arXiv Computer Vision September 15, 2026 By Zhangjie Fu, Jiazhen Yan, Yuanwen Chen, Xinquan Yu, Yanzhe Li, Hui Jiang, Lei Gao, Chenfu Bao

Beyond Natural Images: Rethinking AI-Generated Image Detection in Documents

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benchmarks

arXiv AI

Jun 18

A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2

arXiv:2606. 19259v1 Announce Type: cross Abstract: Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information.

By Yijin Wang, Shuyi Wang, Wenhan Zhang, Yuqi Ouyang

llms diffusion multimodal benchmarks safety

arXiv Computer Vision

Oct 27

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...

By Zengqi Zhao, Weidi Xia, En Wei, Yan Zhang, Jane Mo, Tiannan Zhang, Yuanqin Dai, Zexi Chen, Yiran Tao, Simiao Ren

llms diffusion robotics fine-tuning benchmarks

arXiv Computation and Language

Oct 12

Measuring Artifact Reliance in Machine-Generated Text Detectors

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.

By Anastasia Voznyuk, Andrey Grabovoy

benchmarks

arXiv Computer Vision

Oct 27

AIForge-Doc: A Benchmark for Detecting AI-Forged Tampering in Financial and Form Documents

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...

By Jiaqi Wu, Yuchen Zhou, Muduo Xu, Zisheng Liang, Simiao Ren, Jiayu Xue, Meige Yang, Siying Chen, Jingheng Huan

llms diffusion benchmarks

Hugging Face Trending Papers

Aug 25

Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text

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...

benchmarks

arXiv AI

Jul 28

TextRich: A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2

arXiv:2606. 19259v2 Announce Type: replace-cross Abstract: Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information.