arXiv Computer Vision
AIForge-Doc: A Benchmark for Detecting AI-Forged Tampering in Financial and Form Documents
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arXiv Computer Vision
4d ago
DOCFORGE-BENCH: A Comprehensive 0-shot Benchmark for Document Forgery Detection and Analysis
arXiv:2603.01433v3 Announce Type: replace Abstract: We present DOCFORGE-BENCH, the first unified zero-shot benchmark for document forgery detection, evaluating 14 methods across eight datasets spanni...
arXiv Computer Vision
4d ago
When the Forger Is the Judge: GPT-Image-2 Cannot Recognize Its Own Faked Documents
arXiv:2604.25213v2 Announce Type: replace Abstract: OpenAI's GPT-Image-2 has effectively erased the visual boundary between authentic and AI-edited document images: a single number on a receipt can b...
arXiv AI
4d ago
GPT4o-Receipt: A Dataset and Human Study for AI-Generated Document Forensics
GPT4o-Receipt is a benchmark dataset of 1,235 receipt images that pairs GPT‑4o‑generated receipts with authentic ones from established datasets. The study evaluates five state‑of‑the‑art multimodal LLMs and a 30‑annotator crowdsourced perceptual study, revealing that humans are better at spotting AI artifacts yet perform worse at binary detection than models like Claude Sonnet 4 and Gemini 2.5 Flash. The paradox is explained by the fact that the dominant forensic signals—arithmetic errors—are invisible to visual inspection but can be quickly verified by LLMs.
arXiv Computer Vision
Sep 15
Beyond Natural Images: Rethinking AI-Generated Image Detection in Documents
arXiv:2609.14352v1 Announce Type: new Abstract: AI-generated image detection has attracted increasing attention, but existing evaluations mainly focus on natural images, leaving AI-generated document...
arXiv Computer Vision
Oct 1
Team MSU GenText-Forensics Challenge 2026 Technical Report
arXiv:2609.38391v1 Announce Type: new Abstract: Document text forgery has evolved beyond simple pixel-level manipulation: modern attacks alter not only the appearance of a document but also its meani...
arXiv AI
Aug 12
Field-Localized Forgery Detection for Digital Identity Documents
arXiv:2605. 09089v2 Announce Type: replace-cross Abstract: Digital onboarding and eKYC systems used by banks, fintech platforms, telecom providers, and other third-party services commonly verify users by comparing an uploaded identity document with a selfie or live facial capture.