Would Deep Learning Technologies Help Secure Airports?

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A new initiative seeks the application of deep learning technologies in enhancing airport security. The US Department of Homeland Security is turning to data scientists to improve screening techniques at airports. The department, working with Google, introduced a $1.5 million contest to build computer algorithms that can automatically identify concealed items in images captured by checkpoint body scanners.

According to nytimes.com, the US government is putting up the money, and the six-month contest will be run by Kaggle, a site that hosts more than a million data scientists that was recently acquired by Google.

Although data scientists can apply any technique in building these algorithms, the contest is a way of capitalizing on the progress in a technology called deep neural networks, said the Kaggle founder and chief executive, Anthony Goldbloom.

Neural networks are complex mathematical systems that can learn specific tasks by analyzing vast amounts of data.

Companies like Google and Facebook use the technology to do things like identify faces in online images, recognize commands spoken into smartphones and translate one language into another. But the possibilities extend well beyond smartphone apps and other online services. The hope is that neural networks can also help automated systems read body scans with greater accuracy, so checkpoint workers can spend less time pulling passengers aside and patting them down.

John W. Halinski, a former deputy administrator at the Transportation Security Administration who now works as a security consultant, welcomed the “crowdsourcing” idea because it could draw on the skills of any data scientist.

Homeland Security and other organizations are working on ways to improve the technologies used at airport checkpoints, with the T.S.A. set to roll out new CT systems that can automatically identify items hidden in passenger baggage, and at least one company, Smiths Detection, exploring the use of neural networks at security checkpoints.

To help data scientists and machine-learning researchers train their algorithms, Homeland Security is supplying more than 1,000 three-dimensional body scans. The department is not sharing scans of the more than two million people screened each day at the nation’s airports. Instead, T.S.A. workers volunteered to help create the data for the contest from scratch, repeatedly walking through a set of test scanners at a laboratory in New Jersey. In some cases, the workers carried concealed items through the scanners, and these images are carefully labeled.

By analyzing this data, neural networks and other algorithms can learn to pinpoint concealed items on their own. Jeremy Achin, a founder and the chief executive of the data analysis company DataRobot, said that neural networks were well-suited to such a task, but he also warned that the technology could make mistakes and that in some cases it could be vulnerable to bad actors. Research has shown that after analyzing the performance of an image-recognition system driven by a neural network, miscreants could mark or otherwise alter items in ways that fool the system into seeing things that are not there — or failing to see things that are.

For those reasons, the immediate aim is not to build technology that replaces human screeners but to find a way of removing some of the burden from those screeners.