Model Inversion

Reason about model inversion as a defender. Learn the conceptual attack (adversary reconstructs sensitive features from model outputs), the historical research (Fredrikson et al.), the privacy implications, the evaluation methodology, the defence stack (output truncation, top-k vs full distribution, DP at inference, query-rate limits), and the link to membership-inference defences.