Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models

Understand

Large vision-language models (LVLMs) have demonstrated outstanding performance in many downstream tasks.

• However, LVLMs are trained on large-scale datasets, which can pose privacy risks if training images contain sensitive information.
• Therefore, it is important to detect whether an image is used to train the LVLM.
• Recent studies have investigated membership inference attacks (MIAs) against LVLMs, including detecting image-text pairs and single-modality content.