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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) <noreply@anthropic.com>
This commit is contained in:
test-user
@@ -30,7 +30,7 @@ You are a **principal Python engineer** maintaining a CLI tool and library for r
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- `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).
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- `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.
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- `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: <base64>` + 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.
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- `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.
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- `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.
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- `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`.
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- `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.
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- `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.
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+1
-1
@@ -1,6 +1,6 @@
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[project]
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name = "remove-ai-watermarks"
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version = "0.6.2"
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version = "0.6.3"
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description = "Remove visible and invisible AI watermarks from images (Gemini / Nano Banana, ChatGPT, Stable Diffusion)"
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readme = "README.md"
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requires-python = ">=3.10"
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@@ -1,3 +1,3 @@
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"""Remove-AI-Watermarks: Unified tool for removing visible and invisible AI watermarks."""
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__version__ = "0.6.2"
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__version__ = "0.6.3"
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@@ -145,6 +145,11 @@ _DEVICE_C2PA_PLATFORM: tuple[tuple[bytes, str], ...] = (
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(b"Leica Camera", "Leica (camera, C2PA capture)"),
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(b"NIKON", "Nikon (camera, C2PA capture)"),
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(b"Pixel Camera", "Google Pixel (camera, C2PA capture)"),
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# Sony uses its own ``sony.*`` C2PA assertion namespace (sony.sig / sony.cert);
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# match that, NOT bare "Sony" (which is an EXIF Make on countless photos).
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# Verified on a real Sony-signed file (Sony PXW-Z300, signer "Sony Corporation").
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(b"sony.sig", "Sony (camera, C2PA capture)"),
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(b"sony.cert", "Sony (camera, C2PA capture)"),
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# "Truepic_Lens" (from the Lens SDK claim generator), NOT bare "Truepic" --
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# Truepic is a C2PA signing authority whose name appears in the trust chain
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# of unrelated manifests (e.g. OpenAI), so the bare token mis-attributes.
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@@ -360,6 +360,15 @@ class TestIdentifyC2paDevice:
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assert r.platform == "Google Pixel (camera, C2PA capture)"
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assert r.is_ai_generated is None # camera capture, not AI
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def test_sony_namespace_beats_bare_make(self, tmp_path: Path):
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# Sony's own C2PA assertion namespace (sony.sig), not the bare "Sony"
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# EXIF Make that appears on ordinary photos.
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blob = b"\xff\xd8\xff\xe1 c2pa.claim jumbf Adobe Sony sony.sig.v1_1 \xff\xd9"
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p = tmp_path / "sony_like.jpg"
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p.write_bytes(blob)
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r = identify(p, check_visible=False, check_invisible=False)
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assert r.platform == "Sony (camera, C2PA capture)"
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# ── Open invisible watermark (SD/SDXL/FLUX) integration ─────────────
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@@ -686,7 +686,7 @@ name = "cuda-bindings"
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version = "13.2.0"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "cuda-pathfinder", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" },
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{ name = "cuda-pathfinder", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" },
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@@ -721,34 +721,34 @@ wheels = [
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[package.optional-dependencies]
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cudart = [
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{ name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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{ name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux'" },
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]
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cufft = [
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{ name = "nvidia-cufft", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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{ name = "nvidia-cufft", marker = "sys_platform == 'linux'" },
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]
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cufile = [
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{ name = "nvidia-cufile", marker = "sys_platform == 'linux'" },
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]
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cupti = [
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{ name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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{ name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux'" },
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]
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curand = [
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{ name = "nvidia-curand", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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{ name = "nvidia-curand", marker = "sys_platform == 'linux'" },
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]
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cusolver = [
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{ name = "nvidia-cusolver", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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{ name = "nvidia-cusolver", marker = "sys_platform == 'linux'" },
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]
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cusparse = [
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{ name = "nvidia-cusparse", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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{ name = "nvidia-cusparse", marker = "sys_platform == 'linux'" },
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]
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nvjitlink = [
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{ name = "nvidia-nvjitlink", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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{ name = "nvidia-nvjitlink", marker = "sys_platform == 'linux'" },
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]
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nvrtc = [
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{ name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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{ name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux'" },
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]
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nvtx = [
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{ name = "nvidia-nvtx", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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{ name = "nvidia-nvtx", marker = "sys_platform == 'linux'" },
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]
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[[package]]
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@@ -1844,7 +1844,7 @@ name = "nvidia-cublas"
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version = "13.1.1.3"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "nvidia-cuda-nvrtc", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" },
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{ name = "nvidia-cuda-nvrtc", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" },
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]
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@@ -1883,7 +1883,7 @@ name = "nvidia-cudnn-cu13"
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version = "9.20.0.48"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "nvidia-cublas", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" },
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{ name = "nvidia-cublas", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" },
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]
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@@ -1895,7 +1895,7 @@ name = "nvidia-cufft"
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version = "12.0.0.61"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
|
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{ name = "nvidia-nvjitlink", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" },
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{ name = "nvidia-nvjitlink", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" },
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wheels = [
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@@ -1925,9 +1925,9 @@ name = "nvidia-cusolver"
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version = "12.0.4.66"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "nvidia-cublas", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" },
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{ name = "nvidia-cusparse", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" },
|
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{ name = "nvidia-nvjitlink", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" },
|
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{ name = "nvidia-cublas", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" },
|
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{ name = "nvidia-cusparse", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" },
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{ name = "nvidia-nvjitlink", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" },
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@@ -1939,7 +1939,7 @@ name = "nvidia-cusparse"
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version = "12.6.3.3"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "nvidia-nvjitlink", marker = "python_full_version >= '3.11' or sys_platform != 'darwin'" },
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{ name = "nvidia-nvjitlink", marker = "sys_platform != 'darwin' and sys_platform != 'win32'" },
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]
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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" },
|
||||
|
||||
Reference in New Issue
Block a user