  Membership Inference Attacks (MIAs) determine whether a specific data point
was included in the training set of a target model. In this paper, we introduce
the Semantic Membership Inference Attack (SMIA), a novel approach that enhances
MIA performance by leveraging the semantic content of inputs and their
perturbations. SMIA trains a neural network to analyze the target model's
behavior on perturbed inputs, effectively capturing variations in output
probability distributions between members and non-members. We conduct
comprehensive evaluations on the Pythia and GPT-Neo model families using the
Wikipedia dataset. Our results show that SMIA significantly outperforms
existing MIAs; for instance, SMIA achieves an AUC-ROC of 67.39% on Pythia-12B,
compared to 58.90% by the second-best attack.
