  Adversarial attacks are often considered as threats to the robustness of Deep
Neural Networks (DNNs). Various defending techniques have been developed to
mitigate the potential negative impact of adversarial attacks against task
predictions. This work analyzes adversarial attacks from a different
perspective. Namely, adversarial examples contain implicit information that is
useful to the predictions i.e., image classification, and treat the adversarial
attacks against DNNs for data self-expression as extracted abstract
representations that are capable of facilitating specific learning tasks. We
propose an algorithmic framework that leverages the advantages of the DNNs for
data self-expression and task-specific predictions, to improve image
classification. The framework jointly learns a DNN for attacking Variational
Autoencoder (VAE) networks and a DNN for classification, coined as Attacking
VAE for Improve Classification (AVIC). The experiment results show that AVIC
can achieve higher accuracy on standard datasets compared to the training with
clean examples and the traditional adversarial training.
