Abstract
Coverless information hiding has become a hot topic in recent years. The existing steganalysis tools are invalidated due to coverless steganography without any modification to the carrier. However, for the text coverless has relatively low hiding capacity, this paper proposed a big data text coverless information hiding method based on LDA (latent Dirichlet allocation) topic distribution and keyword TF-IDF (term frequency-inverse document frequency). Firstly, the sender and receiver build codebook, including word segmentation, word frequency and TF-IDF features, LDA topic model clustering. The sender then shreds the secret information, converts it into keyword ID through the keywords-index table, and searches the text containing the secret information keywords. Secondly, the searched text is taken as the index tag according to the topic distribution and TF-IDF features. At the same time, random numbers are introduced to control the keyword order of secret information.
Highlights
For the text coverless has relatively low hiding capacity, this paper proposed a big data text coverless information hiding method based on LDA topic distribution and keyword TF-IDF
This paper proposes a method of coverless text information hiding based on topic distribution and TF-IDF features mixed index
This paper proposes a coverless information hiding method based on LDA topic distribution and TFIDF feature mixed index of big data text
Summary
This method used word rank map and word frequency of words as distance calculation to retrieve ordinary text containing secret information from text database to realize coverless information hiding This method has a low hiding capacity, and a Chinese character can only be hidden in a natural text. Chen et al (2015) proposed coverless information hiding technology based on mathematical expressions (Sun,2002, p.707) of Chinese characters in 2015 (2015, p.133) This method first extracted the secret information vector from the secret information, and retrieved a text containing the secret information vector based on the big data text, so as to achieve the purpose of hiding the secret information without any modification to the text. The hiding capacity has been improved, but it is still relatively small which is difficult to meet the actual demand
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