Embed Size (px)
Citation preview
Image Credit: tsa.gov
Image Credit: idsscorp.net / wcvb.com
ENABLING ACCESSIBL ITY Our goal is to enable Automated Threat Recognition (ATR)
development by gathering and labelling data, providing testing
services for algorithms, and demonstrating algorithms in an open
architecture framework for TSA.
1515 Eubank SE Albuquerque, NM 87123 Ed Jimenez: [email protected] Chris Cuellar: [email protected] Andrew Cox: [email protected] John Parmeter: [email protected]
D a t a s e t s a n d I n f r a s t r u c t u r e
t o s u p p o r t M a c h i n e L e a r n i n g
Sandia National Laboratories
has been working closely with TSA to create a path
that will allow machine learning experts to develop algorithms
enhancing aviation security. We have focused on creating
shareable data and capabilities to reduce the burden of starting
development. The capabilities described in this pamphlet
highlight efforts to directly enable machine learning, testing, and
evaluation.
As part of this effort, Sandia and its partners have developed a
defined interface-the Open Platform Software Library (OPSL) –
which allows for coding to a single environment, streamlining
deployment across multiple devices.
Data provided through the ATR Program currently requires US
Citizenship as well as being approved to handle Sensitive
Security Information by the TSA (“suitability”). Partnership with
Sandia via contract or memorandum of understanding (MOU)
qualify partners to be vetted for suitability by TSA.
Open Threat Assessment Platform (OTAP) and Stream of Commerce (SOC) ATR Program
Transportation Security Administration DHS Science & Technology Directorate
Image Credit: tsa.gov
FEEL FREE TO REACH OUT TO US TO DESCRIBE YOUR NEEDS TO GET STARTED We are available via the emails listed on the back and any of us can redirect your question to make sure it is answered
appropriately
SOC DATASETS CATEGORIES
• SHOES
• LAPTOPS & TABLETS
• OTHER ELECTRONICS
• LIQUID/POWDER/GEL CONTAINER
• FOOD
• PAPER PRODUCTS
• LARGE DARK/ SHIELD/ OPAQUE
• PROHIBITED LIQUIDS
• PROHIBITED GENERAL
• UNKNOWN
ATR program to support machine learning advancements and travel security T S A F U N D E D E F F O R T T O C R E A T E A N A N N O T A T E D , E N R I C H E D D A T A S E T , A N D T O O L I N G
The OTAP ATR program provides two distinct datasets ready for machine learning. Each is designed
to provide a more complete picture of the threat space. All images provided are compliant to the DICOS
standard. The ATR program also provides scoring tools, to provide insight into how performant an ATR
is as well as documentation on integration into the wider OTAP platform.
Two datasets to reduce false alarms and enhance detection SOC Datase t The SOC dataset contains stream of commerce
images that are collected directly at airports
around the United States. Datasets are
enhanced by providing passenger non-PII data
and associating them to their resulting CT
images. Basic bounding-box annotations are
provided for SOC categories that were derived in
consultation with ATR partners.
PBOD Datase t The Passenger Baggage Object Database
(PBOD) dataset contains scans and metadata
produced in TSA and DHS/S&T labs with real
threats. These scans each have a detailed
bag inventory as well as voxel-by-voxel
annotations of the item of interest. The
number of CT images provided in this dataset
is limited since the collection effort is manually
intensive to maintain safety precautions when
handling threat materials.
DATA AVAILABIL ITY Data can be requested by reaching out to any of
the Sandia National Laboratories personnel listed
on the back. A web interface will be available
soon.
PERFORMANCE INFORMATION ATR scoring tools will assist in providing
performance details. Information regarding
runtime requirements and operating environments
will be provided.
INFRASTRUCTURE INTEGRATION Eventual integration into the OTAP platform via the
OPSL SDK will allow for streamlined access
directly into airport checkpoints. OPSL
documentation and interfaces provide a consistent
environment to develop against, allowing for cross
platform solutions.
