A big data text coverless information hiding based on topic distribution and tf-idf

13Citations
Citations of this article
22Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Qin, J., Zhou, Z., Tan, Y., Xiang, X., & He, Z. (2021). A big data text coverless information hiding based on topic distribution and tf-idf. International Journal of Digital Crime and Forensics, 13(4), 40–56. https://doi.org/10.4018/IJDCF.20210701.oa4

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free