Community Forensics: Using Thousands of Generators to Train Fake Image Detectors
Paper • 2411.04125 • Published • 1
Verity detector checkpoints
Mirror of the pretrained checkpoints used by Verity, an open deepfake
examination pipeline for investigators and researchers. Verity downloads the
files below on first run (verity weights pull) and verifies each one
against the SHA-256 in manifest.json before it is used.
This repo re-hosts other people's work. Every file keeps the license its
authors released it under; the repo-level tag is the most restrictive one
(CC BY-NC 4.0). Nothing here may be used commercially. See NOTICE.md for
per-file attribution and LICENSE for the license texts.
Checkpoints that upstream authors did not license for redistribution (NPR, MLEP) are not in this repo; Verity fetches those from the authors' repositories directly.
Assembled 2026-10-21 from Verity's detectors/weights_manifest.yaml.
Files
| Path | Size | License | What it is | SHA-256 |
|---|---|---|---|---|
aasist/AASIST.pth |
1.3 MB | MIT | AASIST audio anti-spoofing checkpoint (ASVspoof2019 LA) | 51d2d9cf0738172f… |
backbones/hrnetv2_w48_imagenet_pretrained.pth |
310.6 MB | MIT | HRNet-W48 ImageNet backbone (Face X-ray; no detector head) | 0efec102d97f2ef5… |
backbones/xception-b5690688.pth |
91.7 MB | BSD-3-Clause | ImageNet Xception backbone (Cadene port, BSD-3-Clause; used by IID; no detector head) | b56906886cbaf573… |
community_forensics/model.safetensors |
87.3 MB | MIT | Community Forensics ViT-S/16-384 detector (~4,800 generators, CVPR 2025) | b89f36275f3bf5e2… |
deepfakebench/I3D_8x8_R50.pth |
112.5 MB | CC-BY-NC-4.0 | Kinetics-pretrained I3D 3D-ResNet-50 backbone (no detector head) | c9e4e6c8e86ce231… |
deepfakebench/capsule_best.pth |
15.7 MB | CC-BY-NC-4.0 | Capsule-Forensics (FF++ c23) | 40e96997c2248aeb… |
deepfakebench/cnnaug_best.pth |
85.3 MB | CC-BY-NC-4.0 | CNN-Aug / ResNet-34 (FF++ c23) | 2ffdc665b9bd723a… |
deepfakebench/core_best.pth |
87.8 MB | CC-BY-NC-4.0 | CORE (FF++ c23) | 3d34dca33ab66ee0… |
deepfakebench/effnb4_best.pth |
71.0 MB | CC-BY-NC-4.0 | EfficientNet-B4 (FF++ c23) | 74cbd82d7be1bf5e… |
deepfakebench/f3net_best.pth |
90.4 MB | CC-BY-NC-4.0 | F3Net (FF++ c23) | deee7d25bfc1b8b5… |
deepfakebench/ffd_best.pth |
87.8 MB | CC-BY-NC-4.0 | FFD (FF++ c23) | 445f5b70f115bf77… |
deepfakebench/meso4Incep_best.pth |
0.1 MB | CC-BY-NC-4.0 | MesoInception-4 (FF++ c23) | 16a2d7cec846e5bc… |
deepfakebench/meso4_best.pth |
0.1 MB | CC-BY-NC-4.0 | MesoNet-4 (FF++ c23) | a362ebbef2658daa… |
deepfakebench/recce_best.pth |
191.5 MB | CC-BY-NC-4.0 | RECCE (FF++ c23); reconstruction decoder untrained in this release | 1349fd80d75cdb2d… |
deepfakebench/spsl_best.pth |
87.8 MB | CC-BY-NC-4.0 | SPSL (FF++ c23) | 7305d660c68aa6ac… |
deepfakebench/srm_best.pth |
222.1 MB | CC-BY-NC-4.0 | SRM (FF++ c23) | 666e8fc7f9938f54… |
deepfakebench/ucf_best.pth |
188.0 MB | CC-BY-NC-4.0 | UCF (FF++ c23) | 4afa2517feeb4730… |
deepfakebench/xception_best.pth |
87.8 MB | CC-BY-NC-4.0 | Xception (FF++ c23) | dd61186f9ac8e99a… |
lipforensics/lipforensics_ff.pth |
144.1 MB | MIT | LipForensics (FF++-trained) mouth-motion detector | 4b7790bc8e02d0c2… |
safe/checkpoint-best.pth |
5.8 MB | Apache-2.0 | SAFE wavelet-HH synthetic-image detector (KDD 2025) | b3f5ecfb46a154ed… |
univfd/fc_weights.pth |
0.0 MB | MIT | UnivFD linear probe on frozen CLIP ViT-L/14 features (768 -> 1) | 477100745713bcc9… |
Sources
AASIST (clovaai/aasist)
- Upstream: https://github.com/clovaai/aasist
- License: MIT
- Copyright (c) 2021-present NAVER Corp.
- Citation: Jung, J.-W., Heo, H.-S., Tak, H., Shim, H.-J., Chung, J.-S., Lee, B.-J., Yu, H.-J., Evans, N. "AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks." ICASSP 2022.
- Note: 2019-era ASVspoof2019 LA baseline; generalises poorly to modern in-the-wild deepfake audio (Deepfake-Eval-2024 AUC 0.43). Shipped as a reference baseline.
- Files:
aasist/AASIST.pth
HRNet-Image-Classification (HRNet/HRNet-Image-Classification)
- Upstream: https://github.com/HRNet/HRNet-Image-Classification
- License: MIT
- Copyright (c) Microsoft Corporation
- Citation: Wang, J. et al. "Deep High-Resolution Representation Learning for Visual Recognition." IEEE TPAMI 2020.
- Files:
backbones/hrnetv2_w48_imagenet_pretrained.pth
DeepfakeBench (SCLBD/DeepfakeBench)
- Upstream: https://github.com/SCLBD/DeepfakeBench
- License: CC-BY-NC-4.0
- Copyright (c) 2023, The Chinese University of Hong Kong, Shenzhen
- Citation: Yan, Z., Zhang, Y., Yuan, X., Lyu, S., Wu, B. "DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection." NeurIPS 2023 (Datasets & Benchmarks). arXiv:2307.01426
- Note: Detector checkpoints were trained on FaceForensics++ (c23), a dataset released for research use. The DeepfakeBench LICENSE text names the source code; the released weights carry no separate statement and are treated as CC BY-NC 4.0 as well.
- Files:
backbones/xception-b5690688.pth,deepfakebench/I3D_8x8_R50.pth,deepfakebench/capsule_best.pth,deepfakebench/cnnaug_best.pth,deepfakebench/core_best.pth,deepfakebench/effnb4_best.pth,deepfakebench/f3net_best.pth,deepfakebench/ffd_best.pth,deepfakebench/meso4Incep_best.pth,deepfakebench/meso4_best.pth,deepfakebench/recce_best.pth,deepfakebench/spsl_best.pth,deepfakebench/srm_best.pth,deepfakebench/ucf_best.pth,deepfakebench/xception_best.pth
Community Forensics (OwensLab/commfor-model-384)
- Upstream: https://huggingface.co/OwensLab/commfor-model-384
- License: MIT
- (c) Jeongsoo Park, Andrew Owens (Harvard Vision Lab)
- Citation: Park, J., Owens, A. "Community Forensics: Using Thousands of Generators to Train Fake Image Detectors." CVPR 2025. arXiv:2411.04125
- Files:
community_forensics/model.safetensors
LipForensics (ahaliassos/LipForensics)
- Upstream: https://github.com/ahaliassos/LipForensics
- License: MIT
- Copyright (c) 2021 Alexandros Haliassos
- Citation: Haliassos, A., Vougioukas, K., Petridis, S., Pantic, M. "Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection." CVPR 2021.
- Note: The checkpoint is linked from the README (Google Drive); the MIT license covers the repository.
- Files:
lipforensics/lipforensics_ff.pth
SAFE (Ouxiang-Li/SAFE)
- Upstream: https://github.com/Ouxiang-Li/SAFE
- License: Apache-2.0
- Citation: Li, O., Cai, J., Hao, Y., Jiang, X., Hu, Y., Feng, F. "Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective." KDD 2025. arXiv:2408.06741
- Files:
safe/checkpoint-best.pth
UniversalFakeDetect (WisconsinAIVision/UniversalFakeDetect)
- Upstream: https://github.com/WisconsinAIVision/UniversalFakeDetect
- License: MIT
- Copyright (c) 2025 Wisconsin AI and Vision Lab (WAIV)
- Citation: Ojha, U., Li, Y., Lee, Y. J. "Towards Universal Fake Image Detectors that Generalize Across Generative Models." CVPR 2023.
- Files:
univfd/fc_weights.pth
Use
pip install -U "huggingface_hub[cli]"
hf download ADPer/verity-weights --local-dir weights
# or let Verity do it:
VERITY_WEIGHTS_HUB=ADPer/verity-weights verity weights pull --trained
These models are statistical indicators, not proof. Their outputs must be interpreted by a qualified examiner alongside the explainability artifacts Verity produces, and never cited as a standalone determination.
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support