135 citations · 210 across the 11 of their papers we have counts for
7 papers · 1 filter
Automated detection of smuggled high-risk security threats using Deep Learning
Nicolas Jaccard, Thomas W. Rogers, Edward J. Morton +1
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 Euro…
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples
Thomas Tanay, Lewis Griffin
Deep neural networks have been shown to suffer from a surprising weakness: their classification outputs can be changed by small, non-random perturbations of their inputs. This adve…
Measuring and correcting wobble in large-scale transmission radiography
Thomas W. Rogers, James Ollier, Edward J. Morton +1
Large-scale transmission radiography scanners are used to image vehicles and cargo containers. Acquired images are inspected for threats by a human operator or a computer algorithm…
Automated X-ray Image Analysis for Cargo Security: Critical Review and Future Promise
Thomas W. Rogers, Nicolas Jaccard, Edward J. Morton +1
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 inspec…
Better Image Segmentation by Exploiting Dense Semantic Predictions
Qiyang Zhao, Lewis D Griffin
It is well accepted that image segmentation can benefit from utilizing multilevel cues. The paper focuses on utilizing the FCNN-based dense semantic predictions in the bottom-up im…
Detection of concealed cars in complex cargo X-ray imagery using Deep Learning
Nicolas Jaccard, Thomas W. Rogers, Edward J. Morton +1
Non-intrusive inspection systems based on X-ray radiography techniques are routinely used at transport hubs to ensure the conformity of cargo content with the supplied shipping man…
Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions
Qiyang Zhao, Lewis D Griffin
Many deep Convolutional Neural Networks (CNN) make incorrect predictions on adversarial samples obtained by imperceptible perturbations of clean samples. We hypothesize that this i…