Here's how they work. First, an image is obtained with an x-ray backscatter or millimeter wave machine like the 385 systems already installed in 70 airports around the country. While the two types of machines have important differences, their basic principles are comparable. The electromagnetic waves (x-rays or radio) used in the machines pass easily through clothing, but bounce back when they encounter human skin (or other denser materials). Those reflections reach the scanner and are transformed into an image of the body sans clothing.

In one of the automated threat detection systems, that image would be fed to an algorithm that would compare it to a database of other images to determine if it was suspicious. Instead of looking at an image of a person, the TSA scanners would see a stick figure that would indicate the general area where a problem existed. They would then follow up with a patdown or other screening procedure.

Unfortunately, the technological task of automated threat detection is not trivial. There are inherent problems that make an accurate machine very, very difficult to build.

The most basic problem is that an algorithm is only as good as its training data. These machines are like a massive game of memory: they compare something new with something they've seen before. In order to make accurate determinations, they need a huge library of suspicious and normal images, said the Pacific Northwest National Laboratory's Doug McMakin, who developed the technology on which the L-3 SafeView system is based.

"To see different threats, you really have to scan a lot of people and put objects on different places on the body and use different kinds of threats too," McMakin said.

Of course, we could easily generate a huge database of images from all the people walking through the scanners right this minute, but the privacy problem that would represent makes it impossible. "You can build up this huge database, but because they don't save any of the imagery, you have to go out and get people to build up this database."

Carey Rappaport, the head of the Center for Subsurface Sensing and Imaging Systems (a multi-university organization that studies automatic threat detection) and a microwave engineer at Northeastern University, agreed that automated threat detection using just this kind of imaging would be very hard. "How do you get a computer algorithm to say this fits in the parameters of what's human and this is something that is not human?" Rappaport asked. "There are a lot of things that could look naturally occurring but that are cleverly disguised explosives."

This problem is not easily sidestepped. It's built into to the detection task: it's just hard to know what you're looking for and even harder to provide a computer with a set of rules to precisely define the characteristics of something you've never seen before.