Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X Ray Security Imagery
X ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items. Particular interest lies in the automatic detection and classification of weapons such as firearms and knives within complex and cluttered X ray security imagery. Here, we address this problem by exploring various end to end object detection Convolutional Neural Network (CNN) architectures. We evaluate several leading variants spanning the Faster R CNN, Mask R CNN, and RetinaNet architectures to explore the transferability of such models between varying X ray scanners with differing imaging geometries, image resolutions and material colour profiles. Whilst the limited availability of X ray threat imagery can pose a challenge, we employ a transfer learning approach to evaluate whether such inter scanner generalisation may exist over a multiple class detection problem. Overall, we achieve maximal detection performance using a Faster R CNN architecture with a ResNet101 classification network, obtaining 0.88 and 0.86 of mean Average Precision (mAP) for a three class and two class item from varying X ray imaging sources. Our results exhibit a remarkable degree of generalisability in terms of cross scanner performance (mAP: 0.87, firearm detection: 0.94 AP) In addition, we examine the inherent adversarial discriminative capability of such networks using a specifically generated adversarial dataset for firearms detection with a variable low false positive, as low as 5% this shows both the challenge and promise of such threat detection within X ray security imagery.
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)