For the research community, the reliability and security of exchanged text through the internet is the most promising and challenging field. In communication technologies, authentication of content and automated text verification of honesty in different languages and formats are of great significance. Numerous applications for instance; e-Banking and e-commerce render information transferred via the internet the most difficult. In terms of content, structure, grammar, and semantics, much of the digital media transferred over the internet is in text form and is very susceptible to online transmission. During the transfer process, malicious attackers can temper such digital content [1].

For information security, many algorithms and techniques are available such as the authentication of content, verification of integrity, detection of tampering, access control, and copyright protection. To overcome these issues, steganography and automated methods of watermarking are commonly used. A technique of digital watermarking (DWM) which can be inserted into digital material through various details such as text, binary pictures, audio, and video [2,3]. A fine-grained text watermarking procedure is proposed based on replacing the white spaces and Latin symbols with homoglyph characters [4].

Several conventional methods and solutions for text watermarking were proposed [4] and categorized into different classifications such as linguistic, structure, and image-based and format-based binary images [5]. To insert the watermark information into the document, most of these solutions require certain upgrades or improvements to the original text in digital format material. Zero-watermarking without any alteration to the original digital material to embed the watermark information is a new technique with smart algorithms that can be used [3]. Several techniques have been proposed to improve the capacity of coverless information hidden in digital media based on various techniques such as anime characters [6], to represent and convey confidential information using unmodified natural stego-carriers [7], and to map the relationships constructed between the inherent features of the images and the secret information [8].

Restricted research has centered on the appropriate solutions to verify the credibility of critical digital media online [9–11]. The verification of digital text and the identification of fraud in research earned great attention. Besides, text watermarking study has several researches concentrated on copyright protection in the last decade, but less interest and attention has been paid to integrity verification, identification of tampering, and authentication of content due to the existence of text content based on the natural language [12].

Proposing the most appropriate approaches and strategies for dissimilar formats and materials, especially in Arabic and English languages, is the most common challenge in this area [13,14]. Therefore, authentication of content, verification of honesty, and detection of sensitive text tampering are major issues in different systems that need critical solutions.

Some instances of such sensitive digital text content are Arabic interactive Holy Qur’an, online, eChecks, tests, and marks. Different Arabic alphabet characteristics such as diacritics lengthened letters and extra symbols of Arabic make it simple to modify the key meaning of the text material by making basic changes such as modifying diacritic arrangements [11,15]. The most popular soft computation and natural language processing (NLP) technique that supported the analysis of the text is HMM.

This paper presents a hybrid text analysis and zero-watermarking approach (HTAZWA) for content authentication and tampering detection of English text. The proposed technique combines the Markov model and zero-watermarking. The fourth-order of the word mechanism of the Markov model has been used for text analysis to extract the interrelationships between contents of the given English text which consequently generates the watermark key. The generated watermark is logically embedded in the original English context without modifying the original text. After text transmission, the embedded watermark is used to detect any tamper that occurred on the received English text and ensures its authenticity.

The primary objective of the HTAZWA strategy is to meet the high accuracy of content authentication and identification of sensitive tampering attacks of English text which is transmitted through the Internet. Therefore, the key contributions of the proposed HTAZWA can be presented as follows.

The rest of the paper has five more sections. Section 2 provides a literature review of the related work. Section 3 presents HTAZWA. Section 4 describes the implementation, simulation, and experimental details. Section 5 describes the comparison and results discussion, and Section 6 offers conclusions.

Natural language is the foundation of linguistic text watermarking approaches. The mechanism of those methods embedding the watermark is based on changes applied to the semantic and syntactic essence of plain text [1].

To enhance the capability and imperceptibility of Arabic text, a method of text watermarking suggested room dependent on the accessible words [16]. In this method, any word-space is used to mask the Boolean bit 0 or 1 that physically modifies the original text. A text steganography technique was proposed to hide information in the Arabic language [17]. The step of this approach considers Harakat’s existence in Arabic diacritics such as Kasra, and Damma as well as reverses Fatha to cover the message.

Kashida-marks invisible method of watermarking [18], based on the features of frequent recurrence of document security and authentication characters, was proposed. The method is based on a predetermined watermark key with a Kashida placed for a bit 1 and a bit omitted. The method of steganography of the text was proposed to use Kashida extensions depending on the characters ‘moon’ and ‘sun’ to write digital contents of the Arabic language [19]. Also, Kashida characters are seen alongside with characters from Arabic to decide which hidden secret bits are kept by specific characters. In this form, four instances are included in the kashida characters: Moon characters representing ‘00’; sun characters representing ‘01’; sun characters representing ‘10’; and moon characters representing ‘11’.

A text steganographic approach [20] based on multilingual Unicode characters has been suggested to cover details in English scripts for the use of the English Unicode alphabet in other languages. Thirteen letters of the English alphabet have been chosen for this approach. It is important to embed dual bits in a timeframe using ASCII code for embedding 00. However, multilingual ones were used by Unicode to embed between 01, and 10, as well as 11. The algorithm of text watermarking is used to secure textual contents from malicious attacks according to Unicode extended characters [21]. The algorithm requires three main steps, the development, incorporation, and extraction of watermarks. The addition of watermarks is focused on the development of predefined coding tables. While scrambling strategies are often used in generation and removal, the watermarking key is safe.

The substitution attack method focused on preserving the position of words in the text document has been proposed [22]. This method depends on manipulating word transitions in the text document. Authentication of Chinese text documents based on the combination of the properties of sentences and text-based watermarking approaches have been suggested [23,24]. The proposed method is presented as follows: A text of the Chinese language is split into a group of sentences, and for each word, the code of a semantic has been obtained. The distribution of semantic codes influences sentence entropy.

A zero-watermarking method has been proposed to preserve the privacy of a person who relies on the Hurst exponent and the nullity of the frames [25]. For watermark embedding, the two steps are determined to evaluate the unvoiced frames. The process of the proposed approach is based on integrating an individual’s identity without notifying any distortion in the signals of medical expression.

A zero-watermarking method was proposed to resolve the security issues of English language text-documents, such as verification of content and copyright protection [26]. A zero-watermarking approach has been suggested based on Markov-model authentication of the content of English text [27,28]. In this approach, to extract the safe watermark information, the probability characteristics of the English text are involved and stored to confirm the validity of the attacked text-document. The approach provides security against popular text attacks with a watermark distortion rate if, for all known attacks, it is greater than one. For the defense of English text copyright, based on the present rate of ASCII non-vowel letters and terms, the conventional watermark approach [29] has been suggested.

This paper proposes a hybrid text analysis and the zero-watermarking approach by integrating zero-watermark technique and the Markov model as NLP technique in which there is no need to embed extra information such as a watermark key, or even to perform any modification on the original text.

The fourth-order of word mechanism of the Markov model has been used as NLP techniques to analyze the contents of the given English text and extract the interrelationships features of these text contents.

The following subsections explain in detail two main processes that should be performed in HTAZWA. The first process is called the watermark generation and embedding process, whereas, the second one is called the watermark extraction and detection process.

A variety of implementation and simulation are conducted to test the accuracy of HTAZWA output and tampering detection. This section outlines the settings for implementation and experimentation, conditions for experiments, typical dataset experimental scenarios, and a discussion of outcomes.

The tampering detection accuracy results were critically analysed. The study impact is investigated and comparisons between HTAZWA and baseline approaches ZWAFWMMM and HNLPZWA showed their effect under the major factors (i.e., dataset size, attack types, and volumes).

HTAZWA approach is implemented in PHP programming language using VS code IDE. The experiments are performed on various standard datasets under different volumes of insertion, deletion, and reorder attacks. HTAZWA has been compared with ZWAFWMMM and HNLPZWA approaches. The comparison results show that HTAZWA outperforms ZWAFWMMM and HNLPZWA approaches in terms of the accuracy of tampering detection. The results also show that HTAZWA applies to all English alphabetic letters, special characters, numbers, and spaces. For future work, the improvement of detection accuracy should be considered for all kinds of attacks.