Harnessing machine learning for baggage scans

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The Transportation Security Administration wants its automated scanners to "intuitively recognize" new objects that come through airports in luggage.

The Transportation Security Administration is looking to Silicon Valley startups to help it bring machine learning to security screening to improve the accuracy of airport baggage scanners.

Through an Other Transaction Solicitation, the Department of Homeland Security's Science & Technology Directorate and TSA's Office of Requirements and Capabilities Analysis are looking for a new way to detect evolving threats carried in airline passenger luggage.

Rapidly changing consumer electronics, the RFI said, are an example of a dynamic threat vector that evolves faster than next-generation detector hardware. TSA personnel looking at baggage scanner images might miss subtle new differences in how newly introduced consumer devices are wired or put together.

The agency wants developers to come up with AI-based methods that could automate detection algorithm training, allowing detection hardware to "intuitively recognize" such subtleties and new objects that come through airports in luggage. If that software can be easily plugged into existing detection gear at airports to identify subtle, but potentially devastating, threats to aircraft, TSA could move away from expensive and proprietary detection capabilities in its luggage screening hardware, while also avoiding labor-intensive hand searches.

The solicitation suggests a system using an image library combined with artificial intelligence could to learn to identify new items and distinguish between benign objects and potential threats.

The OTS would fund development efforts in four three- to six-month $200,000 sprints. TSA is holding an industry day in Menlo Park, Calif., on May 4.

Meanwhile, TSA is also investigating incorporating machine learning into the computer tomography scanners that are starting to be used at airport security checkpoints.

This article was first posted to FCW, a sibling site to GCN.

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