From c196a169000a3b72c131820c5e577e16400cbca1 Mon Sep 17 00:00:00 2001 From: test-user Date: Tue, 26 May 2026 17:56:48 -0700 Subject: [PATCH] feat: detect soft-binding vendors, IPTC 2025.1, video/audio C2PA, TrustMark (v0.6.0) Broadens metadata provenance coverage at the detection and container-strip level. Detection: - C2PA soft-binding `alg` -> forensic-watermark vendor (Adobe TrustMark, Digimarc, Imatag, Steg.AI, Microsoft, ...) via C2PA_SOFT_BINDINGS + soft_binding_vendors_in(); names the watermark vendor even when the watermark itself can't be decoded. - IPTC Photo Metadata 2025.1 AI-disclosure XMP fields (AISystemUsed etc.) via iptc_ai_system() + IPTC_AI_FIELD_MARKERS. - Adobe TrustMark open keyless decoder (trustmark_detector.py, optional extra `trustmark`) -- the watermark behind Adobe Durable Content Credentials. Detects provenance, not AI origin, so it does not assert is_ai. Removal / containers: - isobmff.strip_c2pa_boxes now also drops a top-level XMP uuid box that carries an AI label (matched by AI-marker content, byte-order-robust; plain XMP kept). - remove_ai_metadata routes MP4/MOV/M4V/M4A (and any ftyp-sniffed ISOBMFF) through the box stripper; raises a clear error for non-ISOBMFF audio/video (WebM/MP3/WAV) instead of crashing in the image path. Tests: soft-binding scan, IPTC element/attribute/presence, MP4 + M4A detect/ strip, ISOBMFF XMP surgical strip, content-sniff, unsupported-container guard, TrustMark absent-safety + identify integration. ruff clean; pyright clean on all new modules. Co-Authored-By: Claude Opus 4.7 (1M context) --- CLAUDE.md | 17 +- README.md | 24 +- pyproject.toml | 19 +- src/remove_ai_watermarks/__init__.py | 2 +- src/remove_ai_watermarks/identify.py | 54 +- src/remove_ai_watermarks/metadata.py | 90 +- src/remove_ai_watermarks/noai/c2pa.py | 20 + src/remove_ai_watermarks/noai/constants.py | 20 + src/remove_ai_watermarks/noai/isobmff.py | 55 +- .../trustmark_detector.py | 72 + tests/test_identify.py | 26 + tests/test_metadata.py | 135 ++ tests/test_trustmark_detector.py | 36 + uv.lock | 1276 ++++++++++++++--- 14 files changed, 1573 insertions(+), 273 deletions(-) create mode 100644 src/remove_ai_watermarks/trustmark_detector.py create mode 100644 tests/test_trustmark_detector.py diff --git a/CLAUDE.md b/CLAUDE.md index 93544e9..9451852 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -20,16 +20,18 @@ You are a **principal Python engineer** maintaining a CLI tool and library for r ## Configuration - GPU/ML modules (invisible_engine, ctrlregen, watermark_remover) are optional — guard imports with `is_available()` checks +- Optional detection extras: `detect` (imwatermark — open SD/SDXL/FLUX watermark) and `trustmark` (Adobe TrustMark decoder; pulls torch + downloads weights). Both are guarded by `is_available()` and skipped by `identify` when absent. - Tests for the *model-running* paths are limited to availability checks (multi-GB downloads). But the **pure helpers inside ML-adjacent modules are unit-tested without any download** and must stay that way: `_target_size` (native-vs-downscale, `test_invisible_engine.py`), the MPS->CPU fallback control flow via mocked pipelines (`test_img2img_runner.py`, 100% cover), and the tiling math `tile_positions`/`make_blend_weight`/`resize_center_crop` (`test_tiling.py`; `pytest.importorskip("torch")` since `tiling.py` imports torch at module top). Don't skip these as "ML, needs a model" — only `run_tiled`/`remove_watermark`/the diffusion bodies do. ## Key modules -- `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. -- `noai/constants.py` — PNG_SIGNATURE, C2PA_CHUNK_TYPE, C2PA_SIGNATURES, C2PA_ISSUERS, and `SYNTHID_C2PA_ISSUERS` (issuers that pair SynthID with C2PA: Google, OpenAI). Add a new issuer 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`. -- `identify.py` — `identify(path)` aggregates every locally-readable signal (C2PA issuer→platform, IPTC "Made with AI", embedded SD/ComfyUI params, SynthID proxy, xAI/Grok EXIF signature via `metadata.xai_signature`, visible Gemini sparkle) 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. Add platform mappings 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. +- `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. Add platform mappings 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`. - `invisible_watermark.py` — `detect_invisible_watermark(path)` decodes the OPEN DWT-DCT watermarks (public decoder, no key) embedded by Stable Diffusion / SDXL / FLUX via the `imwatermark` library. Known fixed patterns (verified against upstream source) live in `_BITS_48` (SDXL 48-bit, FLUX.2 48-bit) and `_SD1_STRING` ("StableDiffusionV1", SD 1.x/2.x). Optional dep (extra `detect`); returns None when absent. The `detect` extra pulls **torch** transitively (invisible-watermark declares torch a hard dep, and `WatermarkDecoder` eagerly imports `rivaGan` -> `torch` at import time), so detection needs torch present even though dwtDct runs CPU-only on cv2/numpy/pywavelets — no GPU and no separate `gpu` extra required. **Unlike SynthID this is locally detectable**, but the watermark is fragile (does not survive JPEG re-encode/resize — verified gone after JPEG q90), so it confirms origin only on pristine files. Add new known patterns here. The file carries a top-of-module pyright pragma because imwatermark/cv2 ship no type stubs. +- `trustmark_detector.py` — `detect_trustmark(path)` decodes the OPEN, keyless **Adobe TrustMark** watermark (the soft binding behind Adobe Durable Content Credentials, `alg` `com.adobe.trustmark.P`) via the optional `trustmark` package (extra `trustmark`; pulls torch, downloads model weights on first use). Mirrors `invisible_watermark.py` (lazy singleton, top-of-module pyright pragma, returns None when absent). It detects *provenance*, not AI origin as such (TrustMark also marks human-authored content), so `identify` lists it as a watermark without setting `is_ai_generated`. Other soft-binding vendors (Digimarc/Imatag/Steg.AI/...) have no public decoder — they are only *named* via the `C2PA_SOFT_BINDINGS` scan, not decoded. - `face_protector.py` — YOLO detect + soft-blend pattern; mirror this for any "protect region during diffusion" features ## Watermarking landscape (research 2026-05-24) @@ -42,15 +44,16 @@ Who embeds what, and whether it is locally detectable (so we know which gaps are - **China TC260 AIGC label (caught by `AIGC_MARKERS` / `metadata.aigc_label`, surfaced by `identify` as the `aigc` signal):** China-served generators embed an XMP `{"Label":"1","ContentProducer":...}` block — China's mandatory AI-content labeling (TC260 namespace `tc260.org.cn/ns/AIGC`). **Doubao** (ByteDance) uses it (verified on the real #13 sample 2026-05-25; `ContentProducer` `001191110102MACQD9K64010000`, no C2PA/SynthID/imwatermark — the XMP block is the only signal; GitHub attachment upload did NOT strip it). The same standard is mandatory for Jimeng/Kling/Qwen/Ernie etc., so the one marker covers the whole China-AIGC-labeled ecosystem. `aigc_label` json-decodes the (HTML-entity-encoded) block; container-agnostic raw-byte scan. - **No detectable signal on download (correctly reported `unknown`):** **Recraft** (PNG export is a re-encoded design export — strips everything), **Krea hosting FLUX 2** (no imwatermark despite FLUX — the host omits the encoder, same as Stability's hosted SDXL), and Midjourney (embeds nothing). Lesson: the imwatermark detector only fires on *pristine* output from a pipeline that runs the encoder (diffusers default, official BFL), not from re-hosts (Krea/Stability) or re-encoded exports (Recraft/Canva). - **Invisible but NOT locally detectable (proprietary, API/oracle only — same wall as SynthID):** Amazon Titan Image Generator + Nova Canvas (Bedrock `DetectGeneratedContent` API), Kakao (new SynthID image adopter, May 2026), NVIDIA Cosmos (SynthID video). No local detector possible; treat like SynthID. -- **C2PA 2.4 "Durable Content Credentials" (April 2026; verified against the spec) raise the bar for metadata stripping.** 2.4 defines soft bindings (an invisible watermark or a content fingerprint) plus a server-side manifest repository and a new `c2pa.repository-receipt` assertion. Per the spec: "if a C2PA manifest is removed from an asset, but a copy of that manifest remains in a provenance store elsewhere, the manifest and asset may be matched using available soft bindings." So our local `metadata --remove` deletes the *embedded* manifest, but a fingerprint/watermark soft binding can still re-link the image to its manifest in a repository server-side. Stripping the file is becoming necessary-but-not-sufficient against durable provenance. (Our parsers target the stable embedded-manifest format documented in C2PA 2.1 §11; that format is unchanged in 2.4 -- the new pieces are repository/soft-binding infra, not the on-file box layout, so no parser change is implied.) Spec: https://spec.c2pa.org/specifications/specifications/2.4/specs/C2PA_Specification.html -- **Regulatory driver (context, not a code change):** AI-content labeling mandates are expanding, which pushes more generators toward exactly the C2PA + watermark signals we read. The full per-jurisdiction table lives in README "## Legal" -- keep it there, not duplicated here. Newly added + primary-source verified 2026-05-26: **EU AI Act Article 50** machine-readable marking applicable **2026-08-02** (verified against the article text); **South Korea AI Framework Act Art. 31(3)** in force since Jan 2026 (verified via Kim & Chang; Enforcement Decree accepts an invisible-watermark label); **California AB 853** (amends the CA AI Transparency Act) latent-disclosure duty operative **2026-08-02**, requiring a disclosure "permanent or extraordinarily difficult to remove" (verified against the leginfo bill text -- this is the exact disclosure our tool strips). When extending the README table, verify each date/article against the statute/bill text before committing, not against search summaries. +- **C2PA 2.4 "Durable Content Credentials" (April 2026; verified against the spec) raise the bar for metadata stripping.** 2.4 defines soft bindings (an invisible watermark or a content fingerprint) plus a server-side manifest repository and a new `c2pa.repository-receipt` assertion. Per the spec: "if a C2PA manifest is removed from an asset, but a copy of that manifest remains in a provenance store elsewhere, the manifest and asset may be matched using available soft bindings." So our local `metadata --remove` deletes the *embedded* manifest, but a fingerprint/watermark soft binding can still re-link the image to its manifest in a repository server-side. Stripping the file is becoming necessary-but-not-sufficient against durable provenance. (Our parsers target the stable embedded-manifest format documented in C2PA 2.1 §11; that format is unchanged in 2.4 -- the new pieces are repository/soft-binding infra, not the on-file box layout, so no parser change is implied.) Spec: https://spec.c2pa.org/specifications/specifications/2.4/specs/C2PA_Specification.html We now READ the soft-binding `alg` (`C2PA_SOFT_BINDINGS` / `soft_binding_vendors_in`) to name the forensic-watermark vendor, and locally DECODE the one open scheme, Adobe TrustMark (`trustmark_detector`); the rest (Digimarc/Imatag/Steg.AI/...) stay name-only (proprietary decoders). +- **Built 2026-05-26 (this batch):** soft-binding `alg` vendor detection; IPTC Photo Metadata 2025.1 AI-disclosure fields (`AISystemUsed` etc.); **video C2PA metadata** detect + strip for MP4/MOV/M4V (free — `isobmff.py` is format-agnostic, MP4 is ISOBMFF); Adobe TrustMark open decoder. NOT done (out of cheap reach, per the feasibility review): visible video-logo removal (needs a video frame pipeline) and audio (SynthID/ElevenLabs/Resemble/Suno all oracle-only or unmarked). The soft-binding **box detection window**: non-PNG/video detection scans the first 1 MB, so a C2PA box placed after a large `mdat` in a streaming MP4 can be missed — front-placed manifests (the common case) are caught. +- **Regulatory driver (context, not a code change):** AI-content labeling mandates are expanding, which pushes more generators toward exactly the C2PA + watermark signals we read. The full per-jurisdiction table lives in README "## Legal" -- keep it there, not duplicated here. Newly added + primary-source verified 2026-05-26: **EU AI Act Article 50** machine-readable marking applicable **2026-08-02** (verified against the article text); **South Korea AI Framework Act Art. 31(3)** in force since Jan 2026 (verified via Kim & Chang; Enforcement Decree accepts an invisible-watermark label); **California AB 853** (amends the CA AI Transparency Act) latent-disclosure duty operative **2026-08-02**, requiring a disclosure "permanent or extraordinarily difficult to remove" (verified against the leginfo bill text -- this is the exact disclosure our tool strips); **India IT Amendment Rules 2026** in force **2026-02-20** (verified via Chambers), which prominently-label + permanent-provenance-id all synthetic media AND **expressly prohibit removing/suppressing the label or metadata** -- the first major all-content removal ban outside China. **Removal liability (README "## Legal" disclaimer):** the tool is lawful general-purpose software; liability sits with the remover and is intent-gated -- downstream acts (fraud/deception/IP), plus US DMCA 17 USC 1202 (removing copyright-management info to conceal infringement), plus the removal-as-such bans in China + India. When extending the README table, verify each date/article against the statute/bill text before committing, not against search summaries. ## Known limitations - `invisible` pipeline processes at **native resolution by default** (`max_resolution=0`), matching the hosted raiw.cc backend (fal fast-sdxl, no pre-downscale). The old forced downscale-to-1024 -> upscale-back round-trip was the main quality loss (issue #10) and is gone; at strength ~0.05 SDXL img2img does not need the ~1024 downscale. `--max-resolution N` re-introduces an opt-in long-side cap purely to bound GPU/MPS memory on very large inputs (it reintroduces the lossy round-trip). For huge images that OOM at native, tile-based diffusion is still the proper long-term fix. **Concrete MPS data point (verified 2026-05-25 on a 1254x1254 gpt-image SDXL run, fp32, 20 GB MPS ceiling):** native res OOMs at the *UNet* step (peak ~17 GiB), not only the VAE decode, and the auto-fallback in `img2img_runner` reloads on CPU and finishes (slow, ~13 min) -- the output is still weight-identical and defeats SynthID, so "looks hung/crashed" on Mac is usually this CPU fallback, not a pipeline error. Adding `enable_vae_tiling()` alone does NOT prevent it (the peak is the UNet, not the VAE). The fast Mac workarounds are fp16 on MPS (roughly halves memory) or `--max-resolution` to cap the long side; neither is wired as the default. The native-vs-downscale decision lives in the pure helper `invisible_engine._target_size(w, h, max_resolution)` (returns `None` for native, a clamped target tuple otherwise) so it is unit-tested (`tests/test_invisible_engine.py::TestTargetSize`, the #10/#15 regression guard) without loading the model -- keep that logic in the helper, don't re-inline it. - Pyright first run is slow (2-3 min) due to ML deps (torch/diffusers/transformers stubs); full-project `uv run pyright` can stall for many minutes — scope it to changed files. - `ultralytics` monkey-patches `PIL.Image.open` and tries to autoload `pi_heif`. When `pi_heif` is missing, opening files raises `ModuleNotFoundError`, not `UnidentifiedImageError`. Code that opens user-supplied or unknown-format files should `except Exception`, not just `OSError`/`UnidentifiedImageError`. -- Metadata detection for AVIF/HEIF/JPEG-XL relies on a binary scan for `C2PA_UUID` + `IPTC_AI_MARKERS`, plus EXIF `Software` / XMP `CreatorTool` generator tags via `metadata.exif_generator` (validated with synthesized AVIF/JPEG fixtures + an XMP raw-scan fixture). C2PA removal in those containers is implemented via `noai/isobmff.py` (top-level ``uuid`` / ``jumb`` box stripper, no re-encoding). EXIF/XMP boxes inside those containers are read for detection but not yet **scrubbed** on removal. +- Metadata detection for AVIF/HEIF/JPEG-XL relies on a binary scan for `C2PA_UUID` + `IPTC_AI_MARKERS`, plus EXIF `Software` / XMP `CreatorTool` generator tags via `metadata.exif_generator` (validated with synthesized AVIF/JPEG fixtures + an XMP raw-scan fixture). C2PA removal in those containers is implemented via `noai/isobmff.py` (top-level ``uuid`` / ``jumb`` box stripper, no re-encoding), which now also drops a top-level XMP ``uuid`` box that carries an AI label (matched by AI-marker content, not by the XMP UUID, so byte-order-robust) and covers MP4/MOV/M4V/M4A by content sniff. **Metadata boundary (researched 2026-05-26, deliberately NOT built — low yield / high risk):** EXIF/XMP stored as *items inside the ``meta`` box* (typical for AVIF/HEIF images) needs meta-box surgery (iinf/iloc edit + mdat splice) with corruption risk; WebM/Matroska is EBML (different container, would need a new parser); MP3 ID3 / WAV RIFF audio tags are low-yield (audio provenance is overwhelmingly oracle-only — SynthID/ElevenLabs/Resemble — or unmarked like Suno/Udio). For these, `remove_ai_metadata` raises a clear "not supported" error (`_UNSUPPORTED_CONTAINER_EXTS`) rather than crashing; detection via the `identify` byte scan still fires when the marker is in the first MB. (These are candidates for future work, not a hard ceiling — revisit when there's demand.) - **SynthID detection is metadata-only.** There is no reliable *local* detector of the SynthID *pixel* watermark — Google's decoder is proprietary, no public spec or API (only a waitlisted portal). Authoritative confirmation: Google DeepMind's own paper "SynthID-Image: Image watermarking at internet scale" (Gowal et al., arXiv:2510.09263) states the verification service is restricted to "trusted testers" and does not release detector weights or a reproducible algorithm — so a local pixel detector is infeasible by design, not just unbuilt. https://arxiv.org/abs/2510.09263 We detect SynthID by its C2PA companion (`synthid_source` / `SYNTHID_C2PA_ISSUERS`), which is reliable while the manifest is intact but says nothing once C2PA is stripped. **Surface-dependent blind spot (verified 2026-05-24):** the same Google model emits different metadata per surface -- the Gemini *app* wraps outputs in Google C2PA, but the *API/playground* (AI Studio, Nano Banana / gemini-2.5-flash-image) emits the SynthID *pixel* watermark (confirmed via the Gemini-app oracle) + the visible sparkle but **no C2PA/IPTC at all**, so `synthid_source` returns None despite SynthID being present. Only the pixel oracle or the visible-sparkle detector catches those. (Meta AI is another surface mismatch: it writes the IPTC `digitalSourceType=trainedAlgorithmicMedia` marker, not C2PA and not SynthID.) Google→SynthID is long-standing; OpenAI→SynthID is confirmed by OpenAI's Help Center (ChatGPT/Codex/API "include both C2PA metadata and SynthID watermarks", updated 2026-05-21) but time-gated (pre-rollout OpenAI images carry C2PA without SynthID), so the OpenAI verdict is hedged "likely". Oracles: Gemini app "Verify with SynthID" (Google), openai.com/verify (OpenAI). The spectral phase-coherence approach from `github.com/aloshdenny/reverse-SynthID` was evaluated (May 2026) and **does not work for real-content detection**: on its own shipped codebook + validation set, watermarked and cleaned images were indistinguishable (conf within noise, cleaned often higher); it only fires on pure-black 1024x1024 reference images at exact resolution (the controlled case it was calibrated on). The README's "90% / conf=0.91" reproduces only in that lab condition. Do not build a production detector on it; if revisited, it is experimental/diagnostic only and needs a per-resolution, per-model reference corpus. A from-scratch gpt-image pilot (2026-05-24) confirmed this independently: 5 independent solid-black gpt-image outputs share a near-identical fixed signature (pairwise residual correlation **0.92**, avg-template retains 97% energy), so the watermark/carrier IS strongly present and consistent on flat content — but the carrier frequencies extracted from it do NOT discriminate real content (carrier-to-random ratio: cleaned 1.86 > watermarked 1.53; a non-gpt-image image scored highest at 3.67). The signature drowns in content texture. Net: a perfectly consistent solid-color signature still yields no real-content pixel detector with magnitude/carrier methods. A corpus discrimination test (2026-05-24, `scripts/synthid_pixel_probe.py`, raw zero-mean residual NCC) independently re-confirms this: at matched resolution, SynthID positives do NOT cluster apart from negatives (within-Gemini 0.07; at 1024 px pos-vs-neg >= pos-vs-pos). The only high correlations were near-duplicate *content* (5 ChatGPT renders of one prompt at ~0.92, while a distinct ChatGPT image scored ~0 against them) — content, not a carrier. The probe is solid-fills-only and EXPERIMENTAL/DIAGNOSTIC; do not use it on real content. **Correction (deeper re-examination 2026-05-25):** the carrier IS real on solid fills — the earlier "no carrier" was a *method* artifact of using spatial / FFT-magnitude NCC, which can't see it. The carrier is a fixed *phase* at specific low frequencies, so the right metric is **per-bin phase coherence**. On 8 white `gemini-2.5-flash-image` fills (generated via the reverse-SynthID trick: identity-edit prompt "Recreate this image exactly as it is" on a synthetic pure-white PNG — this bypasses the recitation block that rejects text prompts for pure colors), phase coherence at the white carriers `(0,±7..±12,±20..±23)` = **0.86** vs **0.31** random; single-image leave-one-out phase-match **+0.83** vs real photos **-0.24**. (Black `2.5-flash` fills clip to std≈0 — SynthID can't push values below 0, so no carrier in black; the repo's dark carriers come from nano-banana-pro.) **But it does not generalize:** (a) carriers are model-version + resolution + color specific — the repo's v4 codebook (built for `gemini-3.1-flash-image-preview` + `nano-banana-pro-preview`) scores ~0.527 on my 2.5-flash white fills, indistinguishable from negatives (~0.50), i.e. carriers shift across model versions and need a per-model codebook; (b) on real content (30 `2.5-flash` images) the carrier collapses — set phase coherence at carriers 0.37 ≈ random 0.42, and the repo's v4 detector gives content 0.518 ≈ negatives 0.504 (no separation; a faint +0.24 single-image lean is likely a brightness confound). Net: the spectral/phase approach is a real *controlled-fill* characterizer, NOT an arbitrary-real-content detector, and is brittle to model version. Metadata proxy + visible sparkle + online oracles remain the ceiling for real content. - **External AI-vs-real classifier models are out of scope (decided 2026-05-24).** Generic HuggingFace detectors (`Organika/sdxl-detector` Swin Transformer, `umm-maybe/AI-image-detector`, and fine-tunes) exist and report ~0.98 on their *own* SDXL-vs-real validation sets, but they are per-generator and the model cards themselves note degraded accuracy off-distribution; they are untested on gpt-image / Gemini Nano Banana (the metadata-stripped surfaces we care about), and our own light SDXL pass would likely defeat them the same way it defeats SynthID. Detection here stays local + signal-based (metadata + visible sparkle); do not add a bundled classifier dependency. - **SynthID v2 vs default pipeline:** the SDXL-based default profile (since May 2026) defeats SynthID v2. **Verified end-to-end (May 2026):** local SDXL run on a Gemini 3 Pro output, checked via the Gemini app's "Verify with SynthID" feature, returned "no SynthID watermark detected". Also confirmed against **OpenAI's** SynthID (2026-05-23): a fresh ChatGPT/gpt-image output read "SynthID detected" on openai.com/verify before the local SDXL run and "SynthID not detected" after (corpus regression chain: pos `4ef377bd` -> cleaned `47188e88`). The same configuration is used in raiw-app production (`fal-ai/fast-sdxl/image-to-image`, strength 0.05, steps 50, guidance 7.5, no pre-downscale). fal's own `llms.txt` for `fast-sdxl` names the base checkpoint as `stabilityai/stable-diffusion-xl-base-1.0` (verified 2026-05-25) -- the exact checkpoint the local CLI defaults to (`DEFAULT_MODEL_ID`). So the local `invisible` default is weight-for-weight identical to prod; "fast-sdxl" is fal's optimized serving, not different weights. After the native-resolution fix the local pipeline matches prod on weights + strength + steps + guidance + resolution. SD-1.5 dreamshaper at 768 px was previously the default and does NOT defeat v2 — verified empirically against the same feature (strength 0.04, 0.10, and elastic warp α∈{5,8} all flagged positive). That SD-1.5 path was removed; only `default` (SDXL) and `ctrlregen` profiles remain. **Scope of the claim: defeating the SynthID verifier is NOT the same as forensic invisibility.** "Removing the Watermark Is Not Enough: Forensic Stealth in Generative-AI Watermark Removal" (arXiv:2605.09203, 2026-05) shows that six removal attacks across four families (UnMarker, CtrlRegen+, WatermarkAttacker, etc.) all leave forensic traces: independent detectors flag *removal-processed* images vs genuinely-clean ones at **>98% TPR at 1% FPR**. So our SDXL pass makes the oracle read "SynthID not detected," but the output can still be classifiable as "an image that went through a removal pipeline." Do not over-claim "indistinguishable from a real photo." https://arxiv.org/abs/2605.09203 diff --git a/README.md b/README.md index 5c35ecd..155378d 100644 --- a/README.md +++ b/README.md @@ -16,13 +16,13 @@ If this tool saves you time, consider [sponsoring its development](https://githu - **Visible watermark removal** — Gemini / Nano Banana sparkle logo via reverse alpha blending (fast, offline, deterministic) - **Invisible watermark removal** — SynthID, StableSignature, TreeRing via diffusion-based regeneration (needs a local GPU, or run it with no setup on [raiw.cc](https://raiw.cc)) -- **AI metadata stripping** — EXIF, PNG text chunks, C2PA provenance manifests (PNG / JPEG / AVIF / HEIF / JPEG-XL), XMP DigitalSourceType +- **AI metadata stripping** — EXIF, PNG text chunks, C2PA provenance manifests (PNG / JPEG / AVIF / HEIF / JPEG-XL, and **MP4 / MOV / M4V video** at the container level), XMP DigitalSourceType - **"Made with AI" label removal** — removes the metadata that triggers AI labels on Instagram, Facebook, X (Twitter) - **Analog Humanizer** — film grain and chromatic aberration to bypass AI image classifiers - **Smart Face Protection** — automatic extraction and blending of human faces to prevent AI distortion - **Batch processing** — process entire directories - **Detection** — three-stage NCC watermark detection with confidence scoring -- **Provenance detection (`identify`)** — aggregate C2PA issuer, IPTC "Made with AI", embedded SD/ComfyUI params, EXIF/XMP generator tags, the SynthID metadata proxy, the visible sparkle, and the open SD/SDXL/FLUX invisible watermark into one origin-platform + watermark-inventory verdict (`--json` for machine output) +- **Provenance detection (`identify`)** — aggregate C2PA issuer, the C2PA soft-binding forensic-watermark vendor (Adobe TrustMark, Digimarc, Imatag, ...), IPTC "Made with AI" plus the IPTC 2025.1 `AISystemUsed` field, embedded SD/ComfyUI params, EXIF/XMP generator tags, the xAI/Grok EXIF signature, the SynthID metadata proxy, the visible sparkle, the open SD/SDXL/FLUX invisible watermark, and (with the `trustmark` extra) the open Adobe TrustMark watermark into one origin-platform + watermark-inventory verdict (`--json` for machine output) ## Examples @@ -51,7 +51,7 @@ If this tool saves you time, consider [sponsoring its development](https://githu > Visible watermarks (logo overlays) are currently used only by Google Gemini / Nano Banana. Other services rely on invisible watermarks and/or metadata. Our diffusion-based regeneration works against any invisible watermark in pixel or frequency domain. -> **Detection:** `remove-ai-watermarks identify ` reports the origin platform and watermark inventory for all the signals above — C2PA issuer, IPTC "Made with AI", the China TC260 AIGC label, embedded generation params, EXIF/XMP generator tags, the SynthID metadata proxy, the visible sparkle, and (with the `[detect]` extra) the open SD/SDXL/FLUX invisible watermark. The SynthID *pixel* watermark has no local decoder, so it is reported as a metadata proxy only. +> **Detection:** `remove-ai-watermarks identify ` reports the origin platform and watermark inventory for all the signals above — C2PA issuer, the C2PA soft-binding forensic-watermark vendor (TrustMark / Digimarc / Imatag / ...), IPTC "Made with AI" plus the IPTC 2025.1 `AISystemUsed` field, the China TC260 AIGC label, embedded generation params, EXIF/XMP generator tags, the xAI/Grok EXIF signature, the SynthID metadata proxy, the visible sparkle, and (with the `[detect]` / `[trustmark]` extras) the open SD/SDXL/FLUX and Adobe TrustMark invisible watermarks. SynthID and the proprietary soft-binding watermarks (Digimarc etc.) have no local decoder, so they are reported by metadata proxy / vendor name only. ## How it works @@ -159,6 +159,14 @@ After installation the `remove-ai-watermarks` command is available system-wide. > ```bash > pip install -e ".[detect]" # or: uv pip install -e ".[detect]" > ``` +> +> To also decode the open **Adobe TrustMark** watermark (behind Adobe Durable +> Content Credentials), install the `trustmark` extra (pulls torch and downloads +> model weights on first use): +> +> ```bash +> pip install -e ".[trustmark]" # or: uv pip install -e ".[trustmark]" +> ``` #### Invisible watermark removal @@ -315,7 +323,8 @@ Watermarking and provenance for AI-generated content is now regulated in several | US (state) | CA AB 2655, TX SB 751, similar | TX SB 751 in force; **CA AB 2655 struck down** by a federal court (Aug 2025, Section 230 / First Amendment). | Content-specific (election deepfakes, sexual deepfakes). Not tool-specific. | | US (state) | CA AB 853 (amends the California AI Transparency Act) | Core provider duties operative **2 August 2026** (delayed from 1 January 2026); large platforms 1 January 2027; capture devices 1 January 2028. | Covered providers (1M+ monthly users) must embed a latent disclosure that is "permanent or extraordinarily difficult to remove" and offer a free detection tool. Removing that disclosure is what this tool does. | | South Korea | AI Framework Act (Basic Act on AI), Article 31 | In force since **January 2026** (one-year transition after promulgation). | Art. 31(3): AI output "difficult to distinguish from reality" must be labeled so users "clearly recognize" it; the draft Enforcement Decree accepts a machine-readable (invisible-watermark) label. Artistic/creative works get a presentation exception. | -| China | Measures for Labeling AI-Generated Content (+ GB 45438-2025) | In force since **1 September 2025**. | Mandatory explicit (visible) + implicit (metadata) labels for AI content; tampering with or removing labels is prohibited. | +| China | Measures for Labeling AI-Generated Content (+ GB 45438-2025) | In force since **1 September 2025**. | Mandatory explicit (visible) + implicit (metadata) labels across image / audio / video; tampering with, forging, or removing labels is prohibited. | +| India | IT (Intermediary Guidelines and Digital Media Ethics Code) Amendment Rules, 2026 | In force since **20 February 2026** (notified 10 February 2026). | All "synthetically generated information" must be **prominently labelled** and carry **permanent metadata / a provenance identifier**; the rules expressly **prohibit modifying, suppressing, or removing** that label or metadata. Covers image, audio, and audio-visual content. | | UK | Online Safety Act 2023 / Ofcom guidance | In force, but **no statutory AI-provenance or watermarking obligation**. | Ofcom encourages watermarking / provenance metadata as voluntary "attribution measures"; platform duties, not user obligations. | ## Threat model @@ -340,7 +349,12 @@ This tool is intended for legitimate purposes such as: - Removing false-positive "Made with AI" labels from human-edited photographs. - Security research and watermark robustness study. -Removing AI provenance markers to misrepresent AI-generated content as human-created may violate the laws above, the DMCA, and platform terms of service. Users are solely responsible for ensuring their use complies with all applicable laws. The authors do not condone use of this tool for deception, fraud, or any activity that violates applicable laws or regulations. +**Who bears the liability.** This is general-purpose software and is itself lawful to publish and run; legal responsibility attaches to the person who removes a marker and to how the result is then used, and the hinge is intent. Removing AI provenance to pass AI-generated content off as human-made, to commit fraud, to produce non-consensual deepfakes, or to conceal copyright infringement can expose the remover to liability. Two kinds of exposure are worth knowing: + +- **The downstream act.** Deception, fraud, defamation, IP infringement, or breaking a platform's terms — judged by intent and harm, not by the act of editing metadata itself. In the US, the **DMCA (17 U.S.C. § 1202)** specifically bars removing "copyright management information" *with intent to conceal or enable infringement*. +- **The removal itself.** Some jurisdictions penalise tampering with the label/metadata as such, regardless of downstream use — notably **China** (Labeling Measures) and **India** (IT Amendment Rules 2026), which expressly prohibit removing or suppressing the AI label and provenance metadata. The US **COPIED Act** would do the same if enacted. + +Legitimate uses — publishing your own work, privacy (stripping metadata that leaks an account identifier), security / robustness research, or removing a false-positive "Made with AI" label from a human-edited photograph — are generally lawful. Users are solely responsible for ensuring their use complies with all applicable laws. The authors do not condone use of this tool for deception, fraud, or any activity that violates applicable laws or regulations. None of this is legal advice. ## License diff --git a/pyproject.toml b/pyproject.toml index 82e89c0..cc68451 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "remove-ai-watermarks" -version = "0.5.6" +version = "0.6.0" description = "Remove visible and invisible AI watermarks from images (Gemini / Nano Banana, ChatGPT, Stable Diffusion)" readme = "README.md" requires-python = ">=3.10" @@ -36,6 +36,21 @@ gpu = [ detect = [ "invisible-watermark>=0.2.0", ] +# Adobe TrustMark decoder -- the open, keyless watermark behind Adobe Durable +# Content Credentials (soft-binding alg ``com.adobe.trustmark.P``). Optional +# because it pulls torch and downloads model weights on first use. identify() +# guards the import and skips the TrustMark signal when absent. +trustmark = [ + "trustmark>=0.8.0", +] +# Universal region eraser backend -- big-LaMa via onnxruntime (Carve/LaMa-ONNX, +# Apache-2.0). CPU, no torch. Model (~200 MB) is downloaded on first use and +# cached by huggingface_hub; it is never bundled in this repo. The default cv2 +# eraser backend needs none of this. +lama = [ + "onnxruntime>=1.16.0", + "huggingface-hub>=0.20.0", +] dev = [ "pytest>=8.0.0", "pytest-cov>=4.1.0", @@ -43,7 +58,7 @@ dev = [ "pyright>=1.1.0", "invisible-watermark>=0.2.0", ] -all = ["remove-ai-watermarks[gpu,detect,dev]"] +all = ["remove-ai-watermarks[gpu,detect,trustmark,lama,dev]"] # diffusers 0.38.0 (security fix for GHSA-98h9-4798-4q5v) declares a dependency # on safetensors>=0.8.0rc0 — a pre-release. Allow pre-releases globally so the diff --git a/src/remove_ai_watermarks/__init__.py b/src/remove_ai_watermarks/__init__.py index 72037a6..4162aa7 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__ = "0.5.6" +__version__ = "0.6.0" diff --git a/src/remove_ai_watermarks/identify.py b/src/remove_ai_watermarks/identify.py index e29428a..79778c2 100644 --- a/src/remove_ai_watermarks/identify.py +++ b/src/remove_ai_watermarks/identify.py @@ -26,13 +26,15 @@ from remove_ai_watermarks.metadata import ( AI_METADATA_KEYS, AIGC_MARKERS, C2PA_UUID, + IPTC_AI_FIELD_MARKERS, IPTC_AI_MARKERS, aigc_label, exif_generator, get_ai_metadata, + iptc_ai_system, xai_signature, ) -from remove_ai_watermarks.noai.c2pa import extract_c2pa_info +from remove_ai_watermarks.noai.c2pa import extract_c2pa_info, soft_binding_vendors_in from remove_ai_watermarks.noai.constants import C2PA_AI_TOOLS, C2PA_ISSUERS if TYPE_CHECKING: @@ -162,6 +164,17 @@ def _invisible_watermark(image_path: Path) -> str | None: return detect_invisible_watermark(image_path) +def _trustmark(image_path: Path) -> str | None: + """Adobe TrustMark scheme name or None. + + Optional: needs the ``trustmark`` decoder (extra ``trustmark``). Returns None + if it is not installed or no TrustMark watermark decodes. + """ + from remove_ai_watermarks.trustmark_detector import detect_trustmark + + return detect_trustmark(image_path) + + def identify(image_path: Path, *, check_visible: bool = True, check_invisible: bool = True) -> ProvenanceReport: """Identify an image's origin platform and watermark inventory. @@ -213,6 +226,14 @@ def identify(image_path: Path, *, check_visible: bool = True, check_invisible: b if "OpenAI" in (" ".join(issuers) + synthid): caveats.append(_OPENAI_CAVEAT) + # ── C2PA soft-binding: a named forensic/third-party watermark vendor ─ + # (Adobe TrustMark, Digimarc, Imatag, ...). Present in the manifest even when + # the watermark itself can't be decoded; names whose watermark stamped the pixels. + soft_binding = meta.get("soft_binding") or (", ".join(v) if (v := soft_binding_vendors_in(head)) else None) + if soft_binding: + signals.append(Signal("soft_binding", f"C2PA soft binding: {soft_binding}", "high")) + watermarks.append(f"Forensic watermark soft binding ({soft_binding})") + # ── IPTC "Made with AI" (Meta etc.), only meaningful without C2PA ─ iptc = any(m in head for m in IPTC_AI_MARKERS) if iptc and not has_c2pa: @@ -222,6 +243,18 @@ def identify(image_path: Path, *, check_visible: bool = True, check_invisible: b if platform is None: platform = "Made-with-AI tag (e.g. Meta AI); platform not specified" + # ── IPTC 2025.1 AI-disclosure fields (Iptc4xmpExt:AISystemUsed etc.) ─ + iptc_ai = any(m in head for m in IPTC_AI_FIELD_MARKERS) + if iptc_ai: + system = iptc_ai_system(image_path) + named = bool(system) and system != "fields present" + signals.append( + Signal("iptc_ai_system", f"IPTC AI disclosure ({system})" if named else "IPTC AI disclosure fields", "high") + ) + watermarks.append(f"IPTC 2025.1 AI disclosure ({system})" if named else "IPTC 2025.1 AI disclosure fields") + if platform is None and named: + platform = f"{system} (IPTC AISystemUsed)" + # ── China TC260 AIGC label (Doubao and other China-served gens) ── aigc = any(m in head for m in AIGC_MARKERS) if aigc: @@ -266,12 +299,29 @@ def identify(image_path: Path, *, check_visible: bool = True, check_invisible: b if platform is None: platform = f"{scheme} (open DWT-DCT watermark)" + # ── Adobe TrustMark invisible watermark (open decoder, no key) ─── + # The watermark behind Adobe Durable Content Credentials. Decoded locally, + # but it binds provenance for human-authored content too, so it enriches the + # watermark inventory without by itself asserting AI origin. + if check_invisible and (tm_scheme := _trustmark(image_path)) is not None: + signals.append(Signal("trustmark", tm_scheme, "high")) + watermarks.append(f"Adobe TrustMark invisible watermark ({tm_scheme})") + if platform is None: + platform = "Adobe (TrustMark / Content Credentials)" + # ── Verdict so far (metadata + embedded watermark) ────────────── invisible_wm = any(s.name == "invisible_watermark" for s in signals) exif_gen = any(s.name == "exif_generator" for s in signals) xai_sig = any(s.name == "xai_signature" for s in signals) ai_from_metadata = bool( - (has_c2pa and (c2pa_is_ai or synthid)) or iptc or aigc or local_keys or invisible_wm or exif_gen or xai_sig + (has_c2pa and (c2pa_is_ai or synthid)) + or iptc + or iptc_ai + or aigc + or local_keys + or invisible_wm + or exif_gen + or xai_sig ) # ── Visible Gemini sparkle (fallback for stripped-metadata case) ─ diff --git a/src/remove_ai_watermarks/metadata.py b/src/remove_ai_watermarks/metadata.py index 7517c5e..16f9cca 100644 --- a/src/remove_ai_watermarks/metadata.py +++ b/src/remove_ai_watermarks/metadata.py @@ -74,6 +74,29 @@ IPTC_AI_MARKERS: tuple[bytes, ...] = ( b"compositeWithTrainedAlgorithmicMedia", ) +# IPTC Photo Metadata 2025.1 (published 2025-11-27) added explicit AI-disclosure +# XMP properties in the Iptc4xmpExt namespace. Their mere presence is an AI +# signal; ``AISystemUsed`` additionally carries the generator name. Property +# tokens verified against the IPTC 2025.1 specification. +IPTC_AI_FIELD_MARKERS: tuple[bytes, ...] = ( + b"AISystemUsed", + b"AISystemVersionUsed", + b"AIPromptInformation", + b"AIPromptWriterName", +) + +# ISOBMFF containers whose AI-provenance boxes ``remove_ai_metadata`` strips at +# the container level (image, video, audio -- all ISOBMFF). A content sniff +# (``ftyp``) is also accepted, so this is a fast-path hint, not the sole gate. +_ISOBMFF_EXTS: frozenset[str] = frozenset({".avif", ".heif", ".heic", ".jxl", ".mp4", ".mov", ".m4v", ".m4a"}) + +# Non-ISOBMFF audio/video we can DETECT (binary scan) but not strip at the +# container level (EBML / framed / RIFF need re-encoding). remove_ai_metadata +# fails clearly on these rather than crashing in the image path. +_UNSUPPORTED_CONTAINER_EXTS: frozenset[str] = frozenset( + {".webm", ".mkv", ".mka", ".mp3", ".wav", ".flac", ".ogg", ".oga", ".opus", ".aac"} +) + # China's mandatory AI-content labeling (TC260, the national cybersecurity # standards committee). AI generators serving China embed an XMP block in the # TC260 namespace -- ``{"Label":"1",...}``. Doubao (ByteDance) uses @@ -155,6 +178,9 @@ def has_ai_metadata(image_path: Path) -> bool: return True if any(marker in data for marker in IPTC_AI_MARKERS): return True + # IPTC 2025.1 AI-disclosure XMP properties (their presence flags AI content). + if any(marker in data for marker in IPTC_AI_FIELD_MARKERS): + return True # xAI / Grok: no C2PA/IPTC/XMP -- only the EXIF Signature + UUID-Artist pair. return xai_signature(image_path) @@ -183,6 +209,26 @@ def aigc_label(image_path: Path) -> dict[str, str] | None: return {str(k): str(v) for k, v in parsed.items()} if isinstance(parsed, dict) else None +def iptc_ai_system(image_path: Path) -> str | None: + """Return an IPTC 2025.1 AI-disclosure note if the file carries those XMP + properties, else None. + + IPTC Photo Metadata 2025.1 added ``Iptc4xmpExt`` AI-disclosure properties + (see ``IPTC_AI_FIELD_MARKERS``); their presence alone flags AI content, and + ``AISystemUsed`` names the generator. Returns the ``AISystemUsed`` value when + extractable, otherwise the literal ``"fields present"``. Container-agnostic + raw-byte scan; handles both XMP element and attribute serializations. + """ + with open(image_path, "rb") as f: + data = f.read(1024 * 1024) + if not any(marker in data for marker in IPTC_AI_FIELD_MARKERS): + return None + match = re.search(rb"AISystemUsed[=:\s]*[\"'>]\s*([^<\"']{1,120})", data) + if match and (value := match.group(1).decode("utf-8", "replace").strip()): + return value + return "fields present" + + def synthid_source(image_path: Path) -> str | None: """Return the vendor name(s) if the image carries a SynthID pixel watermark. @@ -380,7 +426,7 @@ def get_ai_metadata(image_path: Path) -> dict[str, str]: """ from PIL import Image - from remove_ai_watermarks.noai.c2pa import extract_c2pa_info, synthid_verdict + from remove_ai_watermarks.noai.c2pa import extract_c2pa_info, soft_binding_vendors_in, synthid_verdict result: dict[str, str] = {} @@ -410,14 +456,21 @@ def get_ai_metadata(image_path: Path) -> dict[str, str]: "source_type", "actions", "synthid_watermark", + "soft_binding", ): if key in c2pa: result.setdefault(key, str(c2pa[key])) - # Non-PNG containers (JPEG/WebP/AVIF): extract_c2pa_info is PNG-only, so - # fall back to the format-agnostic source check for the SynthID verdict. + # Non-PNG containers (JPEG/WebP/AVIF/MP4): extract_c2pa_info is PNG-only, so + # fall back to the format-agnostic source check for the SynthID verdict and + # the soft-binding (forensic-watermark vendor) scan. if "synthid_watermark" not in result and (vendor := synthid_source(image_path)): result.setdefault("synthid_watermark", synthid_verdict(vendor)) + if "soft_binding" not in result: + with open(image_path, "rb") as f: + head = f.read(1024 * 1024) + if vendors := soft_binding_vendors_in(head): + result["soft_binding"] = ", ".join(vendors) # China TC260 AI-content label (Doubao and other China-served generators). if aigc := aigc_label(image_path): @@ -427,6 +480,10 @@ def get_ai_metadata(image_path: Path) -> dict[str, str]: # xAI / Grok EXIF signature scheme (its only provenance signal). if xai_signature(image_path): result.setdefault("xai_signature", "xAI/Grok EXIF signature (Artist UUID + Signature blob)") + + # IPTC 2025.1 AI-disclosure XMP fields (Iptc4xmpExt:AISystemUsed etc.). + if system := iptc_ai_system(image_path): + result.setdefault("ai_system", f"IPTC 2025.1 AI disclosure ({system})") return result @@ -455,19 +512,34 @@ def remove_ai_metadata( if output_path is None: output_path = source_path - # AVIF/HEIF/JPEG-XL: strip C2PA boxes at the container level without - # re-encoding. Avoids needing PIL plugins (pillow-heif / pillow-jxl) and - # preserves pixel data bit-for-bit. - if source_path.suffix.lower() in (".avif", ".heif", ".heic", ".jxl"): - from remove_ai_watermarks.noai.isobmff import strip_c2pa_boxes + # ISOBMFF containers (AVIF/HEIF/JPEG-XL images, MP4/MOV/M4V video, M4A audio): + # strip C2PA + AI-label boxes at the container level without re-encoding. + # Avoids needing PIL plugins (pillow-heif / pillow-jxl) and preserves the + # codestream bit-for-bit. MP4/MOV/M4A are ISOBMFF too, so the same top-level + # uuid/jumb box walker applies. Route by suffix OR by an ``ftyp`` content + # sniff, so a correctly-shaped container is handled whatever its extension. + from remove_ai_watermarks.noai.isobmff import is_isobmff, strip_c2pa_boxes + with open(source_path, "rb") as f: + head = f.read(12) + if source_path.suffix.lower() in _ISOBMFF_EXTS or is_isobmff(head): data = source_path.read_bytes() cleaned, stripped = strip_c2pa_boxes(data) output_path.parent.mkdir(parents=True, exist_ok=True) output_path.write_bytes(cleaned) - logger.info("Stripped %d C2PA box(es) → %s", stripped, output_path) + logger.info("Stripped %d AI-provenance box(es) → %s", stripped, output_path) return output_path + # Containers we can detect (via identify's byte scan) but cannot strip at the + # container level: non-ISOBMFF audio/video (Matroska/WebM are EBML; MP3 is + # framed; WAV is RIFF). Re-encoding them is out of scope, so fail clearly + # rather than crash in the PIL image path below. + if source_path.suffix.lower() in _UNSUPPORTED_CONTAINER_EXTS: + raise ValueError( + f"container-level metadata removal is not supported for {source_path.suffix} " + "(detection via `identify` still works); re-encode it with a media tool to strip metadata" + ) + # Read image and filter metadata with Image.open(source_path) as img: img = img.copy() diff --git a/src/remove_ai_watermarks/noai/c2pa.py b/src/remove_ai_watermarks/noai/c2pa.py index f3ef4a7..2045a1e 100644 --- a/src/remove_ai_watermarks/noai/c2pa.py +++ b/src/remove_ai_watermarks/noai/c2pa.py @@ -28,6 +28,7 @@ from remove_ai_watermarks.noai.constants import ( C2PA_CHUNK_TYPE, C2PA_ISSUERS, C2PA_SIGNATURES, + C2PA_SOFT_BINDINGS, PNG_SIGNATURE, SYNTHID_C2PA_ISSUERS, ) @@ -174,6 +175,18 @@ def synthid_vendors_in(buffer: bytes) -> list[str]: return sorted({name for sig, name in C2PA_ISSUERS.items() if sig in buffer and sig in SYNTHID_C2PA_ISSUERS}) +def soft_binding_vendors_in(buffer: bytes) -> list[str]: + """Return forensic-watermark vendor names whose C2PA soft-binding ``alg`` + identifier appears in ``buffer``. + + A ``c2pa.soft-binding`` assertion names the watermark scheme that stamped the + pixels (Adobe TrustMark, Digimarc, Imatag, Steg.AI, ...). Shared by the PNG + caBX parser and the format-agnostic binary scan so both apply the same + C2PA_SOFT_BINDINGS rule against their respective bytes. + """ + return sorted({name for sig, name in C2PA_SOFT_BINDINGS.items() if sig in buffer}) + + def _parse_c2pa_chunk(chunk_data: bytes, c2pa_info: dict[str, Any]) -> None: """Parse C2PA chunk data and populate info dictionary.""" c2pa_info["c2pa_manifest"] = f"C2PA manifest ({len(chunk_data)} bytes)" @@ -238,6 +251,13 @@ def _parse_c2pa_chunk(chunk_data: bytes, c2pa_info: dict[str, Any]) -> None: c2pa_info["synthid_vendors"] = synthid_vendors c2pa_info["synthid_watermark"] = synthid_verdict(", ".join(synthid_vendors)) + # Soft-binding: a forensic/third-party watermark vendor named in the + # manifest (Adobe TrustMark, Digimarc, ...), independent of the issuer. + soft_binding_vendors = soft_binding_vendors_in(chunk_data) + if soft_binding_vendors: + c2pa_info["soft_binding_vendors"] = soft_binding_vendors + c2pa_info["soft_binding"] = ", ".join(soft_binding_vendors) + def extract_c2pa_chunk(image_path: Path) -> bytes | None: """ diff --git a/src/remove_ai_watermarks/noai/constants.py b/src/remove_ai_watermarks/noai/constants.py index b1026af..0172b51 100644 --- a/src/remove_ai_watermarks/noai/constants.py +++ b/src/remove_ai_watermarks/noai/constants.py @@ -122,6 +122,26 @@ C2PA_AI_TOOLS = { b"Firefly": "Firefly", } +# C2PA ``c2pa.soft-binding`` algorithm identifiers -> the forensic-watermark +# vendor that stamped the pixels. The manifest's ``alg`` field names the +# watermark scheme even when the watermark itself cannot be decoded locally, so +# a byte-scan for these (keyed on a distinctive prefix to catch all variants) +# tells us a third-party forensic watermark is present and whose. Verified +# against the official C2PA registry (github.com/c2pa-org/softbinding-algorithm-list). +# Adobe TrustMark is additionally decodable locally (see ``trustmark_detector``); +# the rest (Digimarc, Imatag, Steg.AI, etc.) are proprietary oracle-only decoders. +C2PA_SOFT_BINDINGS = { + b"com.adobe.trustmark": "Adobe TrustMark", + b"com.digimarc": "Digimarc", + b"com.imatag.lamark": "Imatag (Lamark)", + b"ai.steg": "Steg.AI", + b"com.microsoft.invismark": "Microsoft InvisMark", + b"com.microsoft.wavmark": "Microsoft WavMark", + b"com.verimatrix": "Verimatrix", + b"com.nagra.nexguard": "NAGRA NexGuard", + b"com.aiwatermark": "AIWatermark", +} + # Lowercased substrings that mark an AI generator when found in an EXIF # ``Software`` / XMP ``CreatorTool`` value. Conservative on purpose: plain # editors like "Adobe Photoshop" or "GIMP" must NOT match (no AI token), so only diff --git a/src/remove_ai_watermarks/noai/isobmff.py b/src/remove_ai_watermarks/noai/isobmff.py index af65467..0c27858 100644 --- a/src/remove_ai_watermarks/noai/isobmff.py +++ b/src/remove_ai_watermarks/noai/isobmff.py @@ -23,13 +23,25 @@ from typing import TYPE_CHECKING if TYPE_CHECKING: from collections.abc import Iterator -from remove_ai_watermarks.metadata import C2PA_UUID +from remove_ai_watermarks.metadata import ( + AIGC_MARKERS, + C2PA_UUID, + IPTC_AI_FIELD_MARKERS, + IPTC_AI_MARKERS, +) -# Top-level box types that carry C2PA payload. ``uuid`` boxes are checked -# against ``C2PA_UUID`` before being stripped; ``jumb`` boxes are always -# stripped (JPEG-XL uses them exclusively for JUMBF). +# Top-level box types that may carry AI provenance. ``uuid`` boxes are checked +# against ``C2PA_UUID`` / AI-label markers before being stripped; ``jumb`` boxes +# are always stripped (JPEG-XL uses them exclusively for JUMBF). C2PA_BOX_TYPES: frozenset[bytes] = frozenset({b"uuid", b"jumb"}) +# AI-label byte markers (TC260 AIGC, IPTC "Made with AI", IPTC 2025.1 AI fields) +# whose presence inside an XMP ``uuid`` box means the box carries an AI label. +# Matching the payload rather than a fixed XMP UUID avoids the XMP-box UUID +# byte-order ambiguity and stays surgical: only AI-bearing XMP is dropped, plain +# XMP (copyright, camera info) is kept. +_AI_LABEL_MARKERS: tuple[bytes, ...] = AIGC_MARKERS + IPTC_AI_MARKERS + IPTC_AI_FIELD_MARKERS + def _iter_top_level_boxes(data: bytes) -> Iterator[tuple[int, int, bytes, int]]: """Yield ``(start, end, type, payload_offset)`` for each top-level box. @@ -67,12 +79,22 @@ def is_isobmff(data: bytes) -> bool: def strip_c2pa_boxes(data: bytes) -> tuple[bytes, int]: - """Return ``(cleaned_bytes, stripped_count)``. + """Return ``(cleaned_bytes, stripped_count)`` with AI-provenance boxes removed. - Walks top-level boxes; drops any ``uuid`` box whose UUID equals - ``C2PA_UUID`` and any ``jumb`` box (JPEG-XL JUMBF container). All other - boxes are emitted verbatim. If the input is not ISOBMFF-shaped, returns - it unchanged. + Walks top-level boxes and drops: + - any ``uuid`` box whose UUID equals ``C2PA_UUID`` (a C2PA manifest); + - any ``uuid`` box whose payload carries an AI-label marker (an XMP packet + with a TC260 / IPTC / IPTC-2025.1 AI field -- caught by content, not by the + XMP UUID, so it works regardless of the UUID's byte order, and leaves plain + non-AI XMP intact); + - any ``jumb`` box (JPEG-XL JUMBF container). + + All other boxes (incl. ``mdat`` / codestream) are emitted verbatim, so pixel + and audio data is preserved bit-for-bit. Non-ISOBMFF input is returned + unchanged. Despite the name this also covers MP4/MOV/M4A video and audio + (all ISOBMFF). NOTE: EXIF/XMP stored as *items inside the ``meta`` box* + (typical for AVIF/HEIF images) is not removed -- that needs meta-box surgery + and is a documented limitation. """ if not is_isobmff(data): return data, 0 @@ -80,14 +102,15 @@ def strip_c2pa_boxes(data: bytes) -> tuple[bytes, int]: out = bytearray() stripped = 0 for start, end, box_type, payload_off in _iter_top_level_boxes(data): - if box_type in C2PA_BOX_TYPES: - if box_type == b"uuid": - # uuid boxes carry the 16-byte UUID immediately after the type. - if payload_off + 16 <= end and data[payload_off : payload_off + 16] == C2PA_UUID: - stripped += 1 - continue - else: # b"jumb" + if box_type == b"uuid": + # uuid boxes carry the 16-byte UUID immediately after the type. + is_c2pa = payload_off + 16 <= end and data[payload_off : payload_off + 16] == C2PA_UUID + has_ai_label = any(marker in data[payload_off:end] for marker in _AI_LABEL_MARKERS) + if is_c2pa or has_ai_label: stripped += 1 continue + elif box_type == b"jumb": + stripped += 1 + continue out.extend(data[start:end]) return bytes(out), stripped diff --git a/src/remove_ai_watermarks/trustmark_detector.py b/src/remove_ai_watermarks/trustmark_detector.py new file mode 100644 index 0000000..dd34d13 --- /dev/null +++ b/src/remove_ai_watermarks/trustmark_detector.py @@ -0,0 +1,72 @@ +"""Detect Adobe TrustMark invisible watermarks. + +TrustMark (github.com/adobe/trustmark, MIT) is the open, keyless image watermark +behind Adobe "Durable Content Credentials": when a C2PA manifest is stripped, a +TrustMark soft binding can still re-link the asset to its manifest in a +repository. Unlike SynthID it has a PUBLIC decoder with no secret key, so a +TrustMark-stamped image can be identified locally. Adobe's shipping products use +Variant P (the ``com.adobe.trustmark.P`` soft-binding ``alg``); this wrapper +loads that model. + +Optional dependency (extra: ``trustmark``); the model weights download on first +use. ``detect_trustmark`` returns None when the package is absent. This detects +provenance (Adobe Content Credentials), NOT AI generation as such -- TrustMark +also marks human-authored content -- so callers should treat it as a watermark +signal, not proof of AI origin. +""" + +# trustmark ships no type stubs; relax untyped-library diagnostics for this thin +# wrapper module only. +# pyright: reportMissingTypeStubs=false, reportUnknownMemberType=false, reportUnknownVariableType=false, reportUnknownArgumentType=false, reportMissingImports=false + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from pathlib import Path + +log = logging.getLogger(__name__) + +# Adobe ships Variant P in production (com.adobe.trustmark.P). +_MODEL_TYPE = "P" +# Lazily constructed singleton -- model load + first-use download is expensive. +_tm: Any = None + + +def is_available() -> bool: + """True if the optional ``trustmark`` package is installed.""" + import importlib.util + + return importlib.util.find_spec("trustmark") is not None + + +def _decoder() -> Any: + global _tm + if _tm is None: + from trustmark import TrustMark + + _tm = TrustMark(verbose=False, model_type=_MODEL_TYPE) + return _tm + + +def detect_trustmark(image_path: Path) -> str | None: + """Return a TrustMark scheme note if a TrustMark watermark is decoded, else None. + + Returns e.g. ``"Adobe TrustMark (variant P, schema 0)"`` when the decoder + reports the watermark present, or None if it is absent, the optional + ``trustmark`` package is not installed, or the image cannot be read/decoded. + """ + if not is_available(): + return None + try: + from PIL import Image + + with Image.open(image_path) as img: + cover = img.convert("RGB") + _wm_secret, wm_present, wm_schema = _decoder().decode(cover) + except Exception as exc: # model download / decode failure / unreadable image + log.debug("TrustMark decode failed for %s: %s", image_path, exc) + return None + return f"Adobe TrustMark (variant {_MODEL_TYPE}, schema {wm_schema})" if wm_present else None diff --git a/tests/test_identify.py b/tests/test_identify.py index 38cca3e..59fecc4 100644 --- a/tests/test_identify.py +++ b/tests/test_identify.py @@ -311,6 +311,32 @@ class TestIdentifyXaiSignature: assert any("xAI/Grok" in w for w in r.watermarks) +class TestIdentifySoftBinding: + """A C2PA soft-binding alg names a forensic-watermark vendor in the inventory.""" + + def test_soft_binding_vendor_listed(self, tmp_path: Path): + p = tmp_path / "sb.jpg" + p.write_bytes(b"\xff\xd8\xff\xe1 c2pa jumb com.digimarc.validate.1 \xff\xd9") + r = identify(p, check_visible=False, check_invisible=False) + assert any("Digimarc" in w for w in r.watermarks) + assert any(s.name == "soft_binding" for s in r.signals) + + +class TestIdentifyIptcAi: + """IPTC 2025.1 AISystemUsed drives an AI verdict + platform attribution.""" + + def test_iptc_ai_system_attributed(self, tmp_path: Path): + p = tmp_path / "iptc.jpg" + p.write_bytes( + b"\xff\xd8\xff\xe1Google Gemini" + b"\xff\xd9" + ) + r = identify(p, check_visible=False, check_invisible=False) + assert r.is_ai_generated is True + assert r.platform is not None + assert "Gemini" in r.platform + + # ── Open invisible watermark (SD/SDXL/FLUX) integration ───────────── from remove_ai_watermarks.invisible_watermark import is_available as _wm_available # noqa: E402 diff --git a/tests/test_metadata.py b/tests/test_metadata.py index 37c0664..7904533 100644 --- a/tests/test_metadata.py +++ b/tests/test_metadata.py @@ -14,6 +14,7 @@ from remove_ai_watermarks.metadata import ( exif_generator, get_ai_metadata, has_ai_metadata, + iptc_ai_system, remove_ai_metadata, synthid_source, xai_signature, @@ -567,3 +568,137 @@ class TestAIGCRealSample: def test_doubao_detected_as_ai(self): assert has_ai_metadata(SAMPLES_DIR / "doubao-1.png") assert "aigc_label" in get_ai_metadata(SAMPLES_DIR / "doubao-1.png") + + +class TestSoftBinding: + """C2PA soft-binding alg identifier -> forensic-watermark vendor name.""" + + def test_vendors_in_recognizes_known_algs(self): + from remove_ai_watermarks.noai.c2pa import soft_binding_vendors_in + + assert soft_binding_vendors_in(b"...alg...com.adobe.trustmark.P...") == ["Adobe TrustMark"] + assert soft_binding_vendors_in(b"com.digimarc.validate.1") == ["Digimarc"] + assert soft_binding_vendors_in(b"ai.steg.api blah") == ["Steg.AI"] + + def test_vendors_in_empty_when_absent(self): + from remove_ai_watermarks.noai.c2pa import soft_binding_vendors_in + + assert soft_binding_vendors_in(b"no soft binding here") == [] + + def test_get_ai_metadata_surfaces_soft_binding(self, tmp_path: Path): + # Non-PNG binary-scan path: a manifest naming a soft-binding vendor. + p = tmp_path / "fake.jpg" + p.write_bytes(b"\xff\xd8\xff\xe1 c2pa jumb com.adobe.trustmark.P \xff\xd9") + assert get_ai_metadata(p).get("soft_binding") == "Adobe TrustMark" + + +class TestIptcAiFields: + """IPTC 2025.1 AI-disclosure XMP properties (Iptc4xmpExt:AISystemUsed etc.).""" + + def test_detects_ai_system_used_element_form(self, tmp_path: Path): + p = tmp_path / "iptc_ai.jpg" + p.write_bytes( + b"\xff\xd8\xff\xe1ChatGPT DALL-E" + b"\xff\xd9" + ) + assert has_ai_metadata(p) is True + assert iptc_ai_system(p) == "ChatGPT DALL-E" + assert "ChatGPT DALL-E" in get_ai_metadata(p)["ai_system"] + + def test_attribute_serialization(self, tmp_path: Path): + p = tmp_path / "attr.jpg" + p.write_bytes(b'\xff\xd8\xff\xe1 Iptc4xmpExt:AISystemUsed="Google Gemini" \xff\xd9') + assert iptc_ai_system(p) == "Google Gemini" + + def test_present_without_value(self, tmp_path: Path): + # A disclosure field with no extractable value still flags presence. + p = tmp_path / "novalue.jpg" + p.write_bytes(b"\xff\xd8\xff\xe1 Iptc4xmpExt:AIPromptWriterName \xff\xd9") + assert iptc_ai_system(p) == "fields present" + assert has_ai_metadata(p) is True + + def test_clean_image_none(self, tmp_clean_png: Path): + assert iptc_ai_system(tmp_clean_png) is None + + +# Synthetic MP4 (ISOBMFF): ftyp + C2PA uuid box + mdat. Same box format as AVIF. +_MP4_FTYP = b"\x00\x00\x00\x18ftypmp42\x00\x00\x00\x00mp42isom" +_MP4_MDAT = b"\x00\x00\x00\x10mdat" + b"videodat" + + +class TestVideoC2pa: + """C2PA in MP4 (ISOBMFF) -- detect + strip, reusing the image box walker.""" + + def test_detects_c2pa_in_mp4(self, tmp_path: Path): + from remove_ai_watermarks.metadata import C2PA_UUID + + uuid_box = b"\x00\x00\x00\x20uuid" + C2PA_UUID + b"manifest" + p = tmp_path / "ai.mp4" + p.write_bytes(_MP4_FTYP + uuid_box + _MP4_MDAT) + assert has_ai_metadata(p) is True + + def test_strips_c2pa_in_mp4(self, tmp_path: Path): + from remove_ai_watermarks.metadata import C2PA_UUID + + uuid_box = b"\x00\x00\x00\x20uuid" + C2PA_UUID + b"manifest" + src = tmp_path / "in.mp4" + src.write_bytes(_MP4_FTYP + uuid_box + _MP4_MDAT) + out = tmp_path / "out.mp4" + remove_ai_metadata(src, out) + assert out.read_bytes() == _MP4_FTYP + _MP4_MDAT + assert has_ai_metadata(out) is False + + +class TestIsobmffMetadataRemoval: + """Container-level AI-provenance stripping across ISOBMFF image/video/audio.""" + + def test_strips_ai_xmp_uuid_box(self): + # A uuid box carrying a TC260 AIGC label is dropped by content match, + # regardless of the (non-C2PA) XMP UUID's byte order. + from remove_ai_watermarks.noai.isobmff import strip_c2pa_boxes + + xmp_uuid = bytes(range(16)) # arbitrary, not the C2PA UUID + payload = b'{"Label":"1"}' + box = (24 + len(payload)).to_bytes(4, "big") + b"uuid" + xmp_uuid + payload + cleaned, stripped = strip_c2pa_boxes(_MP4_FTYP + box + _MP4_MDAT) + assert stripped == 1 + assert cleaned == _MP4_FTYP + _MP4_MDAT + + def test_keeps_plain_non_ai_xmp(self): + # A uuid box with ordinary (non-AI) XMP must be preserved. + from remove_ai_watermarks.noai.isobmff import strip_c2pa_boxes + + xmp_uuid = bytes(range(16)) + payload = b"(c) me" + box = (24 + len(payload)).to_bytes(4, "big") + b"uuid" + xmp_uuid + payload + cleaned, stripped = strip_c2pa_boxes(_MP4_FTYP + box + _MP4_MDAT) + assert stripped == 0 + assert cleaned == _MP4_FTYP + box + _MP4_MDAT + + def test_m4a_c2pa_stripped(self, tmp_path: Path): + from remove_ai_watermarks.metadata import C2PA_UUID + + uuid_box = b"\x00\x00\x00\x20uuid" + C2PA_UUID + b"manifest" + src = tmp_path / "voice.m4a" + src.write_bytes(_MP4_FTYP + uuid_box + _MP4_MDAT) + out = tmp_path / "clean.m4a" + remove_ai_metadata(src, out) + assert out.read_bytes() == _MP4_FTYP + _MP4_MDAT + + def test_content_sniff_routes_unknown_suffix(self, tmp_path: Path): + # An ISOBMFF file with a non-standard extension is still box-stripped. + from remove_ai_watermarks.metadata import C2PA_UUID + + uuid_box = b"\x00\x00\x00\x20uuid" + C2PA_UUID + b"manifest" + src = tmp_path / "mystery.bin" + src.write_bytes(_MP4_FTYP + uuid_box + _MP4_MDAT) + out = tmp_path / "out.bin" + remove_ai_metadata(src, out) + assert out.read_bytes() == _MP4_FTYP + _MP4_MDAT + + def test_unsupported_container_raises(self, tmp_path: Path): + src = tmp_path / "audio.mp3" + src.write_bytes(b"ID3\x04\x00\x00\x00\x00\x00\x00 fake mp3 frames") + out = tmp_path / "out.mp3" + with pytest.raises(ValueError, match="not supported"): + remove_ai_metadata(src, out) diff --git a/tests/test_trustmark_detector.py b/tests/test_trustmark_detector.py new file mode 100644 index 0000000..d1697ff --- /dev/null +++ b/tests/test_trustmark_detector.py @@ -0,0 +1,36 @@ +"""Tests for the optional Adobe TrustMark detector. + +TrustMark is an optional dependency (extra ``trustmark``) that downloads model +weights on first use, so the decode path is only exercised when it is installed +(mirrors the imwatermark handling). The always-on test pins the graceful +absent/error behaviour: detect must return None, never raise. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pytest + +from remove_ai_watermarks.trustmark_detector import detect_trustmark, is_available + +if TYPE_CHECKING: + from pathlib import Path + + +def test_detect_never_raises(tmp_clean_png: Path): + # Whether or not trustmark is installed, a clean image must yield None + # (no watermark) without raising. When absent, the import guard returns None. + assert detect_trustmark(tmp_clean_png) is None + + +def test_unreadable_file_returns_none(tmp_path: Path): + bad = tmp_path / "not_an_image.txt" + bad.write_bytes(b"not an image") + assert detect_trustmark(bad) is None + + +@pytest.mark.skipif(not is_available(), reason="trustmark not installed") +def test_clean_image_reports_no_watermark(tmp_clean_png: Path): + # With the decoder present, an un-watermarked image must report absent. + assert detect_trustmark(tmp_clean_png) is None diff --git a/uv.lock b/uv.lock index 74b324c..179d5d4 100644 --- a/uv.lock +++ b/uv.lock @@ -2,10 +2,18 @@ version = 1 revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.11' and sys_platform == 'win32'", - "python_full_version >= '3.11' and sys_platform != 'win32'", + "python_full_version >= '3.12' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version >= '3.12' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')", + "python_full_version == '3.11.*' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')", "python_full_version < '3.11' and sys_platform == 'win32'", - "python_full_version < '3.11' and sys_platform != 'win32'", + "python_full_version < '3.11' and sys_platform == 'darwin'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')", ] [options] @@ -17,8 +25,7 @@ version = "1.13.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "huggingface-hub" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, - { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "numpy" }, { name = "packaging" }, { name = "psutil" }, { name = "pyyaml" }, @@ -30,6 +37,148 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/7e/46/02ac5e262d4af18054b3e922b2baedbb2a03289ee792162de60a865defc5/accelerate-1.13.0-py3-none-any.whl", hash = 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