CristianLazoQuispe/RobustDeepfakeCVPR26-models

RobustDeepfakeCVPR26 — AI4Good greedy6 ensemble

Model checkpoints for Team AI4Good / SoloUNI's submission to the NTIRE 2026 Robust Deepfake Detection Challenge (CVPR 2026 Workshop).

Method — Self-Supervised Adversarial Training. Binary classifiers trained on the 500 real images only using Self-Blended Images (SBI) + PMM degradation. No real fakes are used — pseudo-fakes are generated on-the-fly from pairs of real images via landmark-guided Poisson/Alpha blending. Six diverse backbones are trained independently and combined by probability averaging with 3× deterministic TTA.

• Code & reproduction: https://github.com/CristianLazoQuispe/RobustDeepfakeCVPR26
• Method writeup (arXiv): https://arxiv.org/abs/2604.24163
• Challenge report (CVPR 2026): Hopf et al., Robust Deepfake Detection, NTIRE 2026 Challenge: Report
• License: MIT

The 6 models

Each file is <model>/best.pt — a PyTorch checkpoint dict with keys including model (state_dict), epoch, best_auc, and the full training config. All trained at 224×224, AdamW, cosine 100 epochs, seed: 42 (csl uses 43), deterministic: true, amp: false.

Model	Backbone (timm)	In ch.	Hard-Test AUC	Val AUC
r3d_fa_csb	convnext_base.fb_in22k_ft_in1k	3	0.7644	0.897
lmhm_css	convnext_small.fb_in22k_ft_in1k + LMHM	4	0.7268	0.900
r3e_fa_css	convnext_small.fb_in22k_ft_in1k	3	0.7259	0.844
r3d_fa_csl_s43	convnext_large.fb_in22k_ft_in1k	3	0.7127	0.867
r3d_fa_deit3_s	deit3_small_patch16_224	3	0.7111	0.889
lmhm_vits	vit_small_patch16_224.augreg_in21k_ft_in1k + LMHM	4	0.6945	0.899
Ensemble (6) + TTA	—	—	0.7777	—

CodaBench Public Test AUC: 0.6865 (submission 632762, 6-model ensemble + TTA). Hard-Test AUC is on a local 500-real/500-fake degraded holdout; Val AUC is the in-training best. LMHM = landmark-heatmap models take a 4-channel input (RGB + face-landmark heatmap, σ=4).

Usage

from huggingface_hub import snapshot_download
local = snapshot_download(repo_id="CristianLazoQuispe/RobustDeepfakeCVPR26-models", repo_type="model")
# place each <model>/best.pt under $DATA_ROOT/experiments/<model>/fold0/best.pt, then:

# Reproduce the submission with the code repo (6-model ensemble + TTA)
python scripts/generate_ensemble_submission.py \
    --models r3d_fa_csb r3e_fa_css lmhm_vits r3d_fa_deit3_s r3d_fa_csl_s43 lmhm_css \
    --device cuda:0 --tta --output greedy6_final.zip

Architecture & preprocessing for each model are embedded in its checkpoint's config. The two lmhm_* models require the landmark-heatmap 4th channel (see the code repo's dataset/transform).

Citation

@InProceedings{Hopf_2026_CVPR,
  author    = {Hopf, Benedikt and Timofte, Radu and others},
  title     = {Robust Deepfake Detection, NTIRE 2026 Challenge: Report},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month     = {June},
  year      = {2026},
  pages     = {1925-1938}
}