diff --git a/.claude/rules/development.md b/.claude/rules/development.md index fa7cf8d..e1fb71b 100644 --- a/.claude/rules/development.md +++ b/.claude/rules/development.md @@ -303,3 +303,7 @@ explicit unchanged decodes when comparing stored pixels. Execute Ubuntu Bash workflow harnesses only on POSIX hosts with Bash; keep their pure Python checks active on every platform. Round floating-point image output before uint8 conversion when quantization must preserve constant values across OpenCV builds. +OpenCV 5 draws Hershey `putText` glyphs smaller and heavier than 4.x, so a +calibrated template drawn with `putText` ships as a committed PNG whose recipe +refuses any OpenCV but 4.x (`scripts/render_video_hershey_templates.py`, +`scripts/build_openart_alpha.py`); never render one at import time. diff --git a/.env.example b/.env.example index 7b2641e..a8c97d5 100644 --- a/.env.example +++ b/.env.example @@ -6,6 +6,10 @@ # Create and manage keys at: https://platform.openai.com/api-keys # OPENAI_API_KEY= +# GitHub token (optional; only for scripts/sync_c2pa_soft_bindings.py) +# Raises the api.github.com rate limit from 60 to 5000 requests/hour. GH_TOKEN is read as a fallback. +# GITHUB_TOKEN= + # Local photo-classifier weights (optional; otherwise fetched from Hugging Face) # Directory contents are documented in docs/photo-classify.md. # RAIW_CLASSIFY_WEIGHTS=/path/to/classifier-weights diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 57db320..272f78b 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -14,18 +14,15 @@ updates: update-types: - "minor" - "patch" - # The `all` extra must remain jointly resolvable. These candidates violate - # compatibility constraints: older supported TrustMark builds require NumPy 1.x, OpenCV 4.12+ - # requires NumPy 2 on newer Python versions, and this project keeps tokenizers - # below 0.23 for the stable Transformers path. Keep Dependabot on the - # compatible lines until those constraints move. + # The `all` extra must remain jointly resolvable. PaddleX 3.7 (the OCR + # extra) requires NumPy <2.4, mirrored by `constraint-dependencies` in + # pyproject.toml, and this project keeps tokenizers below 0.23 for the stable + # Transformers path. Keep Dependabot on the compatible lines until those + # constraints move. ignore: - dependency-name: "numpy" versions: - - ">=2" - - dependency-name: "opencv-python-headless" - versions: - - ">=4.12" + - ">=2.4" - dependency-name: "tokenizers" versions: - ">=0.23" diff --git a/.github/workflows/distribute.yml b/.github/workflows/distribute.yml index 2c60148..74a9c48 100644 --- a/.github/workflows/distribute.yml +++ b/.github/workflows/distribute.yml @@ -21,6 +21,8 @@ on: jobs: resolve: + # Model-only prereleases must not reach PyPI, Homebrew, Spaces, or ComfyUI. + if: ${{ github.event_name != 'release' || (startsWith(github.event.release.tag_name, 'v') && !github.event.release.prerelease) }} runs-on: ubuntu-latest # Thirty attempts may spend up to 20 seconds each in the JSON request, # simple-index request, and inter-attempt delay. Leave bounded headroom. diff --git a/.github/workflows/publish-image-source-hf.yml b/.github/workflows/publish-image-source-hf.yml new file mode 100644 index 0000000..52d2e0a --- /dev/null +++ b/.github/workflows/publish-image-source-hf.yml @@ -0,0 +1,59 @@ +name: Publish image-source classifier to Hugging Face + +# Manual only. The derivative weights live in a GitHub model-freeze release; +# private training images and caches never enter this repository. +on: + workflow_dispatch: + inputs: + confirm: + description: "Type publish" + required: true + weights_tag: + description: "GitHub Release tag holding openai-google-source-v1.npz" + required: true + default: image-source-freeze-2026-09-25-v1 + +jobs: + publish: + if: ${{ github.event.inputs.confirm == 'publish' }} + runs-on: ubuntu-latest + timeout-minutes: 15 + permissions: + contents: read + steps: + - uses: actions/checkout@v7 + - uses: astral-sh/setup-uv@v7 + with: + python-version: "3.12" + - name: Download derivative weights + env: + GH_TOKEN: ${{ github.token }} + TAG: ${{ github.event.inputs.weights_tag }} + run: | + set -euo pipefail + mkdir -p weights + gh release download "$TAG" \ + --repo "${{ github.repository }}" \ + --pattern openai-google-source-v1.npz \ + --dir weights + test -s weights/openai-google-source-v1.npz + - name: Verify artifact hash + run: | + set -euo pipefail + expected="$(jq -r .artifact_sha256 docs/image-source-hf/metrics.json)" + test "${#expected}" -eq 64 + echo "$expected weights/openai-google-source-v1.npz" | sha256sum --check + - name: Publish to Hugging Face + env: + HF_TOKEN: ${{ secrets.HF_TOKEN }} + SHA: ${{ github.sha }} + run: | + set -euo pipefail + if [ -z "${HF_TOKEN:-}" ]; then + echo "HF_TOKEN secret is not set" >&2 + exit 1 + fi + uv run --no-project --with huggingface-hub \ + python scripts/publish_image_source_hf.py \ + --src weights \ + --message "Image-source classifier freeze (GitHub ${SHA:0:7})" diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 7da14ac..38501d8 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -10,6 +10,8 @@ permissions: jobs: publish: + # Model-only prereleases are not Python package releases. + if: ${{ startsWith(github.event.release.tag_name, 'v') && !github.event.release.prerelease }} runs-on: ubuntu-latest environment: pypi steps: @@ -38,4 +40,4 @@ jobs: # is needed. uv's uploader also accepts Metadata-Version 2.5 -- the permanent # fix that lets the hatchling pin be relaxed (see pyproject [build-system]). - name: Publish to PyPI - run: uv publish \ No newline at end of file + run: uv publish diff --git a/README.md b/README.md index fd4bbdd..13f694c 100644 --- a/README.md +++ b/README.md @@ -205,9 +205,12 @@ preserved through a timestamped in-memory NUT bridge instead of being flattened to the average frame rate. Non-zero source start timestamps are retained together with the copied audio offset. The default `--mark auto` scans all providers in one decode pass and selects the first stable match in -the specificity order shown below. Pass an explicit mark to restrict detection -to one provider. -Sora covers the moving Sora 2 mascot and wordmark. Veo covers both the current +the specificity order shown below. Valid Google AI-video provenance breaks a +Sora/Veo cross-match in favor of Veo when the diamond is nested in the Sora +region or Veo persists through the complete clip while Sora does not. +Pass an explicit mark to restrict detection to one provider. +Sora covers the moving Sora 2 mascot, with or without the wordmark; mascot-only +matches also require the complete two-eye shape. Veo covers both the current four-point diamond and the legacy `Veo` text. Seedance covers the fixed boxed `AI` label, Dola covers the fixed `Dola AI` text, Hailuo AI covers the composite `MINIMAX | hailuo AI` label, and Kling AI covers the bottom-right `KLING AI` or diff --git a/data/README.md b/data/README.md index c7d5b3b..ceb1a7d 100644 --- a/data/README.md +++ b/data/README.md @@ -10,6 +10,8 @@ data/ visible/ Per-mark example gallery: one synthetic canonical example per registered mark plus documented provider originals where available (see fixtures/visible/README.md) + synthetic/ Frozen synthetic renders that unit tests stamp onto frames, each + produced by the script named in the test that loads it captures/ 2026-09/ Provider outputs from the maintainer's own accounts, stored as served, indexed by manifest.csv (see its README) diff --git a/data/fixtures/synthetic/dola_hershey_simplex.png b/data/fixtures/synthetic/dola_hershey_simplex.png new file mode 100644 index 0000000..8daca7d Binary files /dev/null and b/data/fixtures/synthetic/dola_hershey_simplex.png differ diff --git a/docs/cli.md b/docs/cli.md index e304069..5241a33 100644 --- a/docs/cli.md +++ b/docs/cli.md @@ -393,10 +393,11 @@ remove-ai-watermarks video visible hailuo.mp4 --mark hailuo -o hailuo_clean.mp4 remove-ai-watermarks video visible kling.mp4 --mark kling -o kling_clean.mp4 ``` -The command supports the moving Sora mascot and wordmark, two Veo +The command supports the moving Sora mascot with or without its wordmark, two Veo corner variants, the Seedance boxed `AI` label, the `Dola AI` text label, the composite `MINIMAX | hailuo AI` label, and the bottom-right Kling AI label. Sora -searches the whole frame at multiple scales. The other detectors search bounded +searches the whole frame at multiple scales and verifies the complete mascot +shape when the wordmark is absent. The other detectors search bounded lower-frame regions with separate synthetic silhouettes. Kling additionally requires its bright low-saturation label near the frame edge. Every mark requires a spatially recurring candidate across adjacent frames. Fixed marks @@ -407,8 +408,10 @@ provenance-aware marks; metadata alone never creates a detection. `--mark auto` is the default. It evaluates all providers in one decode pass and selects the first stable match in specificity order: Sora, Veo, Seedance, Doubao, Dola, Hailuo AI, then Kling AI. Their confidence scores are independently calibrated and -are not compared across providers. Pass an explicit `--mark` to scan only that -provider. +are not compared across providers. Valid Google AI-video provenance resolves a +stable Sora/Veo match in favor of Veo when their regions overlap or Veo spans +the full clip while Sora does not; it does not create a visual detection. +Pass an explicit `--mark` to scan only that provider. The video stream is transcoded and the complete original audio stream is copied without truncating an audio tail that extends beyond the final video diff --git a/docs/image-source-hf/README.md b/docs/image-source-hf/README.md new file mode 100644 index 0000000..88df59e --- /dev/null +++ b/docs/image-source-hf/README.md @@ -0,0 +1,64 @@ +--- +license: apache-2.0 +library_name: numpy +pipeline_tag: image-classification +tags: + - image-forensics + - source-attribution + - openai + - google + - open-set-classification +--- + +# OpenAI and Google image source classifier + +This opt-in model classifies decoded image pixels as a likely `openai` or +`google` export pattern, or abstains as `unknown`. It is designed for images +whose metadata was removed without deliberately erasing or regenerating the +pixel pattern. It is **not a SynthID detector**: a provider label does not prove +that SynthID is present, and `unknown` does not prove that it is absent. + +## Artifact and runtime + +The pickle-free `openai-google-source-v1.npz` contains only derivative +spectral templates and numeric thresholds. It contains no training images, +paths, image hashes, embeddings, catalog, or per-image predictions. Its SHA-256 +is `0e94e565c181416e4f63a2bfc06cd5179f4201befda7481ac510f6a168c612f4`. +The corpus used to fit the templates is private and is not redistributed. + +Install `remove-ai-watermarks[source-classify]` and call the explicit Python +API `classify_image_source(path)`. The installed library pins this repository +to a verified commit and verifies the artifact hash. It can use a local NPZ +through `RAIW_IMAGE_SOURCE_WEIGHTS`. The old `classify_source` API is a separate, +earlier model and is not silently replaced by this one. + +The result exposes `label`, `reason`, numeric branch `scores`, and +`watermark_truth="unknown"`. A simultaneous OpenAI and Google match is returned +as label `unknown` with reason `conflict`, not guessed as one provider. + +## Evidence and limits + +On 600 public [Qwen Image Bench](https://huggingface.co/datasets/Qwen/Qwen-Image-Bench) +originals from unused prompt IDs, the frozen model classified 198/200 +OpenAI-labeled, 196/200 Google-labeled, and abstained on 200/200 FLUX-labeled +files. These are uploader source labels, not per-file authenticated provenance; +this public route was opened during earlier model development, so it is a +regression check, not an independent final test. + +An additional post-freeze camera control selected 42 JPEG photographs with +camera EXIF and pre-2022 capture dates from a pinned prefix of +[lightcella/photo-corpus](https://huggingface.co/datasets/lightcella/photo-corpus). +The model returned `unknown` for all 42 original decoded-pixel views and their +JPEG95, JPEG75 and bilinear-1024 transformations. The photos come from one +archive shard; EXIF and the dataset's Flickr-original description are not +cryptographic proof of origin. No originals are redistributed here. + +The model can be wrong on other providers, camera types, editing pipelines, +resizes, crops and compression settings. Some non-target generator and +photographic stress panels found false provider matches in related candidates. +It must not be used as evidence of watermark presence, authorship, fraud, +policy compliance, or provenance. Report the abstention and keep independent +metadata/provenance findings separate from this source-pattern prediction. + +The artifact is Apache-2.0 licensed. Input photographs and their respective +licenses are not part of this repository. diff --git a/docs/image-source-hf/metrics.json b/docs/image-source-hf/metrics.json new file mode 100644 index 0000000..b50f36a --- /dev/null +++ b/docs/image-source-hf/metrics.json @@ -0,0 +1,28 @@ +{ + "claim": "OpenAI/Google export-source patterns; not SynthID detection", + "artifact_sha256": "0e94e565c181416e4f63a2bfc06cd5179f4201befda7481ac510f6a168c612f4", + "classes": ["openai", "google", "unknown"], + "public_qwen_originals": { + "dataset": "Qwen/Qwen-Image-Bench", + "revision": "d2493deb153b020cf169c7e3f57d15e4dd697038", + "label_provenance": "dataset uploader assertion, not per-file signed source", + "selection_status": "opened before this candidate; regression, not independent validation", + "openai": {"n": 200, "correct": 198}, + "google": {"n": 200, "correct": 196}, + "flux_unknown": {"n": 200, "correct": 200} + }, + "camera_controls": { + "dataset": "lightcella/photo-corpus", + "revision": "8d960ee416e50a13f5c73d71f47dc37ee3c1da11", + "shard": "oi/oi-0002.tar", + "selection_status": "prefix and qualification fixed before scoring this model", + "origin_caveat": "camera EXIF is not cryptographic human-origin proof", + "n_sources": 42, + "false_provider_matches": { + "original": 0, + "jpeg95_420": 0, + "jpeg75_420": 0, + "bilinear_max1024": 0 + } + } +} diff --git a/docs/index.md b/docs/index.md index 6511131..1e33829 100644 --- a/docs/index.md +++ b/docs/index.md @@ -42,6 +42,7 @@ The current behavior is defined by the code, tests, README, and user guides. - [Doubao reverse-alpha research](research-doubao-distillation.md) - [AI-generated image classifiers](ai-generated-image-classifiers.md) (photo AI-versus-camera freeze consumed by `classify_pixels`, not `identify`) - [Photo classifier Hugging Face card](photo-classify-hf/README.md) (Hub publication text of the 2026-08-31 freeze) +- [New image-source classifier card](image-source-hf/README.md) (derivative weights, aggregate evaluation, and non-watermark claim) - [SynthID source classifiers](synthid-classifiers.md) (metadata-free OpenAI/Gemini source finding and provider-lineage experiments) - [Source-pipeline classifier model card](source-classify-hf/README.md) (published derivative weights, aggregate evaluation, and intended-use boundary) - [SynthID local detector research](synthid-detector-research.md) diff --git a/docs/known-limitations.md b/docs/known-limitations.md index 5672617..330d370 100644 --- a/docs/known-limitations.md +++ b/docs/known-limitations.md @@ -71,7 +71,14 @@ Known examples: - The Microsoft detector covers one calibrated top-right white pill. Microsoft's [documented feature](https://support.microsoft.com/en-us/topic/include-a-watermark-when-content-from-microsoft-365-is-ai-generated-b00a656e-ae61-4692-8086-67d004421030) - can instead use a Copilot icon, `AI-Generated` text, or another position. + can instead use a Copilot icon, `AI-Generated` text, or another position. A + beige pill with dark `Made with AI` text is also not covered by the current + white-pill silhouette. +- The generic `AI生成` template misses some dark, boxed, and longer + `内容由AI生成` labels. Metadata may still report AI provenance; these visible + variants need independent calibration before automatic removal. Doubao's + bottom-right template also remains specific to its measured glyph layout, + although it now handles marks tinted by saturated backgrounds. - Samsung detection is calibrated for the Italian `Contenuti generati dall'AI` text variant. - The Jimeng top-left pill has a weak visual detector and is intentionally @@ -462,7 +469,7 @@ The `video metadata` command and high level video API inspect and strip supported AI provenance metadata without transcoding streams. `video visible` and `remove_video_visible` additionally support the moving -Sora 2 mascot and wordmark, the current Veo four-point diamond, the legacy +Sora 2 mascot with or without its wordmark, the current Veo four-point diamond, the legacy `Veo` text, the Seedance boxed `AI` label, the fixed Doubao `豆包AI生成` label, the fixed `Dola AI` text, the Hailuo AI MINIMAX/Hailuo AI composite label, and the bottom-right Kling AI `KLING AI` or @@ -476,9 +483,18 @@ The default auto-router evaluates all detectors in one decode pass but does not rank their raw confidence values. Those scores are provider-specific and known to cross-match in some layouts, so the router applies the independent temporal policies and selects the first stable result in specificity order. Use an -explicit mark when the provider is already known. +explicit mark when the provider is already known. One narrow exception resolves +a Sora/Veo cross-match in favor of a stable Veo diamond when valid Google +AI-video provenance corroborates it, Sora provenance does not, and either the +regions overlap or Veo spans the full clip while Sora does not. +When Veo has no stable visual run, this exception does not suppress Sora. +Mascot-only Sora candidates therefore also require a complete-shape match: +the edge-only template previously accepted scene texture on a Google-signed +clip and reappeared after Veo removal stripped the Google provenance. The +measured clips now scan without that false Sora mark, but unmeasured scenes +can still cross-match. Check the pixels before selecting an explicit mark. Historical Sora Turbo exports use a small OpenAI swirl in the corner rather -than the moving mascot-and-wordmark design; that earlier variant is not +than the moving Sora 2 mascot design; that earlier variant is not detected by the `sora` video mark. Hailuo AI and Kling AI coverage is specific to the verified lower-edge layouts; a new provider layout needs a separate calibrated silhouette. Other provider video labels are not supported yet. The `vidu` diff --git a/docs/module-internals.md b/docs/module-internals.md index 111798e..0abd02d 100644 --- a/docs/module-internals.md +++ b/docs/module-internals.md @@ -359,17 +359,26 @@ detected while `0.15` did not. [`video_visible.py`](../src/remove_ai_watermarks/video_visible.py) implements the first pixel stages for Sora, Veo, Seedance, Doubao, Dola, Hailuo AI, Vidu, and Kling AI. The -Sora detector searches a normalized frame with a fully synthetic -mascot-and-text silhouette at several scales. The Veo detector uses separate +Sora detector searches a normalized frame with fully synthetic +mascot-and-text and mascot-only silhouettes at several scales. The mascot-only +candidate must also correlate with the complete grayscale shape, including the +paired eyes; top-hat edge correlation alone can score a Veo diamond or scene +texture above the Sora confidence floor. The shape floor is calibrated below +the independently rendered mascot and above the measured false matches. +The Veo detector uses separate synthetic silhouettes for the current four-point diamond and legacy `Veo` text. Seedance uses a synthetic rounded boxed-`AI` silhouette, while Dola uses -an OpenCV-font `Dola AI` silhouette. Hailuo AI uses a synthetic waveform, +an OpenCV Hershey-font `Dola AI` silhouette. Hailuo AI uses a synthetic waveform, MINIMAX/Hailuo AI text, separator, and ring. Vidu uses two outlined capsules leaning into a V plus a font-rendered `Vidu AI`, sized from the measured 1080p mark (232 x 50 px); its floors (0.52 weak, 0.58 strong) sit between the one real clip (0.72 at 480-1080p, 0.60 at 360p) and the maximum over the 956 other local videos (0.41), measured 2026-09-24. Kling AI combines synthetic font -and capitalization variants with a ring approximation of its swirl; the logo path rescues +and capitalization variants with a ring approximation of its swirl. The Dola and +Kling Hershey renders are frozen PNG assets drawn by OpenCV 4 +(`scripts/render_video_hershey_templates.py`): OpenCV 5 draws the same `putText` +call smaller and heavier, and with runtime rendering the Dola gallery clip was +selected as Kling; the logo path rescues wordmarks whose version or font differs, while the edge and white-label gates reject recurring scene texture. All fixed-mark searches are bounded to the expected lower-frame area and calibrated independently. A strong relocated Veo @@ -385,6 +394,18 @@ takes the first stable result in specificity order (`sora`, `veo`, `seedance`, Kling detector also scores the Vidu wordmark. An explicit mark uses the same scan path with one candidate. Removal also collects authoritative per-frame timestamps for the encoder, while identification omits that unused ffprobe pass. +Cross-matches between providers are settled by the provenance veto below: a +Sora run on a clip with valid Google AI-video provenance and no Sora provenance +is vetoed, so a stable Veo run selects Veo. Metadata alone never selects a +visible mark. A narrower rule that switched to Veo only when the Veo diamond sat +inside the Sora box, or Veo covered every frame and Sora did not, became +unreachable once the veto covered every mark and was removed at the 2026-09-26 +merge; its credential rule survives in the veto. +For metadata-only video identification, a recognized `claim_generator` takes +precedence over certificate issuer names. Searching all marker values at once +previously labeled a Google-generated video as OpenAI when both organizations +appeared in its issuer field. Unknown generators still fall back to the +existing issuer/marker registry. Every per-frame result is untrusted. Each provider's floors, minimum-run policy, fill padding and mask style are one row in `VISIBLE_MARK_POLICIES`, and every mark @@ -410,7 +431,10 @@ rule came from a Veo 3.1 Lite clip whose wood texture matched Kling on every frame at 0.61, and was widened to every mark after a Gemini Omni 1.1 Flash clip from Runway (2026-09-25) matched Sora on a steam wisp at 0.61-0.67 for five frames, a stable run above the strict floor. A re-signing platform such as -YouTube records no source type and vetoes nothing. Provenance cannot veto a clip that really is Kling, so the swirl arm of +YouTube records no source type and vetoes nothing. An invalid credential +(`c2pa_credential_level`) vetoes nothing, and provenance that also confirms the +mark's own vendor (`confirms_video_provenance`: a `Sora` claim generator under +a Google issuer) confirms instead of vetoing. Provenance cannot veto a clip that really is Kling, so the swirl arm of `detect_kling_frame` now passes the same `_reaches_kling_edge` test as the wordmark arm: on Higgsfield's Cinema Studio v2 (a Kling model, TC260 producer `kling`) the swirl matched wood grain at 0.52-0.56 with its box 11% short of the @@ -520,6 +544,9 @@ the official C2PA registry. Refresh it with upstream schema invariants, records the exact source revision and writes a deterministic module. Runtime inspection remains offline, and registry membership is name-only evidence rather than proof that a compatible decoder ships. +The GitHub API revision lookup sends `GITHUB_TOKEN` (or `GH_TOKEN`) when set, +because the unauthenticated 60 requests/hour per IP limit is exhausted on +shared CI runners; `.env.example` documents the variable. `com.microsoft.invismark.1` uses its block value as the pixel-watermark identifier in Microsoft Paint output. An InvisMark soft binding @@ -563,7 +590,7 @@ reach high confidence. Read `untrusted` here as a missing input, not a finding: nothing was checked, because there was nothing to check against. If a maintained trust bundle is ever configured, that stops being true and the confidence mapping in -`_c2pa_credential_level` must be re-read, because only then does a failed trust +`c2pa_credential_level` (in `_internal/c2pa.py`) must be re-read, because only then does a failed trust check mean the signer was rejected. Shipping or fetching an official trust bundle requires a separate update, provenance, and availability policy; do not silently convert `signingCredential.untrusted` into trusted based on a vendor-name match. @@ -683,7 +710,8 @@ metadata extraction from verdict logic: identifier, and not the SynthID vendor inference, in `identify` and in `metadata.synthid_source` alike; the verdict reads SynthID from the decoded C2PA info when the metadata dict lacks it. Claim-generator tokens also match as whole - words (`suno` no longer matches `Sunoco`). The byte scans remain only for + words (`claim_generator_platform`: `suno` no longer matches `Sunoco`), and so do + the C2PA vendor needles the video path reads (`c2pa_vendor_platform`). The byte scans remain only for manifests the reader cannot open (1 of the 1,379), and verdicts over all 1,379 were unchanged by the rule. Device tokens (`NIKON`, `Pixel Camera`, `sony.sig`) exist only in raw bytes, so they stay a byte scan, but of the manifest store: @@ -1217,6 +1245,18 @@ and the threshold mutation guard live in `tests/test_source_classify.py`; evaluation and attack limits live in [`synthid-classifiers.md`](synthid-classifiers.md). +`source_classify_v2.py` provides a separate, opt-in `classify_image_source` +entry point. Its NPZ schema contains only complex spectral filters, spatial +templates, and thresholds; the runtime pins the Hub revision and SHA-256, +rejects unknown fields and invalid shapes, and never loads pickles. The result +has `watermark_truth="unknown"` even when a provider pattern matches. A +simultaneous provider match abstains. `PROVIDERS` exports only the calibrated +provider labels for callers that route processing from a source hint. Tests in +`tests/test_source_classify_v2.py` cover the public seam and changed-threshold +behavior; the [model card](image-source-hf/README.md) records the frozen +aggregate evidence and limits. This does not change the earlier +`classify_source` API. + The CLIP loader suppresses discarded random parameter initialization with Transformers' `no_init_weights` context before applying the complete frozen state dict. Normal `CLIPModel(config)` construction initialized 427 million @@ -1553,7 +1593,7 @@ measured against the cleared provider fixture (0.848) and 132 decoded tracked controls other than that positive (maximum 0.516 on 2026-09-10, zero fires). NCC is not portable across OpenCV builds: issue 108 reported 0.81 on `data/fixtures/provenance/chatgpt-1.png` (sand and denim, no glyph) while this -tree's OpenCV 4.11 scores 0.437 on the same file. A fire also requires +tree's OpenCV scores 0.437 on the same file (4.11, and again 5.0 on 2026-09-23). A fire also requires binarized IoU of the bright residual with the three-lobe silhouette at or above 0.70; the true fixture stays at or above 0.75 down to 512 px, and chatgpt-1 sits at 0.55. `provenance_ncc_factor` remains 1.0, so OpenAI C2PA @@ -1642,6 +1682,11 @@ with large triangular wedges. The aligned sparse mask keeps both frame edges untouched and still clears the detector. `force` cannot align a missing detection, so it retains the shared geometry-box fallback. +Doubao keeps the original low-saturation scan for pale corners. When enough of +its corner is strongly colored and that scan falls below the strict gate, a +wider color filter searches for white glyphs tinted by the background. The +stronger scan wins, preserving previously accepted pale-background marks. + Kling keeps the pixel-derived footprint for release-specific strokes, then unions it with the synthetic alpha aligned to the detector's winning match box. This is necessary because its faint canonical overlay produced a blob covering only the @@ -1801,6 +1846,8 @@ public example image was reconstructed by hand. `scripts/build_openart_alpha.py` instead PROCEDURALLY DRAWS a reconstruction of OpenArt's own public brand mark (the bowtie icon + wordmark shown on openart.ai's own marketing pages) as a grayscale alpha PNG, the same bundled-asset contract every other engine uses. +Its wordmark is a Hershey `putText` render, so like the video Hershey templates +the recipe refuses anything but OpenCV 4.x, whose output the committed asset is. **CALIBRATION IS PROVISIONAL, NOT MEASURED.** There is no recall/precision sweep like Doubao's or the generic-label engine's: no positive corpus exists diff --git a/docs/photo-classify-hf/README.md b/docs/photo-classify-hf/README.md index a16893d..124ef3b 100644 --- a/docs/photo-classify-hf/README.md +++ b/docs/photo-classify-hf/README.md @@ -16,7 +16,13 @@ datasets: - detection-datasets/coco --- -# Photo AI-versus-camera classifier +# Photo AI-versus-camera classifier (archived) + +> **Archived / legacy as of 2026-09-20. Not recommended for new integrations.** +> This model repository is retained for reproducibility and compatibility with +> existing users. Published weights and historical revisions remain available. +> The documentation and measurements below describe the archived model, not +> a current recommendation. No replacement model is linked at this time. Two heads, one call. Model 1 decides whether a **photograph** looks generated or camera-like. Model 2 names a provider only after Model 1 is DEFINITELY AI. diff --git a/docs/python-api.md b/docs/python-api.md index 8dfe161..69cbc14 100644 --- a/docs/python-api.md +++ b/docs/python-api.md @@ -29,6 +29,11 @@ not a SynthID detector and does not establish watermark presence or absence. See [source-pipeline classification](source-classify.md) for the result fields, model pin, evaluation boundary, and offline weights override. +For the newer, separately pinned model, call `raiw.classify_image_source(path)`. +It returns `label`, `reason`, branch `scores`, and +`watermark_truth="unknown"`. A simultaneous provider match abstains with +`reason="conflict"`; it is never reported as SynthID evidence. + ## Remove visible marks Install `remove-ai-watermarks[visible]` before using the visible-removal API. @@ -547,6 +552,8 @@ It reports a recurring registered mark and supported AI metadata as positive signals. When neither is present, `is_ai_generated` is `None`, never `False`. The absence of a public local video SynthID decoder is included in `caveats`. Pass `check_visible=False` for a bounded metadata-only inspection. +For metadata-only platform attribution, a recognized C2PA claim generator +takes precedence over certificate issuers when a manifest names both. For normal product integration, use the complete locally verifiable pipeline: @@ -709,7 +716,10 @@ isolated lookalike in one frame is not enough to authorize inpainting. selects the first stable match in specificity order (`sora`, `veo`, `seedance`, `doubao`, `dola`, `hailuo`, `vidu`, `kling`). Provider confidence values are calibrated independently and are not compared across detectors. Pass one of those explicit -values to restrict the scan to a single provider. The Veo detector recognizes +values to restrict the scan to a single provider. A stable Veo diamond takes +precedence over Sora only with valid Google AI-video provenance, no Sora +provenance, and either substantial overlap or a full-clip Veo run against a +partial Sora run. The Veo detector recognizes the current four-point diamond and the legacy `Veo` text. Seedance recognizes the boxed `AI` label, Doubao recognizes the fixed `豆包AI生成` label, and Dola recognizes diff --git a/docs/release-and-distribution.md b/docs/release-and-distribution.md index a034a0c..89112ed 100644 --- a/docs/release-and-distribution.md +++ b/docs/release-and-distribution.md @@ -102,7 +102,17 @@ catalog, paths, hashes, embeddings, and feature caches are outside this flow. The workflow is manual and requires the model-freeze release tag plus an explicit `publish` confirmation. Keep model-freeze GitHub releases marked as pre-releases so a model artifact cannot replace the latest package release in -GitHub's release UI. +GitHub's release UI. Package publication and distribution jobs run only for +non-prerelease `v*` tags, so model-only prereleases cannot enter PyPI or the +package fan-out. A draft release keeps its target commit when published; target +the commit containing those guards before creating a model-only release, not +merely the then-current `main` after the draft already exists. + +The newer `wiltodelta/openai-google-image-source-classifier` uses the separate +`publish-image-source-hf.yml` workflow. Its release asset is only the +hash-verified derivative `openai-google-source-v1.npz`; the workflow uploads +that artifact plus `docs/image-source-hf/README.md` and `metrics.json`. The +training corpus does not enter the library checkout or this release. If a distribution job fails because a repository or Hugging Face credential is invalid, rotate the corresponding GitHub secret and rerun the failed job. A diff --git a/docs/source-classify-hf/README.md b/docs/source-classify-hf/README.md index e9b7a75..1fcc075 100644 --- a/docs/source-classify-hf/README.md +++ b/docs/source-classify-hf/README.md @@ -8,7 +8,13 @@ tags: - open-set-classification --- -# RAIW source-pipeline classifier +# RAIW source-pipeline classifier (archived) + +> **Archived / legacy as of 2026-09-20. Not recommended for new integrations.** +> This model repository is retained for reproducibility and compatibility with +> existing users. Published weights and historical revisions remain available. +> The documentation and measurements below describe the archived model, not +> a current recommendation. No replacement model is linked at this time. This is a compact, abstaining pixel-forensics classifier for three labels: `openai`, `google`, and `unknown`. It is intended for original-export-looking diff --git a/docs/source-classify.md b/docs/source-classify.md index 24515dd..bde6c39 100644 --- a/docs/source-classify.md +++ b/docs/source-classify.md @@ -1,6 +1,6 @@ # Source-pipeline classification -`classify_source` is an opt-in, metadata-free pixel classifier for images that +`classify_source` is the earlier, opt-in, metadata-free pixel classifier for images that still resemble an original provider export. It returns `openai`, `google`, or an explicit `unknown` abstention. @@ -40,3 +40,23 @@ The model is useful as a routing suggestion for a processing workflow or as one probabilistic signal in a broader forensic report. Do not use it as evidence of authorship, fraud, policy compliance, provenance, or watermark presence. The published model card records the evaluation and robustness limits. + +## New image-source classifier + +`classify_image_source` is a separate, frozen model that incorporates selected +spectral and spatial evidence from the earlier research. It does not silently +change `classify_source` or its thresholds. Call it explicitly: + +```python +result = raiw.classify_image_source("metadata-stripped.png") +print(result.label) # "openai" | "google" | "unknown" +print(result.reason) # "classified" | "abstained" | "conflict" | "feature_unavailable" +print(result.watermark_truth) # always "unknown" +``` + +Its first call downloads a hash-verified artifact from the immutable revision +of [`wiltodelta/openai-google-image-source-classifier`](https://huggingface.co/wiltodelta/openai-google-image-source-classifier). +For offline use, set `RAIW_IMAGE_SOURCE_WEIGHTS` to the NPZ file or its +directory. It classifies likely export-source patterns in decoded pixels, not +SynthID presence. The [model card](image-source-hf/README.md) records its +aggregate tests and limitations. diff --git a/docs/supported-signals.md b/docs/supported-signals.md index 8372619..af1c2e5 100644 --- a/docs/supported-signals.md +++ b/docs/supported-signals.md @@ -49,8 +49,8 @@ pill detector; the generic pill shape does not attribute LiblibAI on its own. | Key | Mark | Motion | Important limit | | --- | --- | --- | --- | -| `sora` | Sora 2 mascot and wordmark | Moves among frame positions | Requires a temporally recurring visual match; the older Sora Turbo corner swirl is a different unsupported mark. OpenAI discontinued the Sora app on 2026-04-26 and the Sora API on 2026-09-24, so this covers existing exports only. | -| `veo` | Current four-point diamond and legacy `Veo` text | Fixed bottom-right corner | Uses separate silhouettes and requires a recurring match; learned fill is preferable on structured backgrounds. | +| `sora` | Sora 2 mascot, with or without the wordmark | Moves among frame positions | Requires a temporally recurring visual match; mascot-only matches also require complete-shape correlation, which rejects a measured Veo-diamond cross-match. The older Sora Turbo corner swirl is a different unsupported mark. OpenAI discontinued the Sora app on 2026-04-26 and the Sora API on 2026-09-24, so this covers existing exports only. | +| `veo` | Current four-point diamond and legacy `Veo` text | Fixed bottom-right corner | Uses separate silhouettes and requires a recurring match; a valid C2PA AI claim from another vendor (Google on a Sora match, for example) vetoes a mark its own vendor does not also confirm. Provenance alone does not create a visual detection. Learned fill is preferable on structured backgrounds. | | `seedance` | Boxed `AI` label | Fixed bottom-right corner | Requires an anchored recurring match; the full localized box is filled because a thinner synthetic shape mask leaves the real translucent rim behind. | | `doubao` | `豆包AI生成` text run | Fixed bottom-right corner | Reuses the image engine's synthetic alpha as the template; a stable run of at least 12 frames at confidence >=0.35 is required. Without Doubao TC260 confirmation, the run also needs a strong anchor at >=0.55. | | `dola` | `Dola AI` text | Fixed bottom-right corner | Requires an anchored recurring match; ByteDance or BytePlus provenance can relax only an existing visual run. | @@ -62,7 +62,8 @@ pill detector; the generic pill shape does not attribute LiblibAI on its own. same temporal arbiter. It is separate from the image registry because selection is made over a sequence rather than one raster. The default `auto` mode scans all seven entries in one decode pass and selects the first temporally stable -match in table order; an explicit mark restricts the scan to that row. +match in table order, subject to the Veo/Sora overlap exception above; an +explicit mark restricts the scan to that row. Accepted fills are motion-aligned across adjacent frames by default. The prior fill contributes only where its warped mask covers the current removal mask and nearby source context agrees. Scene cuts or disjoint marks retain the @@ -136,7 +137,7 @@ detects nor decodes a watermark and never changes the provenance verdict. Pixel based image commands discover these extensions: - PNG; -- JPEG; +- JPEG (`.jpg`, `.jpeg`, `.jpe`, and `.jfif`); - WebP; - HEIC and HEIF; - AVIF. diff --git a/pyproject.toml b/pyproject.toml index e71c5f9..eb89792 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -224,6 +224,12 @@ Repository = "https://github.com/wiltodelta/remove-ai-watermarks" requires = ["hatchling"] build-backend = "hatchling.build" +[tool.uv] +# Lock-resolution only; not published metadata. PaddleX 3.7 (the `ocr` extra's +# PaddleOCR) requires NumPy <2.4, and without this cap `uv lock --upgrade` +# prefers the newest NumPy and silently downgrades PaddleOCR to 3.4. +constraint-dependencies = ["numpy<2.4"] + [tool.hatch.build.targets.wheel] packages = ["src/remove_ai_watermarks"] diff --git a/scripts/README.md b/scripts/README.md index ffdde43..114e88c 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -56,6 +56,7 @@ explicitly names a tracked canonical result. | `retrain_photo_classify.py` | CPU-retrain the 2026-08-31 photo heads from a sha256-keyed cache pack, no images. | | `publish_photo_classify_hf.py` | Upload the photo-classify card and freeze weights to Hugging Face `wiltodelta/raiw-photo-classify`. Manual; the Action `publish-photo-classify-hf.yml` is the write-token path. | | `publish_source_classify_hf.py` | Upload the derivative source-pipeline model and its public card to Hugging Face `wiltodelta/raiw-source-classify`. Manual; the Action `publish-source-classify-hf.yml` is the write-token path. | +| `publish_image_source_hf.py` | Upload only the hash-verified new image-source model and public card to `wiltodelta/openai-google-image-source-classifier`. Manual; `publish-image-source-hf.yml` is the write-token path. | | `verify_engine_selection_fixtures.py` | Verify hashes, dimensions, and prompt pairing in the tracked auto-engine content matrix. | ## Research and diagnostic prototypes diff --git a/scripts/build_openart_alpha.py b/scripts/build_openart_alpha.py index b6c6413..9eaef2e 100644 --- a/scripts/build_openart_alpha.py +++ b/scripts/build_openart_alpha.py @@ -18,9 +18,14 @@ gap explicitly. Replace this asset (and re-run the engine's calibration) the moment a real captured OpenArt export is available under ``data/calibration/openart/``. +The wordmark is a Hershey ``cv2.putText`` render, which OpenCV 5 draws with +different glyphs than the OpenCV 4 build the committed asset came from, so the +script refuses to run on any OpenCV other than 4.x. + Usage:: - uv run python scripts/build_openart_alpha.py + uv run --no-project --with 'opencv-python-headless>=4.8,<5' --with numpy \ + python scripts/build_openart_alpha.py """ # cv2/numpy boundary: third-party libs ship no usable element types; relax the @@ -83,6 +88,8 @@ def build() -> np.ndarray: def main() -> None: + if not cv2.__version__.startswith("4."): + raise SystemExit(f"OpenCV {cv2.__version__} draws different glyphs; run with OpenCV 4.x") canvas = build() _ASSET.parent.mkdir(parents=True, exist_ok=True) cv2.imwrite(str(_ASSET), canvas) diff --git a/scripts/controlnet_sweep.py b/scripts/controlnet_sweep.py index 0ea5912..0252d3b 100644 --- a/scripts/controlnet_sweep.py +++ b/scripts/controlnet_sweep.py @@ -159,7 +159,7 @@ def load_pipeline(control: str, device: str, dtype: Any) -> Any: load_kwargs: dict[str, Any] = {"controlnet": controlnet, "torch_dtype": dtype} if dtype == torch.float16: # The stock SDXL VAE decodes to NaN/black in fp16; the fp16-fix VAE is the - # same swap the production pipeline uses (_SDXL_FP16_VAE_ID). + # same swap the sdxl-zimage profile makes (sdxl_zimage_pipeline.SDXL_VAE_MODEL_ID). load_kwargs["vae"] = AutoencoderKL.from_pretrained(FP16_VAE, torch_dtype=dtype) pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(BASE_MODEL, **load_kwargs) pipe = pipe.to(device) diff --git a/scripts/publish_image_source_hf.py b/scripts/publish_image_source_hf.py new file mode 100644 index 0000000..edcfdd3 --- /dev/null +++ b/scripts/publish_image_source_hf.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python3 +"""Publish the derivative image-source artifact with an explicit allowlist.""" + +from __future__ import annotations + +import argparse +import json +import logging +import os +import shutil +import tempfile +from hashlib import file_digest +from pathlib import Path + +from hf_publish import publish_model_folder + +log = logging.getLogger(__name__) + +HUB_REPO = "wiltodelta/openai-google-image-source-classifier" +MODEL_FILE = "openai-google-source-v1.npz" +REPO_ROOT = Path(__file__).resolve().parents[1] +CARD_DIR = REPO_ROOT / "docs" / "image-source-hf" +PUBLIC_FILES = ("README.md", "metrics.json", MODEL_FILE) + + +def stage_release(dest: Path, src: Path) -> None: + """Copy only the public card, metrics, and hash-verified derivative weights.""" + weights = src.expanduser().resolve() / MODEL_FILE + sources = { + "README.md": CARD_DIR / "README.md", + "metrics.json": CARD_DIR / "metrics.json", + MODEL_FILE: weights, + } + missing = [str(path) for path in sources.values() if not path.is_file()] + if missing: + raise ValueError(f"missing publication files: {', '.join(missing)}") + expected = json.loads(sources["metrics.json"].read_text())["artifact_sha256"] + with weights.open("rb") as source: + actual = file_digest(source, "sha256").hexdigest() + if actual != expected: + raise ValueError("image-source artifact SHA-256 mismatch") + dest.mkdir(parents=True, exist_ok=True) + for name, path in sources.items(): + shutil.copy2(path, dest / name) + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--src", type=Path, required=True) + parser.add_argument("--message", default="Publish image-source classifier") + args = parser.parse_args() + logging.basicConfig(level=logging.INFO, format="%(message)s") + token = os.environ.get("HF_TOKEN", "").strip() + if not token: + raise SystemExit("HF_TOKEN is not set (write role required)") + with tempfile.TemporaryDirectory(prefix="raiw-image-source-hf-") as tmp: + stage = Path(tmp) + stage_release(stage, args.src) + revision = publish_model_folder( + stage, + repo_id=HUB_REPO, + token=token, + message=args.message, + allow_patterns=PUBLIC_FILES, + ) + log.info("published %s revision=%s", HUB_REPO, revision or "(unknown)") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/render_video_hershey_templates.py b/scripts/render_video_hershey_templates.py new file mode 100644 index 0000000..2ade4f0 --- /dev/null +++ b/scripts/render_video_hershey_templates.py @@ -0,0 +1,78 @@ +"""Render the frozen OpenCV Hershey-font video templates for Dola and Kling. + +The video detectors were calibrated against these ``cv2.putText`` silhouettes as +OpenCV 4.x draws them (byte-identical from 4.8 through 4.14). OpenCV 5 reworked +text rendering, so the same call yields smaller, heavier glyphs, and the Dola +gallery clip was then selected as Kling. The package therefore loads committed +PNGs instead of rendering at import time; this script is the recipe that produced +them. It refuses to run on any OpenCV other than 4.x. + +It also renders the independent Dola test fixture: the same text in the other +Hershey face at a smaller scale, which tests/test_video.py stamps onto a frame to +check that the detector is not keyed to its own template's exact pixels. + +Regenerate with: + uv run --no-project --with 'opencv-python-headless>=4.8,<5' --with numpy \ + python scripts/render_video_hershey_templates.py +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +import cv2 +import numpy as np + +log = logging.getLogger(__name__) + +_ROOT = Path(__file__).resolve().parents[1] +_ASSETS = _ROOT / "src" / "remove_ai_watermarks" / "assets" +_FIXTURES = _ROOT / "data" / "fixtures" / "synthetic" + +# Asset name -> (text, font, canvas width). Every template is drawn at scale 2.2, +# thickness 3, anti-aliased, with the baseline origin at (2, 72) on a 100 px canvas. +TEMPLATES = { + "video_dola_hershey_duplex.png": ("Dola AI", cv2.FONT_HERSHEY_DUPLEX, 400), + "video_kling_ai_hershey_simplex.png": ("KLING AI", cv2.FONT_HERSHEY_SIMPLEX, 500), + "video_kling_ai_hershey_duplex.png": ("KLING AI", cv2.FONT_HERSHEY_DUPLEX, 500), + "video_klingai_hershey_simplex.png": ("KlingAI", cv2.FONT_HERSHEY_SIMPLEX, 500), + "video_klingai_hershey_duplex.png": ("KlingAI", cv2.FONT_HERSHEY_DUPLEX, 500), +} + + +def render( + text: str, + font: int, + width: int, + *, + height: int = 100, + origin: tuple[int, int] = (2, 72), + scale: float = 2.2, + thickness: int = 3, +) -> np.ndarray: + canvas = np.zeros((height, width), dtype=np.uint8) + cv2.putText(canvas, text, origin, font, scale, 255, thickness, cv2.LINE_AA) + ys, xs = np.nonzero(canvas) + return canvas[ys.min() : ys.max() + 1, xs.min() : xs.max() + 1] + + +def _write(path: Path, image: np.ndarray) -> None: + if not cv2.imwrite(str(path), image): + raise SystemExit(f"failed to write {path}") + log.info("%s: %dx%d", path.relative_to(_ROOT), image.shape[1], image.shape[0]) + + +def main() -> None: + logging.basicConfig(level=logging.INFO, format="%(message)s") + if not cv2.__version__.startswith("4."): + raise SystemExit(f"OpenCV {cv2.__version__} draws different glyphs; run with OpenCV 4.x") + for name, (text, font, width) in TEMPLATES.items(): + _write(_ASSETS / name, render(text, font, width)) + fixture = render("Dola AI", cv2.FONT_HERSHEY_SIMPLEX, 150, height=40, origin=(2, 28), scale=0.9, thickness=2) + _FIXTURES.mkdir(parents=True, exist_ok=True) + _write(_FIXTURES / "dola_hershey_simplex.png", fixture) + + +if __name__ == "__main__": + main() diff --git a/scripts/render_visible_examples.py b/scripts/render_visible_examples.py index 05e7fc4..b368f70 100644 --- a/scripts/render_visible_examples.py +++ b/scripts/render_visible_examples.py @@ -267,9 +267,8 @@ def _video_mark_frame(key: str, w: int, h: int) -> np.ndarray: if key == "sora": tmpl, scale, x, y = templates["sora-icon"], 0.10, int(w * 0.72), int(h * 0.90) elif key == "veo": - # The legacy "Veo" TEXT form: a perfect synthetic diamond also matches the - # Sora icon template (both are 4-point stars) and table order hands the - # tie to Sora, so the gallery carries the discriminative text variant. + # Keep the legacy text variant in the generated gallery. The provider + # original and the cross-match regression exercise the current diamond. tmpl = templates["veo-text"] th = max(6, round(14 * short / 720)) tw = max(1, round(tmpl.shape[1] * th / tmpl.shape[0])) diff --git a/src/remove_ai_watermarks/__init__.py b/src/remove_ai_watermarks/__init__.py index 6031622..e8d9165 100644 --- a/src/remove_ai_watermarks/__init__.py +++ b/src/remove_ai_watermarks/__init__.py @@ -15,6 +15,7 @@ High-level API (lazy, so ``import remove_ai_watermarks`` stays cheap):: raiw.remove_video_invisible("in.mp4", "out.mp4") # calibrated video-pixel regeneration raiw.remove_video_visible("in.mp4", "out.mp4") # stable visible video-mark removal raiw.classify_source("in.png") # source pipeline, not SynthID detection + raiw.classify_image_source("in.png") # newer source patterns, not SynthID detection For a provenance verdict use the ``identify`` submodule:: @@ -39,12 +40,14 @@ __version__ = "0.42.0" __all__ = [ "BatchItemResult", "BatchSummary", + "ImageSourceClassification", "InvisibleOptions", "MetadataStripIncomplete", "RemoveAllResult", "SourceClassification", "VisibleRemovalResult", "__version__", + "classify_image_source", "classify_source", "identify_video", "inspect_video_metadata", @@ -75,6 +78,7 @@ if TYPE_CHECKING: visible_provenance, ) from remove_ai_watermarks.source_classify import SourceClassification, classify_source + from remove_ai_watermarks.source_classify_v2 import ImageSourceClassification, classify_image_source from remove_ai_watermarks.video import ( identify_video, inspect_video_metadata, @@ -109,6 +113,10 @@ def __getattr__(name: str) -> object: from remove_ai_watermarks import source_classify return getattr(source_classify, name) + if name in ("ImageSourceClassification", "classify_image_source"): + from remove_ai_watermarks import source_classify_v2 + + return getattr(source_classify_v2, name) if name in ( "identify_video", "inspect_video_metadata", diff --git a/src/remove_ai_watermarks/_internal/c2pa.py b/src/remove_ai_watermarks/_internal/c2pa.py index 25932d0..5d23da2 100644 --- a/src/remove_ai_watermarks/_internal/c2pa.py +++ b/src/remove_ai_watermarks/_internal/c2pa.py @@ -338,6 +338,41 @@ def c2pa_info_has_invalid_credential(info: dict[str, Any]) -> bool: ) +def c2pa_credential_level(info: dict[str, Any]) -> str: + """Return invalid, verified, or unverified for provenance attribution. + + ``verified`` means the reader tied this manifest to these bytes: the hard binding + matched and the claim signature validated. Signer trust is deliberately NOT a + condition -- no trust anchors ship, so gating on it made this branch unreachable. + Read the trust-anchor paragraph in docs/module-internals.md before changing this. + """ + if c2pa_info_has_invalid_credential(info): + return "invalid" + if info.get("c2pa_integrity") == "valid" and info.get("c2pa_signature") == "valid": + return "verified" + return "unverified" + + +def claim_generator_platform(generator: str | None) -> str | None: + """The product a claim-generator string names by a whole-word registry token, else None.""" + if not generator: + return None + return _first_word_match(generator.casefold(), _CLAIM_GENERATOR_TOKENS) + + +_CLAIM_GENERATOR_TOKENS = {token.encode(): platform for token, platform in C2PA_CLAIM_GENERATOR_PLATFORMS} +_VENDOR_PLATFORM_NEEDLES = { + vendor.needle.casefold().encode(): vendor.platform + for vendor in C2PA_AI_VENDORS + if vendor.platform and vendor.needle +} + + +def c2pa_vendor_platform(text: str) -> str | None: + """Platform of the first C2PA AI vendor ``text`` names as a whole word, case-insensitively.""" + return _first_word_match(text.casefold(), _VENDOR_PLATFORM_NEEDLES) + + def c2pa_info_has_invismark(info: dict[str, Any]) -> bool: """Return whether parsed C2PA info declares Microsoft InvisMark.""" soft_bindings = info.get("soft_binding_vendors") @@ -735,13 +770,9 @@ def registry_word_matches(text: str, registry: dict[bytes, str]) -> list[str]: ) -_CLAIM_GENERATOR_TOKENS = {token.encode(): platform for token, platform in C2PA_CLAIM_GENERATOR_PLATFORMS} - - -def claim_generator_word_platform(generator: str) -> str | None: - """The product a claim-generator string names by a whole-word registry token, else None.""" - matches = registry_word_matches(generator.casefold(), _CLAIM_GENERATOR_TOKENS) - return matches[0] if matches else None +def _first_word_match(text: str, registry: dict[bytes, str]) -> str | None: + """The first registry label, in registry order, whose token is a whole word of ``text``.""" + return next((label for token, label in registry.items() if _word_pattern(token).search(text) is not None), None) @functools.cache diff --git a/src/remove_ai_watermarks/_internal/constants.py b/src/remove_ai_watermarks/_internal/constants.py index 90daaf4..4f17ca2 100644 --- a/src/remove_ai_watermarks/_internal/constants.py +++ b/src/remove_ai_watermarks/_internal/constants.py @@ -11,7 +11,8 @@ def _tokens(value: str) -> tuple[str, ...]: return tuple(value.split("|")) -SUPPORTED_FORMATS = frozenset(_tokens(".png|.jpg|.jpeg|.webp|.heic|.heif|.avif")) +JPEG_SUFFIXES = frozenset(_tokens(".jpg|.jpeg|.jpe|.jfif")) +SUPPORTED_FORMATS = JPEG_SUFFIXES | frozenset(_tokens(".png|.webp|.heic|.heif|.avif")) AI_METADATA_KEYS = _tokens( "parameters|postprocessing|extras|workflow|prompt|Dream|SD:mode|StableDiffusionVersion|" "generation_time|Model|Model hash|Seed" diff --git a/src/remove_ai_watermarks/_internal/qwen_zimage_pipeline.py b/src/remove_ai_watermarks/_internal/qwen_zimage_pipeline.py index 22da37e..f886141 100644 --- a/src/remove_ai_watermarks/_internal/qwen_zimage_pipeline.py +++ b/src/remove_ai_watermarks/_internal/qwen_zimage_pipeline.py @@ -23,6 +23,7 @@ from PIL import Image from remove_ai_watermarks._internal.two_stage_pipeline import ( _GLOBAL_NEGATIVE, _GLOBAL_PROMPT, + DiffSynthVramConfig, TwoStageZImagePipeline, _cache_static_prompt_embeddings, _resize_to_target, @@ -104,7 +105,7 @@ class QwenZImagePipeline(TwoStageZImagePipeline): total_memory_gib=self._total_vram_gib(), ) - def _qwen_vram_config(self) -> dict[str, Any]: + def _qwen_vram_config(self) -> DiffSynthVramConfig: import torch if self._keep_global_models_resident(): diff --git a/src/remove_ai_watermarks/_internal/two_stage_pipeline.py b/src/remove_ai_watermarks/_internal/two_stage_pipeline.py index c15fcea..4e82c20 100644 --- a/src/remove_ai_watermarks/_internal/two_stage_pipeline.py +++ b/src/remove_ai_watermarks/_internal/two_stage_pipeline.py @@ -21,7 +21,7 @@ import tempfile import urllib.request from dataclasses import dataclass from pathlib import Path -from typing import TYPE_CHECKING, Any, ClassVar +from typing import TYPE_CHECKING, Any, ClassVar, TypedDict import numpy as np from PIL import Image @@ -35,6 +35,20 @@ if TYPE_CHECKING: log = logging.getLogger(__name__) + +class DiffSynthVramConfig(TypedDict): + """DiffSynth's per-model placement: a dtype (or ``"disk"``) and a device per phase.""" + + offload_dtype: Any + offload_device: str + onload_dtype: Any + onload_device: str + preparing_dtype: Any + preparing_device: str + computation_dtype: Any + computation_device: str + + ZIMAGE_TURBO_MODEL_ID = "Tongyi-MAI/Z-Image-Turbo" SAM_MODEL_ID = "facebook/sam-vit-base" @@ -150,19 +164,7 @@ def _pin_vram_managed_models(pipe: Any) -> None: if model is None: continue for module in model.modules(): - if not all( - hasattr(module, attribute) - for attribute in ( - "offload_dtype", - "offload_device", - "onload_dtype", - "onload_device", - "preparing_dtype", - "preparing_device", - "computation_dtype", - "computation_device", - ) - ): + if not all(hasattr(module, attribute) for attribute in DiffSynthVramConfig.__annotations__): continue module.offload_dtype = module.computation_dtype module.offload_device = module.computation_device @@ -456,13 +458,17 @@ def _yunet_model_path() -> Path: YUNET_MODEL_URL, headers={"User-Agent": "remove-ai-watermarks"}, ) - with urllib.request.urlopen(request, timeout=60) as response: # noqa: S310 - fixed HTTPS source - payload = response.read() - log.info( - "YuNet download response: status=%s content_length=%s", - getattr(response, "status", None), - len(payload), - ) + try: + with urllib.request.urlopen(request, timeout=60) as response: # noqa: S310 - fixed HTTPS source + payload = response.read() + log.info( + "YuNet download response: status=%s content_length=%s", + getattr(response, "status", None), + len(payload), + ) + except OSError as exc: + log.warning("YuNet download from %s to %s failed: %s", YUNET_MODEL_URL, model_path, exc) + raise digest = hashlib.sha256(payload).hexdigest() if digest != YUNET_MODEL_SHA256: raise OSError( @@ -724,7 +730,7 @@ class TwoStageZImagePipeline: return cls._zimage_vram_config()["computation_dtype"] @staticmethod - def _zimage_vram_config() -> dict[str, Any]: + def _zimage_vram_config() -> DiffSynthVramConfig: import torch return { @@ -858,7 +864,14 @@ class TwoStageZImagePipeline: ) return _clip_sam_masks_to_boxes(binary_masks, boxes, image.size) except Exception as exc: - log.warning("SAM face-mask refinement failed (%s); using box-derived ellipse masks", exc) + log.warning( + "SAM face-mask refinement failed (%s, model %s on %s, %d boxes); using box-derived ellipse masks", + exc, + SAM_MODEL_ID, + self.device, + len(boxes), + exc_info=True, + ) return _ellipse_masks(boxes, image.size) def preload(self, *, global_only: bool = False) -> None: diff --git a/src/remove_ai_watermarks/_internal/utils.py b/src/remove_ai_watermarks/_internal/utils.py index 78221bc..63164dd 100644 --- a/src/remove_ai_watermarks/_internal/utils.py +++ b/src/remove_ai_watermarks/_internal/utils.py @@ -4,17 +4,11 @@ from __future__ import annotations from typing import TYPE_CHECKING -from remove_ai_watermarks._internal.constants import SUPPORTED_FORMATS +from remove_ai_watermarks._internal.constants import JPEG_SUFFIXES, SUPPORTED_FORMATS if TYPE_CHECKING: from pathlib import Path -_PIL_FORMAT_BY_SUFFIX = { - ".jpg": "JPEG", - ".jpeg": "JPEG", - ".png": "PNG", -} - def is_supported_format(file_path: Path) -> bool: """Return whether ``file_path`` has a supported raster suffix.""" @@ -27,4 +21,4 @@ def get_image_format(file_path: Path) -> str: The metadata writer only has specialized PNG and JPEG paths. Other accepted inputs therefore use its PNG fallback, matching the established API contract. """ - return _PIL_FORMAT_BY_SUFFIX.get(file_path.suffix.casefold(), "PNG") + return "JPEG" if file_path.suffix.casefold() in JPEG_SUFFIXES else "PNG" diff --git a/src/remove_ai_watermarks/_internal/watermark_profiles.py b/src/remove_ai_watermarks/_internal/watermark_profiles.py index 4283c1e..6f927a1 100644 --- a/src/remove_ai_watermarks/_internal/watermark_profiles.py +++ b/src/remove_ai_watermarks/_internal/watermark_profiles.py @@ -1,10 +1,11 @@ """Project-owned configuration for invisible-watermark regeneration profiles. Three profiles remain, and all are CUDA-only: ``qwen-zimage`` (the default), -``sdxl-zimage``. The older ``controlnet``, ``sdxl``, ``qwen`` and ``default`` profiles -were removed rather than kept as a CPU path, because none of them matched the two-stage -recipe's face preservation and keeping them implied a quality this library no longer -offers. Removing invisible watermarks therefore needs a CUDA device; the visible-mark +``sdxl-zimage`` and ``chroma-zimage``, plus the per-vendor ``auto`` selector. The +older ``controlnet``, ``sdxl``, ``qwen`` and ``default`` profiles were removed rather +than kept as a CPU path, because none of them matched the two-stage recipe's face +preservation and keeping them implied a quality this library no longer offers. +Removing invisible watermarks therefore needs a CUDA device; the visible-mark registry and every identify path still run anywhere. """ @@ -75,7 +76,7 @@ SDXL_LIGHTNING_PATTERN = "sdxl_lightning_4step_lora.safetensors" # All profiles are certified at a fixed seed because SynthID removal near the # strength floor is seed-dependent. The step count and CFG are not settable at all -- -# each stage owns them (``GLOBAL_STEPS`` / ``FACE_STEPS`` in qwen_zimage_pipeline). +# each stage owns them (``GLOBAL_STEPS`` in qwen_zimage_pipeline, ``FACE_STEPS`` in two_stage_pipeline). PROFILE_SEED = 0 # sdxl-zimage runs the qwen-zimage recipe on an SDXL global stage, and strength is diff --git a/src/remove_ai_watermarks/_internal/watermark_remover.py b/src/remove_ai_watermarks/_internal/watermark_remover.py index d8047c7..73ba468 100644 --- a/src/remove_ai_watermarks/_internal/watermark_remover.py +++ b/src/remove_ai_watermarks/_internal/watermark_remover.py @@ -102,7 +102,11 @@ def get_device() -> str: if torch.cuda.is_available() and _cuda_works(): # type: ignore[union-attr] return "cuda" if _has_nvidia_gpu(): - logger.warning("NVIDIA GPU detected, but the installed PyTorch build has no working CUDA backend") + logger.warning( + "NVIDIA GPU detected, but the installed PyTorch build (torch %s, CUDA %s) has no working CUDA backend", + torch.__version__, # type: ignore[union-attr] + torch.version.cuda, # type: ignore[union-attr] + ) return "cpu" diff --git a/src/remove_ai_watermarks/_text_mark_engine.py b/src/remove_ai_watermarks/_text_mark_engine.py index 5ee3668..e2747d9 100644 --- a/src/remove_ai_watermarks/_text_mark_engine.py +++ b/src/remove_ai_watermarks/_text_mark_engine.py @@ -66,8 +66,9 @@ _MIN_DETECT_SHORT_SIDE = 200 # Doubao stays at 0.70: both its bands return more true marks than false fills, so # tightening would cost 11 genuine recoveries to prevent 8 false ones. # -# Jimeng moves to 0.85. Its relaxed detector does not key on the "★ 即梦AI" wordmark -# any more -- it keys on "some text in the bottom-right corner": of 68 false +# Jimeng moved to 0.85 (superseded: it is back at 0.70 now that the rival margin +# handles the Doubao cross-fire; see jimeng_engine.PROVENANCE_NCC_FACTOR). Its relaxed detector does not +# key on the "★ 即梦AI" wordmark any more -- it keys on "some text in the bottom-right corner": of 68 false # additions, 33 were DOUBAO marks and 17 were other vendors' AI labels (千问, 百度, # 星绘, 抖音). 45 of those 68 fill a corner nothing else would touch (the other 23 # are harmless -- doubao fires strictly there and fills the same box anyway). At diff --git a/src/remove_ai_watermarks/api.py b/src/remove_ai_watermarks/api.py index 0794764..63e735d 100644 --- a/src/remove_ai_watermarks/api.py +++ b/src/remove_ai_watermarks/api.py @@ -172,10 +172,9 @@ def remove_visible( ) -> tuple[NDArray[Any], list[str]]: """Remove every detected known visible AI mark through localize then fill. - The registry currently covers the Gemini visible watermark; Doubao, Jimeng, - Qwen text or symbol mark, Kling AI, Yuanbao, Samsung, RunningHub, and Baidu text marks; the - LiblibAI wordmark and compact pill; one Microsoft top-right AI-badge variant; - and the Jimeng pill. Returns ``(result_bgr, [labels removed])``. + The registry covers the marks listed by ``known_marks()``: the Gemini visible + watermark, vendor text marks, the brand-less ``AI生成`` label, pills and badges. + Returns ``(result_bgr, [labels removed])``. ``source`` is a file path OR a BGR ndarray. For a PATH, metadata provenance is read automatically (so ``sensitivity="auto"`` recovers a moved/faint mark whenever the diff --git a/src/remove_ai_watermarks/assets/video_dola_hershey_duplex.png b/src/remove_ai_watermarks/assets/video_dola_hershey_duplex.png new file mode 100644 index 0000000..3d59e87 Binary files /dev/null and b/src/remove_ai_watermarks/assets/video_dola_hershey_duplex.png differ diff --git a/src/remove_ai_watermarks/assets/video_kling_ai_hershey_duplex.png b/src/remove_ai_watermarks/assets/video_kling_ai_hershey_duplex.png new file mode 100644 index 0000000..582f7f4 Binary files /dev/null and b/src/remove_ai_watermarks/assets/video_kling_ai_hershey_duplex.png differ diff --git a/src/remove_ai_watermarks/assets/video_kling_ai_hershey_simplex.png b/src/remove_ai_watermarks/assets/video_kling_ai_hershey_simplex.png new file mode 100644 index 0000000..2944646 Binary files /dev/null and b/src/remove_ai_watermarks/assets/video_kling_ai_hershey_simplex.png differ diff --git a/src/remove_ai_watermarks/assets/video_klingai_hershey_duplex.png b/src/remove_ai_watermarks/assets/video_klingai_hershey_duplex.png new file mode 100644 index 0000000..c8bbee1 Binary files /dev/null and b/src/remove_ai_watermarks/assets/video_klingai_hershey_duplex.png differ diff --git a/src/remove_ai_watermarks/assets/video_klingai_hershey_simplex.png b/src/remove_ai_watermarks/assets/video_klingai_hershey_simplex.png new file mode 100644 index 0000000..1a8d213 Binary files /dev/null and b/src/remove_ai_watermarks/assets/video_klingai_hershey_simplex.png differ diff --git a/src/remove_ai_watermarks/baidu_engine.py b/src/remove_ai_watermarks/baidu_engine.py index 5fca940..7233ae7 100644 --- a/src/remove_ai_watermarks/baidu_engine.py +++ b/src/remove_ai_watermarks/baidu_engine.py @@ -7,8 +7,8 @@ two-component template (text + pill tag) was measured and REJECTED -- the solid white pill is a bright-blob magnet and both front-ends scored the clean arm at cohort levels (tophat clean p95 0.445 / gray clean p95 0.487 vs cohort ~0.5, 2026-07-22). The text-only silhouette separates cleanly (below). The white tag -is still removed with the mark: the fill blob covers both bright components in -the corner box. +is still removed with the mark: the fill is the detector's match box, stretched +right to the corner end of the locate box (see :meth:`BaiduEngine._footprint_rect`). Removal is the shared **localize -> fill** (:meth:`footprint_mask` -> ``region_eraser``). This module supplies only Baidu's tuned @@ -17,7 +17,7 @@ synthetic silhouette from ``scripts/render_vendor_silhouettes.py``, never cut from an upload). The detector uses a synthetic silhouette, short-side geometry, a strict -confidence gate, and a Qwen rival margin. The footprint covers both the text +confidence gate, and Doubao and Qwen rival margins. The footprint covers both the text run and its adjacent pill tag. """ # The module-level _alpha_template / _glyph_silhouette / _template_match_score below diff --git a/src/remove_ai_watermarks/doubao_engine.py b/src/remove_ai_watermarks/doubao_engine.py index 25b0eb7..6661028 100644 --- a/src/remove_ai_watermarks/doubao_engine.py +++ b/src/remove_ai_watermarks/doubao_engine.py @@ -22,10 +22,13 @@ supplies only Doubao's tuned :class:`TextMarkConfig` (bottom-right corner, from __future__ import annotations +import dataclasses from typing import TYPE_CHECKING, Any +import numpy as np + from remove_ai_watermarks import _text_mark_engine, image_io -from remove_ai_watermarks._text_mark_engine import TextMarkConfig, TextMarkDetection, TextMarkEngine +from remove_ai_watermarks._text_mark_engine import TextMarkConfig, TextMarkDetection, TextMarkEngine, TextMarkScan if TYPE_CHECKING: from numpy.typing import NDArray @@ -38,9 +41,12 @@ WM_HEIGHT_FRAC = 0.075 MARGIN_RIGHT_FRAC = 0.004 MARGIN_BOTTOM_FRAC = 0.004 -# Glyph appearance: a light, low-saturation gray rendered brighter than the local -# background (white top-hat), so a white-paper document is left untouched. +# Glyph appearance: a light glyph rendered brighter than the local background +# (white top-hat). Preserve the original low-saturation front end, which can +# score better on pale backgrounds than a wider color filter. MAX_SATURATION = 55 # max channel spread to count a pixel as "grayish" +_TINTED_MAX_SATURATION = 100 +_TINTED_CORNER_FRACTION = 0.10 LOGO_MIN_LUMA = 150 # glyphs are at least this bright in absolute terms TOPHAT_DELTA = 12 # glyph must exceed the local background by this many levels @@ -104,6 +110,12 @@ _CONFIG = TextMarkConfig( min_gw=8, ) +# A translucent white mark over yellow/orange imagery can retain enough of the +# background color to miss the original 55-level filter. Search with a wider +# filter only when the corner is substantially colorful. Keep already accepted +# detections and otherwise retain the stronger of the two scans. +_TINTED_ENGINE = TextMarkEngine(dataclasses.replace(_CONFIG, max_saturation=_TINTED_MAX_SATURATION)) + def _alpha_template() -> NDArray[Any] | None: """The bundled Doubao alpha template (float [0,1]), or None.""" @@ -126,6 +138,21 @@ class DoubaoEngine(TextMarkEngine): def __init__(self) -> None: super().__init__(_CONFIG) + def _scan(self, image: NDArray[Any] | None) -> TextMarkScan: + base = super()._scan(image) + if base.loc is None or image is None or (base.score or 0) >= DETECT_NCC_THRESHOLD: + return base + + x, y, w, h = base.loc.bbox + corner = image_io.to_bgr(image[y : y + h, x : x + w]) + channel_spread = corner.max(axis=2) - corner.min(axis=2) + colored = np.count_nonzero(channel_spread >= _TINTED_MAX_SATURATION) + if colored < _TINTED_CORNER_FRACTION * channel_spread.size: + return base + + tinted = _TINTED_ENGINE._scan(image) + return tinted if (tinted.score or 0) > (base.score or 0) else base + def footprint_mask( self, image: NDArray[Any] | None, diff --git a/src/remove_ai_watermarks/forensic_metadata.py b/src/remove_ai_watermarks/forensic_metadata.py index 1c0e37b..24920e6 100644 --- a/src/remove_ai_watermarks/forensic_metadata.py +++ b/src/remove_ai_watermarks/forensic_metadata.py @@ -17,7 +17,7 @@ import re import struct import zlib from pathlib import Path -from typing import Any, cast +from typing import Any, TypedDict, cast import piexif from PIL import Image @@ -28,6 +28,7 @@ from remove_ai_watermarks._internal.constants import ( PNG_METADATA_CHUNKS, RIFF_CODED_IMAGE_CHUNKS, RIFF_METADATA_CHUNKS, + SUPPORTED_FORMATS, ) from remove_ai_watermarks._internal.isobmff import ( C2PA_BOX_TYPES, @@ -46,13 +47,7 @@ __all__ = [ ] SUPPORTED_EXTENSIONS = { - ".png", - ".jpg", - ".jpeg", - ".webp", - ".heic", - ".heif", - ".avif", + *SUPPORTED_FORMATS, ".tif", ".tiff", ".bmp", @@ -65,6 +60,24 @@ SUPPORTED_EXTENSIONS = { ".jxl", } + +class ContainerEntry(TypedDict, total=False): + """One PNG/RIFF chunk or ISOBMFF provenance box in the record, or a walker error.""" + + type: str + length: int + text: str + kind: str + time: str + gamma: float + rendering_intent: int + profile_name: str + base64: str + apple_screenshot_marker: bool + truncated: bool + error: str + + FORENSIC_METADATA_SCHEMA_VERSION = 1 FORENSIC_METADATA_RECORD_TYPE = "forensic_metadata" @@ -196,10 +209,10 @@ def png_text_decode(ctype: str, body: bytes) -> str: return f"{keyword}\x00{tail.decode('utf-8', 'replace')}" -def read_png_chunks(data: bytes) -> tuple[list[dict[str, Any]], bytes]: +def read_png_chunks(data: bytes) -> tuple[list[ContainerEntry], bytes]: """Every PNG chunk in order (type, length; text chunks decoded and inflated, binary chunks as base64) plus the post-IEND trailer bytes.""" - chunks: list[dict[str, Any]] = [] + chunks: list[ContainerEntry] = [] post_iend = b"" try: pos = 8 @@ -207,7 +220,7 @@ def read_png_chunks(data: bytes) -> tuple[list[dict[str, Any]], bytes]: length = struct.unpack(">I", data[pos : pos + 4])[0] ctype = data[pos + 4 : pos + 8].decode("latin-1") body = data[pos + 8 : pos + 8 + length] - entry: dict[str, Any] = {"type": ctype, "length": length} + entry: ContainerEntry = {"type": ctype, "length": length} if ctype in ("tEXt", "zTXt", "iTXt"): entry["text"] = png_text_decode(ctype, body) if entry["text"].startswith("XML:com.adobe.xmp"): @@ -496,9 +509,9 @@ def _jpeg_forensics_bytes(data: bytes) -> dict[str, Any]: return out -def read_webp_chunks(data: bytes) -> list[dict[str, Any]]: +def read_webp_chunks(data: bytes) -> list[ContainerEntry]: """WebP RIFF chunk inventory (VP8X/VP8/VP8L/EXIF/XMP/ICCP/ANIM...).""" - chunks: list[dict[str, Any]] = [] + chunks: list[ContainerEntry] = [] try: pos = 12 declared_end = 8 + struct.unpack("= 12 else len(data) @@ -508,7 +521,7 @@ def read_webp_chunks(data: bytes) -> list[dict[str, Any]]: ctype = chunk_type.decode("latin-1") length = struct.unpack(" list[dict[str, Any]]: return chunks -def read_webp_late_metadata_path(path: Path, window: int = _RAW_SCAN_HEAD) -> list[dict[str, Any]]: +def read_webp_late_metadata_path(path: Path, window: int = _RAW_SCAN_HEAD) -> list[ContainerEntry]: """Stream metadata chunks after ``window`` while seeking over coded frames.""" - chunks: list[dict[str, Any]] = [] + chunks: list[ContainerEntry] = [] try: file_size = path.stat().st_size with open(path, "rb") as handle: @@ -546,7 +559,7 @@ def read_webp_late_metadata_path(path: Path, window: int = _RAW_SCAN_HEAD) -> li if chunk_type in RIFF_METADATA_CHUNKS and start >= window: handle.seek(start) body = handle.read(min(safe_length, _B64_CAP)) - entry: dict[str, Any] = { + entry: ContainerEntry = { "type": chunk_type.decode("latin-1"), "length": length, "base64": _b64(body), @@ -603,7 +616,7 @@ def read_isobmff_inventory(data: bytes) -> dict[str, Any]: top = boxes(0, len(data)) out["boxes"] = [t for t, _, _ in top] - provenance_boxes: list[dict[str, Any]] = [] + provenance_boxes: list[ContainerEntry] = [] for t, s, e in top: if t.encode("latin-1") in C2PA_BOX_TYPES: provenance_boxes.append( @@ -676,7 +689,7 @@ def read_isobmff_provenance_path(path: Path) -> dict[str, Any]: It seeks over media payloads instead of loading them into memory. """ out: dict[str, Any] = {"boxes": []} - provenance_boxes: list[dict[str, Any]] = [] + provenance_boxes: list[ContainerEntry] = [] collected = 0 try: file_size = path.stat().st_size @@ -689,7 +702,7 @@ def read_isobmff_provenance_path(path: Path) -> dict[str, Any]: to_read = min(payload_length, _PROVENANCE_B64_CAP - collected) f.seek(payload_offset) payload = f.read(to_read) - entry: dict[str, Any] = { + entry: ContainerEntry = { "type": box_type, "length": payload_length, "base64": _b64(payload, cap=_PROVENANCE_B64_CAP), @@ -705,9 +718,9 @@ def read_isobmff_provenance_path(path: Path) -> dict[str, Any]: return out -def read_png_late_metadata_path(path: Path, window: int = _RAW_SCAN_HEAD) -> list[dict[str, Any]]: +def read_png_late_metadata_path(path: Path, window: int = _RAW_SCAN_HEAD) -> list[ContainerEntry]: """Stream PNG metadata chunks whose payload starts after ``window``.""" - chunks: list[dict[str, Any]] = [] + chunks: list[ContainerEntry] = [] try: file_size = path.stat().st_size with open(path, "rb") as f: @@ -724,7 +737,7 @@ def read_png_late_metadata_path(path: Path, window: int = _RAW_SCAN_HEAD) -> lis safe_length = max(0, min(length, file_size - data_start)) if chunk_type in PNG_METADATA_CHUNKS and data_start >= window: body = f.read(min(safe_length, _B64_CAP)) - entry: dict[str, Any] = { + entry: ContainerEntry = { "type": chunk_type.decode("latin-1"), "length": length, "base64": _b64(body), diff --git a/src/remove_ai_watermarks/generic_ai_label_engine.py b/src/remove_ai_watermarks/generic_ai_label_engine.py index 427e55e..8869928 100644 --- a/src/remove_ai_watermarks/generic_ai_label_engine.py +++ b/src/remove_ai_watermarks/generic_ai_label_engine.py @@ -47,9 +47,11 @@ CALIBRATION CAVEAT: ``DETECT_NCC_THRESHOLD`` below is fit on a SYNTHETIC corpus scored against generated clean negatives and the six existing brand marks' own composited examples as a false-positive/rival check) -- see ``scripts/calibrate_generic_ai_label.py`` for the recall/precision table it produced. -No real "AI生成"-generator screenshot (vivo/Xiaomi/other gallery output) was available -to calibrate against. Treat this detector as WEAKER evidence than the brand-tuned -engines until it is recalibrated on real captures; the ``rivals`` margin below exists +At initial calibration, no real "AI生成"-generator screenshot was available. A +later retained-corpus review confirmed dark, boxed, and longer-text labels that +this template misses; they are not calibration positives for this silhouette. +Treat this detector as WEAKER evidence than the brand-tuned engines until it is +recalibrated on independently labeled real captures; the ``rivals`` margin below exists specifically so a genuine Doubao/Jimeng/Qwen/Baidu/Kling/Yuanbao mark -- every one of which literally contains the substring "AI生成" in its own glyphs -- is attributed to its own tuned engine instead of double-firing here. diff --git a/src/remove_ai_watermarks/identify.py b/src/remove_ai_watermarks/identify.py index d9bdd04..6a5b695 100644 --- a/src/remove_ai_watermarks/identify.py +++ b/src/remove_ai_watermarks/identify.py @@ -14,7 +14,7 @@ Aggregates every locally-readable signal into a single :class:`ProvenanceReport` Hard limit: a stripped image (re-encoded, screenshotted, social-media upload) loses all metadata, and the SynthID *pixel* watermark is not locally decodable (proprietary decoder). Absence of signals is therefore reported as ``Unknown``, -never as "clean". See CLAUDE.md "SynthID detection is metadata-only". +never as "clean". See docs/synthid.md section 3, "Detectability and verifier access". """ from __future__ import annotations @@ -27,12 +27,12 @@ from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, cast from remove_ai_watermarks._internal.c2pa import ( + c2pa_credential_level, c2pa_info_from_manifest_store, - c2pa_info_has_invalid_credential, c2pa_info_has_invismark, c2pa_info_has_removal_hint, cbor_text_after, - claim_generator_word_platform, + claim_generator_platform, extract_c2pa_info, soft_binding_labels, soft_binding_registry_entries_in, @@ -585,21 +585,6 @@ def _c2pa_validation(info: dict[str, Any]) -> dict[str, Any] | None: } -def _c2pa_credential_level(info: dict[str, Any]) -> str: - """Return invalid, verified, or unverified for provenance attribution. - - ``verified`` means the reader tied this manifest to these bytes: the hard binding - matched and the claim signature validated. Signer trust is deliberately NOT a - condition -- no trust anchors ship, so gating on it made this branch unreachable. - Read the trust-anchor paragraph in docs/module-internals.md before changing this. - """ - if c2pa_info_has_invalid_credential(info): - return "invalid" - if info.get("c2pa_integrity") == "valid" and info.get("c2pa_signature") == "valid": - return "verified" - return "unverified" - - def extract_provenance_evidence(image_path: Path) -> ProvenanceEvidence: """Read all file-backed metadata needed by provenance verdict logic once.""" return ProvenanceEvidence( @@ -827,13 +812,6 @@ def _attribute_platform(issuers: list[str], *, is_ai: bool = True) -> str | None return None -def _claim_generator_platform(generator: str | None) -> str | None: - """Resolve a distinctive C2PA claim generator to its user-facing product.""" - if not generator: - return None - return claim_generator_word_platform(generator) - - # Coarse origin-vendor normalization for integrity-clash detection. Two signals # that resolve to the SAME key are consistent (a C2PA "Google (Gemini)" issuer # and Google SynthID provenance, or Adobe Firefly + its Adobe TrustMark soft @@ -1258,7 +1236,7 @@ def _identify_from_evidence( # ── C2PA Content Credentials ──────────────────────────────────── has_c2pa = bool(info) or c2pa_marker_in(head) c2pa_validation = _c2pa_validation(info) - c2pa_level = _c2pa_credential_level(info) + c2pa_level = c2pa_credential_level(info) c2pa_usable = c2pa_level != "invalid" # The reader already named which failures moved a dimension; re-deriving that here # by substring made the displayed reason a second, looser rule than the verdict. @@ -1309,12 +1287,12 @@ def _identify_from_evidence( camera_label # Exact product generators are useful provenance even when the # signed operation is a non-AI edit (for example, CapCut). - or _claim_generator_platform(generator) + or claim_generator_platform(generator) # An AI product generator recorded under a generic re-signer beats the # signer's own label: Dreamina exports carry a "Dreamina/7.5.0" # ingredient under an active "ByteDance Media Transcode Service" # claim (measured 2026-09-24). - or (_claim_generator_platform(str(info.get("ai_tool"))) if c2pa_is_ai and info.get("ai_tool") else None) + or (claim_generator_platform(str(info.get("ai_tool"))) if c2pa_is_ai and info.get("ai_tool") else None) or signer_label or ( _GOOGLE_PHOTOS_EDIT diff --git a/src/remove_ai_watermarks/image_io.py b/src/remove_ai_watermarks/image_io.py index f027725..70cd91c 100644 --- a/src/remove_ai_watermarks/image_io.py +++ b/src/remove_ai_watermarks/image_io.py @@ -25,7 +25,7 @@ import logging from pathlib import Path from typing import TYPE_CHECKING, Any -from remove_ai_watermarks._internal.constants import PNG_SIGNATURE +from remove_ai_watermarks._internal.constants import JPEG_SUFFIXES, PNG_SIGNATURE if TYPE_CHECKING: from numpy.typing import NDArray @@ -443,7 +443,7 @@ def _encode_params(ext: str) -> list[int]: build without the chroma/subsampling flags still gets quality 100.""" import cv2 - if ext in (".jpg", ".jpeg"): + if ext in JPEG_SUFFIXES: params = [cv2.IMWRITE_JPEG_QUALITY, 100] cq = getattr(cv2, "IMWRITE_JPEG_CHROMA_QUALITY", None) if cq is not None: @@ -533,6 +533,8 @@ def imwrite( ext = (Path(path).suffix or ".png").lower() if ext in _HEIF_WRITE_EXTS: return _pil_write(path, img, icc_profile=icc, orientation=orient) + if ext in JPEG_SUFFIXES: + ext = ".jpg" try: ok, buf = cv2.imencode(ext, img, _encode_params(ext)) except cv2.error: diff --git a/src/remove_ai_watermarks/invisible_engine.py b/src/remove_ai_watermarks/invisible_engine.py index b5b7b87..df7d99c 100644 --- a/src/remove_ai_watermarks/invisible_engine.py +++ b/src/remove_ai_watermarks/invisible_engine.py @@ -62,8 +62,8 @@ def _target_size(width: int, height: int, max_resolution: int) -> tuple[int, int Set only to bound GPU/MPS memory on very large inputs (issue #10). There was also a ``min_resolution`` floor that scaled small inputs UP toward - SDXL's ~1024 training size. It went with the SDXL profiles: both surviving - profiles run at native geometry, so the floor was forced to 0 on every path and + SDXL's ~1024 training size. It went with the removed SDXL-only profiles: every + surviving profile runs at native geometry, so the floor was forced to 0 on every path and could not fire. Returns None when the cap does not apply (native resolution). Pure function so the @@ -117,7 +117,7 @@ def _apply_postprocessing( progress(f"Sharpening (unsharp mask: {unsharp})...") out_cv = humanizer.unsharp_mask(out_cv, amount=unsharp) - # Adaptive polish (CLI default): restore the input's detail level in the + # Adaptive polish (per-profile default, see PROFILE_ADAPTIVE_POLISH): restore the input's detail level in the # softened output, sparing text/edges. Self-limiting where no deficit. if adaptive_polish: ref = reference() @@ -225,6 +225,8 @@ class InvisibleEngine: seed: Random seed for reproducibility. None resolves to 0, because all profiles are certified at a fixed seed. humanize: Intensity of Analog Humanizer film grain (0 = off). + vendor: Provenance vendor that selects the strength cohort and, for the + ``auto`` profile, the engine. unsharp: Final unsharp-mask sharpening strength (0 = off, default). Applied after restoring the original resolution and before adaptive polish and humanize; ~0.5-0.8 is a safe range, higher risks edge halos. @@ -363,7 +365,7 @@ class InvisibleEngine: # single-write output is byte-identical. # Diffusers rounds native dimensions down to the latent grid (multiples # of 8), even when our own resolution policy did not resize the input. - # Route those outputs through the same final resize so --no-polish does + # Route those outputs through the same final resize so --no-adaptive-polish does # not silently change e.g. 1448x1086 into 1448x1080. needs_restore = target is not None or any(dimension % 8 for dimension in orig_size) if humanize > 0.0 or unsharp > 0.0 or adaptive_polish or needs_restore: diff --git a/src/remove_ai_watermarks/liblib_engine.py b/src/remove_ai_watermarks/liblib_engine.py index bb38695..febab6c 100644 --- a/src/remove_ai_watermarks/liblib_engine.py +++ b/src/remove_ai_watermarks/liblib_engine.py @@ -6,7 +6,8 @@ corner -- the locate box is horizontally centered). Current outputs can instead carry a compact top-left ``AI生成`` pill. The wordmark detector matches the bundled font-rendered "LiblibAI" silhouette (the triangle logo is NOT rendered -- logos vary, the wordmark discriminates); removal is the shared **localize -> -fill** (the glyph blob covers logo + wordmark, both bright). +fill** (the fill is the detector's match box, stretched left to take in the +triangle logo). This module supplies LiblibAI's tuned :class:`TextMarkConfig` (``assets/liblib_alpha.png`` from ``scripts/render_vendor_silhouettes.py``, diff --git a/src/remove_ai_watermarks/metadata.py b/src/remove_ai_watermarks/metadata.py index d3548dc..0764fd5 100644 --- a/src/remove_ai_watermarks/metadata.py +++ b/src/remove_ai_watermarks/metadata.py @@ -20,6 +20,7 @@ if TYPE_CHECKING: from remove_ai_watermarks._internal.constants import ( C2PA_CHUNK_TYPE, + JPEG_SUFFIXES, PNG_METADATA_CHUNKS, PNG_SIGNATURE, RIFF_METADATA_CHUNKS, @@ -1362,7 +1363,7 @@ def get_ai_metadata(image_path: Path) -> dict[str, str]: # PIL may not open AVIF/HEIF/JPEG-XL without optional plugins (and a # third-party plugin autoload can raise a non-OSError); fall through to the - # C2PA/binary path on any open failure. See CLAUDE.md. + # C2PA/binary path on any open failure. try: with Image.open(image_path) as img: for key, value in img.info.items(): @@ -1828,7 +1829,7 @@ def _png_chunk_should_be_dropped( # Fallback extension -> PIL save format, used only when the content sniff is # inconclusive (never for JPEG re-encode of lossless content). -_EXT_TO_PIL_FORMAT = {".jpg": "JPEG", ".jpeg": "JPEG", ".webp": "WEBP", ".png": "PNG"} +_EXT_TO_PIL_FORMAT = {**dict.fromkeys(JPEG_SUFFIXES, "JPEG"), ".webp": "WEBP", ".png": "PNG"} def _sniff_image_format(head: bytes) -> str | None: diff --git a/src/remove_ai_watermarks/pixel_evidence.py b/src/remove_ai_watermarks/pixel_evidence.py index 01dd59f..e03e4c6 100644 --- a/src/remove_ai_watermarks/pixel_evidence.py +++ b/src/remove_ai_watermarks/pixel_evidence.py @@ -388,7 +388,8 @@ def extract_pixel_evidence(image_path: Path, *, artifacts: bool = False, timings residual's sliding window and the ELA re-save are the two expensive steps and each family would otherwise redo them. - A family that fails or does not apply is left empty rather than raising: an + A family that does not apply is left empty, and one that fails records + ``{"error": }`` (status ``partial``), rather than raising: an undecodable file, or one too small for the block DCT, still returns a :class:`PixelEvidence` whose ``decoded`` / empty fields say so. Missing numpy is the one hard error, since then nothing can be measured at all. diff --git a/src/remove_ai_watermarks/region_eraser.py b/src/remove_ai_watermarks/region_eraser.py index efb4314..e04bb31 100644 --- a/src/remove_ai_watermarks/region_eraser.py +++ b/src/remove_ai_watermarks/region_eraser.py @@ -224,7 +224,11 @@ def _get_session(name: str, repo_id: str, filename: str, label: str) -> object: import onnxruntime as ort from huggingface_hub import hf_hub_download - model_path = hf_hub_download(repo_id=repo_id, filename=filename) + try: + model_path = hf_hub_download(repo_id=repo_id, filename=filename) + except Exception as exc: + logger.warning("Downloading %s model %s from %s failed: %s", label, filename, repo_id, exc) + raise logger.info("Loading %s model: %s", label, model_path) session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"]) _sessions[name] = session diff --git a/src/remove_ai_watermarks/runninghub_engine.py b/src/remove_ai_watermarks/runninghub_engine.py index f93f793..609f4e1 100644 --- a/src/remove_ai_watermarks/runninghub_engine.py +++ b/src/remove_ai_watermarks/runninghub_engine.py @@ -27,7 +27,7 @@ whose producer USCC names the entity, harvested 2026-07-22 by 1536-wide frames) at ~0.008/0.006 x/y margins; the locate box below covers it with NCC slack. * ``alpha_height_frac`` comes from the silhouette aspect (0.128) at the - measured width (0.27 * 1.25 rung ~= 0.3375 >= 0.32), per the standing rule + measured 0.32 width (0.32 * 0.128 ~= 0.04), per the standing rule that it is measured, not inherited. * STRICT ONLY (``provenance_ncc_factor`` 1.0): raw gray NCC is contrast-DEPENDENT and the sub-gate band of a corner-anchored gray match is diff --git a/src/remove_ai_watermarks/samsung_engine.py b/src/remove_ai_watermarks/samsung_engine.py index de6e445..681bcf3 100644 --- a/src/remove_ai_watermarks/samsung_engine.py +++ b/src/remove_ai_watermarks/samsung_engine.py @@ -56,7 +56,7 @@ DETECT_NCC_THRESHOLD = 0.40 # Detection-silhouette geometry, solved by scripts/visible_alpha_solve.py from the flat # gray capture (native width 1086). Real photos are ~2958 wide, so the captured glyph is # upscaled; width-scale + NCC-align sizes the silhouette for the detection match (removal -# is the template-free glyph-bbox footprint mask). +# aligns this alpha to the detector's match box; see footprint_mask). _ALPHA_NATIVE_WIDTH = 1086 _ALPHA_WIDTH_FRAC = 0.3195 # asset width / image width -- sizes the detection silhouette _ALPHA_HEIGHT_FRAC = 0.0378 diff --git a/src/remove_ai_watermarks/source_classify.py b/src/remove_ai_watermarks/source_classify.py index 0f0cecf..34bcc1f 100644 --- a/src/remove_ai_watermarks/source_classify.py +++ b/src/remove_ai_watermarks/source_classify.py @@ -7,6 +7,7 @@ removed. Call it explicitly; ``identify`` never imports or runs it. from __future__ import annotations +import logging import math import os from dataclasses import dataclass @@ -19,6 +20,8 @@ from typing import TYPE_CHECKING, Any, Literal if TYPE_CHECKING: from numpy.typing import NDArray +log = logging.getLogger(__name__) + SOURCE_CLASSIFY_EXTRA = "'remove-ai-watermarks[source-classify]'" WEIGHTS_ENV = "RAIW_SOURCE_CLASSIFY_WEIGHTS" WEIGHTS_REPO = "wiltodelta/raiw-source-classify" @@ -95,13 +98,17 @@ def _model_path() -> Path: hub: Any = import_module("huggingface_hub") except ModuleNotFoundError as error: raise RuntimeError(f"Install {SOURCE_CLASSIFY_EXTRA} to download the source classifier") from error - return Path( - hub.hf_hub_download( - repo_id=WEIGHTS_REPO, - filename=MODEL_FILE, - revision=WEIGHTS_REVISION, + try: + return Path( + hub.hf_hub_download( + repo_id=WEIGHTS_REPO, + filename=MODEL_FILE, + revision=WEIGHTS_REVISION, + ) ) - ) + except Exception as exc: + log.warning("Downloading %s from %s@%s failed: %s", MODEL_FILE, WEIGHTS_REPO, WEIGHTS_REVISION, exc) + raise @lru_cache(maxsize=2) diff --git a/src/remove_ai_watermarks/source_classify_v2.py b/src/remove_ai_watermarks/source_classify_v2.py new file mode 100644 index 0000000..05e27d1 --- /dev/null +++ b/src/remove_ai_watermarks/source_classify_v2.py @@ -0,0 +1,255 @@ +"""Opt-in OpenAI/Google export-source patterns in decoded image pixels. + +This classifier does not decode SynthID or establish watermark presence. +""" + +from __future__ import annotations + +import logging +import os +from dataclasses import dataclass +from functools import lru_cache +from hashlib import file_digest +from importlib import import_module +from pathlib import Path +from typing import TYPE_CHECKING, Any, Literal, get_args + +if TYPE_CHECKING: + from numpy.typing import NDArray + +log = logging.getLogger(__name__) + +WEIGHTS_REPO = "wiltodelta/openai-google-image-source-classifier" +WEIGHTS_REVISION = "4563fb97e5572030ad8d6acbb6f20eaab7fc3009" +WEIGHTS_ENV = "RAIW_IMAGE_SOURCE_WEIGHTS" +MODEL_FILE = "openai-google-source-v1.npz" +MODEL_SHA256 = "0e94e565c181416e4f63a2bfc06cd5179f4201befda7481ac510f6a168c612f4" +MODEL_SCHEMA = "openai-google-source-v1" +GROUPS = ("square", "portrait", "landscape") +ROUTES = ("qwen", "mark", "direct") +_FILTER_NAMES = ( + "old_google_default_photo_fit", + "old_google_sigma24", + "r152_google", + "r152_openai", + "r161_google", + "r161_openai", + *(f"r163_{group}" for group in GROUPS), +) +_THRESHOLD_NAMES = ( + "old_google_default_parent", + "old_google_sigma24_parent", + "r152_google", + "r152_openai", + "r161_google", + "r161_openai", + "r183_openai_gate", + *(f"r163_{group}" for group in GROUPS), + *(f"openai_{route}" for route in ROUTES), +) +CLAIM = "likely image source/export pattern; not SynthID detection" + +SourceLabel = Literal["openai", "google", "unknown"] +SourceReason = Literal["classified", "abstained", "conflict", "feature_unavailable"] +PROVIDERS = tuple(label for label in get_args(SourceLabel) if label != "unknown") + + +@dataclass(frozen=True) +class ImageSourceClassification: + """Narrow source attribution with honest abstention and watermark unknown.""" + + label: SourceLabel + reason: SourceReason + scores: dict[str, float] + watermark_truth: Literal["unknown"] = "unknown" + claim: str = CLAIM + model_revision: str = WEIGHTS_REVISION + + def to_dict(self) -> dict[str, Any]: + return { + "label": self.label, + "reason": self.reason, + "scores": self.scores, + "watermark_truth": self.watermark_truth, + "claim": self.claim, + "model_revision": self.model_revision, + } + + +@dataclass(frozen=True) +class _Model: + filters: dict[str, tuple[NDArray[Any], NDArray[Any]]] + templates: dict[str, NDArray[Any]] + thresholds: dict[str, float] + + +def _model_path() -> Path: + override = os.environ.get(WEIGHTS_ENV, "").strip() + if override: + path = Path(override).expanduser() + candidate = path / MODEL_FILE if path.is_dir() else path + if not candidate.is_file(): + raise RuntimeError(f"{WEIGHTS_ENV} does not contain {MODEL_FILE}: {path}") + return candidate + try: + hub: Any = import_module("huggingface_hub") + except ModuleNotFoundError as error: + raise RuntimeError("Install remove-ai-watermarks[source-classify] for image-source weights") from error + return Path(hub.hf_hub_download(repo_id=WEIGHTS_REPO, filename=MODEL_FILE, revision=WEIGHTS_REVISION)) + + +@lru_cache(maxsize=2) +def _load_model(path: Path, expected_sha256: str) -> _Model: + import numpy as np + + with path.open("rb") as source: + actual_sha256 = file_digest(source, "sha256").hexdigest() + if actual_sha256 != expected_sha256: + raise ValueError("image-source model SHA-256 mismatch") + required = { + "schema", + *(f"{name}_{field}" for name in _FILTER_NAMES for field in ("mean", "precision")), + *(f"openai_{route}_unit" for route in ROUTES), + *(f"threshold_{name}" for name in _THRESHOLD_NAMES), + } + with np.load(path, allow_pickle=False) as payload: + if set(payload.files) != required or str(payload["schema"]) != MODEL_SCHEMA: + raise ValueError("image-source model schema mismatch") + loaded_filters: dict[str, tuple[NDArray[Any], NDArray[Any]]] = {} + for name in _FILTER_NAMES: + mean = payload[f"{name}_mean"] + precision = payload[f"{name}_precision"] + if mean.shape != (256, 256, 3) or mean.dtype != np.complex64: + raise ValueError(f"invalid {name} mean") + if precision.shape != mean.shape or precision.dtype != np.float32: + raise ValueError(f"invalid {name} precision") + if not np.all(np.isfinite(mean)) or not np.all(np.isfinite(precision)) or np.any(precision < 0): + raise ValueError(f"nonfinite or negative {name} filter") + loaded_filters[name] = (mean, precision) + templates: dict[str, NDArray[Any]] = {} + for route in ROUTES: + unit = payload[f"openai_{route}_unit"] + if unit.shape != (992, 992) or unit.dtype != np.float32 or not np.all(np.isfinite(unit)): + raise ValueError(f"invalid {route} template") + if not np.isclose(np.linalg.norm(unit), 1.0, rtol=1e-5): + raise ValueError(f"nonunit {route} template") + templates[route] = unit.ravel() + limits: dict[str, float] = {} + for name in _THRESHOLD_NAMES: + value = payload[f"threshold_{name}"] + if value.shape != () or value.dtype != np.float64 or not np.isfinite(value): + raise ValueError(f"invalid {name} threshold") + limits[name] = float(value) + return _Model(loaded_filters, templates, limits) + + +def _spectrum(rgb: NDArray[Any], sigma: float) -> NDArray[Any]: + import cv2 + import numpy as np + from PIL import Image + + small = np.asarray(Image.fromarray(rgb).resize((256, 256), Image.Resampling.BILINEAR)) + values = cv2.cvtColor(small.astype(np.float32) / 255, cv2.COLOR_RGB2YCrCb) + high = values - cv2.GaussianBlur(values, (0, 0), sigma) + energy = cv2.GaussianBlur(high * high, (0, 0), 3) + high = np.clip(high / np.sqrt(energy + (1 / 255) ** 2), -4, 4) + if sigma == 1.2: + high -= high.mean(axis=(0, 1), keepdims=True) + else: + high -= high.mean(axis=(0, 1), keepdims=True, dtype=np.float64).astype(np.float32) + high /= np.maximum(np.sqrt(np.sum(high * high, axis=(0, 1), keepdims=True)), 1e-12) + return np.fft.fft2(high, axes=(0, 1), norm="ortho").astype(np.complex64) + + +def _template_score(values: NDArray[Any], weights: tuple[NDArray[Any], NDArray[Any]]) -> float: + import numpy as np + + mean, precision = weights + numerator = np.real(values * np.conj(mean) * precision).sum(axis=(0, 1)) + denominator = max( + float(np.sqrt((np.abs(values) ** 2 * precision).sum() * (np.abs(mean) ** 2 * precision).sum())), 1e-12 + ) + return float(numerator.sum() / denominator) + + +def _openai_template_scores(rgb: NDArray[Any], model: _Model) -> dict[str, float]: + import cv2 + import numpy as np + + if rgb.shape[:2] != (1024, 1024): + return {} + luminance = cv2.cvtColor(rgb, cv2.COLOR_RGB2YCrCb)[..., 0].astype(np.float32) + high = luminance - cv2.GaussianBlur(luminance, (0, 0), 1.2, borderType=cv2.BORDER_REFLECT) + energy = cv2.GaussianBlur(high * high, (0, 0), 3.0, borderType=cv2.BORDER_REFLECT) + residual = np.clip(high / np.sqrt(energy + 1), -4, 4)[8:1000, 8:1000].ravel().astype(np.float32) + residual -= residual.mean() + norm = np.linalg.norm(residual) + if norm <= 0: + return {} + return {route: float(np.dot(residual, unit) / norm) for route, unit in model.templates.items()} + + +def _aspect(width: int, height: int) -> str: + ratio = width / height + if 0.87 <= ratio <= 1.15: + return "square" + if 0.55 <= ratio < 0.87: + return "portrait" + if 1.15 < ratio <= 1.82: + return "landscape" + return "other" + + +def classify_image_source(source: str | os.PathLike[str]) -> ImageSourceClassification: + """Return likely OpenAI/Google source or abstain; never verify SynthID.""" + import numpy as np + from PIL import Image + + with Image.open(source) as opened: + if min(opened.size) < 256: + return ImageSourceClassification("unknown", "feature_unavailable", {}) + rgb = np.asarray(opened.convert("RGB"), dtype=np.uint8) + model = _load_model(_model_path(), MODEL_SHA256) + height, width = rgb.shape[:2] + regular = _spectrum(rgb, 1.2) + old_default = _template_score(regular, model.filters["old_google_default_photo_fit"]) + old_sigma24 = _template_score(_spectrum(rgb, 2.4), model.filters["old_google_sigma24"]) + limits = model.thresholds + old_google = ( + old_default / limits["old_google_default_parent"] + old_sigma24 / limits["old_google_sigma24_parent"] + ) / 2 + r152_google = _template_score(regular, model.filters["r152_google"]) + r152_openai = _template_score(regular, model.filters["r152_openai"]) + r161_google = _template_score(regular, model.filters["r161_google"]) + r161_openai = _template_score(regular, model.filters["r161_openai"]) + aspect = _aspect(width, height) + r163_openai = _template_score(regular, model.filters[f"r163_{aspect}"]) if aspect in GROUPS else None + old_openai_scores = _openai_template_scores(rgb, model) + google = old_google >= 1.0 or r152_google >= limits["r152_google"] or r161_google >= limits["r161_google"] + openai = ( + any(score >= limits[f"openai_{route}"] for route, score in old_openai_scores.items()) + or r152_openai >= limits["r152_openai"] + or r161_openai >= limits["r161_openai"] + or ( + r163_openai is not None + and r163_openai >= limits[f"r163_{aspect}"] + and r152_openai >= limits["r183_openai_gate"] + ) + ) + scores = { + "old_google": old_google, + "r152_google": r152_google, + "r152_openai": r152_openai, + "r161_google": r161_google, + "r161_openai": r161_openai, + } + if r163_openai is not None: + scores["r163_openai"] = r163_openai + scores.update({f"openai_{route}": score for route, score in old_openai_scores.items()}) + if google and openai: + return ImageSourceClassification("unknown", "conflict", scores) + if google: + return ImageSourceClassification("google", "classified", scores) + if openai: + return ImageSourceClassification("openai", "classified", scores) + return ImageSourceClassification("unknown", "abstained", scores) diff --git a/src/remove_ai_watermarks/video.py b/src/remove_ai_watermarks/video.py index f5f2dd1..2d68a9d 100644 --- a/src/remove_ai_watermarks/video.py +++ b/src/remove_ai_watermarks/video.py @@ -274,39 +274,22 @@ def _visible_removal_plan( """Resolve one provider's stable frame regions and fill geometry. Everything provider-specific -- the confidence floors, the run length, the fill - padding and the mask style -- is data on ``VISIBLE_MARK_POLICIES``. The only thing - left here is WHICH metadata predicate confirms which vendor, which genuinely is a - mapping and not a tuning constant. + padding and the mask style -- is data on ``VISIBLE_MARK_POLICIES``; which metadata + confirms or contradicts which vendor lives beside the predicates in + ``video_visible``. """ from remove_ai_watermarks.video_visible import ( VISIBLE_MARK_POLICIES, + confirms_video_provenance, contradicts_video_provenance, - has_bytedance_video_provenance, - has_doubao_video_provenance, - has_hailuo_video_provenance, - has_sora_provenance, - has_veo_provenance, - has_vidu_video_provenance, stabilize_localizations, ) - confirms = { - "sora": has_sora_provenance, - "veo": has_veo_provenance, - "seedance": has_bytedance_video_provenance, - "doubao": has_doubao_video_provenance, - "dola": has_bytedance_video_provenance, - "hailuo": has_hailuo_video_provenance, - "vidu": has_vidu_video_provenance, - }.get(selected_mark) policy = VISIBLE_MARK_POLICIES[selected_mark] if contradicts_video_provenance(selected_mark, markers): return [None] * len(selected_scan.detections), policy.padding_fraction, policy.mask_style - regions = stabilize_localizations( - selected_mark, - selected_scan.detections, - provenance=bool(confirms and confirms(markers)), - ) + confirmed = confirms_video_provenance(selected_mark, markers) + regions = stabilize_localizations(selected_mark, selected_scan.detections, provenance=confirmed) return regions, policy.padding_fraction, policy.mask_style @@ -324,22 +307,11 @@ def _select_stable_visible_mark( ] | None ): - """Select the first stable provider result in the public specificity order.""" + """Select the first stable mark in specificity order that provenance does not veto.""" for candidate_mark in candidate_marks: - candidate_scan = scans[candidate_mark] - candidate_regions, candidate_padding, candidate_mask_style = _visible_removal_plan( - candidate_mark, - candidate_scan, - markers, - ) - if any(region is not None for region in candidate_regions): - return ( - candidate_mark, - candidate_scan, - candidate_regions, - candidate_padding, - candidate_mask_style, - ) + regions, padding, mask_style = _visible_removal_plan(candidate_mark, scans[candidate_mark], markers) + if any(region is not None for region in regions): + return candidate_mark, scans[candidate_mark], regions, padding, mask_style return None @@ -371,7 +343,7 @@ def _video_markers_claim_ai(markers: dict[str, str]) -> bool: so only through an AI-generator identity: a registered AI signer or an AI product named as the claim generator. """ - from remove_ai_watermarks._internal.c2pa import claim_generator_word_platform, registry_word_matches + from remove_ai_watermarks._internal.c2pa import claim_generator_platform, registry_word_matches from remove_ai_watermarks._internal.constants import C2PA_AI_TOOLS, C2PA_IDENTITY_AI_ORGS source_type = markers.get("source_type", "") @@ -383,26 +355,22 @@ def _video_markers_claim_ai(markers: dict[str, str]) -> bool: generator = markers.get("claim_generator", "") return ( any(org in issuer for org in C2PA_IDENTITY_AI_ORGS) - or claim_generator_word_platform(generator) is not None + or claim_generator_platform(generator) is not None or bool(registry_word_matches(generator, C2PA_AI_TOOLS)) ) def _platform_from_video_metadata(markers: dict[str, str]) -> str | None: """Map supported C2PA-derived marker text to its generating platform.""" - from remove_ai_watermarks._internal.c2pa import registry_word_matches - from remove_ai_watermarks._internal.constants import C2PA_AI_VENDORS + from remove_ai_watermarks._internal.c2pa import c2pa_vendor_platform, claim_generator_platform - marker_text = "\n".join(markers.values()).casefold() - if not marker_text: - return None - needles = { - vendor.needle.casefold().encode(): vendor.platform - for vendor in C2PA_AI_VENDORS - if vendor.platform is not None and vendor.needle is not None - } - if hits := registry_word_matches(marker_text, needles): - return hits[0] + generator = markers.get("claim_generator", "") + if platform := ( + claim_generator_platform(generator) + or c2pa_vendor_platform(generator) + or c2pa_vendor_platform("\n".join(markers.values())) + ): + return platform if "aigc_label" in markers: return _tc260_video_platform(markers.get("aigc_producer", "")) or ( "China AIGC-labeled content (TC260 standard)" @@ -555,8 +523,10 @@ def remove_video_visible( """Remove a supported visible AI wordmark from a video. ``mark="auto"`` scans every supported provider in one decode pass and selects - the first stable match in specificity order. Explicit marks are ``sora``, - ``veo``, ``seedance``, ``dola``, ``hailuo``, and ``kling``. The full + the first stable match in specificity order, except a Veo diamond nested in + a Sora cross-match or spanning the full clip against a partial Sora run, + with valid Google AI-video provenance. Explicit marks are + ``sora``, ``veo``, ``seedance``, ``doubao``, ``dola``, ``hailuo``, and ``kling``. The full sequence is scanned before pixels change, and only recurring candidates are accepted. Complete audio is copied without re-encoding; video is transcoded because the pixels change. ``temporal_consistency=True`` motion-aligns a diff --git a/src/remove_ai_watermarks/video_encoding.py b/src/remove_ai_watermarks/video_encoding.py index e89da14..28cce57 100644 --- a/src/remove_ai_watermarks/video_encoding.py +++ b/src/remove_ai_watermarks/video_encoding.py @@ -179,7 +179,7 @@ def _run_ffprobe( """Run one ffprobe query with shared logging and failure handling.""" ffprobe = shutil.which("ffprobe") if ffprobe is None: - log.warning("ffprobe is unavailable; cannot inspect %s", purpose) + log.warning("ffprobe is unavailable; cannot inspect %s of %s", purpose, source) return None command = [ffprobe, "-v", "error", *arguments, "-of", output_format, str(source)] result = subprocess.run( # noqa: S603 diff --git a/src/remove_ai_watermarks/video_visible.py b/src/remove_ai_watermarks/video_visible.py index 74be192..7dd8a32 100644 --- a/src/remove_ai_watermarks/video_visible.py +++ b/src/remove_ai_watermarks/video_visible.py @@ -1,7 +1,7 @@ """Visible AI-watermark localization and removal for video. Supported marks use fully synthetic silhouettes made from geometric primitives, -OpenCV's built-in font, and Pillow's bundled font. Sora detection searches the +frozen renders of OpenCV 4's built-in font, and Pillow's bundled font. Sora detection searches the full frame because the wordmark moves. Veo detection covers both the current four-point diamond and legacy ``Veo`` text. Seedance detects the boxed ``AI`` label, Dola detects its compact text label, Hailuo AI detects the composite @@ -28,7 +28,7 @@ import logging from contextlib import suppress from dataclasses import dataclass, replace from fractions import Fraction -from functools import lru_cache +from functools import cache, lru_cache from itertools import pairwise from typing import TYPE_CHECKING, Any, Literal @@ -68,6 +68,7 @@ _SORA_RELATIVE_HEIGHTS = (0.065, 0.075, 0.085, 0.095, 0.105) _SORA_PROVENANCE_WEAK_CONFIDENCE = 0.58 _SORA_STRICT_WEAK_CONFIDENCE = 0.60 _SORA_STRONG_CONFIDENCE = 0.65 +_SORA_ICON_SHAPE_FLOOR = 0.50 _VEO_PROVENANCE_WEAK_CONFIDENCE = 0.45 _VEO_STRICT_WEAK_CONFIDENCE = 0.50 _VEO_STRONG_CONFIDENCE = 0.55 @@ -240,21 +241,14 @@ def _seedance_template() -> NDArray[Any]: return np.asarray(canvas, dtype=np.uint8) -@lru_cache(maxsize=1) def _dola_template() -> NDArray[Any]: - """Return a synthetic Dola AI text silhouette using OpenCV's font.""" - canvas = np.zeros((100, 400), dtype=np.uint8) - cv2.putText( - canvas, - "Dola AI", - (2, 72), - cv2.FONT_HERSHEY_DUPLEX, - 2.2, - 255, - 3, - cv2.LINE_AA, - ) - return _crop_nonzero(canvas) + """Return the synthetic Dola AI text silhouette drawn in OpenCV 4's Hershey font. + + The Hershey templates are frozen assets, not rendered here: OpenCV 5 draws the + same ``putText`` call with smaller, heavier glyphs than the ones the detectors were + calibrated on. ``scripts/render_video_hershey_templates.py`` is the recipe. + """ + return _asset_template("video_dola_hershey_duplex.png") @lru_cache(maxsize=1) @@ -291,7 +285,7 @@ def _hailuo_template() -> NDArray[Any]: def _kling_templates() -> tuple[NDArray[Any], ...]: """Return synthetic font and capitalization variants for the Kling wordmark core.""" templates: list[NDArray[Any]] = [] - for text in ("KLING AI", "KlingAI"): + for text, asset_stem in (("KLING AI", "kling_ai"), ("KlingAI", "klingai")): canvas = Image.new("L", (430, 104), 0) ImageDraw.Draw(canvas).text( (2, 12), @@ -302,19 +296,8 @@ def _kling_templates() -> tuple[NDArray[Any], ...]: stroke_fill=255, ) templates.append(_crop_nonzero(np.asarray(canvas, dtype=np.uint8))) - for font in (cv2.FONT_HERSHEY_SIMPLEX, cv2.FONT_HERSHEY_DUPLEX): - cv_template = np.zeros((100, 500), dtype=np.uint8) - cv2.putText( - cv_template, - text, - (2, 72), - font, - 2.2, - 255, - 3, - cv2.LINE_AA, - ) - templates.append(_crop_nonzero(cv_template)) + # Frozen OpenCV 4 Hershey renders; see _dola_template for why. + templates.extend(_asset_template(f"video_{asset_stem}_hershey_{font}.png") for font in ("simplex", "duplex")) return tuple(templates) @@ -370,7 +353,7 @@ def _top_hat(gray: NDArray[Any]) -> NDArray[Any]: return cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, kernel) -@lru_cache(maxsize=1) +@cache def _asset_template(name: str) -> NDArray[Any]: """Load a package asset as a video detect template (white glyph on black). @@ -388,7 +371,6 @@ def _asset_template(name: str) -> NDArray[Any]: return at -@lru_cache(maxsize=1) def _doubao_template() -> NDArray[Any]: return _asset_template("doubao_alpha.png") @@ -508,7 +490,7 @@ def detect_sora_frame( template_width = max(1, round(base_template.shape[1] * template_height / base_template.shape[0])) if template_height >= normalized_height or template_width >= normalized_width: continue - _, template_feature = _resized_template_feature( + template, template_feature = _resized_template_feature( template_key, template_width, template_height, @@ -518,6 +500,14 @@ def detect_sora_frame( _, confidence, _, location = cv2.minMaxLoc(scores) if confidence <= best_confidence: continue + if icon_only: + # Edge-only correlation also accepts diamonds and scene texture. + # Confirm the full mascot silhouette, including its two eyes. + x, y = location + candidate = gray[y : y + template_height, x : x + template_width] + shape_confidence = float(cv2.matchTemplate(candidate, template, cv2.TM_CCOEFF_NORMED)[0, 0]) + if shape_confidence < _SORA_ICON_SHAPE_FLOOR: + continue best_confidence = float(confidence) best_region = _expanded_region( location, @@ -1010,15 +1000,15 @@ def detect_veo_frame( return FrameLocalization(frame_index, best_confidence, best_region) -def _region_iou(left: Region, right: Region) -> float: +def _region_intersection(left: Region, right: Region) -> int: lx, ly, lw, lh = left rx, ry, rw, rh = right - x0 = max(lx, rx) - y0 = max(ly, ry) - x1 = min(lx + lw, rx + rw) - y1 = min(ly + lh, ry + rh) - intersection = max(0, x1 - x0) * max(0, y1 - y0) - union = lw * lh + rw * rh - intersection + return max(0, min(lx + lw, rx + rw) - max(lx, rx)) * max(0, min(ly + lh, ry + rh) - max(ly, ry)) + + +def _region_iou(left: Region, right: Region) -> float: + intersection = _region_intersection(left, right) + union = left[2] * left[3] + right[2] * right[3] - intersection return intersection / union if union > 0 else 0.0 @@ -1679,6 +1669,20 @@ _VIDEO_MARK_ISSUER_TOKENS: dict[str, tuple[str, ...]] = { } +def confirms_video_provenance(mark: str, markers: dict[str, str]) -> bool: + """Whether provenance names the visible mark's own vendor; Kling has no such predicate.""" + confirms = { + "sora": has_sora_provenance, + "veo": has_veo_provenance, + "seedance": has_bytedance_video_provenance, + "doubao": has_doubao_video_provenance, + "dola": has_bytedance_video_provenance, + "hailuo": has_hailuo_video_provenance, + "vidu": has_vidu_video_provenance, + }.get(mark) + return bool(confirms and confirms(markers)) + + def contradicts_video_provenance(mark: str, markers: dict[str, str]) -> bool: """Whether provenance names a producer other than the visible mark's vendor. @@ -1696,15 +1700,27 @@ def contradicts_video_provenance(mark: str, markers: dict[str, str]) -> bool: 192-frame run; a Gemini Omni 1.1 Flash clip with Google's C2PA (Runway, 2026-09-25) scored 0.61-0.67 for Sora on a steam wisp over five frames. A re-signing platform (YouTube) records no AI source type, so it vetoes nothing. + An invalid credential vetoes nothing either, and provenance that also confirms + the mark's own vendor (a Sora claim generator under a Google issuer) confirms + instead of vetoing. """ + if confirms_video_provenance(mark, markers): + return False producer = markers.get("aigc_producer", "").strip() if mark == "kling" and producer: from remove_ai_watermarks.watermark_registry import get_mark return not tc260_producer_in(producer, get_mark("kling").tc260_producer_codes) + from remove_ai_watermarks._internal.c2pa import c2pa_credential_level + issuer = markers.get("issuer", "").casefold() claims_ai = "trainedalgorithmicmedia" in markers.get("source_type", "").casefold() - return bool(issuer) and claims_ai and not any(token in issuer for token in _VIDEO_MARK_ISSUER_TOKENS[mark]) + return ( + bool(issuer) + and claims_ai + and c2pa_credential_level(markers) != "invalid" + and not any(token in issuer for token in _VIDEO_MARK_ISSUER_TOKENS[mark]) + ) def has_vidu_video_provenance(markers: dict[str, str]) -> bool: diff --git a/src/remove_ai_watermarks/watermark_registry.py b/src/remove_ai_watermarks/watermark_registry.py index ccf249a..4453044 100644 --- a/src/remove_ai_watermarks/watermark_registry.py +++ b/src/remove_ai_watermarks/watermark_registry.py @@ -620,8 +620,8 @@ def _text_mark( ``product`` defaults to the key (one mark, one product); pass it only when two marks share a product. ``manufacturer`` is separate because unrelated companies use the same TC260 standard. ``label_regime`` and ``provenance_signals`` default - to the China-AIGC label because every text mark registered so far except Samsung - and Microsoft uses it. + to the China-AIGC label because every text mark registered so far except Samsung, + Microsoft, generic_ai_label and OpenArt uses it. """ return KnownMark( key, @@ -841,7 +841,8 @@ _REGISTRY: tuple[KnownMark, ...] = ( label_regime=None, provenance_signals=(), ), - # Same product as the Jimeng wordmark -- the one pair that cross-relaxes. + # Same product as the Jimeng wordmark, so a strict wordmark relaxes it (LiblibAI's + # wordmark and pill share a product the same way). KnownMark( "jimeng_pill", "Jimeng AI生成 pill", @@ -1139,6 +1140,7 @@ def remove_auto_marks_detailed( resolved_backend, bbox, exc, + exc_info=True, ) removals.append( diff --git a/tests/test_dependabot.py b/tests/test_dependabot.py index 041da9a..c1a02a7 100644 --- a/tests/test_dependabot.py +++ b/tests/test_dependabot.py @@ -1,11 +1,16 @@ """Regression tests for Dependabot compatibility constraints.""" +import tomllib from pathlib import Path +from packaging.requirements import Requirement + + +def test_dependabot_numpy_ignore_mirrors_the_lock_constraint() -> None: + constraints = tomllib.loads(Path("pyproject.toml").read_text())["tool"]["uv"]["constraint-dependencies"] + (numpy,) = (r for r in map(Requirement, constraints) if r.name == "numpy") + (upper,) = (spec.version for spec in numpy.specifier if spec.operator == "<") -def test_dependabot_blocks_opencv_releases_that_require_numpy_2() -> None: config = Path(".github/dependabot.yml").read_text() - - opencv_ignore = config.split('dependency-name: "opencv-python-headless"', maxsplit=1)[1] - assert '"<4.12"' not in opencv_ignore - assert '- ">=4.12"' in opencv_ignore + numpy_ignore = config.split('dependency-name: "numpy"', maxsplit=1)[1].split("dependency-name:", maxsplit=1)[0] + assert f'- ">={upper}"' in numpy_ignore diff --git a/tests/test_doubao_engine.py b/tests/test_doubao_engine.py index 81805a0..a465501 100644 --- a/tests/test_doubao_engine.py +++ b/tests/test_doubao_engine.py @@ -9,10 +9,12 @@ import numpy as np import pytest from remove_ai_watermarks import watermark_registry as registry +from remove_ai_watermarks._text_mark_engine import TextMarkEngine from remove_ai_watermarks.doubao_engine import ( _ALPHA_HEIGHT_FRAC, _ALPHA_NATIVE_WIDTH, _ALPHA_WIDTH_FRAC, + _CONFIG, DETECT_NCC_THRESHOLD, DoubaoEngine, _alpha_template, @@ -24,10 +26,11 @@ from remove_ai_watermarks.image_io import load_image_bgr SAMPLE = Path(__file__).resolve().parents[1] / "data" / "fixtures" / "provenance" / "doubao-1.png" -def _compose(w: int, h: int, bg: float = 100.0): +def _compose(w: int, h: int, bg: float | tuple[int, int, int] = 100.0): """Composite the real alpha (scaled to width ``w``) onto a flat bg. Returns ``(watermarked_uint8, mark_bool_mask)``.""" - img = np.full((h, w, 3), bg, np.float32) + img = np.empty((h, w, 3), np.float32) + img[:] = bg at = _alpha_template() gw, gh = int(_ALPHA_WIDTH_FRAC * w), int(_ALPHA_HEIGHT_FRAC * w) margin = int(0.015 * w) @@ -59,6 +62,19 @@ class TestLocate: class TestDetect: + def test_tinted_glyph_on_saturated_background(self): + """A translucent white glyph stays recognizable after a yellow background tints it.""" + wm, _mark = _compose(_ALPHA_NATIVE_WIDTH, _ALPHA_NATIVE_WIDTH, bg=(20, 190, 240)) + eng = DoubaoEngine() + clean = np.empty_like(wm) + clean[:] = (20, 190, 240) + assert not eng.detect(clean).detected + assert eng.detect(wm).detected + out, region = registry.get_mark("doubao").remove(wm, backend="cv2") + assert region is not None + assert not eng.detect(out).detected + assert np.array_equal(wm[:1024, :1024], out[:1024, :1024]) + def test_clean_gradient_not_detected(self): eng = DoubaoEngine() ramp = np.tile(np.linspace(0, 255, 1024, dtype=np.uint8), (1024, 1)) @@ -104,9 +120,11 @@ class TestDetect: @pytest.mark.skipif(not SAMPLE.exists(), reason="sample image not present") class TestRealSample: def test_detects_watermark(self): - det = DoubaoEngine().detect(load_image_bgr(SAMPLE)) + image = load_image_bgr(SAMPLE) + det = DoubaoEngine().detect(image) assert det.detected assert det.confidence >= DETECT_NCC_THRESHOLD + assert det.confidence == TextMarkEngine(_CONFIG).detect(image).confidence def test_fill_lowers_confidence(self): img = load_image_bgr(SAMPLE) diff --git a/tests/test_forensic_metadata.py b/tests/test_forensic_metadata.py index 7186f57..7fe9256 100644 --- a/tests/test_forensic_metadata.py +++ b/tests/test_forensic_metadata.py @@ -52,10 +52,18 @@ def _png_chunk(chunk_type: bytes, payload: bytes) -> bytes: def test_supported_extensions_are_media_not_documents(): - assert {".jpg", ".png", ".webp", ".heic", ".mp4"}.issubset(SUPPORTED_EXTENSIONS) + assert {".jpg", ".jpeg", ".jpe", ".jfif", ".png", ".webp", ".heic", ".mp4"}.issubset(SUPPORTED_EXTENSIONS) assert ".pdf" not in SUPPORTED_EXTENSIONS +@pytest.mark.parametrize("suffix", [".jpe", ".jfif"]) +def test_jpeg_alias_forensic_record_uses_jpeg_content(tmp_path: Path, suffix: str): + path = _jpeg(tmp_path / f"photo{suffix}") + record = collect_forensic_metadata(path) + assert record["extension"] == suffix + assert record["content_format"] == "jpeg" + + def test_json_helpers_and_format_sniffer(): class BadString: def __str__(self): diff --git a/tests/test_identify.py b/tests/test_identify.py index 8326d86..7c43e3f 100644 --- a/tests/test_identify.py +++ b/tests/test_identify.py @@ -1051,7 +1051,7 @@ class TestIdentifyRealSamples: assert any("only the signing time is unproven" in caveat for caveat in report.caveats) def test_no_committed_fixture_reports_a_trusted_signer(self): - """The reachability guard for :func:`_c2pa_credential_level`. + """The reachability guard for :func:`c2pa_credential_level`. The SDK ships no production trust anchors, so ``signingCredential.trusted`` appears in no default installation. Gating high confidence on it made that branch @@ -2324,11 +2324,11 @@ class TestRegistryTokenMatching: assert report.platform != "Nikon (camera, C2PA capture)" def test_claim_generator_tokens_are_whole_words(self): - from remove_ai_watermarks.identify import _claim_generator_platform + from remove_ai_watermarks._internal.c2pa import claim_generator_platform - assert _claim_generator_platform("Sunoco Studio 2.1") is None - assert _claim_generator_platform("suno-v4 export") == "Suno" - assert _claim_generator_platform("Adobe_Firefly") == "Adobe Firefly" + assert claim_generator_platform("Sunoco Studio 2.1") is None + assert claim_generator_platform("suno-v4 export") == "Suno" + assert claim_generator_platform("Adobe_Firefly") == "Adobe Firefly" def test_video_generator_tokens_are_whole_words(self): from remove_ai_watermarks.video import _video_markers_claim_ai diff --git a/tests/test_image_io.py b/tests/test_image_io.py index 60e1967..ad47bc5 100644 --- a/tests/test_image_io.py +++ b/tests/test_image_io.py @@ -61,6 +61,21 @@ class TestUnicodeRoundTrip: assert np.array_equal(out, src) +@pytest.mark.parametrize("suffix", [".jpe", ".jfif"]) +def test_jpeg_aliases_are_supported_and_written_as_jpeg(tmp_path: Path, suffix: str) -> None: + from remove_ai_watermarks._internal.utils import get_image_format, is_supported_format + + path = tmp_path / f"marked{suffix}" + source = np.full((32, 48, 3), (10, 120, 240), dtype=np.uint8) + assert is_supported_format(path) + assert get_image_format(path) == "JPEG" + assert image_io.imwrite(path, source) + assert path.read_bytes().startswith(b"\xff\xd8") + decoded = image_io.imread(path) + assert decoded is not None + assert decoded.shape == source.shape + + class TestToBgr: def test_grayscale_2d_promoted_to_bgr(self) -> None: gray = np.full((4, 5), 120, dtype=np.uint8) diff --git a/tests/test_image_math.py b/tests/test_image_math.py new file mode 100644 index 0000000..b77ab3f --- /dev/null +++ b/tests/test_image_math.py @@ -0,0 +1,18 @@ +"""The shared DCT basis must be the orthonormal DCT-II that OpenCV computes.""" + +from __future__ import annotations + +import cv2 +import numpy as np +import pytest + +from remove_ai_watermarks._internal.image_math import dct_matrix + + +@pytest.mark.parametrize("size", [4, 8, 16]) +def test_dct_matrix_matches_opencv_dct(size: int) -> None: + matrix = dct_matrix(np, size) + + # DCT_ROWS transforms each basis vector e_i separately, so row i is column i of the basis. + np.testing.assert_allclose(matrix, cv2.dct(np.eye(size), flags=cv2.DCT_ROWS).T, atol=1e-12) + np.testing.assert_allclose(matrix @ matrix.T, np.eye(size), atol=1e-12) diff --git a/tests/test_metadata.py b/tests/test_metadata.py index fb8831c..e45364b 100644 --- a/tests/test_metadata.py +++ b/tests/test_metadata.py @@ -33,6 +33,18 @@ from remove_ai_watermarks.metadata import ( SAMPLES_DIR = Path(__file__).resolve().parent.parent / "data" / "fixtures" / "provenance" +@pytest.mark.parametrize("suffix", [".jpe", ".jfif"]) +def test_jpeg_alias_metadata_strip_preserves_image_scan(tmp_path: Path, suffix: str) -> None: + source = tmp_path / f"source{suffix}" + output = tmp_path / f"clean{suffix}" + Image.new("RGB", (48, 32), (20, 80, 160)).save(source, format="JPEG") + original = source.read_bytes() + + remove_ai_metadata(source, output) + + assert output.read_bytes() == original + + def _png_chunk(kind: bytes, payload: bytes, *, corrupt_crc: bool = False) -> bytes: """Encode one PNG chunk, optionally corrupting its CRC.""" import zlib diff --git a/tests/test_publish_image_source_hf.py b/tests/test_publish_image_source_hf.py new file mode 100644 index 0000000..cc10b94 --- /dev/null +++ b/tests/test_publish_image_source_hf.py @@ -0,0 +1,33 @@ +"""The public model staging boundary accepts only verified derivatives.""" + +from __future__ import annotations + +import hashlib +import json +import sys +from pathlib import Path + +import pytest + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts")) +import publish_image_source_hf as publisher + + +def test_stage_release_rejects_changed_weights(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + card = tmp_path / "card" + card.mkdir() + (card / "README.md").write_text("# Public card\n") + weights = tmp_path / "source" + weights.mkdir() + original = b"derivative weights" + (weights / publisher.MODEL_FILE).write_bytes(original) + (card / "metrics.json").write_text(json.dumps({"artifact_sha256": hashlib.sha256(original).hexdigest()})) + monkeypatch.setattr(publisher, "CARD_DIR", card) + + stage = tmp_path / "stage" + publisher.stage_release(stage, weights) + assert sorted(item.name for item in stage.iterdir()) == sorted(publisher.PUBLIC_FILES) + + (weights / publisher.MODEL_FILE).write_bytes(b"changed weights") + with pytest.raises(ValueError, match="SHA-256 mismatch"): + publisher.stage_release(tmp_path / "second", weights) diff --git a/tests/test_publish_photo_classify_hf.py b/tests/test_publish_photo_classify_hf.py index 060ab39..1aca62f 100644 --- a/tests/test_publish_photo_classify_hf.py +++ b/tests/test_publish_photo_classify_hf.py @@ -12,8 +12,14 @@ sys.path.insert(0, str(SCRIPTS)) import publish_photo_classify_hf as publish # noqa: E402 -def test_hub_id_is_the_photo_classify_repo() -> None: - assert publish.HUB_REPO == "wiltodelta/raiw-photo-classify" +def test_hub_ids_match_the_runtime_loader() -> None: + # The workflow runs this script with --no-project, so it cannot import the package constants. + from remove_ai_watermarks.classify import RECEIPT_GATE_FILE, RECEIPT_GATE_STABLE_FILE, WEIGHTS_REPO + + assert publish.HUB_REPO == WEIGHTS_REPO + assert publish.GATE_FILE == RECEIPT_GATE_FILE + assert publish.GATE_STABLE_FILE == RECEIPT_GATE_STABLE_FILE + assert publish.GATE_SOURCE.is_file() def test_publish_freeze_includes_both_clip_runtimes() -> None: diff --git a/tests/test_publish_source_classify_hf.py b/tests/test_publish_source_classify_hf.py index 6c5597c..98b71dd 100644 --- a/tests/test_publish_source_classify_hf.py +++ b/tests/test_publish_source_classify_hf.py @@ -13,8 +13,12 @@ sys.path.insert(0, str(SCRIPTS)) import publish_source_classify_hf as publish # noqa: E402 -def test_hub_id_is_the_source_classify_repo() -> None: - assert publish.HUB_REPO == "wiltodelta/raiw-source-classify" +def test_hub_ids_match_the_runtime_loader() -> None: + # The workflow runs this script with --no-project, so it cannot import the package constants. + from remove_ai_watermarks.source_classify import MODEL_FILE, WEIGHTS_REPO + + assert publish.HUB_REPO == WEIGHTS_REPO + assert publish.MODEL_FILE == MODEL_FILE def test_stage_release_copies_only_public_artifacts(tmp_path: Path) -> None: diff --git a/tests/test_qwen_zimage_pipeline.py b/tests/test_qwen_zimage_pipeline.py index 861d7ad..7336e18 100644 --- a/tests/test_qwen_zimage_pipeline.py +++ b/tests/test_qwen_zimage_pipeline.py @@ -151,6 +151,13 @@ def test_global_stack_is_resident_on_a_large_card_and_streams_on_a_small_one(): assert small["offload_device"] == "disk" assert small["onload_device"] == "cpu" + # The module pragmas silence return-type checks, so pin the key set here: every + # config is splatted into ModelConfig and a misspelled key would load silently. + from remove_ai_watermarks._internal.two_stage_pipeline import DiffSynthVramConfig + + for config in (large, small, QwenZImagePipeline._zimage_vram_config()): + assert list(config) == list(DiffSynthVramConfig.__annotations__) + @pytest.mark.parametrize( ("cpu_offload", "expected"), diff --git a/tests/test_source_classify_v2.py b/tests/test_source_classify_v2.py new file mode 100644 index 0000000..8f61b68 --- /dev/null +++ b/tests/test_source_classify_v2.py @@ -0,0 +1,106 @@ +"""Contracts for the portable OpenAI/Google source-pattern runtime.""" + +from __future__ import annotations + +from hashlib import sha256 +from typing import TYPE_CHECKING + +import numpy as np +import pytest +from PIL import Image, PngImagePlugin + +from remove_ai_watermarks import source_classify_v2 as model + +if TYPE_CHECKING: + from pathlib import Path + + +def _synthetic_weights(path: Path, *, gate: float) -> None: + zero_mean = np.zeros((256, 256, 3), dtype=np.complex64) + zero_precision = np.zeros((256, 256, 3), dtype=np.float32) + unit = np.zeros((992, 992), dtype=np.float32) + unit[0, 0] = 1 + fields: dict[str, np.ndarray] = {"schema": np.asarray(model.MODEL_SCHEMA)} + for name in model._FILTER_NAMES: + fields[f"{name}_mean"] = zero_mean + fields[f"{name}_precision"] = zero_precision + for route in model.ROUTES: + fields[f"openai_{route}_unit"] = unit + limits = dict.fromkeys(model._THRESHOLD_NAMES, 1.0) + limits["r183_openai_gate"] = gate + limits.update({f"r163_{group}": -0.5 for group in model.GROUPS}) + fields.update({f"threshold_{name}": np.asarray(value, dtype=np.float64) for name, value in limits.items()}) + np.savez_compressed(path, **fields) + + +def test_r163_gate_changes_real_prediction(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + image = tmp_path / "photo.png" + Image.new("RGB", (512, 512), (80, 110, 145)).save(image) + blocked = tmp_path / "blocked.npz" + allowed = tmp_path / "allowed.npz" + _synthetic_weights(blocked, gate=0.5) + _synthetic_weights(allowed, gate=-0.5) + + def predict(path: Path) -> model.ImageSourceClassification: + monkeypatch.setenv(model.WEIGHTS_ENV, str(path)) + monkeypatch.setattr(model, "MODEL_SHA256", sha256(path.read_bytes()).hexdigest()) + return model.classify_image_source(image) + + no_gate = predict(blocked) + with_gate = predict(allowed) + + assert no_gate.label == "unknown" + assert no_gate.reason == "abstained" + assert with_gate.label == "openai" + assert with_gate.reason == "classified" + assert with_gate.watermark_truth == "unknown" + + +def test_top_level_api_is_lazy_and_points_to_new_runtime() -> None: + import remove_ai_watermarks as raiw + + assert raiw.classify_image_source is model.classify_image_source + assert raiw.ImageSourceClassification is model.ImageSourceClassification + assert model.PROVIDERS == ("openai", "google") + assert len(model.WEIGHTS_REVISION) == 40 + assert all(character in "0123456789abcdef" for character in model.WEIGHTS_REVISION) + + +def test_metadata_does_not_change_pixel_decision(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + weights = tmp_path / model.MODEL_FILE + _synthetic_weights(weights, gate=-0.5) + monkeypatch.setenv(model.WEIGHTS_ENV, str(weights)) + monkeypatch.setattr(model, "MODEL_SHA256", sha256(weights.read_bytes()).hexdigest()) + pixels = Image.new("RGB", (512, 512), (80, 110, 145)) + clean = tmp_path / "clean.png" + tagged = tmp_path / "tagged.png" + pixels.save(clean) + metadata = PngImagePlugin.PngInfo() + metadata.add_text("Comment", "arbitrary metadata") + pixels.save(tagged, pnginfo=metadata) + + first = model.classify_image_source(clean) + second = model.classify_image_source(tagged) + + assert first.label == second.label == "openai" + assert first.scores == second.scores + + +def test_rejects_wrong_artifact_hash(tmp_path: Path) -> None: + weights = tmp_path / model.MODEL_FILE + _synthetic_weights(weights, gate=0.5) + + with pytest.raises(ValueError, match="SHA-256 mismatch"): + model._load_model(weights, "0" * 64) + + +def test_small_image_abstains_without_watermark_claim(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setattr(model, "_model_path", lambda: pytest.fail("small image loaded weights")) + image = tmp_path / "small.png" + Image.new("RGB", (128, 128), "white").save(image) + + result = model.classify_image_source(image) + + assert result.label == "unknown" + assert result.reason == "feature_unavailable" + assert result.watermark_truth == "unknown" diff --git a/tests/test_video.py b/tests/test_video.py index 3ee6faf..01c845a 100644 --- a/tests/test_video.py +++ b/tests/test_video.py @@ -904,12 +904,56 @@ class TestVideoMetadataApi: class TestVideoProvenanceApi: + @pytest.mark.parametrize( + ("generator", "issuer", "expected"), + [ + ( + "Google C2PA Core Generator Library", + "Google LLC, OpenAI", + "Google (Gemini / Imagen)", + ), + ( + "Dreamina/2.0", + "OpenAI", + "ByteDance Dreamina", + ), + ("Higgsfield AI", "BytePlus (ByteDance)", "Higgsfield AI"), + ("Higgsfield AI", "", "Higgsfield AI"), + ("FastVid", "", "FastVid"), + ], + ) + def test_claim_generator_precedes_mixed_certificate_issuers(self, generator: str, issuer: str, expected: str): + from remove_ai_watermarks.video import _platform_from_video_metadata + + assert _platform_from_video_metadata({"claim_generator": generator, "issuer": issuer}) == expected + def test_c2pa_platform_keeps_the_bytedance_surface_name(self): from remove_ai_watermarks.video import _platform_from_video_metadata assert _platform_from_video_metadata({"issuer": "BytePlus (ByteDance)"}) == "BytePlus (ByteDance)" assert _platform_from_video_metadata({"issuer": "ByteDance (Volcano Engine)"}) == "ByteDance Volcano Engine" + def test_metadata_only_identification_prefers_generator_to_mixed_issuer( + self, tmp_path: Path, monkeypatch: pytest.MonkeyPatch + ): + from remove_ai_watermarks.video import identify_video + + source = _video_with_c2pa(tmp_path / "source.mp4") + monkeypatch.setattr( + "remove_ai_watermarks.metadata.get_ai_metadata", + lambda _path: { + "claim_generator": "Google C2PA Core Generator Library", + "issuer": "Google LLC, OpenAI", + "source_type": "trainedAlgorithmicMedia (AI-generated)", + }, + ) + + report = identify_video(source, check_visible=False) + + assert report.platform == "Google (Gemini / Imagen)" + assert report.visible_mark is None + assert report.has_ai_metadata is True + def test_identifies_metadata_without_pixel_scan(self, tmp_path: Path): from remove_ai_watermarks.video import identify_video @@ -1616,13 +1660,28 @@ class TestSoraFrameLocalization: assert detection.confidence == 0.0 assert detection.region is None + def test_localizes_independently_rendered_mascot_without_wordmark(self): + from remove_ai_watermarks.video_visible import detect_sora_frame + + mark, _region = _independent_sora_mark() + mascot = cv2.resize(mark[:, :45], (55, 55), interpolation=cv2.INTER_AREA) + frame = np.full((720, 1280, 3), 36, dtype=np.uint8) + _stamp_gray_mark(frame, mascot, x=900, y=500, opacity=0.78) + + detection = detect_sora_frame(frame) + + assert detection.confidence >= 0.60 + assert detection.region is not None + assert abs(detection.region[0] - 900) <= 10 + assert abs(detection.region[1] - 500) <= 10 + class TestVeoFrameLocalization: def test_localizes_independently_rendered_diamond_at_relocated_position(self): - from remove_ai_watermarks.video_visible import _region_iou, detect_veo_frame + from remove_ai_watermarks.video_visible import _region_iou, detect_sora_frame, detect_veo_frame frame = np.full((720, 1280, 3), 28, dtype=np.uint8) - size = 48 + size = 55 x, y = 1080, 570 mark = Image.new("L", (size, size), 0) points = ( @@ -1643,6 +1702,7 @@ class TestVeoFrameLocalization: assert detection.region is not None assert detection.confidence >= 0.70 assert _region_iou(detection.region, (x, y, size, size)) >= 0.70 + assert detect_sora_frame(frame).confidence < 0.60 def test_localizes_independently_rendered_legacy_text(self): from remove_ai_watermarks.video_visible import _region_iou, detect_veo_frame @@ -1714,22 +1774,16 @@ class TestByteDanceFrameLocalization: assert _region_iou(detection.region, (x, y, 80, 60)) >= 0.75 def test_localizes_independently_rendered_dola_text(self): + from pathlib import Path + from remove_ai_watermarks.video_visible import _region_iou, detect_dola_frame frame = np.full((720, 1280, 3), 35, dtype=np.uint8) - mark = np.zeros((40, 150), dtype=np.uint8) - cv2.putText( - mark, - "Dola AI", - (2, 28), - cv2.FONT_HERSHEY_SIMPLEX, - 0.9, - 255, - 2, - cv2.LINE_AA, - ) - ys, xs = np.where(mark > 0) - mark = mark[ys.min() : ys.max() + 1, xs.min() : xs.max() + 1] + # A frozen OpenCV 4 render in the other Hershey face: OpenCV 5 draws the same + # putText call smaller and heavier (scripts/render_video_hershey_templates.py). + fixture = Path(__file__).resolve().parents[1] / "data/fixtures/synthetic/dola_hershey_simplex.png" + mark = cv2.imread(str(fixture), cv2.IMREAD_GRAYSCALE) + assert mark is not None mark_height, mark_width = mark.shape x = frame.shape[1] - mark_width - 18 y = frame.shape[0] - mark_height - 14 @@ -2782,6 +2836,73 @@ class TestVideoVisibleFullClip: class TestVideoVisibleApi: + @pytest.mark.parametrize( + ("sora_box", "sora_frames", "integrity", "signature", "signer", "generator", "expected_mark"), + [ + ((1125, 584, 155, 69), 15, "valid", "valid", "unknown", "Google C2PA Core Generator Library", "veo"), + # Valid Google AI provenance vetoes Sora wherever its box sits. + ((300, 200, 155, 69), 15, "valid", "valid", "unknown", "Google C2PA Core Generator Library", "veo"), + ((300, 200, 155, 69), 8, "valid", "valid", "unknown", "Google C2PA Core Generator Library", "veo"), + ((1125, 584, 155, 69), 15, "invalid", "valid", "unknown", "Google C2PA Core Generator Library", "sora"), + ((1125, 584, 155, 69), 15, "valid", "invalid", "unknown", "Google C2PA Core Generator Library", "sora"), + # A revoked or expired signing credential is "invalid" to identify, so it + # cannot veto either. + ((1125, 584, 155, 69), 15, "valid", "valid", "invalid", "Google C2PA Core Generator Library", "sora"), + # Provenance that also names Sora confirms the mark instead of vetoing it. + ((1125, 584, 155, 69), 15, "valid", "valid", "unknown", "Sora export", "sora"), + ], + ) + def test_google_ai_provenance_vetoes_sora_unless_invalid_or_sora_confirmed( + self, + sora_box: tuple[int, int, int, int], + sora_frames: int, + integrity: str, + signature: str, + signer: str, + generator: str, + expected_mark: str, + ): + from remove_ai_watermarks.video import _select_stable_visible_mark + from remove_ai_watermarks.video_visible import FrameLocalization, VideoScan + + veo_box = (1132, 572, 56, 56) + scans = { + "sora": VideoScan( + width=1280, + height=720, + fps=24.0, + detections=tuple( + FrameLocalization( + index, + 0.66 if index < sora_frames else 0.0, + sora_box if index < sora_frames else None, + ) + for index in range(15) + ), + ), + "veo": VideoScan( + width=1280, + height=720, + fps=24.0, + detections=tuple(FrameLocalization(index, 0.82, veo_box) for index in range(15)), + ), + } + google_markers = { + "claim_generator": generator, + "issuer": "Google LLC", + "source_type": "trainedAlgorithmicMedia (AI-generated)", + "c2pa_integrity": integrity, + "c2pa_signature": signature, + "c2pa_signer_validity": signer, + } + + selected = _select_stable_visible_mark(scans, google_markers, ("sora", "veo")) + + assert selected is not None + assert selected[0] == expected_mark + assert selected[2] == [veo_box if expected_mark == "veo" else sora_box] * 15 + assert selected[4] == ("veo" if expected_mark == "veo" else "box") + def test_auto_prefers_specific_sora_run_over_hailuo_cross_match( self, tmp_path: Path, @@ -3198,15 +3319,16 @@ class TestYouTubeReencodedSamples: def test_google_c2pa_vetoes_a_sora_match_on_steam(self): # Gemini Omni 1.1 Flash via Runway (2026-09-25): a steam wisp scored 0.61-0.67 # on the Sora template for five frames, a stable run above the strict floor, - # and the report named OpenAI Sora over the file's valid Google C2PA. - from remove_ai_watermarks.video import _visible_removal_plan, identify_video - from remove_ai_watermarks.video_visible import scan_video_marks, stabilize_localizations + # and the report named OpenAI Sora over the file's valid Google C2PA. The + # mascot-only shape check now rejects the wisp too; the veto stays as the + # independent second guard. + from remove_ai_watermarks.video import identify_video + from remove_ai_watermarks.video_visible import contradicts_video_provenance clip = self._DIR.parents[1] / "captures" / "2026-09" / "runway" / "gemini-omni-1-1-flash.mp4" - scan = scan_video_marks(clip, ("sora",), collect_timestamps=False)["sora"] - assert any(stabilize_localizations("sora", scan.detections)) - markers = identify_video(clip, check_visible=False).metadata_markers - assert not any(_visible_removal_plan("sora", scan, markers)[0]) + report = identify_video(clip) + assert report.visible_mark != "sora" + assert contradicts_video_provenance("sora", report.metadata_markers) def test_real_kling_and_hailuo_labels_still_pass_the_new_gates(self): from remove_ai_watermarks.video_visible import ( diff --git a/tests/test_visible_examples.py b/tests/test_visible_examples.py index 6216b9f..bfbeff9 100644 --- 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