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:2602.20569v2 Announce Type: replace Abstract: We present AIForge-Doc, the first dedicated benchmark targeting exclusively diffusion-model-based inpainting in financial and form documents with p...
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:2606. 07613v1 Announce Type: cross Abstract: Visual evidence has long been treated as a reliable form of legal proof, but advances in artificial intelligence (AI) are undermining that assumption.
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: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...
The paper investigates an agentic framework for open‑world fake image detection that combines specialist detectors with per‑detector triage, prompting, and conflict‑aware evidence arbitration. Experiments across six configurations and three multimodal large language model backbones reveal that naive detector fusion yields high false‑positive rates, while triage and prompting consistently filter unreliable evidence. The most significant improvement comes from the reasoning component: a stronger judge markedly outperforms a weaker one, especially under distribution shift, and overall manipulation recall is nearly saturated, highlighting that the key challenge lies in calibrating trust and arbitrating conflicting forensic evidence rather than detecting manipulations themselves.
The study evaluates machine‑generated text detectors on scientific literature, finding that many detectors perform near perfectly on certain benchmarks due to superficial formatting cues rather than genuine linguistic differences. A logistic regression using 27 superficial features achieved macro‑F1 scores between 0.833 and 0.997, outperforming other detectors on arXiv subsets. The most powerful cue was the PDF extraction artifact—human texts preserve line breaks while generated texts appear as a single block—allowing classification at 0.996 AUC. By introducing format counterfactuals and normalizing formatting, the authors demonstrate that re‑wrapping machine‑written abstracts can flip 20–27% of detector decisions, reducing the generalization gap and improving out‑of‑domain performance.
arXiv:2610.28087v1 Announce Type: new Abstract: AI-generated images linked to news events and online claims can mislead audiences, enable fraud, or cause public controversy. We introduce WildAIGC-Ben...
arXiv:2607. 22745v1 Announce Type: cross Abstract: Rapid advances in image generation are eroding the evidentiary value of visual content in settings where authenticity can affect public safety and personal reputation.
The paper introduces SnapJudge, an image‑based AI detection system for math‑heavy scientific text, trained on screenshots of LaTeX‑generated PDFs. It shows that existing text‑only detectors like Pangram 4 lose accuracy as mathematical content increases, while fine‑tuned LaTeX detectors recover performance. SnapJudge achieves 92–93% detection rates at a 0.1% false‑positive rate, outperforming both Pangram 4 and text‑based models.
Distinguishing machine-generated text (MGT) from human-written text (HWT) becomes increasingly important due to potential misuse. However, most supervised detectors often degrade out-of-domain (OOD) a...
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...