A New GAN-Based Anomaly Detection (GBAD) Approach for Multi-Threat Object Classification on Large-Scale X-Ray Security Images

Joanna Kazzandra DUMAGPI; Woo-Young JUNG; Yong-Jin JEONG

doi:10.1587/transinf.2019EDL8154

IEICE TRANSACTIONS on Information

A New GAN-Based Anomaly Detection (GBAD) Approach for Multi-Threat Object Classification on Large-Scale X-Ray Security Images

Joanna Kazzandra DUMAGPI, Woo-Young JUNG, Yong-Jin JEONG

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Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.

Publication: IEICE TRANSACTIONS on Information Vol.E103-D No.2 pp.454-458

Publication Date: 2020/02/01

Publicized: 2019/10/23

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2019EDL8154

Type of Manuscript: LETTER

Category: Artificial Intelligence, Data Mining

Authors

Joanna Kazzandra DUMAGPI
  Kwangwoon University
Woo-Young JUNG
  Kwangwoon University
Yong-Jin JEONG
  Kwangwoon University

Keyword

anomaly detection, generative adversarial networks, x-ray baggage security, convolutional neural networks, threat classification

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Joanna Kazzandra DUMAGPI, Woo-Young JUNG, Yong-Jin JEONG, "A New GAN-Based Anomaly Detection (GBAD) Approach for Multi-Threat Object Classification on Large-Scale X-Ray Security Images" in IEICE TRANSACTIONS on Information, vol. E103-D, no. 2, pp. 454-458, February 2020, doi: 10.1587/transinf.2019EDL8154.
Abstract: Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8154/_p

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@ARTICLE{e103-d_2_454,
author={Joanna Kazzandra DUMAGPI, Woo-Young JUNG, Yong-Jin JEONG, },
journal={IEICE TRANSACTIONS on Information},
title={A New GAN-Based Anomaly Detection (GBAD) Approach for Multi-Threat Object Classification on Large-Scale X-Ray Security Images},
year={2020},
volume={E103-D},
number={2},
pages={454-458},
abstract={Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.},
keywords={},
doi={10.1587/transinf.2019EDL8154},
ISSN={1745-1361},
month={February},}

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TY - JOUR
TI - A New GAN-Based Anomaly Detection (GBAD) Approach for Multi-Threat Object Classification on Large-Scale X-Ray Security Images
T2 - IEICE TRANSACTIONS on Information
SP - 454
EP - 458
AU - Joanna Kazzandra DUMAGPI
AU - Woo-Young JUNG
AU - Yong-Jin JEONG
PY - 2020
DO - 10.1587/transinf.2019EDL8154
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2020
AB - Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.
ER -

IEICE TRANSACTIONS on Information