  Model Inversion (MI), in which an adversary abuses access to a trained
Machine Learning (ML) model attempting to infer sensitive information about its
original training data, has attracted increasing research attention. During MI,
the trained model under attack (MUA) is usually frozen and used to guide the
training of a generator, such as a Generative Adversarial Network (GAN), to
reconstruct the distribution of the original training data of that model. This
might cause leakage of original training samples, and if successful, the
privacy of dataset subjects will be at risk if the training data contains
Personally Identifiable Information (PII). Therefore, an in-depth investigation
of the potentials of MI techniques is crucial for the development of
corresponding defense techniques. High-quality reconstruction of training data
based on a single model is challenging. However, existing MI literature does
not explore targeting multiple models jointly, which may provide additional
information and diverse perspectives to the adversary.
  We propose the ensemble inversion technique that estimates the distribution
of original training data by training a generator constrained by an ensemble
(or set) of trained models with shared subjects or entities. This technique
leads to noticeable improvements of the quality of the generated samples with
distinguishable features of the dataset entities compared to MI of a single ML
model. We achieve high quality results without any dataset and show how
utilizing an auxiliary dataset that's similar to the presumed training data
improves the results. The impact of model diversity in the ensemble is
thoroughly investigated and additional constraints are utilized to encourage
sharp predictions and high activations for the reconstructed samples, leading
to more accurate reconstruction of training images.
