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*To*: Sergey Goldgaber <[email protected]>*Subject*: Re: standard for steganography?*From*: [email protected] (Norman Hardy)*Date*: Tue, 1 Mar 1994 00:19:25 -0800*Cc*: [email protected]*Reply-To*: [email protected]*Sender*: [email protected]

At 0:56 3/1/94 -0500, Sergey Goldgaber wrote: >On Mon, 28 Feb 1994, Norman Hardy wrote: > >> Has anyone done statistical studies of low bits of pixels or sound samples? >> I suspect that they are often far from random. A flat 50% distribution in >> the low bits might standout like a sore thumb. I can imagine the the low ... >Yes, pure white noise would be anamalous. I have suggested that one use >a Mimic function with a "garbage grammar". Implemented correctly, it should >withstand statistical analysis. > >What is an AD converter? And what are the techniques you speak of that >mimic those AD converters? 'AD converter' = 'Analog to Digital converter'. Here are three schemes each with flaws: Consider an alphabet of 10 bit characters with a probability distribution such that each bit has an expected value of .6 (instead of the normal .5). The character 000000000 has a probability of .4^10 = .000105 and p(1111111111) = .6^10 = .006046. Do a Huffman encoding on this alphabet. 000000000 codes as 13 bits and 1111111111 codes as 7 bits. Take the cipher stream and execute the Huffman decode(!) operation on the cipher stream. Out comes a sequence of 10 bit bytes with 60% ones. To retrieve the original cipher stream execute the normal Huffman coding algorithm and get the original stream. The flaw here is that Huffman assigns some probability to each of the 10 bit characters which is 2^-7, 2^-8, ... 2^-13. The intermediate probabilities are not represented. This would show up without too much data. Another scheme is called 'arithmetic coding'. It avoids the above probability quantization but is tricky to program. I can't find a reference to it just now but it should appear in any modern book in information theory. Unlike Huffman it does not code each character into a definite number of bits but codes a sequence of several characters into a 'real number'. Adapting this to numbers that real computers can use is tricky. Again you feed the flat cipher stream into the decoding end of the algorithm and get biased bits. The above two schemes are information efficient. With a 60% bias you get 97% efficiency. If you are willing to settle for 80% efficiency you can merely establish a RNG synchronized at sender and receiver that sends a bit from the cipher stream with probability .8 and sends a one with probability .2.

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