anomaly detection in x-ray security imaging · in x-ray security imaging. lewis griffin. computer...

Anomaly Detectionin X-ray security imaging

Lewis GriffinComputer Science, University College London, UK

A vision for automated security X-ray analysis

illegal dangerous valuable unusual

dangerous valuablelegal

shielding semantics anomaly

A S AgS

What does the AI revolution offerfor Semantic Analysis in X-ray security?

The Promise

better than human performance!

The Problem

massive training datasets needed

Summary: Deep Learning methods are powerful enough, the main problem is logistical –assembling massive labelled datasets.

illegal dangerous valuable unusual

dangerous valuablelegal

shielding semantics anomaly

A S AgS

A-R = RelativeWhy does that pallet of lemons look different from that one?

P-R = Passenger-relativeStrange luggage for a business traveller.

F-R = Flight-relative…

‘Unexpected item in the bagging area’:Anomaly Detection in X-ray Security Images

Lewis D. Griffin, Matthew Caldwell, Jerone T. A. Andrews an Helene Bohler

stream-of-commerce(n=5000)

staged threat(n=234)

3% 15% 81%

unrecognizable

increasing threat visibility

easily visibledifficult to see

A Parcel Dataset

Threat Detection for this problem is easy. With100 threat examples, we can build a system thatdetects 99% of threats for 1% false alarms.

AUC

number of training SoC images =number of training staged-threat images

Representation is the Keytraining testing

supervised

Trained on male/female label

anomaly detection

Trained on same-person pairs vs. different person pairs

Image Patch

multiple convolution,pooling and rectification layers

pooling layer

logit layer

lant

ern

car

… … …

softmax layer

semanticconfidences

threshold

appearancerepresentation

semanticrepresentation

Convolutional Neural Network (CNN) trained to recognize objects in photographs

Representations used for Anomaly Detection

Schematic of Automated Anomaly Detection

stream-of-commerce parcels

distribution of SoCappearance

01

01

appearance-representationfrom anobject-categorizing CNN

encode

test image

01

01

outlier?

Anomaly Detection Results. 1

anomaly

pass

SoC

staged threats

AUC

appearance

semantic

appearance + semantic

number of SoC images1 8 64 512 4096

• Peak performance requires many more images than for threat detection.• But only unlabelled SoC images are required.• Semantic less effective than appearance.• Small boost from combination



falsealarm

beststandard approach

ourapproach

18% false alarms 65% false alarms

90% detection

50% catch

×15 discovery ×3 discovery

Anomaly Detection works.Possibly well enough to havea role, but improvedperformance is desirable.

Summary

Questions?

Semantics: Its all aboutlabelled datasets

Innovation Workshop Partnered with ICAO Working Group on Innovation in Aviation Security (WGIAS)

Bruno Faviero, Co-Founder and COO, Synapse Technology

Bruno FavieroCo-Founder and COO

The information in this document should be treated as proprietary and confidential to Synapse Technology Corporation

AVSEC World: ICAO Innovation Workshop February 28, 2019


Silicon ValleyCenter of Innovation


“If I had asked people what they wanted, they would have said faster

horses"

Henry Ford


horses

Henry Ford

In the face of unknowable future threats, prioritize solutions that deliver speed and flexibility for maximum responsiveness.


Humans are Human

• Long shifts

• Repetitive task

• Motivation

• Fatigue


Lots of data generated

Highly repetitive

Specific list of items

Few seconds to make decision

AI is a perfect fit for checkpoint security

“Speed matters. Threats emerge quickly. And they evolve fast”.

Time

Performance

Machine

Human


SHARPS

Syntech ONE™ pocketknife detection

Automatic Threat Detection

Syntech ONE™ handgun detection

FIREARMS

Dark Background

•Operator Assist = Operator + AI

•Operator usability and trust is important

•Deploy today


Syntech ONE™

START


Syntech ONE™ Traction

5+ Million Bags

NRT

ARN

PLS KIX

SJC

FPS

Dark Background


Benefits of Platform

• Updates for new threats

• Detects threat components across lanes

• Learning from system-wide data

Functionality Beyond AI

Dark Background


Benefits of Platform

• Intuitive UI

• Analytics

• Customized training

Human-Computer Interaction

“Synapse notices things that the operators don't — things that would have made it through the checkpoint without the system…

security has improved.” - X-Ray Supervisor

“I’m 40-60% more effective with Synapse.”

“I no longer want to quit my job.”

- X-Ray Operator


Challenges to Innovation

Security hardware is closed

AI is held to a higher standard than humans

Regulatory framework needed for a system that improves in the field


Screening Automation Roadmap

Similar to Hold Baggage Screening

On the path towards

• Operators will focus on resolving alarms, not looking at images

• Fewer operators needed to look at X-ray

• Detecting new threats or items that emerge

• Processing bags more quickly

• Taking advantage of new sensors and technologies

Syntech SOLUTION

• First of its kind to integrate the machine with AI

Dark Background

Innovation is not just about technology.It’s also about innovating on:


PEOPLE PROCESS POLICY

“Coordination Innovation”How do we put it all together.


www.gsin.group/resources

Tomorrow’s technology to prepare for tomorrow’s problems.

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