Evaluating the Feature-Based Technique of Text Steganography Based on Capacity and Time Processing Parameters

Din, Roshidi; Utama, Sunariya; Hanizan, S. H; Hilal, Muhammad; Hanif, M.; Zulhazlin, A; Fazali, G. Mohamad

doi:10.1166/asl.2018.12941

Cited by 4 publications

(3 citation statements)

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“…Moreover, the experimental results have been compared with the existing works regarding capacity and invisibility. The secret message and cover text dataset in this study have been utilized in previous studies [8,16,17,71,74,97,98]. The selected secret message has 198 characters, and the cover text has 874 characters [18], as presented in Table 6.…”

Section: Experimental Results and Comparisonsmentioning

confidence: 99%

CSNTSteg: Color Spacing Normalization Text Steganography Model to Improve Capacity and Invisibility of Hidden Data

et al. 2022

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The rapid growth of online communication has increased the demand for secure communication. Most government entities, healthcare providers, the legal sector, financial and banking, and other industries are vulnerable to information security issues. Text steganography is one way to protect secure communication by hiding secret messages in the cover text. Hiding a high amount of secret information without raising the attacker's suspicion is the main challenge in steganography. This paper proposes the Color and Spacing Normalization stego (CSNTSteg) model to resolve the low capacity and invisibility problem on text steganography. CSNTSteg consists of two stages: the pre-embedding stage, which achieves high capacity by utilizing RGB coding and character spacing. It is designed to increase the number of bits per location and usable characters. Besides, it applies the Huffman coding technique to compress the secret message to add more capacity enhancement. The second stage is color and spacing normalization, which accomplishes high invisibility by normalizing the RGB coding and character spacing of the cover and stego text. CSNTSteg overcomes the color differences issue between the cover and stego texts regardless of the color of the cover text. To assess the quality of CSNTSteg, the experimental results are compared with existing works. CSNTSteg shows superior capacity over the existing studies with a percentage of 98.85%. CSNTSteg also achieves high invisibility by reducing the color differences with a percentage of 4.7% and 5.07% for black and colored cover text, respectively. Furthermore, CSNTSteg improves robustness by 94.22% by reducing the distortion in stego text. Overall, the CSNTSteg model embeds a high capacity of secret data while maintaining invisibility and security, offering a new perspective on text steganography to protect against visual and statistical attack issues.

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Section: Experimental Results and Comparisonsmentioning

confidence: 99%

CSNTSteg: Color Spacing Normalization Text Steganography Model to Improve Capacity and Invisibility of Hidden Data

et al. 2022

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“…Meanwhile, the performance metrics such as confidentiality, availability, and integrity have the minimum percentage that used by researchers at 4%, 3% and 3% respectively. Embedding time [3,5,23,[26][27][28][29][30][31][32] Calculates the time of the technique that the embedding process consumes. Hiding capacity [3,6,11,17,18,20,22,25,27,30,[33][34][35][36][37][38][39][40][41][42][43] It is the maximum number of bits that could be embedded in the cover medium.…”

Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%

Analysis review on feature-based and word-rule based techniques in text steganography

Alyousuf¹,

Din²

2020

Bulletin EEI

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This paper presents several techniques used in text steganography in term of feature-based and word-rule based. Additionally, it analyses the performance and the metric evaluation of the techniques used in text steganography. This paper aims to identify the main techniques of text steganography, which are feature-based, and word-rule based, to recognize the various techniques used with them. As a result, the primary technique used in the text steganography was feature-based technique due to its simplicity and secured. Meanwhile, the common parameter metrics utilized in text steganography were security, capacity, robustness, and embedding time. Future efforts are suggested to focus on the methods used in text steganography.

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“…Thus, the carrier object after encryption is meaningless. Meanwhile, information hiding conceals the secret message to make it unnoticed/invisible in the course of its transmission via the public (untrusted) communication channel [3]. Invisibility is the fundamental difference between cryptography and information hiding [4].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Arabic Text Steganography

et al. 2021

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Protecting sensitive information transmitted via public channels is a significant issue faced by governments, militaries, organizations, and individuals. Steganography protects the secret information by concealing it in a transferred object such as video, audio, image, text, network, or DNA. As text uses low bandwidth, it is commonly used by Internet users in their daily activities, resulting a vast amount of text messages sent daily as social media posts and documents. Accordingly, text is the ideal object to be used in steganography, since hiding a secret message in a text makes it difficult for the attacker to detect the hidden message among the massive text content on the Internet. Language’s characteristics are utilized in text steganography. Despite the richness of the Arabic language in linguistic characteristics, only a few studies have been conducted in Arabic text steganography. To draw further attention to Arabic text steganography prospects, this paper reviews the classifications of these methods from its inception. For analysis, this paper presents a comprehensive study based on the key evaluation criteria (i.e., capacity, invisibility, robustness, and security). It opens new areas for further research based on the trends in this field.

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Evaluating the Feature-Based Technique of Text Steganography Based on Capacity and Time Processing Parameters

Cited by 4 publications

References 0 publications

CSNTSteg: Color Spacing Normalization Text Steganography Model to Improve Capacity and Invisibility of Hidden Data

CSNTSteg: Color Spacing Normalization Text Steganography Model to Improve Capacity and Invisibility of Hidden Data

Analysis review on feature-based and word-rule based techniques in text steganography

A Comparative Analysis of Arabic Text Steganography

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