LeakageBench is a new benchmark consisting of 500 document images with 11,954 GDPR‑aligned PII annotations, designed to evaluate document‑level redaction risk. Unlike existing text‑centric PII benchmarks, it measures whether a page remains unsafe if any identifier is missed, using entity‑level F1, group‑wise leakage, and document‑level leakage metrics. Experiments show that even advanced OCR pipelines and vision‑language models improve localization but still leave a high proportion of pages unsafe for release.
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
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...
By Zhangjie Fu, Jiazhen Yan, Yuanwen Chen, Xinquan Yu, Yanzhe Li, Hui Jiang, Lei Gao, Chenfu Bao
arXiv:2606. 18782v1 Announce Type: cross Abstract: Large Language Models are increasingly applied to sensitive domains that require redaction of personally identifiable information (PII).
By Sean Brynj\'olfsson, Shashvat Jayakrishnan, Esha Sali, Diptanshu Purwar, Madhav Aggarwal
arXiv:2607. 16203v1 Announce Type: cross Abstract: Document parsing is a foundational step for document understanding tasks such as visual question answering and key information extraction, as it transforms unstructured scanned images into structured representations by extracting textual, visual, and layout information.
By Zihan Xu, Puzhen Wu, Lawrence Chun Man Lau, Wei Liu, Sirui Li, Yifan Peng, Yihao Ding
arXiv:2606. 07595v1 Announce Type: cross Abstract: Vision-language agents increasingly consume screenshots, documents, and user interfaces before writing to memory, sending messages, or invoking external tools.
By Youting Wang, Yuan Tang, Yitian Qian, Chen Zhao
The paper introduces Strategic 16K, a 16,000‑document corpus of diplomatic cables from WikiLeaks’ Public Library of US Diplomacy, designed to eliminate label leakage. It benchmarks six models—both classical machine learning and transformer-based—on this leakage‑controlled dataset, finding BERT and ELECTRA top performers while TF‑IDF with Logistic Regression offers strong accuracy at lower cost. This work provides the first fully reproducible sensitivity‑classification benchmark built under explicit leakage‑control conditions.
By Aleesha Zainab, Muhammad Ahmed Khalid, Faheem Ullah Khan, Asifullah Khan
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
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
T2LSC-Bench is a new benchmark for evaluating localized semantic control in text-to-image generation, consisting of 50 seed subjects and 1,200 prompt cases per model, producing 7,160 images across six models. The benchmark measures Text-at-Anchor Accuracy, Semantic Subject Preservation, Semantic Leakage Rate, and Conditional Semantic Leakage Rate using a dual‑branch protocol that combines OCR‑VLM verification with structured VLM semantic judgments. Results show that while accurate text rendering remains high, semantic leakage can increase dramatically under stress‑test conditions, and anti‑leakage prompting can reduce leakage without harming rendering accuracy.
By Yan Wang, Xinyi Hou, Weiguo Lin, Junjun Si, Siwei Ma
arXiv:2605.19309v4 Announce Type: replace
Abstract: Document Layout Analysis (DLA) pipelines provide structured page representations for retrieval-augmented generation, long-document question answeri...
By Yue Chen, Yihao Wang, Ziyi Tang, Yongsen Zheng, Keze Wang
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...
By Kirill Koltsov, Aleksandr Gushchin, Dmitriy Vatolin, Anastasia Antsiferova