  Anomaly detection is a classical problem where the aim is to detect anomalous
data that do not belong to the normal data distribution. Current
state-of-the-art methods for anomaly detection on complex high-dimensional data
are based on the generative adversarial network (GAN). However, the traditional
GAN loss is not directly aligned with the anomaly detection objective: it
encourages the distribution of the generated samples to overlap with the real
data and so the resulting discriminator has been found to be ineffective as an
anomaly detector. In this paper, we propose simple modifications to the GAN
loss such that the generated samples lie at the boundary of the real data
distribution. With our modified GAN loss, our anomaly detection method, called
Fence GAN (FGAN), directly uses the discriminator score as an anomaly
threshold. Our experimental results using the MNIST, CIFAR10 and KDD99 datasets
show that Fence GAN yields the best anomaly classification accuracy compared to
state-of-the-art methods.
