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
Authors: Giovanni Affatato, Sara Mandelli, Paolo Bestagini, Stefano Tubaro.
Code: 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.

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. 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

@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}
}