defense arXiv Apr 21, 2026 · 4w ago
Fateme Rahmani, Mahdi Jafari Siavoshani, Mohammad Hossein Rohban · Sharif University of Technology
Empirical study showing advanced generalization techniques (augmentation, early stopping) reduce membership inference attack success by up to 100×
Membership Inference Attack vision
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. We found that utilizing advanced generalization techniques can significantly decrease attack performance, potentially by up to 100 times. Moreover, combining these methods not only improves model generalization but also reduces attack effectiveness by introducing randomness during training. Additionally, our study confirmed the direct impact of generalization on MIA performance through an analysis of over 1K models in a controlled environment.
cnn Sharif University of Technology
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