Deep Automatic Threat Recognition: Considerations for Airport X-Ray Baggage Screening

Abstract

<p>Deep learning has made significant progress in recent years, contributing to major advancements in many fields. One such field is automatic threat recognition, where methods based on neural networks have surpassed more traditional machine learning methods. In particular, we evaluate the performance of convolutional object detection models within the context of X-ray baggage screening at airport checkpoints. To do so, we collected a large dataset of scans containing threats from a diverse set of classes, and then trained and compared a number of models. Many currently deployed X-ray scanners contain multiple X-ray emitter-detector pairs arranged to give multiple views of the scanned object, and we find that combining predictions from these improves overall performance. We select the best-performing models fitting our design criteria and integrate them into the X-ray scanning machines, resulting in functional prototypes capable of simulating live screening deployment.</p><p>We also explore a number of subfields of deep learning with potential to improve these deep automatic threat recognition algorithms. For example, as data collection efforts are scaled up and the number threat categories are expanded, the likelihood of missing annotations will also increase, especially if this new data is collected from real airport traffic. Such a setting is actually common in object detection datasets, and we show that a positive-unlabeled learning assumption better fits the characteristics of the data. Additionally, real-world data distributions tend to drift over time or evolve cyclically with the seasons. Baggage scan images also tend to be sensitive, meaning storing data may represent a security or privacy risk. As a result, a continual learning setting may be more appropriate for these kinds of data, which we examine in the context of generative adversarial networks. Finally, the sensitivity of security applications makes understanding models especially important. We thus spend some time examining how certain popular neural networks emerge from assumptions made starting from kernel methods. Through these works, we find that deep learning methods show considerable promise to improve existing automatic threat recognition systems.</p>