Discriminative Autoencoding Framework for Simple and Efficient Anomaly Detection

Abstract

In this paper, a discriminative autoencoding framework is proposed for semi-supervised anomaly detection using reconstruction errors. The framework only consists of a generator and a discriminative encoder, and the output of the latter is a vector. In the training process, the framework is trained as a generative adversarial network based on quadratic potential divergence. An extra loss added in the objective function enforces the discriminative encoder to use the mean value of the output vector for discrimination, which also empowers it with encoding ability. In the testing process, the trained framework can be used as an autoencoder to reconstruct test samples, where the trained discriminative encoder works as an encoder, and samples with reconstruction errors above a predefined threshold are determined as anomalies. The properties of quadratic potential divergence ensure a simple training process with comparable performance, meanwhile the discriminative encoder with two functions makes full use of training resources and reduces required network structures. Comparisons on benchmark datasets also show the efficiency and superiorities of the proposed methods.

An Integrated Model Based on O-GAN and Density Estimation for Anomaly Detection

“…The adversarial game between the generator and the discriminator enables the generator to produce visually realistic images. At present, GANs have shown excellent performance in many fields such as super-resolution [14], image translation [16,17], video generation [18], text generation [19], and so on, as well as have a large number of applications in the field of anomaly detection [9,11,12,13,20]. Meanwhile, many scholars have also conducted in-depth studies on the network framework and mathematical principle of GAN, which gave rise to a series of GAN variants, such as DCGAN [21], WGAN [22], LSGAN [23], BiGAN [24], and so on.…”

“…Although these methods are excellent in some specific problems, these network structures are very complex. Mao et al proposed a very simple and effective anomaly detection method based on O-GAN [10], namely Dis-AE [12]. This anomaly detection method can make use of a relatively simple network structure and obtain good performance.…”

“…It is well known that the selection of threshold α is very important in the studies of semi-supervised anomaly detection. However, in our study, the determination of threshold was not a major research object, so we followed the setting of threshold used in [52,11,12].…”

“…Nowadays, numerous GAN-based anomaly detection methods have been developed, but most of these methods have very complex network structures. In order to prove that our method (and other methods [12] based on O-GAN) is more simple and efficient than other GAN-based methods, we compare the network structures of various GAN-based anomaly detection methods. As shown in Table 4, GAN-based methods often have very complex network structures.…”

“…Nevertheless, with the deepening of research, the network structure of these GAN-based anomaly detection methods has become more and more complicated. Therefore, we refer to the research of Mao et al [12] and propose an anomaly detection method based on O-GAN [10], which makes the network structure as simple as possible on the premise of ensuring accuracy.…”