Image communication is the act of exchanging images among applications and systems. Image communication can be represented by integrity, confidentiality, and authentication. In the contemporary digital world, image communication has been a highly effective tool for communicating complex ideas quickly. Visual information, such as images, infographics, and diagrams, acts as a universal language that helps people from different backgrounds understand messages easily without relying on text. It also facilitates real-time comprehension, and so it is an essential tool for applications across education to marketing. Image communication is important as it facilitates quick and transparent communication of advanced information, transcending linguistic boundaries, and augmenting comprehension. Visual Cryptography (VC) is a method that divides a secret image into several shares or transparencies, where each share individually contains no information about the original image. The entire image can be reconstructed only by combining the shares. For encryption, the original image is divided into several shares. For example, when an image is divided into two shares, each pixel from the original image is split into two corresponding pixels in the shares. One pixel in each share is randomly assigned, while the other is calculated so that the original pixel becomes visible when the shares are layered together. For decryption, to view the original image, the shares are stacked or overlaid. The visual information from each share combines to reveal the original image. This process ensures secure image transmission, maintaining confidentiality and preventing unauthorized access during communication.

This systematic review adheres to established guidelines to guarantee thorough coverage of visual cryptography research. A systematic search was performed across various academic databases, including IEEE Xplore, ACM Digital Library, Springer, and Google Scholar, utilizing key terms such as “visual cryptography,” “color image encryption,” “secret sharing schemes,” and “visual secret sharing.” The review included literature from 2000 to 2024 to document the progression of techniques from foundational studies to modern developments. The inclusion criteria emphasized peer-reviewed articles, conference proceedings, and book chapters that specifically addressed methods of color visual cryptography, while excluding non-English publications, abstract-only papers, and studies restricted to binary or grayscale images. The initial search produced around 150 papers, which were systematically screened for relevance based on titles and abstracts, followed by a full-text evaluation for final selection. The screening process yielded 30 high-quality papers that fulfilled all inclusion criteria and exhibited substantial contributions to the field of color visual cryptography research. The data extraction emphasized methodology, performance metrics, pixel expansion factors, computational complexity, and reconstructed image quality to facilitate a thorough comparative analysis of various approaches.

VC is a cryptographic method used to secure images by splitting them into multiple shares. At its simplest level, which is referred to as VC (2,2) , an image is split into two shares.¹ A pixel from the original image is split into two sub-pixels, and each is distributed randomly to the two shares. On their own, these shares look like random patterns and reveal nothing about the original image. However, when the two shares are stacked, the original image is reconstructed and becomes visible. This method is particularly useful for secure image sharing and authentication, where the original image can exclusively be revealed by the intended recipient possessing all necessary shares. The strength of VC lies in its simplicity, as it doesn’t require complex algorithms to decrypt the image, only the proper stacking of shares. Additionally, the method offers strong resistance against brute-force and statistical attacks, ensuring human-perceptible visual recovery without the need for computational decryption or cryptographickeys. Moreover, modern VC approaches integrate visual quality enhancement and pixel optimization techniques to improve the clarity and contrast of the reconstructed image.

Figure 1 illustrates a simple VC scheme used to securely share a secret image between two participants. The Naor and Shamir Scheme,³¹ introduced in 1994, is one of the earliest and most influential techniques in Visual Cryptography, enabling secure image sharing by dividing a secret image into multiple shares, each appearing as random noise when viewed individually. Only when these shares are overlaid does the original image become visible, without requiring any complex computation or decryption algorithm. In this scheme, every pixel of the secret image—either black or white—is divided into two sub-pixels distributed across two separate shares, referred to as Share 1 and Share 2. For a white pixel, Share 1 contains one white and one black sub-pixel, while Share 2 contains the reverse pattern, whereas for a black pixel, both shares carry the same sub-pixel arrangement, either black–white or white–black, depending on the encoding pattern used. When the two shares are superimposed, the human visual system perceives the reconstructed image: white pixels appear lighter or grayish due to alternating sub-pixels, while black pixels appear completely dark. This ensures that neither of the individual shares reveals any information about the original secret, thereby maintaining complete confidentiality until both shares are combined. Furthermore, this method is classified as a (2,2) Visual Cryptography Scheme, indicating that both shares are required for decryption, and the reconstruction process relies entirely on human visual perception rather than computational processing. This approach provides a high level of security since no partial information can be extracted from a single share; however, it introduces the limitation of pixel expansion, where each pixel of the original image is represented by multiple sub-pixels in the shares, leading to an increase in image size. Despite this drawback, the Naor and Shamir scheme remains a foundational technique in the field of Visual Cryptography, serving as the basis for advanced methods such as Color Visual Cryptography, Progressive VC, and Meaningful Share Generation. In practical applications, this scheme is widely utilized in secure image transmission, confidential document sharing, and biometric authentication systems, where data privacy and visual integrity are of critical importance. Additionally, recent studies have optimized this scheme by reducing pixel expansion and improving image contrast for better visual quality. It also forms the basis for hybrid models that integrate computational cryptography with VC to enhance security levels.

Figure 2 shows the VC process applied to a binary image containing text. The process starts with the original binary image, where the text is easily readable in black on a white background. This image is split into two distinct shares, referred to as “Share 1” and “Share 2.” Both shares are random noise, and contain no apparent pattern or information that can be seen by the naked eye. This randomness guarantees that each share separately cannot disclose the content of the original image. But, when the two shares are overlap or added together, the original text is reconstructed in the “Reconstructed Image” portion. The reconstructed image appears slightly blurred, with some areas looking less sharp or mixed in gray shades as a result of the overlap of black and white sub-pixels from the two shares. This method safely conceals the data, since only when both the shares are used together can they be disclosed, proving the utility of VC to secure sensitive visual information.

Before Types of VC, it is important to understand the various methods and schemes developed to encode visual information into multiple shares that can only be decoded when the appropriate shares are combined. These types differ in terms of how they handle images, the number of shares required for reconstruction, the complexity of the encryption, and the quality of the reconstructed image. Each type is designed to balance security, visual quality, and computational efficiency based on application needs. Additionally, some schemes focus on reducing pixel expansion to minimize storage and transmission overhead, while others aim to support color images, grayscale images, or even meaningful shares that reveal partial information without compromising the full secret.

Figure 3 illustrates the different types of VC schemes based on the nature of the secret image representation. VC techniques are broadly categorized into three types Binary, Grayscale, and Color depending on the pixel characteristics and visual information used during encryption and decryption.

This section provides a detailed analysis of various VC techniques, based on a review of 30 research papers. The analysis is structured into two key tables: one focuses on Methodology and Outcomes, and the other on Research Characteristics.

The visual quality of shares in VC is crucial for determining the effectiveness of the technique. This quality is quantitatively assessed using three key metrics: PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Squared Error), and SSIM (Structural Similarity Index). PSNR and MSE evaluate the fidelity of the shares compared to the original images, while SSIM provides a measure of perceptual similarity, considering structural information and image degradation. These metrics collectively help in understanding how well the visual shares maintain the integrity and appearance of the original image.

A significant number of these papers, especially from 2020 onwards, focus on the encryption and sharing of color images, reflecting a growing trend in enhancing security for more complex and visually rich data. The papers employ a wide range of methodologies, including hybrid substitution ciphers,¹⁵ chaotic systems,⁴ genetic codes, Harris Hawks Optimization algorithms,⁶ and error diffusion techniques,²⁶ indicating the variety of approaches being explored to improve the effectiveness and efficiency of color image encryption. Many studies center around VC, particularly for color images, which extend traditional black-and-white VC techniques. The use of color increases the complexity but also enhances applicability in real-world scenarios, especially within intelligent networked environments where secure visual data transmission is crucial.

A common theme is the emphasis on secret sharing schemes, where a secret image is divided into multiple shares that are required to be merged in order to create the original image. This method is important in secure transmission and storage of sensitive visual data, such as 5G-enabled IoT ecosystems and edge computing frameworks. With the increasing interconnectivity of devices, ensuring robust encryption and secure communication channels has become a priority. Several recent studies, such as those using chaotic systems and evolutionary frameworks, highlight the trend of integrating advanced mathematical models and optimization algorithms to improve security and unpredictability. The fusion of VC with machine learning and deep learning-based anomaly detection further strengthens security by enabling adaptive threat response mechanisms. Many studies evaluate their methodologies based on specific performance metrics such as pixel expansion, computational complexity, and the quality of the reconstructed image, indicating a strong focus on quantifiable improvements. Additionally, real-time encryption and decryption processes are being optimized for distributed networks, ensuring minimal latency while maintaining high security levels. Several papers explore practical applications of cryptographic schemes in securing medical images, cloud storage, and IoT devices—domains where intelligent networks play a crucial role in data exchange and processing. There is a noticeable trend towards hybrid techniques, combining multiple cryptographic approaches like chaos theory with genetic algorithms or watermarking with VC to enhance security features and address the limitations of individual methods. The complexity of cryptographic schemes has been increasing, with recent studies integrating more sophisticated algorithms and frameworks, such as fractional-order hyperchaotic maps⁴ and multi-level VC, to meet the evolving security challenges in distributed high-speed communication infrastructures.

The paper presents a detailed overview of color VC, with focus on advancements, methodologies, and challenges in the field. It highlights the balance between security and image quality within different cryptographic schemes, particularly in color image encryption. The paper addresses the difficulties in these methods, such as pixel expansion and computational complexity, and investigates hybrid approaches that strive to find balance between security and efficiency. Future advancements are anticipated to further automate secure image sharing with high protection levels. The growing emphasis on improving the quality of reconstructed images while maintaining robust security measures is evident, indicating a significant trend in the development of VC methods for secure communication and data protection.