Membership Inference Attacks against Large Vision-Language Models

Understand

Large vision-language models (VLLMs) exhibit promising capabilities for processing multi-modal tasks across various application scenarios.

• However, their emergence also raises significant data security concerns, given the potential inclusion of sensitive information, such as private photos and medical records, in their training datasets.
• Detecting inappropriately used data in VLLMs remains a critical and unresolved issue, mainly due to the lack of standardized datasets and suitable methodologies.
• In this study, we introduce the first membership inference attack (MIA) benchmark tailored for various VLLMs to facilitate training data detection.