Model Inversion Attacks: Reconstructing Private Training Data from Model Confidence Scores
A model's output probability scores are not neutral summaries — they encode information about the training data that produced them. Model inversion attacks exploit this to reconstruct representative training examples from API-level confidence values. This post covers the foundational Fredrikson et al. work, black-box and GAN-based variants, conditions for vulnerability, and how to distinguish this class from membership inference and training data extraction.