Matlist: Mature linguistic steganography methodology

Abstract

The generated text by Natural Language Generation (NLG) and template systems is meaningful and looks legitimate. Therefore, the Mature Linguistic Steganography Methodology (Matlist) employs NLG and template techniques along with Random Series values (RS), e.g. binary, decimal, hexadecimal, octal, alphabetic, alphanumeric, etc., of Domain-Specific Subject (DSS) to generate noiseless text-cover. This type of DSS, e.g. financial, medical, mathematical, scientific, economical, etc., has plenty of room to conceal data and allows communicating parties to establish a covert channel such as a relationship based on the profession of the communication parties to transmit a text-cover. Matlist embeds data in a form of RS values, function of RS, related semantics of RS, a combination of these, etc. Unlike synonym-based approach, Matlist does not preserve the meaning of text-cover every time it is used. Instead, Matlist Cover retains different legitimate meaning for each message while it remains semantically coherent and rhetorically sound. The presented implementation, validation, and experimental results demonstrate that Matlist is capable of accomplishing the steganographical goal with higher bitrate than all other linguistic steganography approaches.

Secured Approach for Hiding Data in MS Word Document Using MCDFF

“…This approach need the Dictionary and Corpus of synonyms In 2010, Desoky has presented the working of syntactic method with an example -a common, boring, but responsible taskǁ and -a common, boring but responsible task.ǁ The latter phrase omits one comma and may be considered syntactically wrong by certain editors and linguists. The point is that one bit may be hidden in each of the two types of phrases [7] and [12]. This method also has low embedding capacity.…”

Edustega: an Education-Centric Steganography methodology

“…The fifth linguistic steganography methodology, Unlike all other approaches, the Normal Linguistic Steganography Methodology (NORMALS) neither generates noise nor uses noisy text to camouflage data. NORMALS employs Natural Language Generation (NLG) techniques to generate noiseless (flawless) and legitimate text-cover by manipulating the inputs' parameters of NLG system in order to camouflage data in the generated text (Desoky, 2010a(Desoky, , 2012. As a result, NORMALS is capable of fooling both human and machine examinations.…”

“…Notestega pursues the variations among both human notes and the outputs of automatic notetaking techniques to conceal data. The fourth linguistic steganography methodology, Mature Linguistic Steganography Methodology (Matlist) (Desoky, 2011) employs random series of a domain specific subject along with NLG and template techniques to generate a text-cover that is naturally has a different legitimate meaning for concealing different messages while it remains semantically coherent and rhetorically sound. The fifth linguistic steganography methodology, Unlike all other approaches, the Normal Linguistic Steganography Methodology (NORMALS) neither generates noise nor uses noisy text to camouflage data.…”

High-Capacity Data-Hiding Scheme on Synthesized Pitches Using Amplitude Enhancement—A New Vision of Non-Blind Audio Steganography

“…They can be used simply to modify media content, but in so doing, they limit the capacity for and security of the hidden secrets. Currently, much content is transmitted through the Internet, and a fraction of it is made by computer animators, and includes text and simulated media [25][26][27][28][29][30][31][32]. Cayre and Macq [28] were the first to propose a data-hiding method that was based on 3D polygons.…”