--- license: other license_name: polyform-noncommercial-1.0.0 license_link: https://polyformproject.org/licenses/noncommercial/1.0.0 tags: - deepfake-detection - ensemble --- # DeepSafe Ensemble Artifacts The meta-learners that turn 19 individual detector scores into one calibrated verdict, for [DeepSafe](https://github.com/deepsafehq/deepsafe-bench). Unlike the model code and weights in the other DeepSafe repositories, **these are first-party**: trained by us, licensed PolyForm Noncommercial 1.0.0, same as the project. ## Contents | Modality | Meta-learner | Held-out AUC | |---|---|---| | Image | LightGBM over 7 models | 0.9466 | | Audio | Random Forest over 3 models | 0.8290 | | Video | XGBoost over 9 models | 0.6694 | Each modality ships four files: - `_meta_learner.pkl` — the trained model - `_scaler.pkl` — feature scaling - `_calibrator.pkl` — Platt calibration, so scores read as probabilities - `_config.json` — feature order and fallback weights Trained on the 15,499-sample medium evaluation tier. Without these, the inference server produces per-model scores but no ensemble verdict. ## Usage `setup.sh` fetches these automatically. Manually: ```python from huggingface_hub import snapshot_download snapshot_download("deepsafe/ensemble", local_dir="models/ensemble/artifacts") ``` ## The numbers are the point Held-out video AUC is **0.6694**. That is barely above chance on generators the models were not trained for, and it is lower than the cross-validated figure produced during training. We publish the held-out number because the gap between the two is the finding. See [BENCHMARK.md](https://github.com/deepsafehq/deepsafe-bench/blob/main/BENCHMARK.md). ## Security note These are Python pickles, which execute code on load. Only load them from a source you trust. DeepSafe's loader refuses any pickle outside its configured artifacts directory, but that is a guardrail, not a guarantee. If you are security-sensitive, retrain your own with `deepsafe fit --tier 1`.