Score a document scan or photo for tampering likelihood in [0, 1] (higher = more likely forged). Tuned for ID / KYC–style pages in the redrob-verify harness (MIDV authentic + synthetic patches).

Not a face deepfake detector. Not a claim of FMIDV cross-domain pass unless you run that split yourself.

Model


Architecture	ResNet-50 + FFT + HOG streams + upsample localization head
Backbone init	torchvision `ResNet50_Weights.IMAGENET1K_V2` (BSD-3)
Input	RGB 320×320
Serving score	Image-level sigmoid (localization used at train time)
Code	`services/forgery/model.py`

Files in this repo:

model.safetensors — weights for Hub / safetensors loaders
forgerynet_apache.pth — full training checkpoint (model_state + metadata); drop-in for redrob-verify config.yaml
config.json — image size, recommended threshold, provenance pointers

Training data (provenance)

Source	Role	Terms
torchvision ResNet-50 ImageNet-1K V2	Backbone init	BSD-3 / torchvision
MIDV-2020 authentic pages	Train negatives (JPEG-recompressed, train-split only) + eval authentic (held-out docs)	Follow MIDV / portal terms
`tools/gen_forgery.py` synthetic tampers	Train positives + masks (from train docs only)	Synthetic; generated in-repo

Weights are not derived from TruFor.

Evaluation (in-domain)

Document-disjoint holdout (tools/split_forgery_holdout.py): 400 train / 100 eval authentic IDs, eval n=200 (100 auth + 100 forged).

Published checkpoint (seed 7):

Joint TC2/TC3 feasible ≈ [0.69, 0.93]
Recommended threshold 0.87 → TPR ≈ 0.92, F1 ≈ 0.82

Multi-seed check (seeds 7 / 13 / 42; judge by minimum):

Seed	TPR	F1	t*
7	0.92	0.821	0.87
13	0.88	0.811	0.98
42	0.93	0.798	0.96
min	0.88	0.798	—

Protocol: ./run.sh split-forgery-holdout --regenerate-train --rebuild-eval then ./run.sh train-forgery / ./run.sh eval-forgery.

Download & run

bash

# From the redrob-verify checkout
./tools/fetch_models.sh   # pulls face + forgery from Hugging Face

# Or Hub only
huggingface-cli download redrob-labs/redrob-verify-forgery \
  --local-dir models/forgery

Serve with forgery.backend: forgery_net, image_size: 320, and weights_path: models/forgery/forgerynet_apache.pth (or load model.safetensors via the same ForgeryNet class).

Limitations

Domain: MIDV + our synthetic generator; other scanners/tampers may need fine-tuning.
Optional TruFor backend in the code repo is research-only (nonprofit upstream) and is not these weights.

Citation

Cite redrob-verify and MIDV-2020 per their terms when reporting results.