LeakageBench: Document-Level Leakage Risk for Redacting Personally Identifiable Information in Document Images

arXiv:2609.02207 · cs.CV, cs.CL · Submitted 2026-09-02 · Read on arXiv

cs.CV, cs.CL

Submitted: 2026-09-02

Updated: 2026-09-02

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 23× in this paper · explained in 2 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Grounding — 4× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Failure Modes — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Analyzer — 1× in this paper · explained in 1 episode
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Federal Communications Commission — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• UCSF Industry Documents Library — 1× in this paper

Sources

• PRvL: Quantifying the Capabilities and Risks of Large Language Models for PII Redaction
• LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
• ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction
• Qwen3-VL Technical Report
• RedactBuster: Entity Type Recognition from Redacted Documents
• ProPILE: Probing Privacy Leakage in Large Language Models
• OCR-IDL: OCR Annotations for Industry Document Library Dataset
• DocVQA: A Dataset for VQA on Document Images
• LayoutLM: Pre-training of Text and Layout for Document Image Understanding
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

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