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Recently, a serious problem in communications is security. Hiding data is one of the most important security techniques. Steganography is the art and science of hiding information in a cover media. Texts are the most usual method of communication and so they are very suitable for cover objects. In this paper, we give a new technique for text steganography. There exist different models, such as RGB, HSL, HSV to determine a color. The main goal of the proposed algorithm is the fact that some different but very similar colors in RGB have the same code in HSL. We use this fact to hide data. For this purpose, first we find a color B′ in RGB, which is very similar to black in such a way that they have the same code in HSL. Then, by changing the color of each character of the text to black or B′, we conceal the information. We will show that the capacity of this method is better than some other methods of text steganography, and then we show that the invisibility of this algorithm is very high, which is the most prominent feature of the proposed technique. [ABSTRACT FROM AUTHOR]

Recently, a serious problem in communications is security. Hiding data is one of the most important security techniques. Steganography is the art and science of hiding information in a cover media. Texts are the most usual method of communication and so they are very suitable for cover objects. In this paper, we give a new technique for text steganography. There exist different models, such as RGB, HSL, HSV to determine a color. The main goal of the proposed algorithm is the fact that some different but very similar colors in RGB have the same code in HSL. We use this fact to hide data. For this purpose, first we find a color $B'$ in RGB, which is very similar to black in such a way that they have the same code in HSL. Then, by changing the color of each character of the text to black or $B'$, we conceal the information. We will show that the capacity of this method is better than some other methods of text steganography, and then we show that the invisibility of this algorithm is very high, which is the most prominent feature of the proposed technique.

The transmission of confidential data using cover media is called steganography. The three requirements of any effective steganography system are high embedding capacity, security, and imperceptibility. The text file's structure, which makes syntax and grammar more visually obvious than in other media, contributes to its poor imperceptibility. Text steganography is regarded as the most challenging carrier to hide secret data because of its insufficient redundant data compared to other digital objects. Unicode characters, especially non-printing or invisible, are employed for hiding data by mapping a specific amount of secret data bits in each character and inserting the character into cover text spaces. These characters are known with limited spaces to embed secret data. Current studies that used Unicode characters in text steganography focused on increasing the data hiding capacity with insufficient redundant data in a text file. A sequential embedding pattern is often selected and included in all available positions in the cover text. This embedding pattern negatively affects the text steganography system's imperceptibility and security. Thus, this study attempts to solve these limitations using the Part-of-speech (POS) tagging technique combined with the randomization concept in data hiding. Combining these two techniques allows inserting the Unicode characters in randomized patterns with specific positions in the cover text to increase data hiding capacity with minimum effects on imperceptibility and security. Format-preserving encryption (FPE) is also used to encrypt a secret message without changing its size before the embedding processes. By comparing the proposed technique to already existing ones, the results demonstrate that it fulfils the cover file's capacity, imperceptibility, and security requirements. [ABSTRACT FROM AUTHOR]

In or daily lives, maintaining the security of sensitive information has proven to be very difficult. communications sent frequently through a communication channel like the Internet may catch the attention of crackers and hackers, leading to possible attempts to decrypt and reveal the odd communications. With the development of digital communication, the Internet, and cloud-based storage, there is an increasing need for novel and effective methods for protecting the privacy and security of digital data. Steganogra-phy and cryptography are often used together as a solid privacy protection strategy. However, numerous studies on steganography have been established by various academics. Here, a text steganography method has been developed using quantum circuits and XOR-based symmetric cryptography. In this case, the quantum technique also includes boosting security. using a quantum gate truth table to map the logic allows for the embedding of cov-ert and hidden messages. Since text messages are generally regarded as being safe and not requiring further investigation, this technique offers an excel-lent data protection system. Steganography is an art form that can be used to transmit data securely through any form of open communication. [ABSTRACT FROM AUTHOR]

With the development of deep learning, steganography based on text generation has made significant break-throughs. Existing text-based steganography methods suffer from exposure bias, where the input during training comes from real sample labels, while the input during prediction comes from the output predicted in the previous time step. This difference in input samples between training and prediction leads to error accumulation, resulting in a large distribution difference between generated samples and real samples. To address this problem, this paper proposes a text steganography model called TS-GANMA based on generative adversarial networks and multi-head attention. First, a text generator is trained using a generative adversarial network, and multi-head attention mechanisms are used to extract multi-head attention scores to participate in the reward calculation of the reward module, obtaining feedback information more suitable for the generator. Then, the generator and discriminator are trained in an adversarial manner, which can solve the exposure bias problem and optimize the text generation model. Finally, the conditional probability distribution output by the text generation model is encoded to embed secret information. The experimental results demonstrate that the steganography method based on TS-GANMA has a much lower perplexity than the methods based on LSTM-vlc and ADG at the same embedding rate. This is because the steganographic text generated by the TS-GANMA model fits the statistical distribution of the real text better, and can generate higher quality steganographic text. [ABSTRACT FROM AUTHOR]

Digital watermarking may be utilized to identify illicit usage of text as a means to protect its copyright. This research proposes an advanced watermarking method for Arabic text that supports slight verification through the innovative utilization of counting-based secret sharing. The results of the suggested method are compared with several other existing watermarking techniques using applicable tests conducted across identical Arabic benchmarks of the standard Imam Nawawi's Forty Hadiths, as shown to be favorable in terms of speed, capacity, and security. The research further tested its resilience against the majority of conceivable manipulating text assaults showing our anticipated method to be impervious to electronic text attacks. Interestingly, this approach is not limited to Arabic language script only, as it is applicable to any related text-structure such as Urdu and Farsi writings. The work suggests attractive research contributions and exciting findings pointing the way forward to future specific and smart watermarking enhancements. [ABSTRACT FROM AUTHOR]

With the advancement of technology, the maximum data hiding capacity and security of cover objects have become a very challenging task for researchers, particularly in text carrier. Text carrier depicts low hiding capacity but more secure for the detection of confidential information. It demands novelty in data hiding algorithms. In this regard, a novel algorithm is proposed by using steganography and cryptography together for the enhancement of capacity and security of confidential data. The recommended algorithm uses a linguistic steganography method to conceal data into the Arabic text carrier. In the described algorithm, the identification of secret information from text files is hard due to less redundant bits in the text as compared to the image, audio, and video steganographic mediums. The current solution uses Unicode characters such as Zero-Width-Character (ZWC) and Zero-Width-Joiner (ZWJ) to hide the secret information. Before hiding confidential information, secret data is encrypted by using bit inversion due to which algorithm achieved high security. It is observed from the simulation results that the proposed algorithm successfully achieved high cover medium capacity, security, and robustness.

Steganography is one of the oldest methods for securely sending and transferring secret information between two people without raising suspicion. Recently, the use of Artificial Intelligence (AI) has become simpler and more widely used. Since the emergence of natural language processing (NLP), building language models using deep learning has become more. Furthermore, because of the importance of concealing secret information in delivered messages, Artificial Intelligence theories along with Natural Language Processing algorithms were employed to conceal secret information within the text cover. The Arabic language was used because of its large number of words, vocabulary, and linguistic meanings, and its most significant feature is Arabic poetry. This study discovered a new way to hide secret data inside newly formulated Arabic poetry based on previous Arabic poetic texts and a database of a number of Arab poets from the ancient and modern eras using Artificial Intelligence and Long Short-Term Memory (LSTM) theories to increase storage capacity by 45 percent. The linguistic accuracy and volume of secret data hidden within the formulated poetry were increased using a Baudot Code algorithm, where the secret data is hidden at the level of letters rather than words, and the linguistic accuracy and volume of secret data hidden within the formulated poetry were increased to eliminate the drawbacks found in previous studies.

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the advancement of technology, the maximum data hiding capacity and security of cover objects have become a very challenging task for researchers, particularly in text carrier. Text carrier depicts low hiding capacity but more secure for the detection of confidential information. It demands novelty in data hiding algorithms. In this regard, a novel algorithm is proposed by using steganography and cryptography together for the enhancement of capacity and security of confidential data. The recommended algorithm uses a linguistic steganography method to conceal data into the Arabic text carrier. In the described algorithm, the identification of secret information from text files is hard due to less redundant bits in the text as compared to the image, audio, and video steganographic mediums. The current solution uses Unicode characters such as Zero-Width-Character (ZWC) and Zero-Width-Joiner (ZWJ) to hide the secret information. Before hiding confidential information, secret data is encrypted by using bit inversion due to which algorithm achieved high security. It is observed from the simulation results that the proposed algorithm successfully achieved high cover medium capacity, security, and robustness. [ABSTRACT FROM AUTHOR]

This paper addresses the detection of hidden information leakage through the use of text steganography. In this paper, we present research results on the study possibility to detect hidden leakages by detecting changes in users writing styles using neural networks and various types of text features. The framework for hidden leakages detection based on discovering changes in the author's writing style with deep neural networks (RNN, LSTM, GRU) was proposed. To evaluate the hidden leakages detection accuracy were carried out series of experiments on text corpus, contains Russian online texts. The experiments showed that the LSTM and character 4-grams allow achieving the accuracy of 87%. Text preprocessing significantly decreases accuracy.

Text steganography is one of the most imminent and promising research interests in the information security field. With the unprecedented success of the neural network and natural language processing (NLP), the last years have seen a surge of research on generative linguistic steganography (GLS). This paper provides a thorough and comprehensive review to summarize the existing key contributions, and creates a novel taxonomy for GLS according to NLP techniques and steganographic encoding algorithm, then summarizes the characteristics of generative linguistic steganographic methods properly to analyze the relationship and difference between each type of them. Meanwhile, this paper also comprehensively introduces and analyzes several evaluation metrics to evaluate the performance of GLS from diverse perspective. Finally, this paper concludes the future research work, which is more conducive to the follow-up research and innovation of researchers. [ABSTRACT FROM AUTHOR]

The article deals with the new term “trigger-container” in linguistic steganography. The necessity to introduce this term was brought about by the creation a unique kind of steganographic containers of minimal possible size that would allow for insertion of confidential information into Russian written texts. The introduction of “trigger-containers” is intrinsically interwoven with our ongoing systematic attempt to create a linguistic steganographic term apparatus of describing sense information protection on the basis of linguistic experimentation with a strong scientific foundation. Concurrent use of the proposed steganographic term in praxis in conjunction with the data yielding from linguistic experiments would allow to create a method for concealing sensitive information in Russian written texts that is simultaneously highly secure against automated and expert-driven visual steganalysis as is not dependent on the usage of cryptographic cyphers. The novelty of this research is based on the introduction of the new term “trigger-containers”, which describes a phenomenon that is not touched upon in other research. The goal of the paper is, likewise, the introduction of the term, which allows one to describe the proper theoretical basis of qualitative research in the field of linguistic steganography. We introduce the term “trigger container” which allows to create the basis for our method of conducting perceptive experimental research in the field of linguistic steganography.

In every system of security, to keep important data confidential, we need a high degree of protection. Steganography can be defined as a way of sending confidential texts through a secure medium of communications as well as protecting the information during the process of transmission. Steganography is a technology that is used to protect users' security and privacy. Communication is majorly achieved using a network through SMS, e-mail, and so on. The presented work suggested a technology of text hiding for protecting secret texts with Unicode characters. The similarities of glyphs provided invisibility and increased the hiding capacity. In conclusion, the proposed method succeeded in securing confidential data and achieving high payload capacity by using the Huffman compression algorithm, which was implemented on an unlimited text length. In addition, this approach has the ability to hide a single bit in every digit or letter in the cover file. Also, the approach meets the cognitive transparency and does not make the modifications apparent on the original data. The method suggested in this work increases the security level through coding a secret message before embedding it within the cover text, with the use of the Advanced Encryption Standard (AES) algorithm. [ABSTRACT FROM AUTHOR]

Steganography based on generative adversarial networks (GANs) has become a hot topic among researchers. Due to GANs being unsuitable for text fields with discrete characteristics, researchers have proposed GANbased steganography methods that are less dependent on text. In this paper, we propose a new method of generative lyrics steganography based on GANs, called GAN-GLS. The proposed method uses the GAN model and the largescale lyrics corpus to construct and train a lyrics generator. In this method, the GAN uses a previously generated line of a lyric as the input sentence in order to generate the next line of the lyric. Using a strategy based on the penalty mechanism in training, the GAN model generates non-repetitive and diverse lyrics. The secret information is then processed according to the data characteristics of the generated lyrics in order to hide information. Unlike other text generation-based linguistic steganographic methods, our method changes the way that multiple generated candidate items are selected as the candidate groups in order to encode the conditional probability distribution. The experimental results demonstrate that our method can generate highquality lyrics as stego-texts. Moreover, compared with other similar methods, the proposed method achieves good performance in terms of imperceptibility, embedding rate, effectiveness, extraction success rate and security. [ABSTRACT FROM AUTHOR]

We present a Recurrent Neural Network (RNN) Encoder-Decoder model to generate Chinese pop music lyrics to hide secret information. In particular, on a given initial line of a lyric, we use the LSTM model to generate the next Chinese character or word to form a new line. In so doing, we generate the entire lyric from what has been generated so far. Using common lyric formats and rhymes we extracted, we generate lyrics embedded with secret information to meet the visual and pronunciation requirements. We carry out experiments and theoretical analysis, and show that lyrics generated by our method offer higher embedding capacities for steganography, which also look more natural than the existing steganography methods based on text generations.

Email steganography is one of the subcategories of text steganography. This subcategory is noted due to the widespread use of emails for communication. In this paper, we propose a new method for email steganography which provides a high level of capacity along with a strong security level. Furthermore, since no surplus information is added to the cover text, we can use any kinds of email bodies as the cover text. In order to provide a high level of security, we offer several stego keys. In the first step of the proposed scheme, the secret message is compressed by using one of the lossless compression algorithms named LZW to several bit streams. Then in every step, due to number of email body's characters, a part of each bit stream is selected and by using the email addresses will be created. The proposed method presents a capacity level of 10.6 over a common sample used by other studies, which proved a significant increase in term of capacity. [ABSTRACT FROM AUTHOR]

Especially in recent years, the security of information and computer systems appears to be a very important issue. Because the heavily used communication network has made life easier as well as accompany serious security problems. To solve these problems, cryptography and steganography have been widely used. To present a more secure model for audios, a simple and secure stego-crypto method is presented. In this work, the hidden data is encrypted by the proposed Taylor series based encryption method and the encrypted data are hidden audio signals by applying the least significant bit (LSB) method. A real-world application is implemented in the proposed model. According to the results, the used encryption model is more efficient than AES. The results and findings demonstrated that this model can be used in a communication security system. [ABSTRACT FROM AUTHOR]

With the popularity of smartphones and widespread use of high-speed Internet, social media has become a vital part of people’s daily life. Currently, text messages are used in many applications, such as mobile chatting, mobile banking, and mobile commerce. However, when we send a text message via short message service (SMS) or social media, the information contained in the text message transmits as a plain text, which exposes it to attacks. In some cases, this information may be confidential, such as banking credentials, secret missions, and confidential appointments; moreover, it is a major drawback to send such information via SMS or social media, as neither provides security before transmission. In this paper, we propose a novel text steganography technique called AITSteg, which provides end-to-end security during the transmission of text messages via SMS or social media between end users. The AITSteg technique is evaluated by considering a trusted scenario. We then evaluate the efficiency of the proposed technique in terms of embedding capacity, invisibility, robustness, and security. The experiments confirm that the AITSteg is able to prevent various attacks, including man-in-the-middle attacks, message disclosure, and manipulation by readers. Moreover, we compare the experimental results with the existing techniques to show the superiority of the proposed technique. To the best of our knowledge, this is the first text steganography technique that provides end-to-end secure transmission of the text message using symmetric keys via social media.

There has been a persistent requirement for safeguarding documents and the data they contain, either in printed or electronic form. This is because the fabrication and faking of documents is prevalent globally, resulting in significant losses for individuals, societies, and industrial sectors, in addition to national security. Therefore, individuals are concerned about protecting their work and avoiding these unlawful actions. Different techniques, such as steganography, cryptography, and coding, have been deployed to protect valuable information. Steganography is an appropriate method, in which the user is able to conceal a message inside another message (cover media). Most of the research on steganography utilizes cover media, such as videos, images, and sounds. Notably, text steganography is usually not given priority because of the difficulties in identifying redundant bits in a text file. To embed information within a document, its attributes must be changed. These attributes may be non-displayed characters, spaces, resized fonts, or purposeful misspellings scattered throughout the text. However, this would be detectable by an attacker or other third party because of the minor change in the document. To address this issue, it is necessary to change the document in such a manner that the change would not be visible to the eye, but could still be decoded using a computer. In this paper, an overview of existing research in this area is provided. First, we provide basic information about text steganography and its general procedure. Next, three classes of text steganography are explained: statistical and random generation, format-based methodologies, and linguistics. The techniques related to each class are analyzed, and particularly the manner in which a unique strategy is provided for hiding secret data. Furthermore, we review the existing works in the development of approaches and algorithms related to text steganography; this review is not exhaustive, and covers research published from 2016 to 2021. This paper aims to assist fellow researchers by compiling the current methods, challenges, and future directions in this field.

Text steganography is inherently difficult due to minimal redundant information space to hide secret payload. The same fact limits the hiding capacity and security too. In this study, a novel technique has been proposed using a randomized indexed word dictionary, and a list of email addresses to increase the hiding capacity and security. A forward email platform has been used as the cover, and email addresses in the carbon copy (CC) field contain secret data that are encoded using a randomized index-based word dictionary. The email username list and indexed word dictionary are both pre-shared between the communicating parties. But during every new communication, a random bitstream (temporary stego-key) is generated from the system time and communicated separately using public-key cryptography. This temporary stego-key is used to randomize the index values of the words in the dictionary. Most of the existing state-of-the-art techniques provide a hiding capacity of 6–10%. The proposed scheme achieves a capacity of 12.17% using some common secret text and email body text (cover text) as used in all other studies. The proposed technique provides higher hiding capacity and security by randomizing the word indexes every time using temporary stego-key. It is also free from statistical attacks, OCR based attacks, and does not depend on the use of any particular text processor. [ABSTRACT FROM AUTHOR]

With the rapid development of natural language processing (NLP) technology in the past few years, the automatic steganographic texts generation methods have been greatly developed. Benefiting from the powerful feature extraction and expression capabilities of neural networks, these methods can generate steganographic texts with both relatively high concealment and high hidden capacity at the same time. For these steganographic methods, previous steganalysis models show unsatisfactory detection performance, which remains an unsolved problem and poses a great threat to the security of cyberspace. In this paper, we first collect a large text steganalysis (T-Steg) dataset, which contains a total number of 396,000 texts with various embedding rates under various formats. We analyze that there are three kinds of word correlation patterns in texts. Then we propose a new text steganalysis model based on convolutional sliding windows (TS-CSW), which use convolutional sliding windows (CSW) with multiple sizes to extract those correlation features. We observed that these word correlation features in the generated steganographic texts would be distorted after being embedded with secret information. These subtle changes of correlation feature distribution could then be used for text steganalysis. We use the samples collected in T-Steg dataset to train and test the proposed steganalysis method. Experimental results show that the proposed model can not only achieve a high steganalysis performance, but can even estimate the amount of secret information embedded in the generated steganographic texts, which shows a state-of-the-art performance. [ABSTRACT FROM AUTHOR]

With the rapid development of natural language processing technologies, more and more text steganographic methods based on automatic text generation technology have appeared in recent years. These models use the powerful self-learning and feature extraction ability of the neural networks to learn the feature expression of massive normal texts. Then, they can automatically generate dense steganographic texts which conform to such statistical distribution based on the learned statistical patterns. In this letter, we observe that the conditional probability distribution of each word in the automatically generated steganographic texts will be distorted after embedded with hidden information. We use recurrent neural networks to extract these feature distribution differences and then classify those features into cover text and stego text categories. Experimental results show that the proposed model can achieve high detection accuracy. Besides, the proposed model can even make use of the subtle differences of the feature distribution of texts to estimate the amount of hidden information embedded in the generated steganographic text. [ABSTRACT FROM AUTHOR]

Steganography is the science of hiding certain messages (data) in groups of irrelevant data possibly of other form. The purpose of steganography is covert communication to hide the existence of a message from an intermediary. Text Steganography is the process of embedding secret message (text) in another text (cover text) so that the existence of secret message cannot be detected by a third party. This paper presents a novel approach for text steganography using the Blood Group (BG) method based on the behavior of blood group. Experimentally it is found that the proposed method got good results in capacity, hiding capacity, time complexity, robustness, visibility, and similarity which shows its superiority as compared to most several existing methods.

In this paper, capacity and security issues of text steganography have been considered by employing LZW compression technique and color coding based approach. The proposed technique uses the forward mail platform to hide the secret data. This algorithm first compresses secret data and then hides the compressed secret data into the email addresses and also in the cover message of the email. The secret data bits are embedded in the message (or cover text) by making it colored using a color coding table. Experimental results show that the proposed method not only produces a high embedding capacity but also reduces computational complexity. Moreover, the security of the proposed method is significantly improved by employing stego keys. The superiority of the proposed method has been experimentally verified by comparing with recently developed existing techniques.

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of natural language processing technology in the past few years, the steganography by text synthesis has been greatly developed. These methods can analyze the statistical feature distribution of a large number of training samples, and then generate steganographic texts that conform to such statistical distribution. For these steganography methods, previous steganalysis methods show unsatisfactory detection performance, which remains an unsolved problem and poses a great threat to the security of cyberspace. In this letter, we proposed a fast and efficient text steganalysis method to solve this problem. We first analyzed the correlations between words in these generated steganographic texts. Then, we map each word to a semantic space and used a hidden layer to extract the correlations between these words. Finally, based on the extracted correlation features, we used the softmax classifier to classify the input text. Experimental results show that the proposed model can achieve a high detection accuracy, which shows a state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Copyright of Ingénierie des Systèmes d'Information is the property of International Information & Engineering Technology Association (IIETA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)