AI-Powered Document Fraud
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AI-inpainted document forgery benchmarks across GPT-Image-2, GPT-4o, Gemini, and Ideogram. Receipts, financial forms, KYC documents. • 3 items • Updated
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AIForge-Doc: A Benchmark of AI-Forged Document Images
AIForge-Doc is the first large-scale benchmark of AI-forged document images, targeting financial and identity document fraud. Every tampered image was produced by a diffusion-model inpainting pipeline — a threat model that existing forgery detectors cannot reliably handle.
At a Glance
| Attribute | Value |
|---|---|
| Total forged images | 4,061 |
| Training split | 3,249 (80 %) |
| Testing split | 812 (20 %) |
| Authentic baseline images | 812 (mirror of test split) |
| AI inpainting tools used | 2 (Gemini 2.5 Flash Image, Ideogram v2 Edit) |
| Source datasets | CORD v2, WildReceipt, SROIE, XFUND |
| Document types | Receipts (89.7 %), Forms (10.3 %) |
| Languages | 9 (EN, ID, DE, IT, ES, FR, PT, ZH, JA) |
| Output format | DocTamper-compatible (binary grayscale masks) |
| Mask convention | 0 = authentic · 255 = tampered pixel |
Directory Layout
AIForge-Doc/
├── TrainingSet/
│ ├── images/ # 000000001.png … 000003249.png
│ └── masks/ # same filenames; 0=authentic, 255=tampered
├── TestingSet/
│ ├── images/ # 000000001.png … 000000812.png
│ └── masks/
├── metadata.jsonl # full provenance for every image (see schema below)
├── README.md # this file
└── DATASHEET.md # Datasheets for Datasets (Gebru et al., 2021)
File names are 9-digit zero-padded integers (000000001.png), identical to the
DocTamper dataset convention so that existing evaluation pipelines require no modification.
Provenance — metadata.jsonl Schema
Each line is a JSON object with the following fields:
| Field | Type | Description |
|---|---|---|
spec_id |
str | Unique forgery spec identifier |
image_id |
str | Original image ID from source dataset |
source_dataset |
str | cord / wildreceipt / sroie / xfund |
doc_type |
str | receipt or form |
language |
str | ISO 639-1 language code |
field_name |
str | Annotation key of the tampered field |
original_value |
str | Ground-truth text before tampering |
forged_value |
str | Synthesised replacement text |
bbox_xyxy |
list[int] | Tampered region [x1, y1, x2, y2] in full-image pixels |
assigned_tool |
str | Inpainting model used (gemini-nano / qwen-inpaint) |
split |
str | training or testing |
new_id |
str | 9-digit zero-padded filename stem |
final_image_path |
str | Absolute path on generation machine |
final_mask_path |
str | Absolute path on generation machine |
generated_at |
str | ISO 8601 timestamp |
Dataset Statistics
By Inpainting Tool
| Tool | API Provider | Images | Share |
|---|---|---|---|
Gemini 2.5 Flash Image (gemini-nano) |
Google / OpenRouter | 3,639 | 89.6 % |
Ideogram v2 Edit (qwen-inpaint) |
fal.ai | 422 | 10.4 % |
By Source Dataset
| Source | Document Type | Images | Languages |
|---|---|---|---|
| WildReceipt | Receipt | 1,696 | EN |
| CORD v2 | Receipt | 1,000 | ID |
| SROIE | Receipt | 946 | EN |
| XFUND | Form | 419 | DE, IT, ES, FR, PT, ZH, JA |
By Language
| Language | Code | Images |
|---|---|---|
| English | en |
2,642 |
| Indonesian | id |
1,000 |
| Italian | it |
81 |
| German | de |
78 |
| Spanish | es |
67 |
| French | fr |
56 |
| Portuguese | pt |
62 |
| Chinese | zh |
38 |
| Japanese | ja |
37 |
Most Commonly Tampered Field Types
| Field | Count |
|---|---|
| Telephone / store address | 1,388 |
| Free-form text | 880 |
| Store address | 457 |
| Form answer | 419 |
| Menu price | 404 |
Forgery Generation Pipeline
Each forgery is created in four steps to prevent global image drift:
- Field selection — A numeric or key field (price, date, ID, phone) is chosen from the source annotation and a plausible replacement value is generated.
- Context crop — The bounding box is expanded 50 % on each side (minimum 100 px) to provide font and colour context for the inpainting model.
- Masked inpainting — The context crop is inpainted with the replacement text using a diffusion-model API (white mask = tamper region, black = preserve).
- Patch-back — Only the exact field bbox region is pasted back into the full image;
the ground-truth mask marks those pixels as
255.
This technique ensures that only the tampered field is replaced while the rest of the document (background, typography, logos) remains authentic.
Baseline Results
Evaluated on the TestingSet (812 forged + 812 authentic paired images).
Image-Level Detection (AUC-ROC)
| Method | AUC | 95 % CI | AP |
|---|---|---|---|
| TruFor (Guillaro et al., 2023) | 0.751 | [0.726, 0.776] | 0.709 |
| DocTamper (Qu et al., 2023) | 0.563 | [0.535, 0.591] | 0.564 |
| GPT-4o (zero-shot) | 0.509 | [0.481, 0.537] | 0.516 |
Pixel-Level Localisation (TruFor only)
| Metric | Value |
|---|---|
| IoU | 0.358 |
| F1 | 0.434 |
| Pixel-AUC | 0.916 |
Key finding: Even the best-performing detector (TruFor) achieves only 0.751 AUC — well below the ≥ 0.95 reported on traditional Photoshop-tampered benchmarks. DocTamper and GPT-4o are near-random, confirming that AI-generated forgeries represent a qualitatively different and substantially harder threat model.
Licence
The forged images are derived from:
- CORD v2 — CC BY 4.0
- WildReceipt — Apache 2.0
- SROIE — ICDAR 2019 research use
- XFUND — CC BY 4.0
The AIForge-Doc dataset itself (forged images + masks + metadata) is released under CC BY 4.0. You are free to share and adapt the material for any purpose provided you give appropriate credit.
Citation
If you use AIForge-Doc in your research, please cite:
@dataset{aiforgedoc2026,
title = {{AIForge-Doc}: A Benchmark of AI-Forged Document Images},
year = {2026},
note = {Dataset paper under submission},
url = {https://github.com/YOUR_ORG/aiforge-doc}
}
Contact
For questions about the dataset or to report issues, please open a GitHub issue or contact the authors at [your-email@institution.edu].
Related Research from Scam.AI
This dataset is part of Scam.AI's broader research portfolio on deepfake detection, synthetic media forensics, and adversarial robustness. Other relevant work from our group:
- DOCFORGE-BENCH: A Comprehensive Benchmark for Document Forgery Detection and Analysis — Zhao, Xia, Wei et al. (arXiv:2603.01433)
- When the Forger Is the Judge: GPT-Image-2 Cannot Recognize Its Own Faked Documents — Wu, Zhou, Ng et al. (arXiv:2604.25213)
- AIForge-Doc: A Benchmark for Detecting AI-Forged Tampering in Financial and Form Documents — Wu, Zhou, Xu et al. (arXiv:2602.20569)
- Can Multi-modal (reasoning) LLMs detect document manipulation? — Liang, Zewde, Singh et al. (Google Scholar)
Browse our full publications list and dataset catalog at scam.ai/research.
About Scam.AI
Scam.AI builds detection systems for AI-driven fraud — deepfakes, document forgery, AI-generated synthetic media, and adversarial attacks against identity verification. Learn more at scam.ai.
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Collection including Scam-AI/AIForge-Doc-v1
Papers for Scam-AI/AIForge-Doc-v1
When the Forger Is the Judge: GPT-Image-2 Cannot Recognize Its Own Faked Documents
Paper • 2604.25213 • Published
DOCFORGE-BENCH: A Comprehensive 0-shot Benchmark for Document Forgery Detection and Analysis
Paper • 2603.01433 • Published
AIForge-Doc: A Benchmark for Detecting AI-Forged Tampering in Financial and Form Documents
Paper • 2602.20569 • Published