID verifier; the stronger candidate was negative on both carriers, while a weaker candidate was negative on one. A later adversarial follow-up that asked ordinary Gemini to reinterpret the pixel result returned UNAVAILABLE; that follow-up was not a verifier rerun and does not invalidate the built-in verdicts. A 2026-07-31 full-clip check on a public eight-second Veo sample found 0.10 still detected and 0.15 not detected, so 0.15 is now the certified default. The reproducible hashes and verdicts live in data/evaluations/video-synthid-oracle.csv.

Documentation

Start with the documentation index.

• Installation
• CLI guide
• Python API
• Supported signals
• Known limitations
• Scope, safety, and legal notes
• Module internals
• Release and distribution

Research notes and historical experiments are listed separately in the documentation index. They explain past decisions but do not define the current public API.

Contributing

Install the development environment and run the project gate:

uv sync --frozen --extra dev
bash maintain.sh

See module internals before changing a subsystem with documented invariants.

License

Apache 2.0. Copyright 2025-2026 wiltodelta.