ArticlePDF Available

Text based steganography

nt. J.
nformation Privacy, Security and Integrity, Vol. 3, No. 2, 201
Copyright © 2017 Inderscience Enterprises Ltd.
Text based steganography
Robert Lockwood and Kevin Curran*
School of Computing and Intelligent Systems,
Faculty of Computing and Engineering,
Ulster University,
Londonderry BT48 7JL, Northern Ireland
*Corresponding author
Abstract: Steganography is the art of hiding information within other less
conspicuous information to prevent eavesdropping by way of hiding its
existence in the first place. Image based steganography is the most common
form but text based steganography can also be used. Text based steganography
can be generally classified as format based, linguistic and random/statistical
generation. In general, random and statistical generated methods create a cover
text but do not necessarily make semantic sense; that is, the subject matter of
each sentence has little or no relation to the next sentence. Linguistic
steganography can use natural language processing to hide information but
again is still subject to analysis particularly if the basis for the cover text is an
existing document. Here, we examine the leading methods of text based
steganography. We evaluate a variety of steganographic techniques including
open space encoding, synonym replacement, UK/US English translation
algorithm and Wayner’s Mimic Functions using five benchmarks which
compare speed, capacity, complexity, compromisability and size. We find that
the best methods to hide information should not use a single scheme, but a
hybrid of many schemes. In order to further hide information, text should be
compressed, encrypted and then hidden in a cover document.
Keywords: steganography; text based steganography; cryptography; security.
Reference to this paper should be made as follows: Lockwood, R. and
Curran, K. (2017) ‘Text based steganography’, Int. J. Information Privacy,
Security and Integrity, Vol. 3, No. 2, pp.134–153.
Biographical notes: Robert Lockwood is a graduate of Computer Science from
the Ulster University. His research interests include text based steganography
Kevin Curran is a Professor of Cyber Security and Group Leader for the
Ambient Intelligence and Virtual Worlds Research Group at the Ulster
University. He is also a senior member of the IEEE. He is most well-known for
his work on location positioning within indoor environments and internet
security. His expertise has been acknowledged by invitations to present his
work at international conferences, overseas universities and research
laboratories. He is a regular contributor on TV and radio and in trade and
consumer IT magazines.
Text based steganography 135
1 Introduction
Encryption of messages is now a common occurrence (Gupta et al., 2016). Popular
applications include messaging, email and website queries. Whilst we feel fairly secure in
the knowledge that encryption takes place, the very existence of the encryption can alert
network peers, rogue routers and so forth to the presence of hidden information.
Steganography is the art of hiding information inside a carrier such as an image, a sound
file or network packets. The field of steganography has had much research especially
with image based steganography but lesser research has taken place with text based
steganography. Beyond email and watermarking, steganography has not become
mainstream, yet the purpose of steganography is not to secure information as encryption
but to hide its very existence in the first place. The origins of steganography was first
coined by Trithemus who coined ‘steganographia’ which means ‘concealed writing’
(Bennett, 2004). Today steganography has been extended to not only include text but also
images and any other object. For example, text can be embedded in images, video or
other objects and vice versa with enough data to hide information in steganography can
fall into five categories: images, video, audio, text (Bhattacharyya et al., 2010) and other
objects such as executables which does not fit into the four original categories that
Bhattacharyya described.
In general no matter the cover medium, steganography can be classified into two
areas; key based systems and keyless based systems. A key based system hides
information in a cover medium and generates a key for transmission on a separate
channel. Only the sender and target receiver are aware of this key which would be used to
expose the hidden information in a cover material. Keyless systems employ only the
insecure channel to transmit and receive information but the sender and the receiver must
be aware of the encoding algorithm in order to decipher the original information
(Atawneh et al., 2016).
Image based steganography is usually the process of hiding text in an image by
various means without distorting the picture noticeably to the user (Li et al., 2017). Other
information can also be inserted such as other images. Significant research has taken
place in this area (Bennett, 2004) and as such a brief overview of the most common
methods will be explained. Some Image based methods do not employ modification of
the image itself but can the file container in which the image is stored. One such scheme
shown by Cheddad et al. (2010) explains that files can be appended to the EOF marker to
hide data. Whilst this is ultimately very simple to implement for a small amount of
information an image file significantly larger than the expected file size for the resolution
may raise eyebrows and in itself cause further investigation. Certain Image formats also
have areas within the format to hide small amount of data such as the EXIF field in
images. Various research papers have used the encoding of data within the least
significant bits (LSBs herein) within the pixels of the cover image. For example (Rig and
Tuithung, 2012) also shown that the letter A can be coded into 3 pixels using the 3 LSBs
of each pixel (3 BPP × 3 pixels = 9 bits which is enough to cover the 8 bits of the letter
A). Figure 1 shows 3 pixels one without encoding and one with the letter ‘A’ encoded
136 R. Lockwood and K. Curran
Figure 1 Original and difference encoding ‘A’ (see online version for colours)
As you can see this method cannot easily be identified by a person simply examining the
image with their eyes. A steganalyst could detect the hidden data however if the image
was significantly malformed which could arise when one attempts to insert too much
information. Detection of hidden information is easier if one has the original image and is
able to directly compare to the cover image. Given (in this case) a 1,024 × 768 image,
using 3 pixels per character, 262,144 characters can be encoded or squashed together to
form 294,912. Given that much of the ASCII character set is unused, a way to convert
more information into fewer pixels would be to use of a custom character set that omits
unused characters. Rig and Tuithung (2012) does this by way of Huffman Encoding. In
the case of (Rig and Tuithung, 2012), they modify the DCT blocks of pixels in JPEGs but
in essence any format can be used to encode information such as within bitmaps. The
frequency of the characters being used form shorter bit lengths (such as ‘A’). The letter
‘Z’ would less often be used so is located at the bottom of the binary tree and thus has a
longer bit length.
Other methods of encoding information into images can be by manipulating the way
the file is formatted by itself (Yu et al., 2017). Rig and Tuithung (2012) notes JPEG uses
DCT blocks of 8 × 8 pixels as a form of compressing pixels and near pixels. Beyond
JPEGs, different solutions can be applied to PNGs and other types. Videos on their very
size make an attractive alternative to extremely large amounts of information in. For
small amounts of data video based steganography would take a considerable amount of
computational time (Balaji and Naveen, 2011) and network bandwidth, however, it can
be suitable for large amounts of information. Depending on the format data can be held in
frame by frame (within the pixels of the frame). Videos have another dimension in which
information can be held, time. As with image based steganography, individual frames
(which are images in their own right) can also be modified by changing the LSB pixels of
the frame. As this has already been covered in image based steganography, it will not be
repeated here as the concept is the same. Videos are divided into set of frames. Video
formats can fall into one of two categories and some video formats support both: CBR
and VBR. Beneath that frame rates can also vary. On high frame rate video formats, a
single frame can contain a hidden frame. Due to the way our eyes work if the colour is
nearly matching the rest of the frames the watcher would not notice. Whilst it can be used
in steganography, it has also been used in subliminal messaging.
Text based steganography 137
Steganography can take place in other objects and in theory any object. Executable
files for the most part can also hide data and often do. Executable files do not necessary
harbour the main application program itself but in some cases viruses, spyware and
adware also. The Microsoft portable executable not only has sections for code (.text/.code
segment) but data also; such as strings. Images are often included to form icons or
embedded resources that are embedded into the application without having the resources
externally stored. To the user the embedded content is hidden but exposable by using a
resource extraction tool. Al-Nabhani et al. (2010) propose the use of header field of the
portable executable. Immediately after the header, the hidden information would be
stored. By updating the offsets of the starting program code, data and text segments, the
capacity is high. Al-Nabhani et al. (2010) do note, however, in order for the application
to execute it must first be downloaded and run (or installed and run). Like images and
video, the least significant bits of audio data can also be modified. Because of the
minimal modification to the generated sound, to human ears no distortion is identified.
As with all plain LSB methods, steganalysis can potentially uncover hidden information.
To overcome this (Asad et al., 2001) proposes selective modification of lower bits
depending on the value of the most significant bits. This would make steganalysis more
difficult but not immune particularly of the steganalyst is aware of the algorithm.
The audible range of human hearing is 20Hz (cycles per second) to 20KHz (Cuttnel
and Johnson, 1998). Outside if this range humans cannot hear. If a significant safety
margin is applied, we can encode audio below and above this range. In fact Gopalan and
Wenndt (1998) did just this, while the selected frequencies in use are not outside of the
audible range, low frequencies were used with the cover audio on top. Gopalan and
Wenndt (1998) noted that this method is susceptible to noise and can cause the method to
fail, any lossy medium could also cause data loss. Hiding videos in audio, images and
text is possible but impractical because of the large size requirements. Images and audio
can be encoded in such a way that they can be embedded into text with enough cover
text, but most of all, all information can be interchangeable (McBrearty et al., 2017).
This paper provides an overview of text steganography and presents the stegaid text
steganography system in addition to evaluation common text steganographic methods.
We evaluate a variety of steganographic techniques including open space encoding,
synonym replacement, UK/US English translation algorithm and Wayner’s Mimic
Functions using five benchmarks which compare speed, capacity, complexity,
compromisability and size.
2 Text based steganography
This form of steganography has had lesser research with comparison of other methods
such as images, therefore is the focus of this research. Steganographia, literally means
‘covered writing’ (Bennett, 2004), and although this has now been extended to include
images and other formats, the origins of steganography involve text. Text Based
Steganography can fall into one of three categories: format based, linguistic and
random/statistical generation (Bhattacharyya et al., 2010). In this chapter, we uncover the
basic methods that can be applied to any language and not a specific language such as
Chinese or Indian.
138 R. Lockwood and K. Curran
2.1 Format based systems
Format based steganography relies on a selected cover text and changing properties
within the cover text such as punctuation, or spelling to hide information. More
commonly information can be held in white space and non-printing characters.
The use of punctuation has been suggested as a way of hiding bits. Commas and
full-stops are explored by Agarwal (2013). By selecting appropriate points of insertion of
punctuation bits can be represented. For example, a full stop might represent 00, comma
01, exclamation 10 and question mark 11. Whilst, if the punctuation is logically correct,
there is no reason to believe such an algorithm would ever be discovered.
Shirali-Shahreza (2008) propose the way in which words are spelled is a method in
which to hide information. For example, in UK English the word ‘favourite’ and US
‘favourite’ have the same meaning but is inconspicuous in that the difference could
represent a zero or one. Whilst this method is very simple, a selective approach to chosen
words would have to take place as there are more English word spellings in common than
different. In order to encode a large message, it could be estimated you get one bit per
word, eight words to make a single character. On its own, the method would not be
feasible for a significant amount of text due to the low encode result. If the method was
combined with other methods to form a hybrid more bits can be encoded.
In some documents (namely HTML), spaces are ignored as are carriage returns. For
example in order to structure the document <p> and <br> tags are used. The first space is
accepted, but any additional spaces are explicitly ignored by the user’s browser. In order
to overcome this, website developers have to encode the &nbsp; code. Indeed (Barilnik
et al., 2007) has explored this and whilst not suitable for normal documents can be used
to hide information in source code, html documents or anywhere formatting is ignored
and justified text. This can be useful for hiding a signatures (a form of watermarking) for
copyright or hidden data. To a trained eye, this can be easily be subjected to identification
through steganalysis. Barilnik et al. (2007) also noted that opening a HTML document in
Microsoft Word and enabling the formatting marks, spaces are easily identified.
Other ideas include the colouring of white spaces (a hybrid method of the open space
method above and applying colour). Naturally a white space coloured red on a white
background is still white. Consequently for each space 3 bytes of information can be
encoded (R, G and B, 1 byte each not including a possible Alpha channel).
Documents such as ODF and DOCX store document formatting by modulating the
line spacing (by tiny amounts) to encode bits. Jalil and Mirza (2009) explains line
shifting by a small amount of pixels can be used in watermarking as a form of document
protection. Such a method can also be used in steganography to hide small amount of
information or as part of a larger strategy in combination.
For printed material and direct to screen there are a number of using fonts to hide
information. For some documents such as Microsoft Word, the user can easily see where
the font changes by looking at the font field in the top icon bar. In HTML this could be
used as viewers are not immediately aware of the font change. The following example
contains two x characters, one bit 0 encoded and the other bit 1.
hh Hello World! How are you today?
We can easily see the difference, but as part of a larger sentence it is difficult to pick up
the differences. A computer application that can detect pixel level differences would
Text based steganography 139
easily pick up on these differences. Whilst these images are enlarged for visibility, what
you may not notice is the additional character encoded in the ‘Hello Wor’. In this case the
letter is ‘H’. Whilst un-zoomed and appropriately encoded, it is not immediately obvious.
On paper, a pixel can be very small, for example a 600dpi Printer, 1dot equal 1/600th of
an inch. The above methods are all extremely simple and whilst can either on their own
or by using hybridisation are all candidates for steganalysis. The more encoding schemes
employed, the more difficult it is to decipher the hidden information. In these cases the
cover text can already exist but must be modified to hide the information in.
2.2 Random and statistical generation
The second category of text based steganography involves generation of a cover text
based on either randomisation or likelihood of correctness. This differs from linguistic
based steganography which attempts to create a valid natural language text using a range
of algorithms. This area borders the realms of computer science and language by way of
natural language processing and computer generated texts.
Hernon Moraldo (2012) suggests the use of generation of a cover text hiding the
information by way of Markov chains. Markov chains are often used to generate
language based on words, bi-grams and so forth). Hernon Moraldo (2012) uses such a
method to create a Markov chain based on a cover text. In the example provided, the
book ‘War and Peace’ is used as the Markov generation source. It is noted, that whilst
unigram based steganography is low quality, bigram generated texts are better. Despite
this, it can still be identified there are issues as in the following example:
“Be a square for fuel and kindled fires there. Secondly it was hard to hide
behind the cart and remained silent. He feels a pain in the now cold face
appeared that the man continually glanced at her as though they stumbled and
panted with fatigue. With a deep.”
Certain words are out of context in that “it was hard to hide behind the cart and remain
silent”. In this case, this approach can fall under linguistics and statistical generation.
Wayner (1991) created the well-known mimic functions and has been cited by a large
proportion of text steganography research papers. He describes the process of the
generating context free grammar using his mimic functions which are based on
probabilities. Whilst the generated text is legible, the result does not make sense as in the
following example.
“Paul is dead! I am the walrus! Buy something right now. Do not shoplift. Buy!
“Here are the plans to the Overthruster, Sergei. Yoyodyne forever.”
2.3 Linguistic steganography
Linguistic steganography is the third major category, this involves the creation of a
natural language text (or modification of an existing text) in order to hide information.
Coupled with a thesaurus, where the sender and receiver have the same thesaurus.
Words based on existing text can be modified to represent values. This was proposed by
Topkara et al. (2006) and others to act as a watermarking technique to protect documents.
It has come to the attention of this researcher that you can have multiple related words. A
140 R. Lockwood and K. Curran
proposed method could be to use a thesaurus and based on the value of the word an
alternative is used. For example ‘cat’ == ‘feline’. This could be extended further, there
are 16 words or more (synonyms) for cat:
bobcat cheetah cougar jaguar kitten leopard lion lynx panther puma puss pussy
tabby tiger tom tomcat
In order to put things in context the thesaurus would have to have ‘context’. Topkara
et al. (2006) notes that a generated sentence may lose its context and may not make
semantic sense. You’ll note the following sentence contains a syntactically correct
original sentence, a possible flawed sentence and a steganographic sentence:
“My pet cat has been to the vet today.”
“My pet tiger has been to the vet today.
Tomcat is tagged with ‘cat’, ‘pet’ and ‘tiger’, whilst tiger is still valid, it may raise some
eyebrows. Tomcat is the 16th word (0b10000). Reverse lookup suggests (with some
computation) tomcat is a synonym for cat.
The above method would work if the thesaurus on the sender and receiver are
identical, and assuming we have a thesaurus capable of identifying ‘is a’ relationships
reverse lookup is not a problem. Thinkmap Inc. (2013) has such an application that shows
relationships between words such as ‘is part of’, ‘pertains to’ and more specifically ‘is a
type of’. Other methods can also be used such as negativity (antonyms) of positivity to
represent bits also.
A variation on synonym replacement is to replace the whole structure of the sentence.
The previous method focuses on the modification of words (either synonyms or
antonyms), this method requires use of natural language processing tools. Chang and
Clark (2010) proposes the use of Google n-gram data to verify the correctness of the
sentence although they have not proposed any type of medium in which the cover text be
a basis for steganography. They have suggested news articles, but comparison with the
original article and the modified text would yield suspicion. If we examine the following
“The beginning of this month.”
The sentence can be modified whilst still meaning the same thing:
“This month in the beginning ...”
The sentence is correct but has bits (zeroes and ones) encoded depending on its structure.
This method is the simplest whilst remaining understandable to us. The issue arises when,
if based on an existing article such as a news story, differences would be noticeable.
3 System implementation
We developed a tool for examining many popular text based steganography algorithms.
After the splash screen has loaded (see Figure 2), the system prompts for user credentials.
After the failure of input of credentials three times, the application will terminate. If
authentication is successful, the user is passed through to the main menu interface.
Specifically the password has hidden characters for security. A guest mode button
appears if guest mode is checked in the system settings. If successful authentication
Text based steganography 141
occurs within three attempts, the main menu displays. The main menu allows for the
sending and receipt of mail, the encoding and decoding of files and access to settings. If
you belong to a group elevated permissions you will see the settings area, likewise, if you
do not have access to mail, these options will also not be visible. In the example provided
in Figure 2 the user has full administrative privileges. The buttons stand out significantly
as users felt they could not identify these controls. Icons were also added as appropriate
(along with the original planned shortcuts).
Figure 2 Implementation – main menu (see online version for colours)
3.1 Encode text
The primary capability of Stegaid is to encode and decode data using a variety of formats
by way of text based steganography. Four selected methods currently exist using the
plugin architecture. To hide information, the encode button can be selected (see
Figure 3). At first all controls are hidden and activated only as appropriate to prevent
confusion to the user, although a HTML Help File is also provided. Firstly the user
should select a file to be hidden, be it some text, an image or other document. The only
way to enter file is to click ‘select file’ which will provide the user with an open file
Upon selection of a file, more controls become active. All registered libraries
(plugins) are shown using a radio option select. If an option has been selected, an
‘encode’ button appears. Once encode has been selected, the core application,
(Controller) loads the selected plugin, encodes the information before returning with the
result and status messages. Encoding provides additional information such as the time
taken to encode a file which can be useful for evaluation algorithms. In order to validate
the encoding was successful, the performance timer is stopped and the cover text decoded
142 R. Lockwood and K. Curran
and validated against the original file. There is currently no way to disable this feature
and is considered essential to inform the user when things fail. Also some algorithms
have capacity limits, these checks also occur.
Figure 3 Implementation – encode text file selection (see online version for colours)
Figure 4 Implementation – encode text status (see online version for colours)
After encoding has taken place (see Figure 4), all invalid controls become disabled, and a
‘save as’ button becomes activated. The user can then activate this button to show a ‘save
as’ dialogue and save the resulting cover text. Upon success confirmation is provided as
to the status of the save process.
3.2 Decode text
The decode text, opens a cover text and attempts to decode as appropriate. Like the
encode text function, a stepwise approach to information elicitation from the user takes
place to help avoid confusion to new users. After selecting the appropriate option from
the main menu a decode text display is provided. After a file has been selected the
additional options become active. The user must know how the file was encoded. Most
steganographic systems have a single method, Stegaid has many. Consideration was
made to placing a signature or embedded header into the cover text, however, this
approach was not adopted in this release as it was felt a security risk to put
implementation details of how a file is encoded into the cover text. If an incorrect method
is selected, the decoding process will still occur, except the returned bytes of information
will differ from the originally encoded data.
Text based steganography 143
After the file selection and appropriate method selected the user can save the result
file be it an image, or document. If the result was successful confirmation is provided as
appropriate (see Figure 5).
Figure 5 Implementation – decode text status (see online version for colours)
3.3 Receiving encoded messages
The Stegaid application toolkit provides functionality for encoded communications.
Currently through the use of support libraries, SMTP, SMTP/TLS, POP3 and POP3/TLS
email protocols are supported.
Figure 6 Implementation – receive messages (see online version for colours)
In Figure 6, we can see that currently no message has been selected, so no operations can
be performed, hence no options have been enabled. Once a message is clicked relevant
options become available.
The transformed display next shows once such decoded message. As a rule messages
are downloaded but not deleted, this is intentional as Stegaid only supports encoded
messages. Multipart MIME type messages are listed as un-encoded messages as they
were not encoded by Stegaid.
If the user does not have any mail accounts set up but does have mail permissions,
then a message box is displayed as soon as the display is loaded. As well as receiving
144 R. Lockwood and K. Curran
encoded messages, mail messages can be encoded and sent using a compatible mail
server such as Gmail and Go Daddy. As with the receiving of messages, the system
checks for presence of mail accounts for the current user (along with other tests). This is
to prevent the user typing a mail message only to find out that the message cannot be
sent. Upon the successful sending of the message a confirmation message is displayed as
appropriate. To manage the system and enable some customisability a ‘settings’ feature
was implemented. This section has been locked down to any Group that has the
appropriate permissions. Using a tabbed interface, items are in related groups, such as
‘general settings’, ‘groups’, ‘users’, ‘libraries’ and ‘mail’. The general settings tab enable
the ability to disable the console, allow for external authentication using a data plugin,
enable mail and guest mode. Significantly there is no ‘save’ button, save occur internally
either when the display is closed or a tab switch takes place.
Figure 7 Implementation – received message view (see online version for colours)
Figure 8 Implementation – users (see online version for colours)
Groups are logical grouping of users that have shared permissions. A certain group may
have access to mail but not system settings, likewise, another Group may have access to
no mail or system settings. Group names must be unique and are case insensitive along
Text based steganography 145
with other validation requirements. Plain English helpers will always alert the user to any
issue before appending to the database. Administration is designed to be as simple as
possible so the only options are to delete and add using the + and – icons, showing only
relevant fields at the time. The system also checks for orphan users that are a member of
the group to be deleted and will prevent group deletion until they are deleted. There can
be many users to the system and they can be managed from this tab. A simple create, read
and delete interface has been provided for user administration. Users can be added if
certain validation proves true such as the password containing at least six character is
contains both alphabetic and numeric characters.
As with groups, no fields are visible until a request to add a user has been made. This
is to simplify the interface to the system administrator. The system was designed to be
extensible from the outset, the system administrator can on demand register and de-
register libraries as needed. Buggy libraries can also be removed if necessary such as
development versions and new versions be loaded on the fly without restarting the whole
application. More importantly whilst version 1.2 solely targets text based steganography,
version 2 will support other plugins such as encryption and compression by using a
prioritisation system (only minimal change in the core application is required to
accomplish this).
Figure 9 Implementation – libraries (see online version for colours)
To add a library, the + icon is clicked. A ‘select file’ option is provided that loads a file
dialogue box showing only relevant *.dll or *.so files. The library is then loaded and
tested to ensure it meets the criteria before registering it to the system. Valid libraries
have a unique class identifier and a useful description. If the mail option is enabled,
another interface becomes available, the mail administration area. A user can have one
email address but many protocols to support the email capability. Further to this there
may also be backup servers and so forth. Upon changing the protocol, the default
standard port changes as appropriate but is amendable as some servers have irregular
4 Evaluation
Each method was evaluated using set criterion (both qualitative and quantitative) and
displayed via a result set like shown in Figure 10.
We can see from the example diagram the evaluation is based on five benchmarks:
146 R. Lockwood and K. Curran
Speed – The speed in milliseconds for the encoding to take place. Note in the
interested of fairness, our versions are all single thread and uncompressed. The more
speed the lower the score.
Capacity – The number of bits that can be encoded within set limit. The less capacity
the lower the score.
Complexity – The difficulty (estimated) in implementing the algorithm. The more
complexity the lower the score.
Compromisability – The difficulty (without knowing the algorithm) on suspecting
hidden information. The ease of breaking the algorithm the lower the score.
Size – The result set size in comparison to other methods. The more the result set
size the lower the score.
The target for any algorithm is to have a score of 5 in all key areas:
The application evaluation is the process of applying critique to the resulting
software. Evaluation takes multiple forms and includes the following methods:
Usability testing – The evaluation of responses by ten users who have tried this
software in order to obtain directions for either improvement in the current release or
a future release.
Project evaluation – The personal evaluation of the project detailing issues that have
arisen, what should be done to improve and recommendations should a future release
occur. Time constraints or other reasons will be noted as to why these are not
incorporated into this first release.
Figure 10 Example evaluation radial chart (see online version for colours)
Text based steganography 147
4.1 Steganographic methods
In order for a fair testing and evaluation take place all methods use a single thread (as
some can only be performed sequentially). Whilst Stegaid is perfectly capable of
compression and encryption (should a plugin be developed), no algorithms shall make
use of a these technologies. Some research papers were using compression and/or
encryption in their tests and are disregarded in this application.
4.1.1 Open space encoding
This method is the simplest method that was chosen, which involves the encoding of bits
between the spaces of words. Depending on the implementation, the encoding can be
different. In this implementation, a simple option was chosen whereby a zero bit (off) has
one space and a one bit (on) has two spaces. The encode limit is 1 bit per word and across
1,000 words, therefore 125 bytes of information can be encoded. The current
implementation using a randomly generated document from Project Gutenberg, an image
of 513 Bytes could be encoded into 5,205 bytes.
Encode Partial result Decode
The Project Gutenberg EBook of Beyond Good and Evil, by Friedrich
This eBook is for the use of anyone anywhere at no cost and with almost
no restrictions whatsoever. You may copy it, give it away or re-use it under
the terms of the Project Gutenberg License included with this eBook or
online at
The tests in this implementation show that whilst open space is a simple solution however
only small images such as icons and/or a small amount of textual information can be
4.1.2 Synonym replacement
The process of using a thesaurus which is identical on both clients can be used to store
information. For each word in a given text a word is replaced with a synonym of that
word. Our implementation is simpler in that a given word is searched through the
thesaurus to generate a suitable word list. The greater the number of words in the
generated list, the more bits can be encoded (variable bit encoding). If there are three
synonyms, two bits can be encoded or seven synonyms 3 bits. In some cases there are
more than 60 synonyms for a given word so the number of bits that can be encoded is
higher. It is noted that currently this plugin does not identify a context for the given
synonym, for example:
“The ‘cat’ sat on my lap.”
“The ‘lion’ sat on my lap.”
Logically this sentence is correct but does not make sense given the average weight of a
lion is very heavy and do not make good lap pets.
148 R. Lockwood and K. Curran
Figure 11 Open space algorithm evaluation (see online version for colours)
Figure 12 Synonym replacement algorithm evaluation (see online version for colours)
Text based steganography 149
Encode Partial result Decode
Hello World! The Beget Gutenberg EBook anent ‘Smiles’, obsolete
Eliot H. Robinson
Hello World!
This eBook is hereby the benignity atop anybody anywhere
helium einsteinium cost borrow with almost europium
restrictions what. Her may blowup me, bear alterum
backwards dolophine
The issue with our implementation is that in some cases the sentence makes sense, other
parts of the sentence do not. The system has no way of knowing that the grammar makes
semantic sense.
4.1.3 English diversity
Given the difference in spelling between UK and US English, these differences in itself
can be used as a storage media. Whilst simple to implement an extremely large text could
only encode a limited amount of information. The problem with this solution is the
limited number of words with differences between the UK English language and the US
variant. In our tests a large amount of text is required to encode even a short message,
obviously this is entirely dependent on the number of words that can be translated exist.
In the current implementation, the UK English variant represents zero and US variant
represents one.
Encode Partial result Decode
Hello World! If there be any one feature in this textbook more to be
commended than another, it is the exposition in Part III. The
situations arising in many different kinds of business are
here analyzed.
Hello World!
In our results, we were able to encode only limited information. The encoding of ‘Hello
World’ took 146 KB using a Gutenberg text. To maximise the encode rate in future it is
therefore suggested that a text be created that specifically has words in the US/UK cross
4.1.4 Wayner’s mimic functions
This method tested does not use a pre-existing text to store information, instead using a
random selected grammar file; phrases are encoded depending on the bits that are
required to be stored. The recipient must have the same grammar file in order to
successfully decode the data. Issues arise when the end of the grammar file is reached and
the cover text information becomes repeated. The information that can be encoded (as the
cover text is generated) is unlimited, however, because of the previous point it is not
recommended in case of discovery.
Our implementation differs slightly from the original C code developed by Wayner
(1991). Unfortunately the application no longer operates on Microsoft Windows or
compiles successfully. Wayner’s version uses a probabilistic approach using a syntax
such as: *AAStart = Fred went to *con/.1/.AAStart is the start of the text (the variable).
‘Fred went to’ > Next Variable and the weight is the final number, the higher the weight,
the more probable (Defcon, 2003). Our implementation differs, using the same grammar
150 R. Lockwood and K. Curran
files we can encode bits by counting the appropriate variables and identifying the number
of bits we can embed using a counting algorithm. So if there are two values
corresponding to a variable, we can encode two bits, 0 or 1. The more values to each
variable, the more bits can be encoded.
Figure 13 UK/US English algorithm evaluation (see online version for colours)
Figure 14 Wayner’s mimic functions evaluation (see online version for colours)
Text based steganography 151
Encode Partial result Decode
Hello World! Let’s get going! Top of the inning. Hello World!
Through this evaluation we have tested and evaluated four Steganographic algorithms
that can be used to hide information such as files and documents. A summary is presented
in Table 1.
Table 1 Evaluation summary
Method Summary
Open space
Open space encoding was the simplest method to implement. Through our
implementation it was shown that capacity is approximately 10% of the size of
the cover text (language dependent).
Synonym replacement (the process of changing words with comparable
thesaurus words). The better the thesaurus, the greater the probability of
occurrence of a synonym. Our specific implementation checks for a word and
replaces it using a thesaurus. It was noted, however, that the current library is
unable to discern the context of the grammar at hand. For example, project
(hurl) and project (task) are the same words but have different meanings. Test
results indicate that Synonym replacement has a higher encode rate than that of
Open Space encoding.
This method involves the use of encode bits (zeroes and ones) by way of
selecting UK (or US) English as bit zero and the other as bit one. Whilst in itself
would be unexpected to a reader of the cover text to notice the difference, the
capacity is extremely small. This is due to the likelihood in occurrence of a word
that is spelled differently across the two nations.
Wayner (1991) developed the mimic functions to mimic natural language using
a Grammar File to generate context free grammar. It was found that the encode
rate was low, the more variable paths in a grammar file the more information
could be encoded. Whilst having a low bitrate, our versions of the Mimic
Functions have an unlimited capacity. Once the end of a path is reached, the
beginning of the path is taken again. This would cause repeating text which
would be suspicious to users viewing the cover text.
5 Conclusions
Various algorithms with relation to text based steganography have been investigated, four
of which have also been implemented. This research firstly involved the investigation of
various methods within steganography as a whole and drilled down further specifically
into text based steganography. The next step involved the investigation into
Requirements of a potential system in order to provide a direction for the project to move
forward. Whilst most steganographic research focuses on a single method of encoding
such as the famous mimic functions, (Wayner, 1991), this implementation focuses on
four, although not limited to four. The eventual design was designed with future
enhancements in mind. To aid further in future development, the current user interface
(the view) can be ‘stripped’ and replaced without affecting how the application operates.
In fact the application can support multiple Views, as the logic within the application
does not reside here. The application logic and validation occur within the base classes
(controllers) and the data is provided within the embedded database system. To enable
152 R. Lockwood and K. Curran
further future proofing the use of a plugin architecture is provided, thus allowing an
administrator to register new methods as they become available. Whilst in this version we
concentrated solely on text based steganography, the system in its current form can be
used with other steganography methods, encryption or compression out of the box.
The application designed is not inherently aware of the encoding of the cover text. To
date all known steganographic applications encode only one method, it is obvious to that
application as to which method must be used. Stegaid uses multiple methods of encoding
depending on the user’s choice. As such the information (binary bits) are encoded but not
how it is encoded. The receiver must know how the method used in order to decode the
information. In future, the application could encode information about the encoding also,
which raises another issue. If the embed how the information is encoded into the cover
text, any peer network users can instantly decode the text (if they know the information is
Finally, Stegaid is not limited to steganography but could potentially perform
encryption and compression as part of a hybrid system. This would make Stegaid far
more secure in that data is hidden but if discovered, still encrypted. Given recent
developments with the Heartbleed bug in OpenSSL (Jeske et al., 2017; Gupta and Gupta,
2016; Harran et al., 2017) it makes sense to have an additional level of security beyond
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... Common subtasks include style transfer [78], in which a given model attempts to retain the topic of a given text whilst changing some specified stylistic attribute (e.g., sentiment, toxicity, formality). Moreover, style transfer can be adapted to preserve user privacy by removing or otherwise obfuscating aspects of authorial style [79,80], thereby protecting the source of the original text. Other rewriting subtasks include summarisation [81], in which the summarising model attempts to generate a shortened version of a given input whilst retaining its overall content. ...
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In recent years there has been substantial growth in the capabilities of systems designed to generate text that mimics the fluency and coherence of human language. From this, there has been considerable research aimed at examining the potential uses of these natural language generators (NLG) towards a wide number of tasks. The increasing capabilities of powerful text generators to mimic human writing convincingly raises the potential for deception and other forms of dangerous misuse. As these systems improve, and it becomes ever harder to distinguish between human-written and machine-generated text, malicious actors could leverage these powerful NLG systems to a wide variety of ends, including the creation of fake news and misinformation, the generation of fake online product reviews, or via chatbots as means of convincing users to divulge private information. In this paper, we provide an overview of the NLG field via the identification and examination of 119 survey-like papers focused on NLG research. From these identified papers, we outline a proposed high-level taxonomy of the central concepts that constitute NLG, including the methods used to develop generalised NLG systems, the means by which these systems are evaluated, and the popular NLG tasks and subtasks that exist. In turn, we provide an overview and discussion of each of these items with respect to current research and offer an examination of the potential roles of NLG in deception and detection systems to counteract these threats. Moreover, we discuss the broader challenges of NLG, including the risks of bias that are often exhibited by existing text generation systems. This work offers a broad overview of the field of NLG with respect to its potential for misuse, aiming to provide a high-level understanding of this rapidly developing area of research.
... Text steganography refers to all the techniques and methods used for hiding secret messages in textual documents (Agarwal, 2013;Lockwood and Curran, 2017;Taleby Ahvanooey et al., 2019;Kumar and Singh, 2020;Majeed et al., 2021). Unlike other media, text documents are more challenging to use as cover due to the lack of redundant information that can be used for hiding secret messages. ...
Conference Paper
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The ancient technique of information hiding known as text steganography has enjoyed much research in recent years due to the rising popularity of social media platforms and the abundant availability of online literature and other text as cover media for steganography. Whilst the majority of the research approaches have focused on manipulating or replacing text, in some form or another, to embed secret information, the utilisation of the structure of the document itself for such embedding has rarely been researched. Therefore, we propose in this short paper a new approach for embedding secret messages in textual documents based on the splitting, merging, and resizing of paragraph text. The size comparison between adjacent paragraphs embeds one bit of information. We outline only the basic idea and define the syntax and semantics of the embedding language.
... Essentially, intruders may temper the information for their own interests. The ultimate goal of any steganography technique is to embed secret messages into a chosen media, such as images [1,2], text-based documents [3,4], audio [5][6][7], and video [5,7] files without easily noticed by any unintended third party. Essentially, the steganography strategy successfully hides the message in innocuous looking media in a camouflage manner. ...
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span>The study of steganography focuses on strengthening the security in protecting content message by hiding the true intention behind the texts. However, existing linguistic steganography approach especially in synonym-based substitution is prone to attack. In this paper, a new substitution-based approach for linguistic steganography is proposed using antonyms. The antonym-based stego-text generation algorithm is implemented in a tool called the Antonym Substitution-based (ASb). Evaluation of ASb was carried out via verification and validation. The results showed highly favorable performance of this approach.</span
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Information hiding has received a lot of attention recently. Various methods have been developed for steganography, and at the same time, proper steganalysis has been designed to detect hidden data. However, an approach that is introduced in a new type of cover can be less doubtful due to the lack of attention to steganalysis analyses. This paper proposes a method that can convert a message into a mathematical expression. The generated mathematical expression can be used as a cover to transmit the message along with a highly secure text. The proposed method, using a suitable stochastic grammar, has the ability to create a mathematical expression based on a given text. Probability of grammar rules can be determined in accordance with the carrier text and based on the type of mathematical expressions.
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A novel four-image encryption scheme based on the quaternion Fresnel transforms (QFST), computer generated hologram and the two-dimensional (2D) Logistic-adjusted-Sine map (LASM) is presented. To treat the four images in a holistic manner, two types of the quaternion Fresnel transform (QFST) are defined and the corresponding calculation method for a quaternion matrix is derived. In the proposed method, the four original images, which are represented by quaternion algebra, are processed holistically in a vector manner by using QFST first. Then the input complex amplitude, which is constructed by the components of the QFST-transformed plaintext images, is encoded by Fresnel transform with two virtual independent random phase masks (RPM). In order to avoid sending entire RPMs to the receiver side for decryption, the RPMs are generated by utilizing 2D–LASM, which results that the amount of the key data is reduced dramatically. Subsequently, by using Burch’s method and the phase-shifting interferometry, the encrypted computer generated hologram is fabricated. To improve the security and weaken the correlation, the encrypted hologram is scrambled base on 2D–LASM. Experiments demonstrate the validity of the proposed image encryption technique.
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A text steganography method based on Markov chains is introduced, together with a reference implementation. This method allows for information hiding in texts that are automatically generated following a given Markov model. Other Markov - based systems of this kind rely on big simplifications of the language model to work, which produces less natural looking and more easily detectable texts. The method described here is designed to generate texts within a good approximation of the original language model provided.
The current paper outlines an exploratory case study in which we examined the extent to which specific communities of Twitter users engaged with the debate about the security threat known as “Heartbleed” in the first few days after this threat was exposed. The case study explored which professional groups appeared to lead the debate about Heartbleed, the nature of the communication (tweets and retweets), and evidence about behaviour change. Using keywords from the Twitter user profiles, six occupational groups were identified, each of which were likely to have a direct interest in learning about Heartbleed (including legal, financial, entrepreneurial, press, and IT professionals). The groups participated to different degrees in the debate about Heartbleed. This exploratory case study provides an insight into information sharing, potential communities of influence, and points for future research in the absence of a voice of authority in the field of cybersecurity.
Internet usage has become a facet of everyday life, especially as more technological advances have made it easier to connect to the web from virtually anywhere in the developed world. However, with this increased usage comes heightened threats to security within digital environments. The Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security identifies emergent research and techniques being utilized in the field of cryptology and cyber threat prevention. Featuring theoretical perspectives, best practices, and future research directions, this handbook of research is a vital resource for professionals, researchers, faculty members, scientists, graduate students, scholars, and software developers interested in threat identification and prevention.
Over last two decades, due to hostilities of environment over the internet the concerns about confidentiality of information have increased at phenomenal rate. Therefore preventing unauthorized information access has been a prime consideration for growing use of steganography techniques for applications like copyright protection, feature tagging and secret communication. As a result, steganography has become an interesting and challenging field of research striving to achieve greater immunity of hidden data against signal processing operations on the host cover media like image, audio, or text.A good steganography technique should offer immunity of hidden data against lossy compression, scaling, interception, modification, or removal etc. and ensure that embedded data remains inviolate and recoverable.In this work the authors propose a new text based steganographic model along with a novel steganography method for hiding the information. The proposed approach works by selecting the embedding position of the secret information in the cover text using some mathematical function and map each two bit of the secret information in those selected position in a specified manner. As a further improvement of security level, the information has been encrypted first through the genetic operator crossover and then embedded into an innocuous cover text to form the stego text with minimum degradation. At the receiving end different opposite processes should run to get the back the original secret message.The proposed system is also capable of checking the authenticity of the secret message through integer wavelet transform.
Conference Paper
Increased use of electronic communication has given birth to new ways of transmitting information securely. Audio steganography is the science of hiding some secret text or audio information in a host message. The host message before steganography and stego message after steganography have the same characteristics. Least Significant Bit (LSB) modification technique is the most simple and efficient technique used for audio steganography. The conventional LSB modification technique is vulnerable to steganalysis. This paper proposes two ways to improve the conventional LSB modification technique. The first way is to randomize bit number of host message used for embedding secret message while the second way is to randomize sample number containing next secret message bit. The improvised proposed technique works against steganalysis and decreases the probability of secret message being extracted by an intruder. Advanced Encryption Standard (AES) with 256 bits key length is used to secure secret message in case the steganography technique breaks. Proposed technique has been tested successfully on a. wav file at a sampling frequency of 8000 samples/second with each sample containing 8 bits.