Optical Cryptography
chill writes: "In Cryptonomicon, Neil Stephenson wrote about Bell Labs' research into using static, or chaotic signals to mask communications. A message would be generated, then the signal masked in noise. Someone on the other end would subtract out the noise to get the signal. Works great if both ends have the exact same noise. Now, Jia-ming Liu, professor of electrical engineering at UCLA, is giving a presentation on doing essentially the same thing using OC-48 (2.5 Gbps) optical circuits. The presentation will be at the upcoming Optical Fiber Communications Conference and Exhibit. There is an article covering this and some other nice advances in optical over in Wired."
so how is this any different than steg
where a message is hidden in noise (the image) then when the image (noise) is subtracted the message appears.
are we still trying to re-invent the wheel here or am i missing something ?
This is called traffic masking, and is a useful, known tool. However, it can also be viewed as security through obscurity, typically a bad thing. (tm)
This is true only if the two waves being added have the same frequency spectra, or if one of the waves is contained in the other in the frequency domain. If you add a 10 nanometer-wide signal centered at 700 nm to a 10 nanometer-wide signal centered at 710 nm, the resultant wave has a bandwidth of 20 nm.
This wave would take up more bandwidth than either of the other two.
Well, look at it this way: if your background traffic is random noise, and your "signal" cannot be differentiated from random noise, one must question what kind of signal actually is present.
It's really, really hard to mask a legitimate messages in random noise and hope that the bad guy won't be able to differentiate the two.
Ok...
So you're saying Rissanen gave the theoretical limit for how quickly a compression algorithm asymptotically approaches maximum entropy in its output, and Context Tree Weighing and other algorithms actually reach that limit?
Or is this only proven for certain classes of input, like Markov models?