docverify — payslip tampering detection
A forensic segmentation network that finds where a payslip was edited — copy-move, splice from
another document, or a re-typed amount — and the small models and thresholds that turn its pixel
map into a verdict an analyst can act on.
Part of docverify, a complete system with arithmetic checks, PDF forensics and a local LLM note:
project ·
YOLO field detector
Files
| File |
What it is |
tampernet.onnx |
the served model — ONNX FP32, 1.56× faster than PyTorch on CPU, identical decisions |
tampernet.pt |
the same weights for PyTorch |
threshold.json |
page threshold 7.86 (5% false alarms on genuine payslips), field threshold 9.19 (1% of genuine pages rated high risk) |
siglip_probe.json |
linear probe on frozen Google SigLIP image embeddings: payslip / RIB / invoice |
version.txt |
release 2 |
Model
TamperNet (~33M parameters): a pretrained ConvNeXt encoder on RGB, plus two forensic inputs —
fixed SRM noise residual filters (edits break the sensor/print noise) and a JPEG/DCT branch
(a pasted region carries its own 8×8 compression grid) — fused into a U-Net-style decoder that
outputs one logit per pixel. The page score is the mean of the top-200 pixel logits.
Inference runs at full resolution (1000 × 1414 px pages, padded to a multiple of 32 to keep the JPEG grid).
Results
Frozen test split: 793 synthetic payslips (390 genuine, 403 forged), never seen in training.
| Metric |
v2 (this release) |
v1 (U-Net) |
| ROC-AUC |
0.902 |
0.877 |
| Forgeries caught at 1% false alarms |
61% |
55% |
| Forgeries caught at 5% false alarms |
72% |
66% |
| Pixel F1 on forged pages |
0.81 |
0.77 |
| Pixel IoU |
0.68 |
0.62 |
| Segment (recall at 5% false alarms) |
v2 |
| Splice (pasted from another document) |
90% |
| Copy-move (inside the document) |
77% |
| Rewritten amount |
50% |
| Scanned |
77% |
| Phone photo |
63% |
Rewritten amounts are the hard case for pixels alone. In the full system, seven payslip
arithmetic rules raise recall on them from 56% to 92%, and on all forgeries to 95% at 1.7% false
alarms (240-document OCR subset):
Speed
A full page needs ~4 GB of RAM and ~5 s on 4 CPU threads (~0.44 s on a T4).
Limits
- Synthetic data only. Trained and evaluated on generated French payslips (Faker
fr_FR, fake
people and companies). Never measured on real documents.
- Built for one layout family (French payslips). Other documents need adaptation; the forensic
branches are generic.
- It flags; it does not decide. In docverify, a human analyst always makes the decision.
- EU AI Act: fraud detection is excluded from the Annex III 5(b) high-risk category. Using this
score directly to grant or refuse credit would not be.
License
Apache-2.0. The ConvNeXt encoder was initialised from timm ImageNet weights (Apache-2.0).
The SigLIP probe applies to Google's SigLIP (Apache-2.0), downloaded from Google's repository.
