‘Unexpected item in the bagging area’: Anomaly Detection in X-ray Security Images
- Submitting institution
-
University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14546
- Type
- D - Journal article
- DOI
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10.1109/TIFS.2018.2881700
- Title of journal
- IEEE Transactions on Information Forensics and Security
- Article number
- -
- First page
- 1539
- Volume
- 14
- Issue
- 6
- ISSN
- 1556-6013
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper pioneers the use of Anomaly Detection (deviation from normal) as an alternative to Threat Detection in the field of automated analysis of X-ray security images (e.g. cabin baggage in airports). It establishes a taxonomy of anomaly types for the domain, and presents and evaluates an algorithm for detection of two of the types: appearance and semantic anomalies. The work has directly influenced the UK HMG’s Future Aviation Security Systems funding programme, appearing as an example technology in several of its calls for research. Invited presentations on it have been delivered to the US conferences ADSA18 and AVSEC19.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -