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  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.2   pp.454-458
Publication Date: 2020/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDL8154
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
anomaly detection,  generative adversarial networks,  x-ray baggage security,  convolutional neural networks,  threat classification,  

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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.