license: apache-2.0
language:
- fr
- en
library_name: onnx
pipeline_tag: image-segmentation
tags:
- document-forensics
- image-manipulation-detection
- tampering-detection
- fraud-detection
- payslip
- banking
- onnx
- pytorch
- synthetic-data
metrics:
- roc_auc
- f1
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.