Logit-based MIAs perform comparably on MLLMs with or without visual inputs in-distribution but visual inputs mask membership signals in out-of-distribution settings.
Privacy risk in ma- chine learning: Analyzing the connection to overfitting
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Lost in Modality: Evaluating the Effectiveness of Text-Based Membership Inference Attacks on Large Multimodal Models
Logit-based MIAs perform comparably on MLLMs with or without visual inputs in-distribution but visual inputs mask membership signals in out-of-distribution settings.