‘Unexpected item in the bagging area’: Anomaly Detection in X-ray Security Images

Abstract

The role of Anomaly Detection in X-ray security imaging, as a supplement to targeted threat detection, is described; and a taxonomy of anomalies types in this domain is presented. Algorithms are described for detecting appearance anomalies, of shape, texture and density; and semantic anomalies of object category presence. The anomalies are detected on the basis of representations extracted from a convolutional neural network pre-trained to identify object categories in photographs: from the final pooling layer for appearance anomalies, and from the logit layer for semantic anomalies. The distribution of representations in normal data are modelled using high-dimensional, full- covariance, Gaussians; and anomalies are scored according to their likelihood relative to those models. The algorithms are tested on X-ray parcel images using stream-of-commerce data as the normal class, and parcels with firearms present as examples of anomalies to be detected. Despite the representations being learnt for photographic images, and the varied contents of stream-of- commerce parcels; the system, trained on stream-of-commerce images only, is able to detect 90% of firearms as anomalies, while raising false alarms on 18% of stream-of-commerce.

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Reproductions