#model-vulnerability News & Analysis

Assessing Per-Sample Membership Inference Vulnerability without Retraining

Researchers propose a novel method to assess individual training data vulnerability to membership inference attacks without requiring shadow models. The approach combines theoretical analysis in linear settings with a practical surrogate score for deep networks, using only geometry and loss information from a single trained model.