Compulsive tinkerers at the Georgia Institute of Technology have built a prototype device that they say can stop digital cameras functioning in a given area. The device uses off-the-shelf equipment – camera-mounted sensors, lighting equipment, a projector and a computer – to scan for, identify and neutralize both still and video digital cameras. The researchers have their eye on two markets – protecting limited areas against clandestine photography and stopping video copying in larger areas such as theaters, explained Geogia Tech’s Gregory Abowd.
Abowd said the device could be used to prevent espionage photography in government buildings, industrial settings or trade shows. Co-researcher James Clawson added that preventing movie copying could be a major application for the camera-blocking technology. “Movie piracy is a $3 billion-a-year problem. If someone videotapes a movie in a theater and then puts it up on the web that night or burns half a million copies to sell on the street – then the movie industry has lost a lot of in-theater revenue,” said Clawson. The researchers say that movie theaters are likely to be a good setting for the technology, as a camera’s CCD image sensor is retroreflective, meaning it sends light back directly to its origin rather than scattering it. Such retroreflection would make it relatively easy to detect and identify video cameras in a darkened theater.
To identify a digital camera, the system looks for the reflectivity and shape of the CCD image-producing sensors used in digital cameras. The current prototype uses visible light and two cameras to find CCDs, but a future commercial system might use invisible infrared lasers and photo-detecting transistors. Once the system has found a suspicious spot, it would feed information on the reflection’s properties to a computer for a determination. “The biggest problem is making sure we don’t get false positives from, say, a large shiny earring,” said Summet. “We need to make our system work well enough so that it can find a dot, then test to see if it’s reflective, then see if it’s retroreflective, and then test to see if it’s the right shape.”
Team member Jay Summet said there were still some hurdles to overcome. “Most of the major work that we have left involves algorithmic development,” he said. “False positives will be eliminated by making a system with fast, efficient computing,” he explained.
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