One-class anomaly detection via novelty normalization

Anomaly detection is an important task in many real-world applications, such as within cybersecurity and surveillance. As with most data these days, the size and dimensionality of the data within these fields are constantly growing, which makes it essential to develop an approach that can both accurately and efficiently identify anomalies within these datasets. In this paper, we address the problem of one-class anomaly detection, where after training on a singular class, we try to determine whether or not inputs belong to that said class. Most of the currently existing methods have limitations in which the criterion of the novel class relies solely on the reconstruction error term. We attempt to break away from this restriction by proposing the use of an autoencoder network with a normalization term. We pair this with an additive novelty scoring module during the training procedure as a way to determine the difference between a given image and our determined normal class, therefore improving the efficiency of our model. We evaluate our model on MNIST, CIFAR-10, and Fashion-MNIST, three popular datasets for image classification, and compare the results against other various state-of-the-art models to determine the efficacy of our efforts. Our model not only outperforms the existing methods, but it also gives us a narrower range of AUCs for the tested classes, suggesting a stark improvement in both accuracy and precision. Moreover, we discover that introducing this “Novelty Normalization” concept into our model allows us to expand its usage into multiclass scenarios without a steep drop in accuracy.