Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items

X-ray baggage security screening is widely used to maintain aviation and transport safety and security. To address the future challenges of increasing volumes and complexities, the recent focus on the use of automated screening approaches are of particular interest. This includes the potential for automatic threat detection as a methodology for concealment detection within complex electronics and electrical items screened using low-cost, 2D X-ray imagery (single or multiple view). In this work, we use automatic object segmentation algorithms enabled by deep Convolutional Neural Networks (CNN, e.g. Mask R-CNN) together with the concept of image over-segmentation to the sub-component level and subsequently use CNN classification to determine the presence of anomalies at both an object or sub-component level. We evaluate the performance impact of three strategies: full frame, object segmentation, and object over-segmentation, for threat/anomaly detection within consumer electronics items. The experimental results exhibit that the object over-segmentation produces superior performance for the anomaly detection via classification, with $< 5\%$ false positive and ~99% true positive

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

Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption

Anomaly detection is the task of recognising novel samples which deviate significantly from pre-established normality. Abnormal classes are not present during training meaning that models must learn effective representations solely across normal class data samples. Deep Autoencoders (AE) have been widely used for anomaly detection tasks, but suffer from overfitting to a null identity function. To address this problem, we implement a training scheme applied to a Denoising Autoencoder (DAE) which introduces an efficient method of producing Adversarially Learned Continuous Noise (ALCN) to maximally globally corrupt the input prior to denoising. Prior methods have applied similar approaches of adversarial training to increase the robustness of DAE, however they exhibit limitations such as slow inference speed reducing their real-world applicability or producing generalised obfuscation which is more trivial to denoise. We show through rigorous evaluation that our ALCN method of regularisation during training improves AUC performance during inference while remaining efficient over both classical, leave-one-out novelty detection tasks with the variations-: 9 (normal) vs. 1 (abnormal) & 1 (normal) vs. 9 (abnormal); MNIST - AUCavg: 0.890 & 0.989, CIFAR-10 - AUCavg: 0.670 & 0.742, in addition to challenging real-world anomaly detection tasks: industrial inspection (MVTEC-AD - AUCavg: 0.780) and plant disease detection (Plant Village - AUC: 0.770) when compared to prior approaches

Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample size, and inadequate distribution coverage for the other class (abnormal). In this work, we propose the dual use of both visual appearance and localized motion characteristics, derived from optic flow, applied on a per-region basis to facilitate object-wise anomaly detection within this context. Leveraging established object localization techniques from a region proposal network, optic flow is extracted from each object region and combined with appearance in the far infrared (thermal) band to give a 3-channel spatiotemporal tensor representation for each object (1 × thermal - spatial appearance; 2 × optic flow magnitude as x and y components - temporal motion). This formulation is used as the basis for training contemporary semi-supervised anomaly detection approaches in a region-based manner such that anomalous objects can be detected as a combination of appearance and/or motion within the scene. Evaluation is performed using the LongTerm infrared (thermal) Imaging (LTD) benchmark dataset against which successful detection of both anomalous object appearance and motion characteristics are demonstrated using a range of semi-supervised anomaly detection approaches

On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks

Automatic detection of prohibited items within complex and cluttered X-ray security imagery is essential to maintaining transport security, where prior work on automatic prohibited item detection focus primarily on pseudo-colour (rgb) X-ray imagery. In this work we study the impact of variant X-ray imagery, i.e., X-ray energy response (high, low) and effectivez compared to rgb, via the use of deep Convolutional Neural Networks (CNN) for the joint object detection and segmentation task posed within X-ray baggage security screening. We evaluate state-of-the-art CNN architectures (Mask R-CNN, YOLACT, CARAFE and Cascade Mask R-CNN) to explore the transferability of models trained with such ‘raw’ variant imagery between the varying X-ray security scanners that exhibits differing imaging geometries, image resolutions and material colour profiles. Overall, we observe maximal detection performance using CARAFE, attributable to training using combination of rgb, high, low, and effective-z Xray imagery, obtaining 0.7 mean Average Precision (mAP) for a six class object detection problem. Our results also exhibit a remarkable degree of generalisation capability in terms of cross-scanner transferability (AP: 0.835/0.611) for a one class object detection problem by combining rgb, high, low, and effective-z imagery

On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery

X-ray imagery security screening is essential to maintaining transport security against a varying profile of prohibited items. Particular interest lies in the automatic detection and classification of prohibited items such as firearms and firearm components within complex and cluttered X-ray security imagery. 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. Overall, we achieve maximal detection performance using a Faster R-CNN architecture with a ResNet 101 classification network, obtaining 0.91 and 0.88 of mean Average Precision (mAP) for a two-class problem from varying X-ray imaging dataset. Our results offer very low false positive (FP) complimented by a high accuracy (A) $(\mathrm{FP}=0.00\%,\ \mathrm{A}=99.96\%)$ . This result illustrates the applicability and superiority of such integrated region based detection models within this X-ray security imagery context

Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery

Lossy image compression strategies allow for more efficient storage and transmission of data by encoding data to a reduced form. This is essential enable training with larger datasets on less storage-equipped environments. However, such compression can cause severe decline in performance of deep Convolution Neural Network (CNN) architectures even when mild compression is applied and the resulting compressed imagery is visually identical. In this work, we apply the lossy JPEG compression method with six discrete levels of increasing compression {95, 75, 50, 15, 10, 5} to infrared band (thermal) imagery. Our study quantitatively evaluates the affect that increasing levels of lossy compression has upon the performance of characteristically diverse object detection architectures (Cascade-RCNN, FSAF and Deformable DETR) with respect to varying sizes of objects present in the dataset. When training and evaluating on uncompressed data as a baseline, we achieve maximal mean Average Precision (mAP) of 0.823 with Cascade RCNN across the FLIR dataset, outperforming prior work. The impact of the lossy compression is more extreme at higher compression levels (15, 10, 5) across all three CNN architectures. However, re-training models on lossy compressed imagery notably ameliorated performances for all three CNN models with an average increment of ∼ 76% (at higher compression level 5). Additionally, we demonstrate the relative sensitivity of differing object areas {tiny, small, medium, large} with respect to the compression level. We show that tiny and small objects are more sensitive to compression than medium and large objects. Overall, Cascade R-CNN attains the maximal mAP across most of the object area categories