ops: DotCheck Apache-2.0 open heads + card

CITATION.cff

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+cff-version: 1.2.0
+message: "If you cite DotCheck engines or measured holdout gates, use the citation below."
+title: "DotCheck AI-likeness engines (inhouse@14, inhouse-text@11, inhouse-video@4)"
+authors:
+  - name: "DotCheck"
+url: "https://dotcheck.ai"
+repository-code: "https://huggingface.co/DotCheck"
+license: "Apache-2.0"
+date-released: "2026-08-25"
+abstract: >-
+  DotCheck serves binary AI-likeness scores for images (wire inhouse@14 /
+  Vermeer@14.2, pair stack: max-side 256 transport + native 224 center),
+  text (inhouse-text@11 / Valla@11, seven languages), and video stamps
+  (inhouse-video@4 / Muybridge@4.4, same pair views then stamp-cap max)
+  using commercial-clean SigLIP2 spines plus Apache-2.0 heads. Live heads
+  are published on Hugging Face; everyday product scoring runs through
+  DotCheck.
+keywords:
+  - ai-detection
+  - image-classification
+  - text-classification
+  - video
+  - siglip2
+preferred-citation:
+  type: soft
+  authors:
+    - name: "DotCheck"
+  title: "DotCheck engines (inhouse@14, inhouse-text@11, inhouse-video@4)"
+  url: "https://dotcheck.ai/docs"
+  year: 2026

LICENSE

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NOTICE

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+DotCheck NOTICE — third-party attributions
+=========================================
+
+DotCheck in-house engines use the following upstream models as frozen bases /
+backbones. Their licenses remain those of the upstream projects. DotCheck heads
+trained on top of these bases are Apache-2.0 in this package (see LICENSE).
+Upstream backbone licenses remain those of the upstream projects.
+
+1) google/siglip2-base-patch16-224
+   Used by: Vermeer + Muybridge
+   Hub: https://huggingface.co/google/siglip2-base-patch16-224
+   Typical license: Apache-2.0 (see upstream card)
+
+2) Oxidane/tmr-ai-text-detector
+   Used by: Valla
+   Hub: https://huggingface.co/Oxidane/tmr-ai-text-detector
+   Typical license: MIT (see upstream card)
+
+3) fakespot-ai/roberta-base-ai-text-detection-v1
+   Used by: Valla
+   Hub: https://huggingface.co/fakespot-ai/roberta-base-ai-text-detection-v1
+   Typical license: Apache-2.0 (see upstream card)
+
+4) mispeech/dasheng-base
+   Used by: Helmholtz
+   Hub: https://huggingface.co/mispeech/dasheng-base
+   Typical license: Apache-2.0 (see upstream card)
+
+5) hfl/chinese-macbert-base
+   Used by: Valla (zh cls_emb)
+   Hub: https://huggingface.co/hfl/chinese-macbert-base
+   Typical license: Apache-2.0 (see upstream card)
+
+DotCheck does not redistribute upstream full checkpoints via DotCheck Hub
+public model repos. Everyday product scoring runs through DotCheck services.

README.md

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+---
+language:
+  - en
+license: apache-2.0
+library_name: dotcheck
+pipeline_tag: image-classification
+tags:
+  - image-classification
+  - video
+  - ai-detection
+  - synthetic-media
+  - siglip2
+  - apache-2.0
+  - dotcheck
+  - muybridge
+base_model: google/siglip2-base-patch16-224
+base_model_relation: adapter
+model-index:
+  - name: Muybridge@4.4
+    results:
+      - task:
+          type: image-classification
+          name: binary AI detection (video pair)
+        dataset:
+          name: DotCheck video holdout (4.4 homework)
+          type: other
+          split: homework
+        metrics:
+          - name: mean_P_AI_real
+            type: mean_score_real
+            value: 0.022
+          - name: mean_P_AI_ai
+            type: mean_score_ai
+            value: 0.976
+          - name: balanced_accuracy
+            type: balanced_accuracy
+            value: 0.9859
+        source:
+          name: RULER_video_v4_4 / video_gates_v4_4
+          url: https://dotcheck.ai/docs
+---
+
+# DotCheck/muybridge-video-v4_4
+
+**Muybridge@4.4** is DotCheck’s live video pair stack (`inhouse-video@4`): Muybridge 3.8 on the transport view plus Blau 1.4 on the native center crop, combined by one stack, then stamp-cap (max) across stamps. This repo is new. It does not replace [muybridge-video-v2](https://huggingface.co/DotCheck/muybridge-video-v2) (archive).
+
+Scores are estimates under this model, not proof of authorship. Public Layer A is homework at cut 0.5.
+
+| Field | Value |
+|-------|--------|
+| Hub id | `DotCheck/muybridge-video-v4_4` |
+| Wire id | `inhouse-video@4` |
+| Label | Muybridge@4.4 |
+| Artifact | `siglip2_base_patch16_224_linear_head_v4_4.npz` |
+| Arms | `…_v3_8.npz` (transport) · `…_v1_4.npz` (center) · shared `…_v6_6.npz` |
+| Spines | same 13.12 + 6.6 as Vermeer@14.2 |
+| Backbone | [`google/siglip2-base-patch16-224`](https://huggingface.co/google/siglip2-base-patch16-224) (Apache-2.0) |
+| Stamps | pair2: n=2 inner 33%/67% when duration ≤ 180s; else `min(12, max(2, floor(dur/60)))` |
+| Fold | stamp-cap **max** (not bag p90) |
+| Encode | same pair views as stills (max-side 256 + native 224) |
+| Output | `p ∈ [0,1]` — estimated P(AI) |
+| Serve | CPU FastAPI `POST /v1/analyze-pair` `kind=video` |
+
+## Model description
+
+Two shared SigLIP2 towers and a stack combiner. Same spines as Vermeer@14.2. Live on `inhouse-video@4`. Clip fold is stamp-cap max, not bag p90.
+
+Product clip scores that fuse frames with audio use a private seating path (Covenant: Muybridge + Helmholtz). Do not read the homework table as a fused eyes-and-ears claim.
+
+**In this repo:** `README.md`, `LICENSE`, `NOTICE`, `CITATION.cff`, the 4.4 stack `.npz`, the video arm heads, the shared center arm, and the two spine safetensors.
+
+## Architecture
+
+```text
+clip → pair2 stamps
+  each stamp raster (min side ≥ 224; else refuse)
+    → transport: max-side 256, aspect kept, JPEG q70
+          → 13.12 spine → v3_8 logit
+    → center: native 224×224 crop, JPEG q85
+          → 6.6 spine  → v1_4 logit
+    → stack_w · [z_v, z_j] + stack_b → sigmoid → p_stamp
+  clip p = max(p_stamp)
+```
+
+## Inference
+
+Open weights: the stack `.npz`, video arm heads, shared center arm, and spines (Apache-2.0). Product scoring: [Check](https://dotcheck.ai/check) or [Pro API](https://dotcheck.ai/api).
+
+Not `AutoModel.from_pretrained("DotCheck/…")`. Everyday scoring runs through DotCheck.
+
+## Training data
+
+Holdout real n=602 / AI n=180. See eval/MUYBRIDGE_4.md.
+
+## Evaluation
+
+Homework Layer A at thr 0.5. Source: RULER_video_v4_4.json and video_gates_v4_4.json.
+
+| Metric | n | Measured |
+|--------|--:|---------:|
+| mean P(AI) real | 602 | **0.022** |
+| mean P(AI) AI | 180 | **0.976** |
+| balanced accuracy |  | **0.9859** |
+
+Gold 164×25 is a Vermeer exam, not a 4.4 public claim.
+
+## Intended use
+
+Video pair scores. Citation of the homework table. Not a v2 overwrite.
+
+## Limitations
+
+Two-spine serve. Homework is Layer A, not a posterior over every generator. Stamp-cap is not a temporal model.
+
+## License
+
+Apache License 2.0 for DotCheck heads in this repo.
+
+## Citation
+
+Prefer Muybridge@4.4 and https://dotcheck.ai/docs.

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