模型逆向攻擊重建訓練數據

Risk Description

Attackers reconstruct sensitive training data samples through carefully designed query sequences, exploiting model output probability distributions. Unlike membership inference attacks (determining if a sample is in the training set), model inversion attacks can actually reconstruct approximate content of training data, including facial images, medical records, or financial data. Recent research shows increasing success rates of inversion attacks on diffusion models.