| --- |
| license: mit |
| library_name: pytorch |
| pipeline_tag: video-classification |
| tags: |
| - deepfake-detection |
| - face-forensics |
| - person-of-interest |
| - facial-dynamics |
| - temporal-transformer |
| --- |
| |
| # MOTIF — Person-of-Interest Deepfake Detection |
|
|
| Inference assets for **MOTIF: Person-of-Interest Deepfake Detection Beyond 3DMM Coefficients**, accepted at IEEE WIFS 2026. |
|
|
| **Paper:** [arXiv:2610.09830](https://arxiv.org/abs/2610.09830) |
| **Authors:** Giovanni Affatato, Sara Mandelli, Paolo Bestagini, Stefano Tubaro. |
| **Code:** [polimi-ispl/MOTIF](https://github.com/polimi-ispl/MOTIF) |
|
|
| MOTIF enrolls a person from genuine reference videos and scores a query video without retraining. Two independently trained temporal transformers process global 3D morphable-model coefficients and temporally centered reconstructed mouth motion in 75-frame windows with 50% overlap. Per-person whitening and leave-one-reference-video-out calibration produce branch scores; their arithmetic mean is the final authenticity score. Higher scores indicate greater consistency with the enrolled person. |
|
|
| The models were trained on real videos only, without manipulated videos or person-of-interest-specific training data. This repository contains inference checkpoints and normalization statistics, not training code or data. |
|
|
| ## Usage |
|
|
| Requires an NVIDIA GPU and Python 3.10 or newer. |
|
|
| ```bash |
| python -m pip install git+https://github.com/polimi-ispl/MOTIF |
| motif extract-reference --reference ref1.mp4 ref2.mp4 ref3.mp4 -o person.npz |
| motif score --reference-set person.npz --test query.mp4 |
| ``` |
|
|
| The first run downloads these files automatically. Ten independent genuine reference videos match the paper protocol; at least two usable references and enough valid windows are required for leave-one-out calibration. |
|
|
| ## Files |
|
|
| | File | Purpose | SHA-256 | |
| | --- | --- | --- | |
| | `motif_global.pth` | Global temporal transformer | `fd93ba656c7d2ad71481659b9cab723343a285d55174eeb277c15dc27312b99e` | |
| | `motif_mouth.pth` | Mouth temporal transformer | `e900ab14013072a0da2164d47b44e1635e56ec4a69682a7951a138b84203fa5a` | |
| | `norm_stats_all.npz` | Global normalization statistics | `7e3796edd3c452a98eab2b0e069903c70eb15b2488423053a7d1aed1f0e211d9` | |
| | `norm_stats_mvrecon.npz` | Mouth reconstruction statistics and projection bases | `7b506ff093ff48bb467e8c1c567f216a7e9e4c91da41be1e0e3802d9ee148c7c` | |
|
|
| ## Third-party weights |
|
|
| Face-analysis assets (`retinaface_resnet50_2020-07-20_old_torch.pth`, `large_base_net.pth`, and `net_recon.pth`) are downloaded directly from the upstream [3DDFA-V3 Hub repository](https://huggingface.co/datasets/Zidu-Wang/3DDFA-V3). They are not mirrored here and retain their respective licenses and provenance (RetinaFace, HRN, and 3DDFA-V3). |
|
|
| ## License |
|
|
| The MOTIF checkpoints and normalization assets are released under the MIT license. Third-party assets retain their own terms. This model license is separate from the paper's license. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{affatato2026motif, |
| title = {{MOTIF}: Person-of-Interest Deepfake Detection Beyond {3DMM} Coefficients}, |
| author = {Affatato, Giovanni and Mandelli, Sara and Bestagini, Paolo and Tubaro, Stefano}, |
| booktitle = {2026 IEEE International Workshop on Information Forensics and Security (WIFS)}, |
| year = {2026}, |
| eprint = {2610.09830}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CV}, |
| url = {https://arxiv.org/abs/2610.09830} |
| } |
| ``` |
|
|
