My tl;dr understanding: Drones send video back to operator. Video is typically compressed so that it only updates part of the picture that have changed. Even if the video is encrypted, the researchers are able to measure the bitrate. Thus when the researchers make a significant change like putting a board against a window, and seeing if the traffic increases, the researchers can determine whether the drone is looking at that window.
Couldn't this be bypassed by regulating a drone's bitrate to a constant steady flow? The sacrifice will be either video quality or freshness of data, but it would be a straightforward way get past that protection.
Or and even easier way: just have the drone fill up the stream with random data so that the data stream never goes "quiet" when the image is mostly unchanged.
No sacrificing video quality or freshness required.
> Why compress at all? Wouldn't just having a pixel perfect stream solve the same issue and give better quality video?
Because compression reduces the amount of data that needs to be transmitted, reducing network and battery usage, especially if the radio channel is congested.
Instead of filling the stream and having a constant stream size, you could just code the drone do add random bursts of data, making any prediction from the observer useless.
> Because compression reduces the amount of data that needs to be transmitted, reducing network and battery usage
> Instead of filling the stream and having a constant stream size, you could just code the drone do add random bursts of data
You cannot simultaneously reduce network bandwidth and also increase it. Of course padding with noise increases bandwidth, but it's silly to do that when you could instead be padding it with more bits relevant to the video stream.
The vulnerability here isn't inherent to video compression; it's that the compression rate (and therefore network packet rate) is variable (so as to minimize global size of a video stream). It would not be that difficult to modify an encoder to guarantee fixed bit rate over whatever time chunk you want (depending on how many frames is tolerable to buffer before sending out), then send out those chunks at regular intervals.
All that would do is increase the number of intervals that would need to be observed. Adding noise to a channel only reduces the bandwidth of the channel.
I could be wrong, but I don't know if that is true here. First, if the receiver is aware exactly what parts to ignore, and the transmission itself is not interfered with or altered, then it's not really noise as far as the channel is concerned. Second, the assumption here is that spurious packets would be sent when data is not being transmitted, so this principle probably wouldn't hold anyway because the "noise" is inversely correlated to the signal.
You are forgetting that the user of the drone can set the transmission protocol beforehand, and thus is still an important part of the process.
Given that you have a channel between A and B and an eavesdropper C:
Transmission between A and B can be done by any of several obfuscatory protocols, where (for example) a certain flag sent by A tells B to ignore some component of the transmission, to do no error correction on that part, etc. That flag is sent when updates are sparse, and filler (which is to be ignored) is added by B. Without determining how to identify between filler and data, C sees no variations in traffic volume.
Overcoming real-time spikes is easy with this protocol. Just keep a buffer of a few seconds before you begin transmitting.
This approach is a common answer to any sort of traffic analysis. There's actually a Tor alternative that operates on this principle of constant traffic between nodes (traffic analysis is an effective attack versus Tor), but the name escapes me at the moment.
It takes more CPU and battery to compress the video than to leave it raw. So sending a lossless or semi lossless version of the video should theoretically work.
> Instead of filling the stream and having a constant stream size, you could just code the drone do add random bursts of data, making any prediction from the observer useless.
If it's actually random, wouldn't the average bitrate of "still scene + occasional random data" still be lower than "active scene + occasional random data"? Given enough samples, of course.
The average doesn't really matter. The researchers are looking for "steps" in data streamed caused by changes in the video. If you're adding random levels noise it will drown the signal that the researchers are looking for.
The random noise becomes the background and unless it is a constant addition of noise (which wouldn't save any bandwidth, might as well use uncompressed stream) you could still time changes in the signal.
The 'solution' of adding random data to hide these "steps" is a common misunderstanding of the theory behind preventing bandwidth side-channel attacks. It'll probably work in practice as long as the amount of randomness relative to the delta is sufficiently large and the attacker only can only make a limited amount of samples.
Indeed. However, it is a very good practical example of why encryption alone doesn't do much. (If the attacker can measure your data stream, they can make statistical assumptions and pull a lot of useful data out without breaking the encryption).
Well, It assumes that the amount of delta you can create in the transmitted image is sufficiently large that you can discern it from other sources of random variation in the image.
So if the drone is moving around dramatically and there are huge deltas in the image, making your windows opaque / translucent repeatedly may not be sufficient signal. But if the drone is observing a dark compound and you flash lights everywhere repeatedly, it might not matter that the drone is moving around a lot - there might still be a dramatic change in the data transmitted by the drone.
Ultimately you're putting some signal into a potentially noisy channel and trying to reconstruct whether or not your observations contain that signal. The example is simple enough that normal human eyeballs looking at the output can detect it. Using more sophisticated techniques might be able to tease out smaller signals from noisier channels.
Generally speaking, inter-frame video compression uses an amount of data directly related to the amount that things change from one frame to the next. So while noise like leaves moving shows up as small changes moment to moment, larger changes are still realistically visible.
My younger years spent screwing around with video for a hobby are coming back to me now.
If you do a regular sequence of major changes, you could pick that signal out. This would create a larger and more regular signal in the data than the random noise from leaves. I don't think you could accurately predict observation with just one set of environmental changes.
Yes. The compression works by analyzing changes in the screen. A simple example of this would be if you are displaying data. Say you are taking real time data and it is scrolling across the screen. Your display moves the data to the left, drops off what moved out of the frame, and adds what is new. Pictures are more complex, but the idea is the same. Only take what is new.
