Andrews, JTA;
Jaccard, N;
Rogers, TW;
Griffin, LD;
(2017)
Representation-learning for anomaly detection in complex x-ray cargo imagery.
In:
Anomaly Detection and Imaging with X-Rays (ADIX) II.
(pp. 101870E-1).
SPIE
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Representation-learning for anomaly detection in complex x-ray cargo imagery |
| Event: | SPIE Defense + Commercial Sensing 2017 |
| Location: | Anaheim, California, United States |
| Dates: | 09 April 2017 - 13 April 2017 |
| ISBN-13: | 9781510608757 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/12.2261101 |
| Publisher version: | http://doi.org/10.1117/12.2261101 |
| Language: | English |
| Additional information: | © 2017 SPIE. This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Anomaly detection, representation-learning, machine learning, deep learning, cargo screening, X-ray imaging, security imaging |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10024872 |
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