Threat Object-based anomaly detection in X-ray images using GAN-based ensembles

The problem of detecting dangerous or prohibited objects in luggage is a very important step during the implementation of Security setup at Airports, Banks, Government buildings, etc. At present, the most common techniques for detecting such dangerous objects are by using intelligent data analysis algorithms such as deep learning techniques on X-ray imaging or employing a human workforce for inferring the presence of these threat objects in the obtained X-ray images. One of the major challenges while using deep-learning methods to detect such objects is the lack of high-quality threat image data containing the “dangerous” objects (objects of interest) versus the non-threat image data in practical scenarios. So, to tackle this data scarcity problem, anomaly detection techniques using normal data samples have shown great promise. Also, among the available Deep Learning Strategies for anomaly detection for computer vision applications, generative adversarial networks have achieved state-of-the-art results. Considering these insights, we adopted a newly proposed architecture known as Skip-GANomaly and devised a modified version of it by using a UNet++ style generator which performed better than Skip-GANomaly, getting an AUC of 94.94% on Compass-XP, a public X-ray dataset. Finally, for targeting better latent space exploration, we combine these two architectures into an Ensemble, which gives another boost to the performance, getting an AUC of 96.8% on the same Compass-XP, a public X-ray dataset. To further validate the effectiveness of ensemble-based architecture, its performance was tested on patch-based training data on a subset of randomly chosen images of another huge public X-ray dataset named as SIXray, and obtained an AUC of 75.3% on this reduced dataset. To demonstrate the prowess of the discriminator and to bring some explainability to the working of our ensemble, we have used Uniform Manifold Approximation and Projection to plot the latent-space vectors for the dangerous and non-dangerous objects of the test-set; this analysis indicates that the Ensemble learns better features for separating the anomalous class from non-anomalous with respect to the individual architectures. Thus, our proposed architecture provides state-of-the-art results for threat object detection. Most importantly, our models are able to detect threat objects without ever being trained on images containing threat objects.