  Despite inherent ill-definition, anomaly detection is a research endeavor of
great interest within machine learning and visual scene understanding alike.
Most commonly, anomaly detection is considered as the detection of outliers
within a given data distribution based on some measure of normality. The most
significant challenge in real-world anomaly detection problems is that
available data is highly imbalanced towards normality (i.e. non-anomalous) and
contains a most a subset of all possible anomalous samples - hence limiting the
use of well-established supervised learning methods. By contrast, we introduce
an unsupervised anomaly detection model, trained only on the normal
(non-anomalous, plentiful) samples in order to learn the normality distribution
of the domain and hence detect abnormality based on deviation from this model.
Our proposed approach employs an encoder-decoder convolutional neural network
with skip connections to thoroughly capture the multi-scale distribution of the
normal data distribution in high-dimensional image space. Furthermore,
utilizing an adversarial training scheme for this chosen architecture provides
superior reconstruction both within high-dimensional image space and a
lower-dimensional latent vector space encoding. Minimizing the reconstruction
error metric within both the image and hidden vector spaces during training
aids the model to learn the distribution of normality as required. Higher
reconstruction metrics during subsequent test and deployment are thus
indicative of a deviation from this normal distribution, hence indicative of an
anomaly. Experimentation over established anomaly detection benchmarks and
challenging real-world datasets, within the context of X-ray security
screening, shows the unique promise of such a proposed approach.
