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moyuan10086/aigc-safety-models
aigc-safety-models is a machine learning model from moyuan10086. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
This repository contains the local model artifacts used by the AIGC Safety System. It combines the project-trained Deepfake detector with the local retrieval, face-detection, and Shadow-audit models.
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Updated Sep 2, 2026
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.ckpt607 MB · 56%
From the Hugging Face model README
This repository contains the local model artifacts used by the AIGC Safety System. It combines the project-trained Deepfake detector with the local retrieval, face-detection, and Shadow-audit models.
| Path | Model | Role | Origin / license note |
|---|---|---|---|
deepfake/model-epoch6.ckpt | AIGC Safety DFDet Multi-Dataset PEFT | Project Deepfake detector | Project checkpoint trained on DF40 + DeepFakeFace; use under the project license and dataset terms |
deepfake/face_detection_yunet_2023mar.onnx | YuNet | Face detection and five-point alignment | OpenCV Zoo model; retain upstream attribution and license |
embedding/paraphrase-multilingual-MiniLM-L12-v2/ | Multilingual MiniLM | RAG semantic retrieval | Sentence-Transformers model; see its upstream model card and license |
shadow/hybrid_safety_model_xgboost_color.json | Hybrid Safety XGBoost | Out-of-band Shadow audit | Local project/vendor artifact; treat serialized model data as trusted code and verify its digest |
The repository also documents models that are part of the production system but are not distributed here:
qwen3guard-gen-0.6b: called through the configured OpenAI-compatible Qwen3Guard service.singguard-nsfa-0.8b: called through the configured OpenAI-compatible SingGuard service.MLLM_MODEL and CHAT_MODEL_NAME and supplied by an external gateway.Their weights remain on the authorized inference or deployment host and are not copied into this public repository.
model-epoch6.ckpt)The checkpoint is intended for use with the inference code in moyuan10086/aigc-safety-system. It is not a general-purpose authenticity guarantee and should be combined with review and provenance evidence.
deepfake/model-epoch6.ckpt: PyTorch Lightning checkpoint for the project inference service.deepfake/face_detection_yunet_2023mar.onnx: YuNet face detector used by the preprocessing pipeline.embedding/paraphrase-multilingual-MiniLM-L12-v2/: Sentence-Transformers snapshot used by the RAG service.shadow/hybrid_safety_model_xgboost_color.json: XGBoost Shadow model used only for observation and evaluation; it never overrides the production guardrail verdict.Download the files locally and set the application paths, for example:
DEEPFAKE_MODEL_PATH=/models/aigc-safety-models/deepfake/model-epoch6.ckpt
DEEPFAKE_FACE_MODEL_PATH=/models/aigc-safety-models/deepfake/face_detection_yunet_2023mar.onnx
GUARDRAIL_XGBOOST_SHADOW_MODEL_PATH=/models/aigc-safety-models/shadow/hybrid_safety_model_xgboost_color.json
Configure the Sentence-Transformers cache to use embedding/paraphrase-multilingual-MiniLM-L12-v2/. Keep downloaded artifacts outside the application Git repository.
The project checkpoint SHA-256 is:
model-epoch6.ckpt fd0b967e9ab6a19aa0127c5c2301b3883ff8013b1a5d7af282e29737132c1fb9
Recompute hashes after download and compare them with the release metadata before enabling a model in production.
The project checkpoint is released under the repository license where permitted. Dataset terms and the CLIP base model terms remain applicable. Review the upstream licenses before redistribution or commercial use.