Automated detection of smuggled high-risk security threats using Deep Learning

The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called "Small Metallic Threats" (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in stream-of-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image)

Transferring X-ray based automated threat detection between scanners with different energies and resolution

A significant obstacle to developing high performance Deep Learning algorithms for Automated Threat Detection (ATD) in security X-ray imagery, is the difficulty of obtaining large training datasets. In our previous work, we circumvented this problem for ATD in cargo containers, using Threat Image Projection and data augmentation. In this work, we investigate whether data scarcity for other modalities, such as parcels and baggage, can be ameliorated by transforming data from one domain so that it approximates the appearance of another. We present an ontology of ATD datasets to assess where transfer learning may be applied. We define frameworks for transfer at the training and testing stages, and compare the results for both methods against ATD where a common data source is used for training and testing. Our results show very poor transfer, which we attribute to the difficulty of accurately matching the blur and contrast characteristics of different scanners

Automated Analysis of X-ray Images for Cargo Security

Customs and border officers are overwhelmed by the hundreds of millions of cargo containers that constitute the backbone of the global supply chain, any one of which could contain a security- or customs-related threat. Searching for these threats is akin to searching for needles in an ever-growing field of haystacks. This thesis considers novel automated image analysis methods to automate or assist elements of cargo inspection. The four main contributions of this thesis are as follows. Methods are proposed for the measurement and correction of detector wobble in large-scale transmission radiography using Beam Position Detectors (BPDs). Wobble is estimated from BPD measurements using a Random Regression Forest (RRF) model, Bayesian fused with a prior estimate from an Auto-Regression (AR). Next, a series of image corrections are derived, and it is shown that 87% of image error due to wobble can be corrected. This is the first proposed method for correction of wobble in large-scale transmission radiography. A Threat Image Projection (TIP) framework is proposed, for training, probing and evaluating Automated Threat Detection (ATD) algorithms. The TIP method is validated experimentally, and a method is proposed to test whether algorithms can learn to exploit TIP artefacts. A system for Empty Container Verification (ECV) is proposed. The system, trained using TIP, is based on Random Forest (RF) classification of image patches according to fixed geometric features and container location. The method outperforms previous reported results, and is able to detect very small amounts of synthetically concealed smuggled contraband. Finally, a method for ATD is proposed, based on a deep Convolutional Neural Network (CNN), trained from scratch using TIP, and exploits the material information encoded within dual-energy X-ray images to suppress false alarms. The system offers a 100-fold improvement in the false positive rate over prior work

Automated X-ray image analysis for cargo security: Critical review and future promise

We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo

Eagle Eye Tracker

The goal of the Eagle Eye Tracker is to create an automated targeting and tracking system to counteract illegal actions that are committed using small unmanned aerial vehicles. This system will be ideal in locations where aerial security is of utmost importance – such as airports, prisons, and international borders. The Eagle Eye Tracker’s design is highly versatile due to its deep learning-based algorithm, and thus has many more potential applications – including pest control and photograph

Amphibian disease risks and the anthropogenic dispersal of invasive Litoria species : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Ecology at Massey University, Albany, New Zealand

The scope of this research is to provide a broad outline of the interaction between anthropogenic disease spread, risk of an invasive amphibian species establishing into the New Zealand environs, and prevalence of amphibian chytrid in wild introduced amphibian populations. The objective of this overview is to identify risk pathways that could threaten New Zealand’s four endemic Leiopelma species of frogs. Worldwide amphibian populations are in decline with an estimated 32.5% of amphibians globally threatened (IUCN 2008). New Zealand’s four endemic amphibians Leiopelma spp. are high on the ICUN list of critically endangered animals, further, two of the three introduced tree frogs (Litoria spp..) are listed endangered and vulnerable in their native home. Disease has been one factor implicated in the worldwide amphibian decline in particular the two diseases Chytridiomycosis and Ranavirus. Although Chytridiomycosis has had the most profound effect on the decline of amphibian species. The spread of such diseases is, at least in part, human-mediated through media such as the bait trade, food industry, and possibly the pet trade. To date, there have been no reports of Ranavirus in New Zealand amphibians. Conversely, amphibians chytrid fungus is widespread and has been implicated in the decline of the endemic Leiopelma archeyi. This makes amphibian chytrid an ideal disease to model disease transmission with particular reference to the anthropogenic movement of amphibians. The two main goals of this Ph.D. were to investigate specific anthropogenic mediated risks of spreading disease using the pathogen Batrachochytrium dendrobatidis (Bd), which is responsible for amphibian chytrid fungus, as a modality to model this. Included in this will be the enquiry into how Bd entered New Zealand and how it spread so quickly via the movement of Litoria spp.. Furthermore, to look at invasive amphibian species incursion risks by evaluating previous border seizures. Currently, it is unknown how the amphibian chytrid entered New Zealand and whether New Zealand’s borders are a high-risk entry pathway for amphibian disease. Examining the anthropogenic dispersal of Litoria in New Zealand and developing systems that reduce the risk of introducing disease into naive populations is an important role in ensuring the long-term survival of New Zealand’s endangered Leiopelma spp. frogs. The presence of Bd in New Zealand has been recorded but the prevalence of the pathogen in populations is unknown. Identifying the prevalence of infection within populations will provide insight into how populations of Litoria spp. are surviving Bd infection. Furthermore, this Ph.D. project will assess the risk of invasive exotic amphibians entering New Zealand and becoming naturalised. Education is one of the important areas that will greatly help the plight of New Zealand’s frogs. For education to be successful it needs to be targeted, therefore assessing risk areas of amphibian disease is imperative. Furthermore, understanding the public’s knowledge of frogs in New Zealand will further help in the development of resource material and targeting the main groups where education is needed. Key findings of this research are that the three species of Litoria frogs are moved around New Zealand in large numbers via the pet trade. The spread of amphibian chytrid has most likely been so rapid due to the frequency and volume of tadpoles and frogs being bought and sold. The pet trade thereby effectively and inadvertently is a major means of the unregulated translocation of Litoria amphibians throughout New Zealand. Results of this research also show there is a gap in the knowledge about amphibians in areas of husbandry, disease, species identification, and legal responsibilities in the ownership and containment of amphibians in New Zealand. Additionally, the introduction of a new disease is more likely to occur than the risk of an invasive species becoming established. Finally, the wild populations of Litoria frogs were surviving with a high prevalence of amphibian chytrid fungus in two of the three study sites in this research, the third site which had the presence of a reservoir species had low numbers of frogs present

Machine Learning-powered Artificial Intelligence in Arms Control

Artificial intelligence (AI), especially AI driven by machine learning, is on everyone’s lips. Even in armaments such systems are playing an increasingly important role: Some weapons systems are already able to identify targets independently and engage in combat with them. This poses problems for traditional forms of arms control originally designed to monitor physical objects such as mines and small arms and their internal function. In addition, important additional effects of reliable control such as confidence- building and stabilization of diplomatic relations are not addressed. It is important for arms control to address such risks as well. At the same time, the deployment of Machine Learning-powered Artificial Intelligence (MLpAI) as a tool offers tremendous potential for improving arms control processes. Here, more precise and comprehensive data processing can engender more trust between states in particular. This tension between the risks and the opportunities connected with the use of MLpAI in arms control is highlighted in this report