Fence GAN: Towards Better Anomaly Detection
This addresses the issue of ineffective anomaly detection in complex data for applications like security and quality control, though it is incremental as it builds on existing GAN-based methods.
The paper tackled the problem of anomaly detection in high-dimensional data by modifying GAN loss to generate samples at the boundary of the real data distribution, resulting in Fence GAN achieving the best anomaly classification accuracy on MNIST, CIFAR10, and KDD99 datasets compared to state-of-the-art methods.
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.