Linguistic Steganography Research Articles

Steganography Arabic Text Based on Natural Language Process Documents

: Obscurity is a main reason whereas computers can not know natural language. It have made great transaction steps trend developing instrument to morphological and syntactic analyzers for Arabic . One of the manners used in security areas is steganography. The rapid development of steganography scripts, it is a large security and confidentiality problem, it becomes necessary to find appropriate protection because of the significance, accuracy and sensitivity of the data during transmitted.
 In this research is offer in a new method and to use one level to hide, this level is hiding by embedding and addition. The one level is embed a secret message twice, one bit in the LSB in the FFT and the addition of one kashida and add Single-Double Quotation in the same secret message. Using Random Singular Value Decomposition (RSVD) is NRG to find positions that are hiding within the text.
 Linguistic steganography is covering all the techniques that deal with using written natural language to hide secret message. in this research presents a linguistic steganography for scripts written in Arabic language, using kashida, Single-Double Quotation and Fast Fourier Transform on the bases of using new technique entitled Random Singular Value Decomposition (RSVD) as allocation to hide secret message. The proposed approach is an attempt to present a transform linguistic steganography using one level for hiding to improve implementation of kashida and Single-Double Quotation , and improve the security of the secret message by using Random Singular Value Decomposition (RSVD). Are testing this method in terms of security and capacity, transparency, and robustness and this is way better than previous methods. The proposed algorithm ideal steganography properties.

A modified approach to data hiding in Microsoft Word documents by change-tracking technique

Steganography is a hidden way of communication, where one person communicates with the other by using some medium under the garb of a cover, without any suspicion created on the part of any unauthorized intermediary about the existence of the secret message. Linguistic steganography is about hiding information in natural language text. Synonym substitution is an important transformation used in linguistic steganography. In this paper, we are going to show a method of redesigning the change tracking technique for hiding the secret message in Microsoft Word document to increase the embedding capacity and to decrease the suspicion of any intermediary about the message’s existence. The secret message in bit form is concealed by performing linguistic steganography using synonym substitution method. In conventional change tracking technique for information hiding in Microsoft Word document, the generated Huffman’s codes of rare words are lengthier as compared to common words, leading to more suspicion in the stego-document due to the usage of rare words as compared to common words as synonyms. To overcome this problem, the proposed algorithm is designed in such a way that familiar synonyms are used more often to hide more bits and avoid suspicions.

Researcher’s Perspective of Substitution Method on Text Steganography

The linguistic steganography studies are still in the stage of development and empowerment practices. This paper will present several text steganography on substitution methods based on the researcher’s perspective, all scholar paper will analyse and compared. The objective of this paper is to give basic information in the substitution method of text domain steganography that has been applied by previous researchers. The typical ways of this method also will be identified in this paper to reveal the most effective method in text domain steganography. Finally, the advantage of the characteristic and drawback on these techniques in generally also presented in this paper.

Text based steganography

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.

Text based steganography

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.

A Novel Linguistic Steganography Based on Synonym Run-Length Encoding

A Novel Linguistic Steganography Based on Synonym Run-Length Encoding

A Proficient Two Level Security Contrivances for Storing Data in Cloud

Objectives: Cloud Computing provides vast storage facility. The requirement of this system is to improve the security and transmission performance in the cloud storage environment. Methods: This system provides two level of security for the cloud data. The Client Data Security Contrivance (CDSC) and Cloud Service Provider (CSP) Data Security Contrivance are the two methods which transforms the original data to cipher text. The security algorithm used in CDSC is Linguistic Steganography. Blowfish algorithm is used in CSP Data Security Contrivance to convert the cipher text using a secretkey block cipher of 64-bits. This system is designed to increase security and to improve the transmission performance. Storage-cheating attack model and Privacy-cheating attack model are implemented to protect the data on cloud. Findings: This system increases security and improves transmission performance. The blowfish algorithm is secure against unauthorized attacks and runs faster than the popular existing algorithms. Storage-cheating attack model and Privacy-cheating attack model provides the data confidentiality for data stored in the cloud in higher way. Improvements: The system can be implemented in real cloud system in the future.

Comparison of Eight Proposed Security Methods using Linguistic Steganography Text

Comparison of Eight Proposed Security Methods using Linguistic Steganography Text

Steganography Based Secure Data Storage and Intrusion Detection for Cloud Computing Using Signcryption and Artificial Neural Network

An efficient approach for providing very high secure storage data to the cloud system is proposed in this study. In order to effectively access data from the cloud server, a secure authentication is highly essential. After the authentication the user data is examined to ascertain whether the specified data is normal or intrusive in accordance with the intrusive recognition system, such as the Artificial Neural Network (ANN). It is accompanied by the encryption of the file by means of the signcryption technique. For the purpose of enhancing the storage safety of the novel approach, the steganography methods are carried out immediately after the encryption technique. In the innovative technique, the linguistic steganography approach is employed to conceal the data from the TPA. Subsequently, the data is subdivided arbitrarily and is amassed into cloud. The epoch-making steganography based secure storage and intrusion recognition technique is performed with the mighty assistance of the Cloud simulator in the working platform of Java software.

Avoiding detection on twitter: embedding strategies for linguistic steganography

Any serious steganography system should make use of coding. Here, we investigate the performance of our prior linguistic steganographic method for tweets, combined with perfect coding. We propose distortion measures for linguistic steganography, the first of their kind, and investigate the best embedding strategy for the steganographer. These distortion measures are tested with fully automatically generated stego objects, as well as stego tweets filtered by a human operator. We also observed a square root law of capacity in this linguistic stegosystem.

Unicode-based method for text steganography with malayalam text

Recent researches regarding information hiding is mostly concentrating on Linguistic steganography. The Steganography is the art and science of hiding a message inside another message without drawing any suspicion to the others so that the message can only be detected by its intended recipient. Now days, concentration is made on local language encryption based steganography to provide high security for the secret information sharing. In this paper, a method to steganography is proposed with an Indian local language, Malayalam. The proposed method consists of a custom Unicode based technique with embedding based on indexing, i.e. the original message is encoded to a Malayalam text with custom UNICODE values generated for the Malayalam text. After that an embedding algorithm will be designed to mix the encoded original message with the Malayalam text. The experimental study was done to evaluate the efficiency of the proposed approach. The comparison study of the proposed method against an existing method revealed that, the proposed steganography methods is more precise in the encoding process and balanced in the decoding process. The proposed method achieved a precision rate of.95 and decoding rate of.81.

A Method for Text Steganography Using Malayalam Text

Recent researches regarding information hiding is mostly concentrating on Linguistic steganography. In this paper, a method to steganography is proposed with an Indian local language, Malayalam. The proposed method consists of custom Unicode based technique with embedding based on indexing, i.e. the original message is encoded to Malayalam text with custom UNICODE values generated for the Malayalam text. The comparison study of the proposed method against an existing method revealed that, the proposed steganography methods is more precise in the encoding process and in the decoding process. The method achieved a precision rate of .95 and decoding rate of .81.

ЛИНГВИСТИЧЕСКАЯ СТЕГАНОГРАФИЯ: СОВРЕМЕННЫЕ ПОДХОДЫ. ЧАСТЬ 2

In the article the approaches aimed at overcoming the drawbacks of the formal approach are presented. Unlike the formal methods, semantic and transformational approaches are directed at concealing the fact of passing the message not only from automatic systems, but also from being revealed by a human. Different methods accounting for the semantics of the generated stegotext are observed. Machine translation based steganography is described as a special type of linguistic steganography. Advantages and disadvantages of the techniques presented are analyzed.

Esteganografía lingüística en lengua española basada en modelo N-gram y ley de Zipf

Linguistic Steganography is a science that utilises computational linguistics to design systems that can be used to protect and ensure the privacy of digital communications and for the digital marking of texts. Various proposed ways of achieving this goal have been documented in recent years. This paper analyses the possibility of generating natural language texts in Spanish that conceal information automatically. A number of hypotheses are put forward and tested using an algorithm. Experimental evidence suggests that it is feasible to use N-gram models and specific features of the Zipf law to generate stegotexts with a good linguistic quality where human readers could not differentiate the stegotext from authentic texts. The stegotexts obtained allow the concealment of at least 0.5 bits per word generated.

Practical Linguistic Steganography using Contextual Synonym Substitution and a Novel Vertex Coding Method

Linguistic steganography is concerned with hiding information in natural language text. One of the major transformations used in linguistic steganography is synonym substitution. However, few existing studies have studied the practical application of this approach. In this article we propose two improvements to the use of synonym substitution for encoding hidden bits of information. First, we use the Google n-gram corpus for checking the applicability of a synonym in context, and we evaluate this method using data from the SemEval lexical substitution task and human annotated data. Second, we address the problem that arises from words with more than one sense, which creates a potential ambiguity in terms of which bits are represented by a particular word. We develop a novel method in which words are the vertices in a graph, synonyms are linked by edges, and the bits assigned to a word are determined by a vertex coding algorithm. This method ensures that each word represents a unique sequence of bits, without cutting out large numbers of synonyms, and thus maintains a reasonable embedding capacity.

A Steganography on Synonym Frequency Distribution

Linguistic steganography is concerned with hiding secret information in natural language text imperceptibly. One of the major methods for doing so is synonym substitution. Existing schemes hardly consider the influence on word frequency so that they can’t resist the text steganalysis which bases on synonym frequency. This study takes the features of word frequency into account, and proposes a novel method using a combination of synonym substitution and multiple-base. This method mainly consists of three steps. Firstly, synonym database is to be preprocessed according to synonym frequency. Then, the secret data will be transformed into the expression of multiple-base expressions. Finally, the chosen synonym will be used to hide the secret message according to its continuous dimension and the proposed algorithm. In this paper, continuous dimension is a new concept to be proposed, and it is important to the embedding and extracting processes. The proposed method possesses superiority to other related methods in terms of security and robustness. Moreover, the most important goal in this method is trying to remain the synonym frequency distribution and achieve good imperceptibility. This paper describes in detail the steganography algorithm that involves the embedding and extracting processes, and presents its advantages and limitations.

A Universal Lexical Steganography Technique

A literally meaning of Steganography is covered writing. There are several methods of steganography, these include: Image steganography, Audio steganography, Video steganography and Linguistic steganography which use the cover to hide information. Each method has its own algorithm to embedding secret information inside the media cover. Linguistic steganography is basically hiding information in a text in such a way without making the text suspicious, so we have to take into our account possible characteristics of natural languages. In linguistic steganography, digital numbers like (0010100101001) data is to be encoded to innocuous natural language text by using synonym. In this paper, English language will be used as an instance of natural languages as we will be concerned with the set of all natural language texts. this research tries to employ a set of all synonyms as a way to hide secret message inside a natural language text. The main objective of this paper is to develop a general technique of lexical steganography to support different natural languages texts and decrease the bits used for encoding and increase the information. An evaluation of the proposed method has been carried out. The obtained results are encouraging and promising.

Text split‐based steganography in OOXML format documents for covert communication

ABSTRACTA new steganographic method for data hiding in Microsoft Word 2007–2010 (Microsoft Corp., Redmond, WA, USA) files that use Office Open XML (OOXML) format is proposed. Secret information can be imperceptibly embedded into OOXML documents by splitting up the printable text, which is defined by the main document body of the OOXML format document. The number of printable words contained in each segment represents the secret message. Theoretical analysis demonstrates that embedding bit rate of the proposed method can take the maximum value (0.8) when 2 bits of secret message are embedded into each segment. Experiments show that 0.44 bit is embedded into each word and 1/151 bit is embedded into each bit of the document on average, which is higher than contemporary linguistic steganography approaches. The method can resist “Format”, “Impersonation”, “Save As”, “Copy”, and other active attacks, and all these changes will not be shown on the MS Office screen display. Therefore, the proposed method can apply to the fields of covert communication and security protection for OOXML format documents. Copyright © 2011 John Wiley & Sons, Ltd.

Extended Encoding of Telugu Text for Hiding Compatibility

Conveying information secretly and establishing a hidden relationship between the message and its counterpart has been of great interest since long time. This paper presents a new scheme for encoding of the Telugu alphabet set, its diacritics and compounds which can be used in the Linguistic Steganography as well as cryptic transmission of Telugu text. This encoding scheme helps its users to have better support in implementing the hiding techniques and this scheme can be extended to other similar Indian Languages.

Steganalysis against substitution-based linguistic steganography based on context clusters

Linguistic steganalysis has been an increasing interest stimulated by the emerging research area of linguistic steganography during the past few years. However, due to limitations of computer natural language processing capability, linguistic steganalysis is a challenging task. Existing steganalysis methods are inefficient to analyze most substitution-based linguistic steganography methods which preserve the syntactic and semantic correctness of cover texts. This paper provides a new steganalysis scheme against substitution-based linguistic steganography based on context clusters. In this scheme, we introduce context clusters to estimate the context fitness and show how to use the statistics of context fitness values to distinguish between normal texts and stego texts. Finally, under this scheme, we present the steganalysis method for synonym substitution-based linguistic steganography. Our experimental results show that the proposed steganalysis method can analyze synonym substitution-based linguistic steganography efficiently and the steganalysis accuracy reaches as high as 98.86%.