Privacy-Aware Visual Language Models

TMLR Paper7001 Authors

Abstract: As Visual Language Models (VLMs) become increasingly embedded in everyday applications. Ensuring they can recognise and appropriately handle privacy-sensitive content is thus essential to protect users. To this end, we conduct a comprehensive evaluation of ten state-of-the-art VLMs and identify limitations in their understanding of visual privacy. However, existing privacy-related datasets often suffer from label inconsistencies, limiting their reliability. To address this, we introduce two compact, high-quality benchmarks, PrivBench and PrivBench-H, that focus on commonly recognised visual privacy categories aligned with the General Data Protection Regulation (GDPR). Additionally, we present PrivTune, an instruction-tuning dataset specifically curated to improve privacy sensitivity. We obtain a Privacy VLM by fine-tuning an off-the-shelf VLM on only 100 samples from PrivTune, which leads to substantial gains on all benchmarks, surpassing even GPT-4, while maintaining strong performance on other tasks. Our findings show that privacy-awareness in VLMs can be substantially improved with minimal data and careful dataset design, setting the stage for safer, more privacy-aligned AI systems.

Submission Type: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission: The last submission was rejected due to margin violations. We fixed the margins.

Assigned Action Editor: ~Alain_Durmus1

Submission Number: 7001