用 Python 工具处理自有媒体的 AI 标记

Remove visible and invisible AI watermarks from images and video, and AI provenance metadata from images, video, and audio. Python librar…

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 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] Every production feature available on the active Python remove-ai-watermarks[all] 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 <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). 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