Researchers at Ben Gurion University in Beer Sheva, Israel have built a proof-of-concept system for counter-surveillance against spy drones that demonstrates a clever, if not exactly simple, way to determine whether a certain person or object is under aerial surveillance. They first generate a recognizable pattern on whatever subject -- a window, say -- someone might want to guard from potential surveillance. Then they remotely intercept a drone's radio signals to look for that pattern in the streaming video the drone sends back to its operator. If they spot it, they can determine that the drone is looking at their subject.
In other words, they can see what the drone sees, pulling out their recognizable pattern from the radio signal, even without breaking the drone's encrypted video.
The details have to do with the way drone video is compressed:
The researchers' technique takes advantage of an efficiency feature streaming video has used for years, known as "delta frames." Instead of encoding video as a series of raw images, it's compressed into a series of changes from the previous image in the video. That means when a streaming video shows a still object, it transmits fewer bytes of data than when it shows one that moves or changes color.
That compression feature can reveal key information about the content of the video to someone who's intercepting the streaming data, security researchers have shown in recent research, even when the data is encrypted.
Imagine a low-cost drone with the range of a Canada goose, a bird that can cover 1,500 miles in a single day at an average speed of 60 miles per hour. Planet Earth profiled a single flock of snow geese, birds that make similar marathon journeys, albeit slower. The flock of six-pound snow geese was so large it formed a sky-darkening cloud 12 miles long. How would an aircraft carrier battlegroup respond to an attack from millions of aerial kamikaze explosive drones that, like geese, can fly hundreds of miles? A single aircraft carrier costs billions of dollars, and the United States relies heavily on its ten aircraft carrier strike groups to project power around the globe. But as military robots match more capabilities found in nature, some of the major systems and strategies upon which U.S. national security currently relies -- perhaps even the fearsome aircraft carrier strike group -- might experience the same sort of technological disruption that the smartphone revolution brought about in the consumer world.
The objective of this CRA is to perform enabling basic and applied research to extend the reach, situational awareness, and operational effectiveness of large heterogeneous teams of intelligent systems and Soldiers against dynamic threats in complex and contested environments and provide technical and operational superiority through fast, intelligent, resilient and collaborative behaviors. To achieve this, ARL is requesting proposals that address three key Research Areas (RAs):
RA1: Distributed Intelligence: Establish the theoretical foundations of multi-faceted distributed networked intelligent systems combining autonomous agents, sensors, tactical super-computing, knowledge bases in the tactical cloud, and human experts to acquire and apply knowledge to affect and inform decisions of the collective team.
RA2: Heterogeneous Group Control: Develop theory and algorithms for control of large autonomous teams with varying levels of heterogeneity and modularity across sensing, computing, platforms, and degree of autonomy.
RA3: Adaptive and Resilient Behaviors: Develop theory and experimental methods for heterogeneous teams to carry out tasks under the dynamic and varying conditions in the physical world.
Researchers have configured two computers to talk to each other using a laser and a scanner.
Scanners work by detecting reflected light on their glass pane. The light creates a charge that the scanner translates into binary, which gets converted into an image. But scanners are sensitive to any changes of light in a room -- even when paper is on the glass pane or when the light source is infrared -- which changes the charges that get converted to binary. This means signals can be sent through the scanner by flashing light at its glass pane using either a visible light source or an infrared laser that is invisible to human eyes.
There are a couple of caveats to the attack -- the malware to decode the signals has to already be installed on a system on the network, and the lid on the scanner has to be at least partially open to receive the light. It's not unusual for workers to leave scanner lids open after using them, however, and an attacker could also pay a cleaning crew or other worker to leave the lid open at night.
The setup is that there's malware on the computer connected to the scanner, and that computer isn't on the Internet. This technique allows an attacker to communicate with that computer. For extra coolness, the laser can be mounted on a drone.