Covert communication (also known as steganography) is the practice of
concealing a secret inside an innocuous-looking public object (cover) so that
the modified public object (covert code) makes sense to everyone but only
someone who knows the code can extract the secret (message). Linguistic
steganography is the practice of encoding a secret message in natural language
text such as spoken conversation or short public communications such as
tweets.. While ad hoc methods for covert communications in specific domains
exist ( JPEG images, Chinese poetry, etc), there is no general model for
linguistic steganography specifically. We present a novel mathematical
formalism for creating linguistic steganographic codes, with three parameters:
Decodability (probability that the receiver of the coded message will decode
the cover correctly), density (frequency of code words in a cover code), and
detectability (probability that an attacker can tell the difference between an
untampered cover compared to its steganized version). Verbal or linguistic
steganography is most challenging because of its lack of artifacts to hide the
secret message in. We detail a practical construction in Python of a
steganographic code for Tweets using inserted words to encode hidden digits
while using n-gram frequency distortion as the measure of detectability of the
insertions. Using the publicly accessible Stanford Sentiment Analysis dataset
we implemented the tweet steganization scheme -- a codeword (an existing word
in the data set) inserted in random positions in random existing tweets to find
the tweet that has the least possible n-gram distortion. We argue that this
approximates KL distance in a localized manner at low cost and thus we get a
linguistic steganography scheme that is both formal and practical and permits a
tradeoff between codeword density and detectability of the covert message.

使用 Python 编写了一个用于推文的隐写码实现，采用插入词以隐藏数字，并使用 n-gram 频率扭曲作为插入检测度量，从而获得一种既形式化又实用的语言隐写方案，允许在编码字密度和隐秘信息的可检测性之间进行权衡。