Existing approaches to automated security image analysis focus on the detection of particular classes of threat. However, this mode of inspection is ineffectual when dealing with mature classes of threat, for which adversaries have refined effective concealment techniques. Furthermore, these methods may be unable to detect potential threats that have never been seen before. Therefore, in this paper, we investigate an anomaly detection framework, at X-ray image patch-level, based on: (i) image representations, and (ii) the detection of anomalies relative to those representations. We present encouraging preliminary results, using representations learnt using convolutional neural networks, as well as several contributions to a general-purpose anomaly detection algorithm based on decision-tree learning.
Jerone T. A. Andrews, Nicolas Jaccard, Thomas W. Rogers, and Lewis D. Griffin, "Representation-learning for anomaly detection in complex x-ray cargo imagery," Proc. SPIE 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II, 101870E (Presented at SPIE Defense + Security: April 12, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2261101.
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