  This paper presents a simple yet effective method for anomaly detection. The
main idea is to learn small perturbations to perturb normal data and learn a
classifier to classify the normal data and the perturbed data into two
different classes. The perturbator and classifier are jointly learned using
deep neural networks. Importantly, the perturbations should be as small as
possible but the classifier is still able to recognize the perturbed data from
unperturbed data. Therefore, the perturbed data are regarded as abnormal data
and the classifier provides a decision boundary between the normal data and
abnormal data, although the training data do not include any abnormal data.
Compared with the state-of-the-art of anomaly detection, our method does not
require any assumption about the shape (e.g. hypersphere) of the decision
boundary and has fewer hyper-parameters to determine. Empirical studies on
benchmark datasets verify the effectiveness and superiority of our method.
