HtStego as a Utility Used for Halftone Steganography

Metadata: The Rationale and Importance

The digital age has brought forth several opportunities, such as improved communication techniques and streamlined data processing. Nevertheless, these advancements have also brought forth new obstacles, particularly the need to communicate confidential data while minimizing the potential for interception or manipulation (such as eavesdropping, injection, phishing, spoofing, etc.) (Dastres & Soori, 2021; Kadhim & Sadkhan, 2021). In light of the current extensive use of digital platforms for both work and social purposes, ensuring the confidentiality and security of information, as well as protecting sensitive data, has become a very important issue.

While encryption technologies provide significant security (Alexan et al., 2023; Lai et al., 2023; Song et al., 2023), the conspicuous existence of encrypted data impairs their efficacy (Artz, 2001). Consequently, there is an increasing need for methods that function covertly and inconspicuously.

Digital steganography has emerged as a method to meet this need by hiding sensitive information inside innocuous digital material. Steganography is the act of concealing important information behind a seemingly innocent disguise, ensuring that the concealed data stays difficult to detect (Cheddad et al., 2010). Throughout recorded human history, there have been numerous instances of steganography, which is the practice of concealing information. These examples range from ancient civilizations using invisible inks to the embedding of microdots in the 20th century. Steganography has consistently shown cleverness in preserving secrecy in communication. Since the latter part of the 20th century, a new epoch of digital steganography has arisen as contemporary communication routes shifted into the digital domain. Digital steganography involves the use of steganographic techniques on digital data, including photos, audio files, movies, and documents. Steganography in the digital domain refers to the practice of concealing confidential information inside a carrier file, making it look unaltered to an observer (Evsutin et al., 2020).

Steganography techniques that use digital picture carriers may be categorized into two groups: those that operate in the spatial domain (Alhomoud, 2021; Ali et al., 2019; Bhuiyan et al., 2019; Hameed et al., 2023; Sahu et al., 2021) and those that act in the frequency domain (Ayub & Selwal, 2020; Kaur & Singh, 2021; Khandelwal et al., 2022; Liu et al., 2020; Sharda & Budhiraja, 2013). The spatial domain refers to the physical space in which a phenomenon or data is seen or measured. There are five methods that focus on hiding the payload by using the visual relationship between the pixels in the image. On the other hand, frequency domain methods target the low and high frequencies in the image and hide the payload by modifying frequency coefficients that do not affect the visual perception of the image. Various forms of digital pictures, including as 1-bit binary images, 8-bit grayscale images, and 24-bit color images, may be used as carriers. However, each type presents distinct issues due to changes in their properties and complexity.

The development of the steganography utility discussed in this research is driven by the need to address the constraints and difficulties of current techniques that include halftone pictures, as well as the absence of ways that involve plaintext payloads. Halftone pictures are produced by applying certain algorithms to grayscale photographs, resulting in visuals that use just black and white hues while maintaining a resemblance to the original image. Various techniques may be used to carry out digital halftoning, with error diffusion being the prevailing one.

Due to the inherent constraints of this image type in comparison to grayscale/color images, implementing steganography methods for these images is more difficult. For instance, embedding in the spatial domain, rather than the frequency domain, is the preferred approach for halftone images (Lu et al., 2019).

The tool described in this research employs an innovative steganography and extraction approach, as discussed in Çiftci and Sümer (2022), which was developed using MATLAB. Subsequently, the method underwent a thorough overhaul in Python. Additionally, new interfaces were created, including novel capabilities that were not included in the original version. Furthermore, the code was refactored to facilitate future enhancements. The steganography tool described in this research generates 39 halftone stego pictures by concealing plaintext payloads inside grayscale or color graphics. The software provides many halftoning techniques and input settings to achieve diverse outcomes. The developed steganography algorithm employs the secret sharing mechanism (Karnin et al., 1983) to ensure that the whole payload is not concealed inside a single stego picture. The steganography tool provides a range of functions that may be accessible via both a command line interface and a graphical user interface.

The software is described as follows: This section provides a comprehensive overview of the steganography utility. It includes specific information on the software architecture, the hiding and extraction algorithms that have been developed, the many features offered by the utility, and a description of how the utility works.