From 9f93d9c0c587cb93440eb62a12227e929cfd1ffb Mon Sep 17 00:00:00 2001 From: test-user Date: Tue, 26 May 2026 21:13:49 -0700 Subject: [PATCH] feat(identify): add Sony C2PA device attribution, verified (v0.6.3) Adds Sony to _DEVICE_C2PA_PLATFORM, matching Sony's own `sony.sig` / `sony.cert` C2PA assertion namespace (NOT bare "Sony", which is a common EXIF Make). Verified against a real Sony-signed file (Sony PXW-Z300, signer "Sony Corporation") found in the Security4Media/c2pa-video-player repo. The sample is video (MP4) -- our ISOBMFF C2PA path detects it; Sony Alpha stills likely share the namespace. Verified device set is now Leica, Nikon, Google Pixel, Sony, Truepic. Canon / Samsung / Bria still have no public direct-download C2PA sample to verify. Co-Authored-By: Claude Opus 4.7 (1M context) --- CLAUDE.md | 2 +- pyproject.toml | 2 +- src/remove_ai_watermarks/__init__.py | 2 +- src/remove_ai_watermarks/identify.py | 5 ++++ tests/test_identify.py | 9 +++++++ uv.lock | 36 ++++++++++++++-------------- 6 files changed, 35 insertions(+), 21 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index a7452ee..397983a 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -30,7 +30,7 @@ You are a **principal Python engineer** maintaining a CLI tool and library for r - `noai/c2pa.py` — PNG chunk parser; use `extract_c2pa_chunk(path)` to get raw caBX payload, `has_c2pa_metadata(path)` to detect. Do not reimplement chunk parsing. `extract_c2pa_info(path)` sets `synthid_watermark`/`synthid_vendors` when the manifest is signed by a SynthID-using vendor, and `soft_binding`/`soft_binding_vendors` when a `c2pa.soft-binding` `alg` names a forensic-watermark vendor (`soft_binding_vendors_in(buffer)` is the shared byte-scan, used by both the PNG parser and the non-PNG binary path). - `noai/constants.py` — PNG_SIGNATURE, C2PA_CHUNK_TYPE, C2PA_SIGNATURES, C2PA_ISSUERS, `SYNTHID_C2PA_ISSUERS` (issuers that pair SynthID with C2PA: Google, OpenAI), and `C2PA_SOFT_BINDINGS` (soft-binding `alg` prefix → forensic-watermark vendor: Adobe TrustMark, Digimarc, Imatag, Steg.AI, Microsoft, ...). Add a new issuer/binding here, not inline. - `metadata.py` — `synthid_source(path)` returns the vendor name(s) if the C2PA manifest implies a SynthID pixel watermark, else None. Format-agnostic: PNG via the caBX parser, JPEG/WebP/AVIF/HEIF/JXL via a binary scan (C2PA marker + SynthID issuer + AI-source marker). `get_ai_metadata` surfaces the verdict, and `metadata --check` prints it as a callout. Both `get_ai_metadata` and `has_ai_metadata` guard the PIL open with `except Exception` (HEIC/unknown formats raise non-OSError) and fall through to the binary scan. `xai_signature(path)` detects xAI/Grok's EXIF-only scheme (`ImageDescription` = `Signature: ` + UUID `Artist`); it feeds `has_ai_metadata`, `get_ai_metadata` (key `xai_signature`), and `identify`. `iptc_ai_system(path)` detects the IPTC Photo Metadata 2025.1 AI-disclosure XMP properties (`IPTC_AI_FIELD_MARKERS` = `AISystemUsed`/`AISystemVersionUsed`/`AIPromptInformation`/`AIPromptWriterName`) and returns the `AISystemUsed` generator name (or `"fields present"`). `remove_ai_metadata` routes **ISOBMFF video** (`.mp4`/`.mov`/`.m4v`) through the same `isobmff.strip_c2pa_boxes` as AVIF/HEIF (MP4 is ISOBMFF), and `_scrub_ai_exif` removes the xAI signature + AI-generator EXIF tags on JPEG output. -- `identify.py` — `identify(path)` aggregates every locally-readable signal (C2PA issuer→platform, C2PA soft-binding forensic-watermark vendor, IPTC "Made with AI" + IPTC 2025.1 `AISystemUsed`, embedded SD/ComfyUI params, SynthID proxy, xAI/Grok EXIF signature via `metadata.xai_signature`, visible Gemini sparkle, open invisible watermark, Adobe TrustMark via `trustmark_detector`) into one `ProvenanceReport`. `is_ai_generated` is True or None (never asserted False — stripped metadata is not proof of clean origin). Visible-sparkle is promoted only at confidence ≥ `_SPARKLE_THRESHOLD` (0.5; corpus-tuned to separate Gemini sparkles ≥0.56 from non-sparkle ≤0.49). The cv2 dependency lives in `gemini_engine.detect_sparkle_confidence`, not here. **C2PA platform attribution is device-token-first, issuer-scan fallback** (`_device_platform` scans manifest bytes for `_DEVICE_C2PA_PLATFORM` tokens, then `_attribute_platform`/`_ISSUER_PLATFORM`). **Why, verified on real signed files 2026-05-26:** the old issuer-only byte-scan matched ANY issuer substring anywhere, so multi-entity manifests mis-attributed -- Leica→"Truepic" (a signing authority in the trust chain), Nikon→"Adobe Firefly" (XMP-toolkit "Adobe" + the sample's "Adobe_MAX" name), Pixel→"Google (Gemini)" ("Google LLC" cert org), Truepic→"Google". A distinctive device token wins instead. **Token distinctiveness is load-bearing:** bare `b"Truepic"` mis-fires (it appears in unrelated trust chains -- it mis-attributed the OpenAI `chatgpt-1.png` fixture), so the token is the specific `b"Truepic_Lens"` from the Lens SDK claim generator; likewise `b"Pixel Camera"` (cert CN) not bare `b"Pixel"`. `_DEVICE_C2PA_PLATFORM` lists ONLY tokens **verified against a real C2PA file**: Leica (`lc_c2pa`/`Leica Camera`), Nikon (`NIKON`), Pixel (`Pixel Camera` -- from a real Pixel 10 Pro file attached to c2pa-rs issue #1609/#1554), Truepic (`Truepic_Lens`). Sony/Canon/Samsung/Bria have **no public direct-download C2PA sample** (checked: upload-to-verify or token-gated only), so they stay unmapped until a real file is captured (same fixture discipline as Grok/Doubao). Camera C2PA marks capture authenticity, not AI (Pixel carries `computationalCapture`, not `trainedAlgorithmicMedia`), so these never set `is_ai` -- that stays driven by digital-source-type. `c2pa.cbor_text_after` (now public) is best-effort for the `generator` detail string only and can be None when the manifest keys it `claim_generator_info` (Pixel). Add device tokens to `_DEVICE_C2PA_PLATFORM`, generator/issuer platforms to `_ISSUER_PLATFORM`, not inline. For non-PNG containers (JPEG/WebP/AVIF/HEIF/JXL) the caBX parser returns nothing, so issuer (`_issuers_in`) and generator (`_ai_tools_in`, reusing `C2PA_AI_TOOLS`) are recovered by binary-scanning the first MB. EXIF `Software` / `Make` / `Artist` / `ImageDescription` and XMP `CreatorTool` generator tags are read by `metadata.exif_generator` (PIL+piexif for any format PIL opens incl. AVIF, plus a container-agnostic XMP raw-byte scan that also covers HEIF/JXL), matched against `AI_GENERATOR_TOKENS` so ordinary editors (plain "Adobe Photoshop") and real-camera `Make` ("Apple"/"Canon") are not flagged. **Ideogram tags its output with EXIF `Make="Ideogram AI"`** (verified on a real download 2026-05-24) — that's why `Make` is read. +- `identify.py` — `identify(path)` aggregates every locally-readable signal (C2PA issuer→platform, C2PA soft-binding forensic-watermark vendor, IPTC "Made with AI" + IPTC 2025.1 `AISystemUsed`, embedded SD/ComfyUI params, SynthID proxy, xAI/Grok EXIF signature via `metadata.xai_signature`, visible Gemini sparkle, open invisible watermark, Adobe TrustMark via `trustmark_detector`) into one `ProvenanceReport`. `is_ai_generated` is True or None (never asserted False — stripped metadata is not proof of clean origin). Visible-sparkle is promoted only at confidence ≥ `_SPARKLE_THRESHOLD` (0.5; corpus-tuned to separate Gemini sparkles ≥0.56 from non-sparkle ≤0.49). The cv2 dependency lives in `gemini_engine.detect_sparkle_confidence`, not here. **C2PA platform attribution is device-token-first, issuer-scan fallback** (`_device_platform` scans manifest bytes for `_DEVICE_C2PA_PLATFORM` tokens, then `_attribute_platform`/`_ISSUER_PLATFORM`). **Why, verified on real signed files 2026-05-26:** the old issuer-only byte-scan matched ANY issuer substring anywhere, so multi-entity manifests mis-attributed -- Leica→"Truepic" (a signing authority in the trust chain), Nikon→"Adobe Firefly" (XMP-toolkit "Adobe" + the sample's "Adobe_MAX" name), Pixel→"Google (Gemini)" ("Google LLC" cert org), Truepic→"Google". A distinctive device token wins instead. **Token distinctiveness is load-bearing:** bare `b"Truepic"` mis-fires (it appears in unrelated trust chains -- it mis-attributed the OpenAI `chatgpt-1.png` fixture), so the token is the specific `b"Truepic_Lens"` from the Lens SDK claim generator; likewise `b"Pixel Camera"` (cert CN) not bare `b"Pixel"`. `_DEVICE_C2PA_PLATFORM` lists ONLY tokens **verified against a real C2PA file**: Leica (`lc_c2pa`/`Leica Camera`), Nikon (`NIKON`), Pixel (`Pixel Camera` -- from a real Pixel 10 Pro file attached to c2pa-rs issue #1609/#1554), Sony (`sony.sig`/`sony.cert` -- Sony's own C2PA assertion namespace, verified on a real Sony PXW-Z300 file; NOT bare "Sony" which is a common EXIF Make), Truepic (`Truepic_Lens`). Canon/Samsung/Bria have **no public direct-download C2PA sample** (checked exhaustively: GitHub issue/PR attachments, contentcredentials gallery, HF datasets -- all upload-to-verify or token-gated; Canon's only public file was a self-signed hobbyist CR3, not factory), so they stay unmapped until a real file is captured (same fixture discipline as Grok/Doubao). The Sony sample is video (MP4) -- our ISOBMFF C2PA path detects it; Sony Alpha stills likely share the `sony.*` namespace but are not separately verified. Camera C2PA marks capture authenticity, not AI (Pixel carries `computationalCapture`, not `trainedAlgorithmicMedia`), so these never set `is_ai` -- that stays driven by digital-source-type. `c2pa.cbor_text_after` (now public) is best-effort for the `generator` detail string only and can be None when the manifest keys it `claim_generator_info` (Pixel). Add device tokens to `_DEVICE_C2PA_PLATFORM`, generator/issuer platforms to `_ISSUER_PLATFORM`, not inline. For non-PNG containers (JPEG/WebP/AVIF/HEIF/JXL) the caBX parser returns nothing, so issuer (`_issuers_in`) and generator (`_ai_tools_in`, reusing `C2PA_AI_TOOLS`) are recovered by binary-scanning the first MB. EXIF `Software` / `Make` / `Artist` / `ImageDescription` and XMP `CreatorTool` generator tags are read by `metadata.exif_generator` (PIL+piexif for any format PIL opens incl. AVIF, plus a container-agnostic XMP raw-byte scan that also covers HEIF/JXL), matched against `AI_GENERATOR_TOKENS` so ordinary editors (plain "Adobe Photoshop") and real-camera `Make` ("Apple"/"Canon") are not flagged. **Ideogram tags its output with EXIF `Make="Ideogram AI"`** (verified on a real download 2026-05-24) — that's why `Make` is read. - `gemini_engine.py` — visible Gemini-sparkle remover/detector (cv2/numpy, no GPU). `detect_sparkle_confidence(path)` is the file-level entry point used by `identify.py`. - `doubao_engine.py` — visible Doubao "豆包AI生成" remover/detector (cv2/numpy, no GPU). `DoubaoEngine.locate` anchors a bottom-right box by **geometry** (mark scales with image WIDTH, fractions in module constants; no bundled template), `extract_mask` pulls the light low-saturation glyphs with a **polarity-aware white top-hat** (brighter-than-blurred-local-bg, so white-paper documents are left untouched instead of smeared), `detect` thresholds glyph coverage (`DETECT_MIN_COVERAGE` 0.16 separates real marks ≥0.20 from corner noise, which stays ≤0.06 on large images but can spike to ~0.15 on tiny ones), `remove_watermark` inpaints (cv2 Telea/NS) and **bails when coverage > `MAX_INPAINT_COVERAGE` 0.50** (dense-text background → would smear). Wired into `visible --mark` via `cli._run_doubao_if_selected`. **Logo is near-white (~253), not the gray some third-party tools assume.** Best on photo/illustration backgrounds; high-contrast edges leave faint residue (cv2-inpaint limit). Clean per-pixel reverse-alpha (Gemini-style) is the future upgrade but needs a captured/distilled alpha map — see below. - `region_eraser.py` — universal region eraser (`erase` CLI). `erase(image, boxes=|mask=, backend=)`: `boxes_to_mask` → `cv2.inpaint` (`cv2` backend, default, no deps) or big-LaMa via onnxruntime (`lama` backend, extra `lama`, `Carve/LaMa-ONNX` Apache-2.0 model downloaded on first use, never bundled). `erase_lama` crops a padded region around the mask, runs LaMa at its fixed 512² input, pastes only masked pixels back (untouched areas stay pixel-exact). Lazy `_get_lama_session` singleton; `lama_available()` guards the optional import. **LaMa-ONNX costs ~3.5-4 GB peak RAM and ~5-6 s/call on CPU** (FFC working set, not arena — `enable_cpu_mem_arena=False` does not help), so it does NOT fit a minimal droplet; the cv2 backend (tens of MB, ~30 ms) does. LaMa quality at low RAM = serverless/GPU, mirroring how raiw.cc offloads SDXL to fal. diff --git a/pyproject.toml b/pyproject.toml index 422e86c..d64f4dd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "remove-ai-watermarks" -version = "0.6.2" +version = "0.6.3" description = "Remove visible and invisible AI watermarks from images (Gemini / Nano Banana, ChatGPT, Stable Diffusion)" readme = "README.md" requires-python = ">=3.10" diff --git a/src/remove_ai_watermarks/__init__.py b/src/remove_ai_watermarks/__init__.py index 5467be6..2fb7892 100644 --- a/src/remove_ai_watermarks/__init__.py +++ b/src/remove_ai_watermarks/__init__.py @@ -1,3 +1,3 @@ """Remove-AI-Watermarks: Unified tool for removing visible and invisible AI watermarks.""" -__version__ = "0.6.2" +__version__ = "0.6.3" diff --git a/src/remove_ai_watermarks/identify.py b/src/remove_ai_watermarks/identify.py index f89e485..d6ec0c5 100644 --- a/src/remove_ai_watermarks/identify.py +++ b/src/remove_ai_watermarks/identify.py @@ -145,6 +145,11 @@ _DEVICE_C2PA_PLATFORM: tuple[tuple[bytes, str], ...] = ( (b"Leica Camera", "Leica (camera, C2PA capture)"), (b"NIKON", "Nikon (camera, C2PA capture)"), (b"Pixel Camera", "Google Pixel (camera, C2PA capture)"), + # Sony uses its own ``sony.*`` C2PA assertion namespace (sony.sig / sony.cert); + # match that, NOT bare "Sony" (which is an EXIF Make on countless photos). + # Verified on a real Sony-signed file (Sony PXW-Z300, signer "Sony Corporation"). + (b"sony.sig", "Sony (camera, C2PA capture)"), + (b"sony.cert", "Sony (camera, C2PA capture)"), # "Truepic_Lens" (from the Lens SDK claim generator), NOT bare "Truepic" -- # Truepic is a C2PA signing authority whose name appears in the trust chain # of unrelated manifests (e.g. OpenAI), so the bare token mis-attributes. diff --git a/tests/test_identify.py b/tests/test_identify.py index a177a4c..1a64cca 100644 --- a/tests/test_identify.py +++ b/tests/test_identify.py @@ -360,6 +360,15 @@ class TestIdentifyC2paDevice: assert r.platform == "Google Pixel (camera, C2PA capture)" assert r.is_ai_generated is None # camera capture, not AI + def test_sony_namespace_beats_bare_make(self, tmp_path: Path): + # Sony's own C2PA assertion namespace (sony.sig), not the bare "Sony" + # EXIF Make that appears on ordinary photos. + blob = b"\xff\xd8\xff\xe1 c2pa.claim jumbf Adobe Sony sony.sig.v1_1 \xff\xd9" + p = tmp_path / "sony_like.jpg" + p.write_bytes(blob) + r = identify(p, check_visible=False, check_invisible=False) + assert r.platform == "Sony (camera, C2PA capture)" + # ── Open invisible watermark (SD/SDXL/FLUX) integration ───────────── diff --git a/uv.lock b/uv.lock index 7d3e518..358702c 100644 --- a/uv.lock +++ b/uv.lock @@ -686,7 +686,7 @@ name = "cuda-bindings" version = "13.2.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "cuda-pathfinder", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" }, + { name = "cuda-pathfinder", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/1a/fe/7351d7e586a8b4c9f89731bfe4cf0148223e8f9903ff09571f78b3fb0682/cuda_bindings-13.2.0-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:08b395f79cb89ce0cd8effff07c4a1e20101b873c256a1aeb286e8fd7bd0f556", size = 5744254, upload-time = "2026-03-11T00:12:29.798Z" }, @@ -721,34 +721,34 @@ wheels = [ [package.optional-dependencies] cudart = [ - { name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux'" }, ] cufft = [ - { name = "nvidia-cufft", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cufft", marker = "sys_platform == 'linux'" }, ] cufile = [ { name = "nvidia-cufile", marker = "sys_platform == 'linux'" }, ] cupti = [ - { name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux'" }, ] curand = [ - { name = "nvidia-curand", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-curand", marker = "sys_platform == 'linux'" }, ] cusolver = [ - { name = "nvidia-cusolver", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cusolver", marker = "sys_platform == 'linux'" }, ] cusparse = [ - { name = "nvidia-cusparse", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cusparse", marker = "sys_platform == 'linux'" }, ] nvjitlink = [ - { name = "nvidia-nvjitlink", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-nvjitlink", marker = "sys_platform == 'linux'" }, ] nvrtc = [ - { name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux'" }, ] nvtx = [ - { name = "nvidia-nvtx", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-nvtx", marker = "sys_platform == 'linux'" }, ] [[package]] @@ -1844,7 +1844,7 @@ name = "nvidia-cublas" version = "13.1.1.3" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cuda-nvrtc", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" }, + { name = "nvidia-cuda-nvrtc", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/a7/a1/0bd24ee8c8d03adac032fd2909426a00c88f8c57961b1277ded97f91119f/nvidia_cublas-13.1.1.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b7a210458267ac818974c53038fbec2e969d5c99f305ab15c72522fa9f001dd5", size = 542848918, upload-time = "2026-04-08T18:46:22.985Z" }, @@ -1883,7 +1883,7 @@ name = "nvidia-cudnn-cu13" version = "9.20.0.48" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" }, + { name = "nvidia-cublas", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/56/c5/83384d846b2fd17c44bd499b36c75a45ed4f095fbbb2252294e89cea5c5c/nvidia_cudnn_cu13-9.20.0.48-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:e31454ae00094b0c55319d9d15b6fa2fc50a9e1c0f5c8c80fb75258234e731e1", size = 444574296, upload-time = "2026-03-09T19:28:27.751Z" }, @@ -1895,7 +1895,7 @@ name = "nvidia-cufft" version = "12.0.0.61" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-nvjitlink", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" }, + { name = "nvidia-nvjitlink", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/8b/ae/f417a75c0259e85c1d2f83ca4e960289a5f814ed0cea74d18c353d3e989d/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2708c852ef8cd89d1d2068bdbece0aa188813a0c934db3779b9b1faa8442e5f5", size = 214053554, upload-time = "2025-09-04T08:31:38.196Z" }, @@ -1925,9 +1925,9 @@ name = "nvidia-cusolver" version = "12.0.4.66" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" }, - { name = "nvidia-cusparse", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" }, - { name = "nvidia-nvjitlink", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" }, + { name = "nvidia-cublas", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" }, + { name = "nvidia-cusparse", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" }, + { name = "nvidia-nvjitlink", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/c8/c3/b30c9e935fc01e3da443ec0116ed1b2a009bb867f5324d3f2d7e533e776b/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:02c2457eaa9e39de20f880f4bd8820e6a1cfb9f9a34f820eb12a155aa5bc92d2", size = 223467760, upload-time = "2025-09-04T08:33:04.222Z" }, @@ -1939,7 +1939,7 @@ name = "nvidia-cusparse" version = "12.6.3.3" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-nvjitlink", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" }, + { name = "nvidia-nvjitlink", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/f8/94/5c26f33738ae35276672f12615a64bd008ed5be6d1ebcb23579285d960a9/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:80bcc4662f23f1054ee334a15c72b8940402975e0eab63178fc7e670aa59472c", size = 162155568, upload-time = "2025-09-04T08:33:42.864Z" }, @@ -2865,7 +2865,7 @@ wheels = [ [[package]] name = "remove-ai-watermarks" -version = "0.6.2" +version = "0.6.3" source = { editable = "." } dependencies = [ { name = "click" },