  This paper proposes an ensemble learning model that is resistant to
adversarial attacks. To build resilience, we introduced a training process
where each member learns a radically distinct latent space. Member models are
added one at a time to the ensemble. Simultaneously, the loss function is
regulated by a reverse knowledge distillation, forcing the new member to learn
different features and map to a latent space safely distanced from those of
existing members. We assessed the security and performance of the proposed
solution on image classification tasks using CIFAR10 and MNIST datasets and
showed security and performance improvement compared to the state of the art
defense methods.
