ArmorOCR framework enhances adversarial OCR perception with new AdvSpot benchmark

Researchers have introduced ArmorOCR, a novel two-stage training framework designed to enhance the robustness of optical character recognition (OCR) against adversarial attacks. This framework addresses the limitations of existing OCR benchmarks by proposing AdvSpot, the first benchmark specifically for grounded adversarial OCR evaluation, featuring 390 images with region-level annotations across various adversarial OCR types. ArmorOCR utilizes On-Policy Self-Distillation (OPSD) to acquire adversarial OCR perception from transformed observations and refines this perception through Group Relative Policy Optimization (GRPO), demonstrating improved adversarial OCR capabilities while maintaining general OCR performance. AI

IMPACT This research could lead to more robust OCR systems capable of withstanding adversarial attacks, improving security and reliability in applications that rely on text recognition.

RANK_REASON The cluster contains a research paper detailing a new method and benchmark for adversarial OCR. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• AdvSpot
• ArmorOCR
• arXiv
• Group Relative Policy Optimization
• Large Multimodal Models
• On-Policy Self-Distillation
• optical character recognition
• visual question answering

• paper
• model release

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