mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
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Victor KuznetsovandClaude Opus 5.5
f855c4700f
Main's claim_generator_platform and c2pa_vendor_platform now use the branch's whole-word registry match, which replaces claim_generator_word_platform. The branch's provenance veto covers every video mark and made main's Veo-over-Sora override unreachable, so the override is removed. Main's credential rule carries into the veto: an invalid credential vetoes nothing. Provenance that confirms the mark's own vendor also vetoes nothing. Over 78 local videos with a C2PA AI claim, the only verdict changes against main are Kling/Hailuo false positives on Veo and FLUX clips that the veto now removes. C2PA verdicts over 1,379 files are unchanged against the pre-merge branch. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
579 lines
25 KiB
Markdown
579 lines
25 KiB
Markdown
# Remove AI Watermarks
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Remove AI provenance marks from images and video you generated yourself:
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- known visible labels such as the Google Gemini sparkle watermark and vendor text marks;
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- invisible pixel watermarks through direct local-format disruption or diffusion regeneration;
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- C2PA, EXIF, XMP, IPTC, and related AI metadata.
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Video support covers provenance identification, complete visible-plus-metadata
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cleaning, directory batches, visible Sora, Veo, Seedance, Doubao, Dola,
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Hailuo AI, and Kling AI mark removal, and oracle-certified VAE regeneration for video SynthID
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removal.
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> [raiw.cc](https://raiw.cc) runs this library as a hosted service, with the GPU
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> included and nothing to install. Visible mark and metadata removal at Standard
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> output up to 12 MP are free there; original resolution above 12 MP and invisible
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> watermark removal are paid.
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[](https://pypi.org/project/remove-ai-watermarks/)
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[](https://pypi.org/project/remove-ai-watermarks/)
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[](https://pepy.tech/project/remove-ai-watermarks)
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[](LICENSE)
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[](https://github.com/wiltodelta/remove-ai-watermarks/actions/workflows/test.yml)
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[](https://github.com/sponsors/wiltodelta)
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[](https://skills.sh/wiltodelta/remove-ai-watermarks)
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> This is a research project on AI provenance signals, for lawful use on content
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> you own. It is provided as is, without warranty or liability, and each user is
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> responsible for how they use it. It does not target stock agency previews or
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> other watermarks that protect third party paid content. Some jurisdictions,
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> including China, restrict removing AI labels or supplying tools that do. See
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> [scope, safety, and legal notes](docs/legal-and-safety.md).
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## Choose what you want to do
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| Goal | Command | GPU |
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| --- | --- | --- |
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| Find provenance signals and watermarks | `identify` | No |
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| Classify a photograph from pixels (opt-in, not provenance) | `classify` | No |
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| Remove known visible AI marks | `visible` | No |
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| Erase a region you select | `erase` | No |
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| Strip AI metadata | `metadata` | No |
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| Identify supported video provenance | `video identify` | No |
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| Remove visible marks and AI metadata from video | `video all` | No |
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| Strip AI metadata from video | `video metadata` | No |
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| Remove a registered visible AI mark from video | `video visible` | No |
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| Process a directory of videos | `video batch` | Depends on mode |
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| Apply the calibrated video-pixel SynthID-removal profile | `video invisible` | Recommended |
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| Regenerate an image to disrupt invisible watermarks | `invisible` | Required (CUDA) |
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| Run visible, invisible, and metadata removal | `all` | Recommended |
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| Process a directory | `batch` | Depends on mode |
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Microsoft Paint and Photos InvisMark declarations are routed automatically to
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pixel regeneration. The `all` command removes both the hidden pixel watermark and
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its linked C2PA manifest; metadata stripping alone removes only the manifest. A
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specialized Python API can inspect and disrupt the validated local
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`Watermarker.dll` payload without diffusion.
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## Installation modes
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| Need | Install |
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| --- | --- |
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| Metadata inspection and stripping | `remove-ai-watermarks` |
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| Photograph AI-versus-camera classification | `remove-ai-watermarks[classify]` |
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| OpenAI/Google/unknown source-export classification | `remove-ai-watermarks[source-classify]` |
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| Visible detection and removal | `remove-ai-watermarks[visible]` |
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| Visible video processing | `remove-ai-watermarks[video]` |
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| Video SynthID removal | `remove-ai-watermarks[video,diffusion]` |
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| Torch-free DWT-DCT detection | `remove-ai-watermarks[detect]` |
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| Direct local Paint InvisMark disruption | `remove-ai-watermarks[pixels]` |
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| Invisible image removal (needs CUDA) | `remove-ai-watermarks[qwen-zimage]` |
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| Every production feature available on the active Python | `remove-ai-watermarks[all]` |
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Lower-level and specialized extras include `pixels`, `heif`, `trustmark`,
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`migan`, `lama`, `diffusion`, `classify-onnx`, and `source-classify`. The
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[installation guide](docs/installation.md#feature-extras) documents their exact
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dependency composition, Python compatibility, and model requirements.
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## Quick start
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Install the metadata-focused default CLI:
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```bash
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uv tool install remove-ai-watermarks
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```
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Inspect an image:
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```bash
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remove-ai-watermarks identify image.png
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```
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To classify a photograph from pixels (AI versus camera, optional provider),
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install the extra and call `classify`. `identify` never starts it:
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```bash
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uv tool install --force "remove-ai-watermarks[classify]"
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remove-ai-watermarks classify image.png
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```
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Guide: [photo pixel classification](docs/photo-classify.md).
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For a lightweight, abstaining OpenAI/Google/unknown source-export signal after
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metadata removal, use the separate Python API. It is not a SynthID detector:
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```python
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import remove_ai_watermarks as raiw
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result = raiw.classify_source("image.png")
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print(result.label, result.reason)
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```
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Install `remove-ai-watermarks[source-classify]`. Guide:
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[source-pipeline classification](docs/source-classify.md).
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Signed provenance is the supported route for SynthID and `identify` reads it.
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There is no local SynthID pixel detector in the package. Research on a
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periodic lattice expert is in `scripts/synthid_runtime/` and
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[synthid-detector-research.md](docs/synthid-detector-research.md).
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For visible watermark removal, install the pixel dependencies:
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```bash
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uv tool install --force "remove-ai-watermarks[visible]"
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```
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Then remove a known visible mark and AI metadata:
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```bash
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remove-ai-watermarks visible image.png -o clean.png
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```
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Strip metadata without running visible inpainting or diffusion:
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```bash
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remove-ai-watermarks metadata image.png --remove -o clean.png
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```
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Without `-o` this command overwrites the source in place.
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Inspect or remove AI metadata from an MP4, MOV, M4V, WebM, MKV, AVI, or FLV
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file:
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```bash
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remove-ai-watermarks video metadata input.mp4 --check
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remove-ai-watermarks video metadata input.mp4 --remove -o clean.mp4
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```
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The `video metadata` command does not transcode video or audio streams. Unlike
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the image command above, when `-o` is omitted it writes `<source>_clean` and
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preserves the original. MP4 and MOV
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inspection includes the native TC260 `AIGC` tag in
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`moov.udta.meta.keys/ilst`, including a `moov` placed after the media payload,
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plus the QuickTime-form `meta` variants Doubao's iOS export writes (a bare
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`meta` box as a direct `moov` child, and a keyless `hdlr=mdir` metadata list).
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Keyed `workflow` and `prompt` entries in the same metadata-list structure are
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also detected and removed, including ComfyUI exports.
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MKV and WebM inspection reads the normative
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`Segment.Tags.Tag.SimpleTag` placement. AVI uses `LIST/INFO/AIGC`, while FLV
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uses `script.onMetaData.AIGC`. The non-ISOBMFF formats are remuxed with stream
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copy for removal.
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Use the product-oriented video path to identify or clean a file:
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```bash
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uv tool install --force "remove-ai-watermarks[video]"
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remove-ai-watermarks video identify input.mp4
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remove-ai-watermarks video all input.mp4 -o clean.mp4
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```
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`video all` removes a stable registered visible mark when present and always
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strips verified AI metadata. If neither signal is found, it still writes a
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same-container passthrough, so application callers get one predictable output
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contract. Proprietary invisible-video removal is excluded by default.
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`--invisible` opts into the lossy, oracle-certified video SynthID profile.
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Process a directory with the same contract:
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```bash
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remove-ai-watermarks video batch ./videos --mode all
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```
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Remove a supported visible video mark:
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```bash
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remove-ai-watermarks video visible input.mp4 -o clean.mp4
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remove-ai-watermarks video visible veo.mp4 --mark veo -o veo_clean.mp4
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remove-ai-watermarks video visible seedance.mp4 --mark seedance -o seedance_clean.mp4
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remove-ai-watermarks video visible dola.mp4 --mark dola -o dola_clean.mp4
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remove-ai-watermarks video visible hailuo.mp4 --mark hailuo -o hailuo_clean.mp4
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remove-ai-watermarks video visible kling.mp4 --mark kling -o kling_clean.mp4
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```
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This path scans the complete sequence before changing pixels. It accepts only a
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mark that repeats at a stable position across adjacent frames, then reuses the
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same OpenCV, MI-GAN, or LaMa fill backends as image removal. Audio is copied
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without re-encoding and is allowed to reach its natural end; the video stream
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is transcoded because its pixels change. By default, a guarded optical-flow
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pass motion-aligns the preceding accepted fill and blends it only when the
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nearby source context agrees; use `--no-temporal-consistency` to disable it.
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The encoder preserves supported
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8-bit source chroma sampling, color tags, and MP4/MOV track timescale instead
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of relying on ffmpeg's implicit raw-BGR defaults. Variable frame intervals are
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preserved through a timestamped in-memory NUT bridge instead of being flattened
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to the average frame rate. Non-zero source start timestamps are retained
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together with the copied audio offset. The default `--mark auto`
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scans all providers in one decode pass and selects the first stable match in
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the specificity order shown below. Valid Google AI-video provenance breaks a
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Sora/Veo cross-match in favor of Veo when the diamond is nested in the Sora
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region or Veo persists through the complete clip while Sora does not.
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Pass an explicit mark to restrict detection to one provider.
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Sora covers the moving Sora 2 mascot, with or without the wordmark; mascot-only
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matches also require the complete two-eye shape. Veo covers both the current
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four-point diamond and the legacy `Veo` text. Seedance covers the fixed boxed
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`AI` label, Dola covers the fixed `Dola AI` text, Hailuo AI covers the composite
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`MINIMAX | hailuo AI` label, and Kling AI covers the bottom-right `KLING AI` or
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`KlingAI` label with its version suffix. A completed encode is published
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atomically. No output is written when no stable mark is found.
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HDR, PQ/HLG, and greater-than-8-bit inputs are rejected before encoding rather
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than silently reduced through OpenCV's 8-bit BGR boundary.
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Apply the calibrated video-pixel SynthID-removal profile:
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```bash
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uv tool install --force "remove-ai-watermarks[video,diffusion]"
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remove-ai-watermarks video invisible input.mp4 -o clean.mp4
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```
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This path regenerates the complete sequence with one latent-noise field shared
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across time, copies complete audio, strips source metadata, and publishes the
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completed encode atomically. The default `noise_std=0.15` profile passed both
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the two-carrier calibration and a complete public eight-second Veo oracle
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check. Google does not publish a local decoder, so a fresh provider check
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remains useful for unusually important files or after provider changes, but it
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is not a product result state. The CLI therefore describes video-pixel
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regeneration instead of claiming a per-file SynthID verdict, and reports the
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copied audio watermark as `UNVERIFIED`.
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For invisible watermark removal through the CLI or high-level API, install the
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`qwen-zimage` extra. **An NVIDIA GPU is required**: all diffusion profiles are
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CUDA-only, and there is no CPU or MPS fallback. The specialized local Paint API
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uses the `pixels` extra without CUDA.
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```bash
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uv tool install --force "remove-ai-watermarks[qwen-zimage]"
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remove-ai-watermarks invisible image.png -o clean.png
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```
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If the local detectors cannot confirm an invisible watermark but you know the
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image came from an AI generator, add `--force`:
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```bash
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remove-ai-watermarks invisible image.png -o clean.png --force
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```
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Typography-heavy images can opt into the experimental verified-text post-pass.
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It accepts manually reviewed strings and line boxes or an operator-verified
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geometry-only manifest; it never treats raw OCR output as ground truth or runs
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automatically:
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```bash
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uv tool install --force "remove-ai-watermarks[text-restoration]"
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remove-ai-watermarks invisible image.png -o clean.png \
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--text-manifest verified-lines.json --force
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```
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See the [CLI guide](docs/cli.md#restore-operator-verified-text) for the manifest
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schema, compatibility restrictions, and oracle caveats.
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See the [installation guide](docs/installation.md) for Homebrew, uv, optional
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features, and development setup.
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## Examples
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### Visible Gemini mark
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| Before | After |
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| --- | --- |
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|  |  |
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The `after` raster is generated from the tracked `before` raster by the public path:
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```bash
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uv run remove-ai-watermarks visible demo_banana_before.png \
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--backend cv2 -o demo_banana_after.png
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```
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### High quality invisible removal
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`qwen-zimage` is the default profile: a Qwen-Image-2512 Lightning pass under Canny
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ControlNet, followed by SAM-masked Z-Image repair of any detected face. The
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alternative, `sdxl-zimage`, swaps the global stage for SDXL and keeps the same face
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stage. A third profile, `chroma-zimage`, uses the Apache-2.0 Chroma1 global pass
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with its own flat vendor floors; see `docs/chroma1-engine-research.md` for the
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calibration. `--pipeline auto` picks sdxl-zimage for Google, chroma-zimage for
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Microsoft, and qwen-zimage otherwise. All are CUDA only.
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```bash
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uv tool install --force "remove-ai-watermarks[qwen-zimage]"
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remove-ai-watermarks invisible image.png -o clean.png --force
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```
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| OpenAI example before | OpenAI example after |
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| --- | --- |
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| [](data/synthid/originals/ChatGPT%20Image%20May%2030,%202026,%2010_31_08%20AM.png) | [](docs/images/qwen-zimage/ChatGPT/ChatGPT%20Image%20May%2030,%202026,%2010_31_08%20AM_full_clean.png) |
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| Gemini example before | Gemini example after |
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| --- | --- |
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| [](data/synthid/originals/Gemini_Generated_Image_633uuy633uuy633u.png) | [](docs/images/qwen-zimage/Gemini/Gemini_Generated_Image_633uuy633uuy633u_full_clean.png) |
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These exact output files were checked with the matching provider verifiers. That
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result applies to these files, not to every seed, image, or future watermark
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version.
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## Common recipes
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### Remove every detected visible mark
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```bash
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remove-ai-watermarks visible image.png -o clean.png
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```
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The default `--mark auto` checks all registered visible marks and removes every
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match. If the mark is visible to you but the detector misses it, select its
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region explicitly:
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```bash
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remove-ai-watermarks erase image.png \
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--region 1640,1930,400,100 \
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-o clean.png
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```
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`--region` uses `x,y,width,height` and may be repeated.
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### Use a learned fill backend
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The `visible` extra uses OpenCV inpainting when no learned backend is installed.
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For more difficult backgrounds, the learned-backend extras include the same
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pixel dependencies automatically:
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```bash
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uv tool install --force "remove-ai-watermarks[migan]"
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remove-ai-watermarks visible image.png -o clean.png --backend migan
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```
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```bash
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uv tool install --force "remove-ai-watermarks[lama]"
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remove-ai-watermarks visible image.png -o clean.png --backend lama
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```
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### Reduce CUDA memory use
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```bash
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remove-ai-watermarks invisible image.png -o clean.png \
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--cpu-offload --force
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```
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CPU offload lowers CUDA memory pressure by moving model components between CPU
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and GPU, at the cost of speed.
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### Process a directory
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```bash
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remove-ai-watermarks batch ./images --mode visible
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remove-ai-watermarks batch ./images --mode all
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```
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## What the tool can recognize
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Visible mark support includes:
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- Google Gemini and Nano Banana visible sparkle watermark;
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- Doubao, the Jimeng wordmark and top-left `AI生成` pill, Qwen, Kling AI,
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Yuanbao, Baidu, LiblibAI's bottom-center wordmark and compact top-left pill,
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and RunningHub labels;
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- one calibrated Microsoft top-right white AI-badge variant;
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- one calibrated Samsung Galaxy AI label variant;
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- a generic brand-less bottom-right `AI生成` fallback for OS/gallery-level
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AI-edit stamps that carry no vendor wordmark of their own (vivo and Xiaomi
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Gallery confirmed); synthetic-only calibration, weaker evidence than the
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vendor-tuned detectors above;
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- the OpenArt wordmark, placed at the frame center rather than a corner;
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uncalibrated (one confirmed real case, no captured corpus).
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Metadata and provenance inspection covers C2PA, EXIF, XMP, IPTC, common
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generator parameters in image and video containers, China TC260 AIGC labels,
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and several vendor specific signals. Optional decoders add support for open
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DWT-DCT watermarks and Adobe TrustMark.
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The exact support matrix, including important locale and detector limits, lives
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in [supported signals](docs/supported-signals.md). The
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[synthetic example gallery](data/fixtures/visible/README.md) carries one canonical
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sample for every registered image and video mark.
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## How it works
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Visible removal follows three steps:
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1. Detect a registered mark in its expected area.
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2. Build a mask around the mark.
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3. Fill only the masked region with OpenCV, MI-GAN, or LaMa.
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Metadata removal uses format aware stripping. JPEG metadata removal preserves
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the encoded image scan instead of recompressing it. Native MP4/MOV TC260 values
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and keyed generation parameters are blanked without changing box sizes or
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media offsets. Other supported containers use their corresponding metadata
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path.
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Invisible removal through the CLI and high-level API regenerates the image through
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a diffusion pipeline. Specialized callers can instead decode and disrupt the
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positively identified local Microsoft Paint format with much smaller pixel changes.
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Neither path can guarantee that every proprietary verifier will reject every output.
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See [supported signals](docs/supported-signals.md) and
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[known limitations](docs/known-limitations.md) for the full technical boundary.
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## Python API
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The visible-removal API requires `remove-ai-watermarks[visible]`.
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```python
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import remove_ai_watermarks as raiw
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result, removed = raiw.remove_visible("watermarked.png", "clean.png")
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print(removed)
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report = raiw.remove_visible_detailed("watermarked.png", "clean.png")
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print(report.status) # cleaned | partial | unvalidated | no_watermark
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complete = raiw.remove_all("watermarked.png", "clean.png")
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print(complete.visible_status, complete.visible_marks)
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images = raiw.remove_batch("images", "images_clean", mode="visible")
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for item in images.items:
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print(item.source, item.visible_status, item.visible_marks)
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|
|
provenance = raiw.identify_video("input.mp4")
|
|
report = raiw.inspect_video_metadata("input.mp4")
|
|
complete = raiw.remove_video_all("input.mp4", "clean.mp4")
|
|
print(complete.visual_invisible_action, complete.audio.watermark_status)
|
|
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")
|
|
print(synthid_cleaned.visual_invisible_action, synthid_cleaned.audio.watermark_status)
|
|
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](docs/python-api.md) for visible removal, the full
|
|
`remove_all` and `remove_batch` pipeline, provenance inspection, metadata
|
|
stripping, and diffusion usage.
|
|
|
|
## Agent skill
|
|
|
|
Coding agents can install the published skill and drive this CLI:
|
|
|
|
```bash
|
|
npx skills add wiltodelta/remove-ai-watermarks
|
|
```
|
|
|
|
Claude Code can add this repository as a plugin marketplace:
|
|
|
|
```text
|
|
/plugin marketplace add wiltodelta/remove-ai-watermarks
|
|
/plugin install remove-ai-watermarks@remove-ai-watermarks
|
|
```
|
|
|
|
The skill refuses stock-agency and other third-party paid-asset marks. See
|
|
[the agent skill guide](docs/agent-skill.md).
|
|
|
|
## ComfyUI
|
|
|
|
The separate
|
|
[ComfyUI Remove AI Watermarks](https://github.com/wiltodelta/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
|
|
Doubao, Dola, Hailuo AI, Vidu, 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 through the CLI and high-level API requires CUDA.
|
|
All diffusion profiles refuse any other device at construction rather than
|
|
falling back to one that cannot run them. The specialized local Paint API,
|
|
visible removal, metadata stripping and `identify` run without CUDA.
|
|
- 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:
|
|
|
|
```bash
|
|
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 after the input resize and before final
|
|
encoding, so only the probe sees the delivered picture.
|
|
|
|
```bash
|
|
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 for that full-clip check -- one carrier, two
|
|
rows -- live in `data/evaluations/video-synthid-oracle.csv`. The earlier
|
|
two-clip calibration is narrative only: its verdicts were not recorded in a
|
|
tracked manifest, so treat the certified default as resting on the 2026-07-31
|
|
rows.
|
|
|
|
## Documentation
|
|
|
|
Start with the [documentation index](docs/index.md).
|
|
|
|
- [Installation](docs/installation.md)
|
|
- [CLI guide](docs/cli.md)
|
|
- [Python API](docs/python-api.md)
|
|
- [Supported signals](docs/supported-signals.md)
|
|
- [Known limitations](docs/known-limitations.md)
|
|
- [Scope, safety, and legal notes](docs/legal-and-safety.md)
|
|
- [Module internals](docs/module-internals.md)
|
|
- [Release and distribution](docs/release-and-distribution.md)
|
|
- [Agent skill](docs/agent-skill.md)
|
|
|
|
Research notes and historical experiments are listed separately in the
|
|
[documentation index](docs/index.md). They explain past decisions but do not
|
|
define the current public API.
|
|
|
|
## Contributing
|
|
|
|
Install the development environment and run the project gate:
|
|
|
|
```bash
|
|
uv sync --frozen --extra dev
|
|
bash maintain.sh
|
|
```
|
|
|
|
See [module internals](docs/module-internals.md) before changing a subsystem
|
|
with documented invariants.
|
|
|
|
## License
|
|
|
|
[Apache 2.0](LICENSE). Copyright 2025-2026 wiltodelta.
|