AIForge-Doc: A Benchmark for Detecting AI-Forged Tampering in Financial and Form Documents
We present AIForge-Doc, the first dedicated benchmark targeting exclusively diffusion-model-based inpainting in financial and form documents with pixel-level annotation. Existing document forgery datasets rely on traditional digital editing tools (e.g., Adobe Photoshop, GIMP), creating a critical gap: state-of-the-art detectors are blind to the rapidly growing threat of AI-forged document fraud. AIForge-Doc addresses this gap by systematically forging numeric fields in real-world receipt and form images using two AI inpainting APIs -- Gemini 2.5 Flash Image and Ideogram v2 Edit -- yielding 4,061 forged images from four public document datasets (CORD, WildReceipt, SROIE, XFUND) across nine...
| Authors | Jiaqi Wu, Yuchen Zhou, Muduo Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 February 2026 |
| Last updated at source | 24 February 2026 |
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- Scam-AI/gpt4o-receiptDataset · cited by
- Scam-AI/gpt-image-2Dataset · cited by
- Scam-AI/AIForge-Doc-v1Dataset · cited by
- Scam-AI/AIForge-Doc-v2Dataset · cited by
Models and datasets referencing this paper
- Scam-AI/AIForge-Doc-v1Dataset
- Scam-AI/AIForge-Doc-v2Dataset
- Scam-AI/gpt-image-2Dataset
- Scam-AI/gpt4o-receiptDataset
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