  Model inversion (MI) attacks are aimed at reconstructing training data from
model parameters. Such attacks have triggered increasing concerns about
privacy, especially given a growing number of online model repositories.
However, existing MI attacks against deep neural networks (DNNs) have large
room for performance improvement. We present a novel inversion-specific GAN
that can better distill knowledge useful for performing attacks on private
models from public data. In particular, we train the discriminator to
differentiate not only the real and fake samples but the soft-labels provided
by the target model. Moreover, unlike previous work that directly searches for
a single data point to represent a target class, we propose to model a private
data distribution for each target class. Our experiments show that the
combination of these techniques can significantly boost the success rate of the
state-of-the-art MI attacks by 150%, and generalize better to a variety of
datasets and models. Our code is available at
https://github.com/SCccc21/Knowledge-Enriched-DMI.
