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The security infrastructure is ill-equipped to detect and deter the smugglingof non-explosive devices that enable terror attacks such as those recentlyperpetrated in western Europe. The detection of so-called "small metallicthreats" (SMTs) in cargo containers currently relies on statistical riskanalysis, intelligence reports, and visual inspection of X-ray images bysecurity officers. The latter is very slow and unreliable due to the difficultyof the task: objects potentially spanning less than 50 pixels have to bedetected in images containing more than 2 million pixels against very complexand cluttered backgrounds. In this contribution, we demonstrate for the firsttime 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-scratchdespite the scarcity of data available. We report fewer than 6% false alarmswhen detecting 90% SMTs synthetically concealed in stream-of-commerce images,which corresponds to an improvement of over an order of magnitude overconventional approaches such as Bag-of-Words (BoWs). The proposed scheme offerspotentially super-human performance for a fraction of the time it would takefor a security officers to carry out visual inspection (processing time isapproximately 3.5s per container image).

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