AI RESEARCH
Generalization and Membership Inference Attack a Practical Perspective
arXiv CS.LG
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ArXi:2604.19936v1 Announce Type: new With the emergence of new evaluation metrics and attack methodologies for Membership Inference Attacks (MIA), it becomes essential to reevaluate previously accepted assumptions. In this paper, we revisit the longstanding debate regarding the correlation between MIA success rates and model generalization using an empirical approach. We focused on employing augmentation techniques and early stopping to enhance model generalization and examined their impact on MIA success rates.