Nowadays, steganography stands as one of the most dynamic, private, and secure communication techniques [1], proving to be a prevalent and renowned method that offers enhanced security and serves as an alternative communication system. It is the procedure of concealing information within various mediums like images, audio, and videos, essential for secure communication [2]. The medium containing the hidden message is referred to as the stego-object. Among all media, images are the most utilized in steganography due to their popularity and availability [3]. In digital image steganography, methods are classified into spatial domain techniques [4] which manipulate pixel values directly, and frequency domain techniques [5], which alter the frequency components of the image. The application of steganography used worldwide in every field includes hiding the medical history of patients within their X-rays and scans [6–8], secure localization of nodes in sensor networks [9], smart tags on mobile devices [10], applied to the protection of biometric data, protection of IP and hiding individual information in smart identity card [11], employ for authentication purposes [12], sharing information between mobile devices and clouds [13]. This integration of steganography with cybersecurity measures underscores its importance in maintaining data integrity and confidentiality across various applications [14,15].

Despite its prevalence, steganography encounters a pressing challenge in safeguarding the confidentiality of transmitted data. Concealing the message within an image alters its statistics. Steganographic techniques must ensure that this alteration is minimal, thereby evading suspicion by unauthorized parties. With detection techniques evolving rapidly, traditional steganographic methods are increasingly vulnerable, posing a significant threat to secure communication channels. Furthermore, intruders often exploit vulnerabilities in steganographic systems through histogram analysis or by assessing the visual imperceptibility of the image. These factors greatly impact the effectiveness of steganographic methods; for instance, increasing the number of bits used to hide data alters the image’s statistics and affects its visual imperceptibility. As a result, researchers are continually striving to develop approaches that allow for the hiding of more data with minimal changes to the image, ensuring that the message remains secure from attackers. Payload capacity, imperceptibility, and robustness are three essential properties to test the effectiveness of any steganographic approach [16,17]: (1) Payload capacity refers to the amount of data hidden within a cover media (2) Imperceptibility refers to the human visual system identifying the changes in the cover images with the naked eye or using statistics. It is commonly assessed through metrics such as PSNR, where higher PSNR values indicate better imperceptibility. In essence, a higher PSNR signifies reduced distortion or alteration in the cover image due to the embedded information. (3) Robustness is measured as the ability of the hidden message to remain accurate even if the stego image undergoes various processing such as cropping, blurring, scaling, filtering, etc. The increase in these three parameters suggests the better security of the data communications algorithm. However, improving all three parameters at once is a challenging task.

The earlier approaches to steganography primarily relied on LSB (Least Significant Bit) substitution [18–22], where secret data is directly embedded into specific regions of the image by replacing the least significant bits. This method was popular due to its simplicity and minimal impact on the visual appearance of the cover image. However, it had its limitations. As the demand for hiding more data increased, it required changing more bits directly. Typically, these methods could utilize at most three LSBs to minimize the chances of detection, as changes in LSBs are less noticeable. Nonetheless, the direct alteration of more bits increased the risk of detection and compromised the security of the hidden message. Our work addresses the limitations of earlier steganographic approaches, particularly the direct changing of LSBs, by introducing a method. Instead of directly substituting message bits into the LSB, our approach involves comparing pairs of message bits with pairs of MSB (Most Significant Bit) bits. When a pair matches, we encode this matching information into the LSB. By doing so, we can store 2 bits of data with only one change in the LSB. We extend this concept by consecutively matching pairs in all MSBs, forming three pairs in total, to fully utilize the byte capacity and store more data efficiently.

The proposed approach offers several advantages over traditional LSB-based steganographic methods. Firstly, by utilizing pair-wise matching of message and MSB bits, we can hide more data with minimal changes to the LSB, thereby reducing the risk of detection. This not only enhances the security of the hidden message but also maintains the visual integrity of the cover image. However, traditional LSB-based methods often face limitations in achieving a balance between payload capacity and imperceptibility. Increasing the payload capacity by directly altering LSBs may lead to noticeable distortions in the cover image, compromising its visual quality and raising suspicion among potential adversaries. Secondly, while our method maximizes the utilization of the byte capacity by encoding information in three pairs of MSBs, allowing for efficient storage of additional data, traditional LSB-based techniques may struggle to maintain robustness against various image processing operations. These limitations can undermine the effectiveness of steganographic communication, particularly in scenarios where data integrity and covert communication are paramount. To evaluate the effectiveness of our approach, we conducted extensive experiments comparing it with existing LSB-based techniques. The results demonstrate that our method outperforms traditional approaches in terms of data hiding capacity and resistance to detection. This novel approach represents a significant advancement in steganographic techniques, offering improved security and efficiency in covert communication.

The results of our experiments provide quantitative insights into the efficacy of our proposed steganographic approach. We measured the embedding capacity of our method, which quantifies the amount of data that can be hidden within the cover image. Additionally, we evaluated the Peak Signal-to-Noise Ratio (PSNR) value, which indicates the quality of the stego-image compared to the original cover image. Our findings indicate that our approach achieves a significant increase in embedding capacity while maintaining high PSNR values, indicating minimal distortion to the cover image. These results demonstrate the effectiveness of our method in efficiently hiding large amounts of data within cover images while preserving their visual quality.

The proposed study makes the following significant contributions:

Development of a steganography scheme focused on achieving high embedding capacity while maintaining visual imperceptibility by utilizing images as cover media to conceal data.

The rest of the paper is structured as follows. Section 2 describes the details of earlier studies. In Section 3, we present our proposed embedding and extracting scheme. Section 4 demonstrates experimental details and results. Finally, the conclusion is presented in Section 5.

This section will offer a comprehensive literature review of current steganography techniques, with a primary focus on the amalgamation of LSB and MSB methodologies. This emphasis is chosen as the proposed work centers on MSB matching and LSB substitution. Steganography techniques have been used for centuries. However, it gained more popularity during World War II when undetectable ink was utilized to compose data on paper so that no one could detect its hidden message [3] and since digital technologies. Although many highly protected and robust techniques have been presented so far [23–26], where these are progressing towards optimizing for better performance. Numerous steganographic algorithms have been proposed by the researchers based on LSB substitution [27–30], some key ones have been discussed in the following paragraphs.

Yang et al. [19] pioneered the adaptive LSB method of steganography, which exploits the cover object’s edges, brightness, and texture to compute the number of k LSBs for hiding information. His work shows that a pixel in the noise-non-sensitive area has a higher value of k than that in noise-sensitive regions. The edge area can tolerate fewer changes compared to a highly textured portion of an image but not more than a smooth area. Also, the LSBs are computed by the high-order bits instead of all bits of a pixel to achieve a more secure system. This method achieves the value of PSNR of 39 dB. Similarly, another adaptive LSB embedding technique is proposed by Khodaei et al. [23] which utilizes the pixel value differencing (PVD) characteristics. The cover images are divided into two consecutive pixel blocks. Further, the difference between the two pixels is computed to estimate the number of secret bits that can be embedded into the LSB of these neighboring pixels. This method also employs a readjustment process to keep the difference of stego-pixel with respective ranges before and after embedding. This method achieves a larger payload capacity by retaining the visual imperceptibility of 37 dB. In [20], an adaptive LSB substitution approach for data hiding is proposed based on edge detection. This method employs 4 LSBs during the embedding and shows an improvement in the payload capacity. However, it suffered from low imperceptibility and has a value of PSNR less than 35 dB.

Lee [30] applied the adaptive LSB method to color images of the smartphone, where various LSB replacements are made based on color channels (RGB), i.e., the method embedded 4 bits in the R channel, 2 bits in G, and 2 bits in B of the secret message. This method could hide 2.8 bits per channel and maintain the value of PSNR 43 dB. Lee’s technique employs only LSBs of every channel to hide the secret data. Similarly, in [24], another approach is presented by utilizing the RGB channels of color images. This study combines human visual properties with an adaptive LSB method to develop techniques for hiding data in an image. Two techniques are presented: One that takes into account the different sensitivities of the human eye to different color channels, allowing for different numbers of bits to be hidden in each channel, and another that utilizes the natural tendency for images to focus on their middle area, hiding data in a spiral pattern starting from the edges and moving towards the center. The amount of data hidden is determined by the length of the message, with any remaining bits hidden in the blue channel. The average PSNR for this method is 43.95 dB for various-sized secret messages. In [25], the LSB substitution approach is employed which works by dividing the cover image into two sections: The first section is used to hide data, and the other to store the embedding changes. The number of LSB substitutions is adaptive and varies from 1 to 5. This study shows an increase in the payload capacity; however, the visual quality of the stego image is affected due to extensive modifications of LSB values.

Sahoo et al. [6] proposed an LSB-based steganography method to protect the data of COVID-19 patients within their X-ray scans. Before hiding, the data is dually encrypted using DNA with a combination of a Baconian cipher. Firstly, the DNA encoding was applied to the binary-coded data, and then the resultant data was replaced with a 26-letter Baconian cipher. The X-ray scan is partitioned into n overlapping windows, and within each window, the specific region of interest is determined by analyzing the minimum mean intensity. This metric calculates the average brightness level within the window, allowing the identification of areas with the lowest average intensity, and aiding in the detection or analysis of targeted features or anomalies within the X-ray image. The data in each window is embedded with XOR operation between the window and cipher matrix. This algorithm is tested on various X-ray scans where the arbitrary information is hidden in each scan. However, the author does not provide any performance measures. Another study [31] introduces a novel Local Binary Pattern-based Reversible Data Hiding (LBP-RDH) technique, achieving a balance between perceptual transparency and hiding capacity by dividing the image into 3 × 3 blocks and utilizing LBP-based descriptors for embedding. Through XORing embedding bits with LBP codes and employing a pixel readjustment process, the method ensures minimal information loss during extraction, outperforming recent techniques in transparency measures and demonstrating robustness against various stego-attacks.

Several recent studies also utilize data compression techniques and LSB substitution to extend embedding capacity. Akhter et al. [21] proposed a combined LSB substitution and modulo-function method that compresses the message before embedding. The original message is divided into two sections, where each section of a message is stored into a different cover image using the 4-LSBs of a pixel. The result of the study shows that there is an improvement in payload capacity when using the modulo function by maintaining the value of PSNR from 34 to 40 dB. In [32], before hiding the secret message is compressed using the Huffman coding scheme. Also, the secret data is encoded with the Rivest-Shamir-Adleman (RSA) algorithm to increase security. Further, four sub-bands are obtained from the cover image using the discrete wavelet transform (DWT) technique. The compressed message is embedded in the LSB of the selected sub-band. The method has a value of PSNR 40 dB. Similarly, another image steganography was proposed with the concept of message compression before the embedding [26]. In this study, a compression algorithm known as GoldBach has been adopted to compress the secret message. GoldBach algorithm states that every even value or more significant than four can be represented as the sum of two odd primes. Then coded message is embedded in the LSBs of cover media. The algorithm is tested on various size messages, i.e., 16, 32, 48 KB, and obtains the value of PSNR 60, 57 dB, and 48 KB, respectively.

Fateh et al. [18] proposed a coding scheme to improve steganography capacity with the LSB revisiting approach. The secret message is divided into several blocks with a different n number of bits. To hide these secret bits, 2n−1 pixels are computed. The algorithm has achieved the maximum capacity when the author sets the value of n equal to 3 and maintains higher imperceptibility by holding the value of psnr in the range of 56 to 66 dB. Swain [28] implemented an approach that combines LSB substitution along with pixel value difference (PVD) to increase the hiding capacity and improve PSNR. This method partitioned an image into 22-pixel blocks and then applied k-bits LSB on the upper left block. Further, the PVD embedding process employs the remaining pixels of a block with the upper left block. The range of k-bits is from 1–3, which indicates that this study maximum modified the 3 LSBs during embedding. In [27], the authors proposed two-level security with the combination of encryption and steganography. The encryption step is completed using the Vigenère cipher, and then encoded message bits are substituted with the pixel LSBs of the cover image. In [33], authors introduce the CSOES-DIS technique, combining competitive swarm optimization with encryption-based steganography for digital image security. The model encrypts the secret image using a double chaotic digital image encryption technique before the embedding process. Additionally, it employs an optimal pixel selection process through the CSO algorithm, enhancing the secrecy level. Comparative analysis against recent methods demonstrates the superior performance of the CSOES-DIS model across various measures, showcasing its enhanced outcomes for digital image security.

Liao et al. [34] explore the use of cloud storage for steganography, focusing on embedding secret information into multiple images. It addresses the challenge of allocating embedding payload among a sequence of images to enhance security. The proposed adaptive payload distribution strategies leverage image texture features, offering improved security performance against modern steganalysis techniques. Furthermore, the article evaluates the detectability of multiple-image steganographic schemes compared to single-image steganalysis methods. Extensive experimental results highlight the effectiveness of the proposed payload distribution strategies in achieving better security performance. In [35], the authors address the issue of optimizing payload allocation in color image steganography to enhance security. Traditional schemes often allocate payloads equally across RGB channels, leaving room for improvement. The authors proposed a novel strategy that involves a channel-dependent payload partition approach based on amplifying channel modification probabilities. By simultaneously increasing modification probabilities across corresponding pixels in RGB channels, the embedding impacts can be clustered, enhancing steganographic security against channel co-occurrence detection. Experimental results demonstrate that the new schemes effectively concentrate embedding changes in textured regions, leading to improved resistance against modern color image steganalysis techniques.

In [22], a recursive approach to information hiding is presented through LSB, PVD shift, and modification of prediction error (MPE). This study image is divided into pixel blocks (2 × 1) classified into higher and lower texture areas. This pixel block is recursively utilized through MPE and PVD shifts to improve embedding capacity. The result of the study showed an improvement in embedding capacity by utilizing up to 4 LSB and maintaining visual quality to 38 dB. In [36], an enhanced LSB image steganography approach is presented, where the author proposes the embedding of a secret message specifically along the edges of an image. The proposed technique involves the categorization of image pixels into two and three categories. The first method distinguishes pixels in the smooth area and edge area, while the second method further divides pixels into the smooth area, intersection area, and edge area. Through this approach, the author demonstrates that the second method exhibits superior embedding capacity. In [4], authors focused on improving message capacity and security using the divide and modulus function in spatial domain steganography. The secret message is partitioned based on divide and modulus functions. Each byte in a message is divided by 16, the dividend is embedded into a cove. By keeping an image size of 128×128 and message size to 25% of cover media, the steganographic image can achieve a PSNR of 57.2226 dB and a mean square error (MSE) value of 0.1582.

Mohammad [37] presents a novel EMD-based reversible data hiding technique using dual-image modification lookup tables, replacing larger matrices. The method enables direct reversibility with minimal computational cost by modifying pixels in alternate columns of cover images. It achieves a one bit per pixel (bpp) embedding rate by embedding one 4-ary secret digit into each pixel. Simulation results exhibit a one bpp embedding rate and over 49 dB average Peak Signal Noise Ratio (PSNR) across test images. Singh et al. [38] introduce a DNA-based cryptographic scheme and access control model (DNACDS) to address security challenges in IoE-based cloud computing and big data. DNACDS utilizes Deoxyribonucleic Acid (DNA) computing to enhance security measures, incorporating the Station-to-Station Key Agreement Protocol (StS KAP) and Feistel cipher algorithms. Experimental results demonstrate DNACDS’s superior performance compared to other DNA-based security schemes, supported by theoretical security analysis highlighting its robust resistance capabilities. Similarly, Namasudra [39] proposes a novel cryptosystem employing DNA cryptography and steganography for securing cloud-based IoT infrastructures. The proposed system encrypts confidential data using a long secret key and hides it within an image, offering dual-layered security. Experimental evaluations demonstrate the effectiveness of the scheme in resisting security attacks and safeguarding data stored in the cloud-based IoT environment.

In [40], an edge detection-based steganography method is discussed. First, the image is divided into small blocks based on the histogram of the oriented gradient algorithm. Then, blocks of interest are selected based on the gradient magnitude and angle of the cover image. The Block of interest is further divided into 2×2 pixels, where two pixels are used for the PVD algorithm, and the remaining pixels are used for LSB substitution. This algorithm has achieved a PSNR of 42.09 dB for embedding 385358 bits in 512×512 pixels. Zakariya et al. [29] proposed a high embedding capacity steganographic approach using MSB matching and LSB substitution. This study employs the 4-MSBs of the cover pixel to match with secret bits and 2-LSBs to indicate these matches. The secret message is taken in the pair form; this pair is compared with the MSB pairs. If the message pair is satisfied with any of the MSB pairs, then the respective LSB is set to be 1; otherwise, 0. If both MSB pairs do not match the secret message pair, the message pair is attempted in the next pixel, and this process continues–in the best-case scenario, a pixel stores a maximum of 4 bits of a secret message with possible changes in the 2 bits and the worst-case scenario a pixel does not store anything. The next section presents the proposed method with extended hiding capacity compared with Zakariya et al. [29]. In Table 1, we present a summary of the literature review findings on various image steganography techniques along with their corresponding PSNR values.

While various steganography techniques demonstrate commendable performance in concealing information within digital media, it is essential to acknowledge their inherent limitations. For instance, techniques [18] are susceptible to LSB detection and exhibit limited capacity for accommodating large datasets. The technique presented in [19] shows improved resistance to certain steganalysis methods, but it has a lower PSNR, indicating the possibility of visual artifacts. Furthermore, techniques [23] work well for grayscale images but have limited capacity for color images and are vulnerable to advanced steganalysis. Furthermore, the technique [20] reduces embedding capacity and displays sensitivity to image changes. Despite achieving high PSNR, the technique [26] is complex to implement and may be vulnerable to algorithm-specific attacks. Another technique [22] involves higher computational complexity and a limited capacity for high-quality images. The approach [32] has increased computational overhead and limited payload capacity. While offering high PSNR and good visual quality, reference [4] has limited embedding capacity and is vulnerable to spatial domain attacks. Finally, technique [40] shows improved resistance to specific steganalysis techniques but has limited robustness against advanced steganalysis methods.

The above literature review has provided a comprehensive overview of existing steganographic techniques and their applications. We have focused on studies that are directly comparable to our work or serve as the foundation for our proposed approach. It is important to note that while our methodology and results are based on these comparable studies, there are inherent differences that may affect direct comparison. For instance, many studies utilize color images with three channels per byte, allowing for the storage of three bytes of data per pixel. Additionally, variations in image sizes among different studies may impact the embedding capacity and overall performance of steganographic methods. Despite these differences, our review highlights the advancements and challenges in the field of steganography, paving the way for further research and development in secure communication techniques.

This paper proposes a steganographic algorithm based on MSB matching and LSB substitution to achieve higher embedding capacity with good invisibility of the stego-image. The proposed technique employs the mapping of secret message bits with the MSBs of cover media, where LSBs are utilized to store information on mapping bits. The embedding method requires input data from a cover image and a secret message to generate a stego image. On the other hand, the extraction process requires only a stego image and produces a hidden secret message. In the subsequent section, we presented our embedding and extracting algorithms. Fig. 1 depicts the proposed embedding scheme, it has three components which consist of a secrete message M, cover image C and a steganography image S. The secrete message is divided into multiple small two bits of message Mi where i is an index. The first 4 bits (MSBs) of each byte Cpixel in C is used for comparison. 4 MSBs of Cpixel are arranged in three set of two bits called CL,pixel, CM,pixel and CR,pixel. CL,pixel is composed for 1^st and 2^nd MSBs, CM,pixel is composed of 2^nd and 3^rd MSBs and CR,pixel is composed of 3^rd and 4^th MSBs. 5 MSBs for Cpixel are similar to 5 MSBs of Bpixel. Last three bits of each pixel Bpixel in S is used to store bits comparison. 1^st, 2^nd, and 3^rd LSBs (BR,pixel,BM,pixel and BL,pixel) are used to store comparion results of CR,pixel, CM,pixel and CL,pixel, respectively.

Today the secure transmission of confidential data is an essential requirement of any communication network. Steganography is considered one of the best techniques to hide data from an intruder. Researchers from information security are continuously working to propose new algorithms or enhance existing steganography algorithms to achieve more reliable systems to hide data as much as possible and maintain the quality of cover media. This study presented a high embedding capacity steganography algorithm based on MSB matching and LSB substitution. The proposed solution offers high embedding capacity while guaranteeing the visual quality of stego images. This system covers the secret communication in the image, where each pixel of the cover image has been utilized to map a maximum of 6 bits of a secret message within each pixel’s 4 MSBs. This mapping process has been performed so that every message bit pair is matched with the consecutive MSB pair of the cover image in increasing form. A flag has been maintained to store the result of the comparison (true=1,false=0) in corresponding LSBs. In this way, if a given pixel successfully matches all MSB’s pairs with the message pairs, we can hide the maximum 6 bits in every 8 bits of cover media by adjusting only the 3 LSBs per pixel.

The proposed algorithm was tested on a standard dataset, including grayscale images of multiple dimensions. We have used embedding capacity, psnr, and bpp to measure the effectiveness of this study. The proposed algorithm’s highest embedding capacity for 256×256 images is 7921 bytes, 46859 bytes for 512×512 images, and 177373 bytes for 1024×1024 images. The proposed algorithm is compared with another recent approach by Zakariya [29] based on MSB matching and LSB substitution. The proposed algorithm has shown 15%, 11%, and 22% more hiding capacity for all three categories of images, respectively while keeping the minimum psnr to 37 dB. Furthermore, this algorithm has shown better embedding capacity while reducing psnr only by 14%.

As for future research directions, there are opportunities to further enhance the hiding capacity of steganography algorithms while simultaneously preserving PSNR values, particularly in the context of grayscale and color images. Expanding the scope of investigation to include other types of cover media, such as audio signals and video frames, presents an intriguing avenue for exploration. By extending the application of steganography to these domains, researchers can unlock new possibilities for secure data transmission and communication across diverse multimedia platforms. Additionally, continued advancements in steganography methodologies and techniques will contribute to the ongoing evolution of secure data concealment strategies in the digital age.