Learning to Defend: Auto-Augmentation Search Against Model Inversion Attacks

Model Inversion Attacks (MIAs, where attackers recover private training data by accessing a model's weights or outputs) pose serious privacy risks, and existing defenses don't protect well against attackers with different levels of knowledge. The paper proposes DAAS (Defense via Auto-Augmentation Search), which automatically finds the best combinations of data augmentation (transformations like cropping applied to images) that balance privacy protection and model usefulness better than current methods.