News

What's been going on @ COMPASS

AvSec World 2019

Lewis Griffin is an invited speaker at the "AvSec World 2019" conference in Miami, 26-28 February.

26-02-2019

AI & Future Crime

COMPASS is hosting an AI & Future Crime sandpit, 14-15 February 2019

14-02-2019

Unexpected item in the bagging area

Our new Anomaly Detection paper for IEEE Transactions on Information Forensics and Security is now online: "Unexpected item in the bagging area".

16-11-2018

Invited Talk at ADSA18

Dr Lewis Griffin will give an invited talk at the 18th workshop on Advanced Development for Security Applications (ADSA18).

24-03-2018

FASS Phase 2 project award

COMPASS awarded FASS phase 2 funding for project 'Next-generation automated image analysis for security: semantics and anomalies'

26-01-2018

AI & Future Crime

COMPASS awarded project from Dawes Foundation on 'AI & Future Crime'.

23-09-2017

Defence Science and Technology Laboratory

COMPASS & Renzoni lab awarded DSTL project on 'Machine learning aided electromagnetic imaging with atomic magnetometers'.

29-07-2017

Roundtable on gun trafficking into the UK

Lewis Griffin will take part in this roundtable at the International Crime Science Conference 2017.

11-07-2017

Knowledge Exchange & Innovation

COMPASS receive Knowledge Exchange & Innovation funds to pursue commercialisation.

01-06-2017

BMVA Security and Surveillance

Thomas Rogers is co-organising a BMVA technical meeting on Security and Surveillance. Sign up here!

25-02-2017

New project on Anomaly Detection

New CDE-funded FASS Phase 1 Project: "Anomaly Detection for Aviation Security"

25-02-2017

Invited Talk on 'Future Crime & AI'

Dr Lewis Griffin gives an invited talk on the 'Future Crime and AI' at the 'What works in Crime Reduction Conference'.

24-01-2017

Economist article on our research

The Economist, in a recent article and podcast, have covered our research.

02-12-2016

Best paper prize at ICDP

COMPASS has been awarded the best paper prize at the 7th International Conference on Crime Detection and Prevention in Madrid. The paper, entitled "Automated detection of smuggled high-risk security threats using Deep Learning", was presented by Dr Nicolas Jaccard.

25-11-2016

New projects on Automated Threat Detection

COMPASS will be working on two new funded Automated Threat Detection (ATD) projects: "Deep Learning for firearms ATD" and "Anomaly Detection for firearms ATD". More information soon!

11-10-2016

New projects for Aviation Security

COMPASS will be working on two new funded projects in Aviation Security: "Anomaly Detection for Aviation Security" and "Virtual Inspection Environment for Aviation Security". More information soon!

26-09-2016

Website Launch!

The official COMPASS website is live! This news feed will contain updates about on-going projects, conferences we attend, and new funding opportunities. You can also follow us on twitter: @COMPASS_UCL

22-09-2016

The Team

Meet the members of the COMPASS team

Dr. Lewis Griffin (PI)

Dr. Lewis Griffin

Group Leader | Principal Investigator

Dr. Matthew Caldwell (RA)

Dr. Matthew Caldwell

Research Associate

Jerone T. Andrews (PhD student)

Jerone Andrews

PhD Student

Thomas Tanay (PhD student)

Thomas Tanay

PhD Student

Former Members

Dr. Nicolas Jaccard (RA)

Dr. Nicolas Jaccard

Former Research Associate

Thomas W. Rogers (PhD student)

Dr. Thomas Rogers

Former PhD Student

Mark Ransley (PhD student)

Mark Ransley

Former PhD Researcher

Research Projects

Projects that COMPASS are currently working on

Anomaly Detection in x-ray images

Security staff inspect x-ray images by: threat detection, where they look for particular items (e.g. knives, detonators); and anomaly detection, where they look for deviations from normal. This project will automate anomaly detection, for which there are no current systems. For firearms, anomaly detection is particularly important for ISO containers and vehicles, where the fabric of the container or vehicle provides opportunities for concealment. Firearms so concealed may not be visible as such, hence not detectable by threat detection methods, but may still be noticed by security staff who spot a darkening out of place (e.g. in the roof of an ISO container) or a shape not quite right (e.g. the engine block of a car). The system we develop for anomaly detection, like experienced security staff, will 'know' what is normal so that it can spot such deviations.

Threat Detection in x-ray images

This project will develop firearm detection algorithms using the most recent methods of Computer Vision. Specifically, we will use Convolutional Neural Networks (CNNs), with parameters Deep Learnt from training images. CNNs approximate the action and connections of neurons in the human brain. ‘Deep’ because of the many layers of the network of artificial neurons that they employ. ‘Learning’ because only the broad architecture of the network is engineered, its detailed parameters being learnt by exposing it to relevant images. Across the two phases of the project, we will develop: a single algorithm for detection of firearms in x-ray images; with performance validated as being at least at human-level; applicable to images from scanners of any modality and manufacturer, after a one-off automated tuning process for operation on a new scanner type, requiring only a sample dataset of benign images.

Anomaly Detection for Aviation Security

Since 9/11 commercial flights have been attacked by groups with political aims. Their strategic aim is to provoke fear, leading to pressure for political change. Reactive security measures, which visibly guard against a repeat attack, achieve the purpose of the attack since they constantly remind of the existence of the adversaries, who are confirmed as national enemies, not mere criminals. Understanding this, the terrorists readily vary their tactics. Each variation provokes a new measure layered on top of existing measures. The apparent potency of the adversaries is hugely magnified: we walk unshod, and have strangers touch our `junk' and taste our children's food. To exit this cycle a novel approach is needed that has the capability to detect the next attack, not the previous; and to do so invisibly, so that fear is not further magnified. In this project, we will apply the latest Computer Science to automate Anomaly Detection (noticing the suspicious or unusual), as used by experienced, trained security staff. Deep Learning algorithms, that mimic the operation of the human brain, makes this feasible: they recognize faces better than humans. If automated Anomaly Detection at human-levels of performance can be achieved, then computers calculating out of sight, can pore over every luggage and baggage scan and every airport CCTV feed, as if a thousand trained, experienced, unflagging security staff were constantly employed in every airport looking for oddities and passing them up to security staff when found.

Magnetic Induction Tomography

In 2013 the Renzoni lab at UCL demonstrated, for the first time, the possibility of imaging using atomic magnetometers in the Magnetic Induction Tomography modality. This opened up a new realm for electromagnetic imaging, given the extreme sensitivity of atomic magnetometers at low frequency. Electromagnetic imaging has potential in many security domains where x-rays are not applicable, such as universal fast parcel screening. COMPASS is working with the Renzoni lab, applying machine learning methods to produce tomographic images from magnetometer measurements that depend non-linearly on the scene. The Machine Learning approach provides an alternative to the standard Inverse Problem approach requiring many Finite Element simulations to produce each image. By shifting the computational burden onto a training stage, we permit fast enough inversion to be applicable in security applications with high throughput.

Publications

Selection of recent COMPASS research output

Filter by type of application:
Year Title Journal/Conf. Authors Type
2018 Unexpected item in the bagging area: Anomaly Detection in X-ray Security Images TIFS Lewis Griffin LDG, Matthew Caldwell MC, Jerone Andrews JTAA, Helene Bohler HB Anomaly Detection
2018 Machine Learning Based Localization and Classification with Atomic Magnetometers PRL Cameron Deans CD, Lewis Griffin LDG, Luca Marmugi LM, Ferruccio Renzoni FR Electromagnetic Imaging
2017 Transferring x-ray based automated threat detection between scanners with different energies and resolution SPIE D+S Matthew Caldwell MC, Mark Ransley MR, Thomas Rogers TWR, Lewis Griffin LDG Object Detection
2017 Representation-learning for anomaly detection in complex x-ray cargo imagery SPIE D+S Jerone Andrews JTAA, Nicolas Jaccard NJ, Thomas Rogers TWR, Thomas Tanay TT, Lewis Griffin LDG Anomaly Detection
2017 L2 Regularization and the Adversarial Distance ICML Jerone Andrews JTAA, Nicolas Jaccard NJ, Thomas Rogers TWR, Thomas Tanay TT, Lewis Griffin LDG Anomaly Detection
2017 A deep learning framework for the automated inspection of complex dual-energy x-ray cargo imagery SPIE D+S Thomas Rogers TWR, Nicolas Jaccard NJ, Edward Morton EJM, Lewis Griffin LDG Object Detection
2016 A boundary tilting perspective on the phenomenon of adversarial examples arXiv Thomas Tanay TT, Lewis Griffin LDG Adversarial
2016 Anomaly Detection for Security Imaging DSDS Jerone Andrews JTAA, Nicolas Jaccard NJ, Thomas Rogers TWR, Thomas Tanay TT, Lewis Griffin LDG Anomaly Detection
2016 Automated detection of smuggled high-risk security threats using Deep Learning ICDP Nicolas Jaccard NJ, Thomas Rogers TWR, Edward Morton EJM, Lewis Griffin LDG Object Detection
2016 Detection of concealed cars in complex cargo X-ray imagery using deep learning JXST Nicolas Jaccard NJ, Thomas Rogers TWR, Edward Morton EJM, Lewis Griffin LDG Object Detection
2016 Automated X-ray Image Analysis for Cargo Security: Critical Review and Future Promise JXST Thomas Rogers TWR, Nicolas Jaccard NJ, Lewis Griffin LDG Review Object Detection Anomaly Detection Image Pre-Processing
2016 Measuring and correcting wobble in large-scale transmission radiography JXST Thomas Rogers TWR, James Ollier JO, Edward Morton EJM, Lewis Griffin LDG Image Pre-Processing
2016 Threat Image Projection (TIP) into X-ray images of cargo containers for training humans and machines IEEE ICCST Thomas Rogers TWR, Nicolas Jaccard NJ, Emmanouil Protonotarios EPD, James Ollier JO, Edward Morton EM, Lewis Griffin LDG Object Detection
2016 Transfer Representation-Learning for Anomaly Detection ICML Jerone Andrews JTAA, Thomas Tanay TT, Edward Morton EJM, Lewis Griffin LDG Anomaly Detection
2016 Tackling the x-ray cargo inspection challenge using machine learning SPIE D+S Nicolas Jaccard NJ, Thomas Rogers TWR, Edward Morton EJM, Lewis Griffin LDG Review Object Detection Image Pre-Processing
2016 Detecting Anomalous Data Using Auto-Encoders IJMLC Jerone Andrews JTAA, Edward Morton WJM, Lewis Griffin LDG Anomaly Detection
2015 Using deep learning on X-ray images to detect threats DSDS Nicolas Jaccard NJ, Thomas Rogers TWR, Edward Morton EJM, Lewis Griffin LDG Object Detection
2015 Detection of cargo container loads from X-ray images IET ICISP Thomas Rogers TWR, Nicolas Jaccard NJ, Edward Morton EJM, Lewis Griffin LDG Object Detection
2014 Labelling images without classifiers YDS Theodore Boyd TB, Lewis Griffin LDG Object Detection
2014 Automated detection of cars in transmission X-ray images of freight containers IEEE AVSS Nicolas Jaccard NJ, Thomas Rogers TWR, Lewis Griffin LDG Object Detection
2014 Reduction of Wobble Artefacts in Images From Mobile Transmission X-ray Vehicle Scanners IEEE ICIST Thomas Rogers TWR, James Ollier JO, Edward Morton EJM, Lewis Griffin LDG Image Pre-Processing

Partners and Funding

Rapiscan Systems
EPSRC
Home Office
DfT
Defense and Security Accelerator

Contact us

Lewis D. Griffin
Department of Computer Science
University College London
Gower Street
London
WC1E 6BT
Telephone: +44 20 3108 7107
E-mail: l.griffin@cs.ucl.ac.uk

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