Embedding Capacity Research Articles

A high-capacity Coverless Image Steganography Based on OMR and Mapping Rules

Securing private information during its transmission over the internet is of paramount importance, as it safeguards against unauthorized access and guarantees the integrity of data. Steganography serves as a technique for enhancing security by concealing sensitive information within digital content such as images, videos, audio, and textual carriers, thus thwarting unauthorized interception. Current methodologies involve manipulating pixels to create stego-images, effectively embedding confidential messages within the image structure. However, this approach has led to steganalysis experts uncovering these hidden messages. To address this issue, an innovative strategy for covert data concealment is proposed. This paper proposes a robust, secure, and embedding capacity coverless image steganography method using OMR and Mapping Rules. The secret information is represented by a binary string, and the bubble sheet is used to represent binary fragments. This approach ensures the confidentiality of sensitive data communicated over the internet. Notably, this approach surpasses previous alternatives in terms of embedding capacity, as substantiated by result analysis. Furthermore, it is adaptable for refinement as needed. Significantly, it exhibits remarkable resilience, safeguarding against a spectrum of attacks including scaling, color space conversion, JPEG compression, thresholding, "salt and pepper" noise, file format conversion, and steganalysis tools, among others.

Dynamic data hiding capacity enhancement for the Hybrid Near Maximum Histogram image steganography based on Multi-Pixel-Pair approach

AbstractIncreasing data hiding capacity and making it difficult to detect presence of any confidential data in stego images are the key objectives in contemporary image steganography research ever-improving the embedding efficiency. With regard to these crucial and challenging points, a new Multi-Pixel-Pair (MPP)-based data hiding approach is proposed in this paper. It can dynamically increase data embedding capacity of the classical Hybrid Near Maximum Histogram (HNMH) image steganography method while well-maintaining the embedding efficiency. In the proposed MPP approach, the peak value as a reference point in the histogram distribution of a cover image is obtained, and accordingly the pixel-pairs of interest where secret data is to be embedded are determined. Then, the pixels of the cover image are scanned sequentially and data hiding is performed on the relevant pixels by using the Least Significant Bit (LSB) method. In an extensive experimental study of the proposed MPP approach, it is shown that the classical HNMH data hiding capacity is dynamically improved 4.74 to 37.48 times while the Peak Signal to Noise Ratio (PSNR) decreases between 6.62 and 13.75 dB, implying both a reasonable trade-off and 7.61% enhanced embedding efficiency performance. Moreover, using the well-known cover image partitioning technique, offering significant improvements in image steganography, and the proposed MPP approach together, can further extend the data hiding capacity about 7.05%.

A Self-Defense Copyright Protection Scheme for NFT Image Art Based on Information Embedding

Non-convertible tokens (NFTs) have become a fundamental part of the metaverse ecosystem due to its uniqueness and immutability. However, existing copyright protection schemes of NFT image art relied on the NFTs itself minted by third-party platforms. A minted NFT image art only tracks and verifies the entire transaction process, but the legitimacy of the source and ownership of its mapped digital image art cannot be determined. The original author or authorized publisher lack an active defense mechanism to prove ownership of the digital image art mapped by the unauthorized NFT. Therefore, we propose a self-defense copyright protection scheme for NFT image art based on information embedding in this paper, called SDCP-IE. The original author or authorized publisher can embed the copyright information into the published digital image art without damaging its visual effect in advance. Different from the existing information embedding works, the proposed SDCP-IE can generally enhance the invisibility of copyright information with different embedding capacity. Furthermore, considering the scenario of copyright information being discovered or even destroyed by unauthorized parties, the designed SDCP-IE can efficiently generate enhanced digital image art to improve the security performance of embedded image, thus resisting the detection of multiple known and unknown detection models simultaneously. The experimental results have also shown that the PSNR values of enhanced embedded image are all over 57db on three datasets BOSSBase, BOWS2 and ALASKA#2. Moreover, compared with existing information embedding works, the enhanced embedded images generated by SDCP-IE reaches the best transferability performance on the advanced CNN-based detection models. When the target detector is the pre-trained SRNet at 0.4bpp, the test error rate of SDCP-IE at 0.4bpp on the evaluated detection model YeNet reaches 53.38%, which is 4.92%, 28.62% and 7.05% higher than that of the UTGAN, SPS-ENH and Xie-Model, respectively.

Reversible data hiding using morphology based pixel classification

Pixel value ordering (PVO) prediction has become a research hotspot in reversible data hiding community and existing achievements have proved the necessity of predicting more pixels in a block. The increase in predicted pixels is beneficial to fully exploiting the redundancy of the cover image, but a single reference pixel may lead to decreased prediction quality. Given this situation, this paper proposes to develop PVO in correlated pixels construction and introduce morphology as an important theoretical tool. Several largest pixels in a block are first selected as candidate reference pixels which are to be activated in descending order. Except for the last one, each active reference pixel is used to predict all pixels to generate a correlation matrix, on which the dilation operation is carried out to pick out connected pixels that are likely to be strongly correlated with the reference pixel. Whether to activate the next reference pixel depends on the presence of disconnected pixels. Once the last reference pixel is activated, all remaining pixels will be taken as connected pixels. After the first round of embedding ends, several smallest pixels are similarly selected as candidate reference pixels to realize the second round of embedding. Experimental results demonstrate that satisfactory superiority in fidelity is achieved, e.g., the average PSNR for the Kodak image database is 63.85 dB with an embedding capacity of 10,000 bits.

An Effective Multiple-Bit Data Abstraction and Lossless Retrieval without Shifting

Cryptography and Steganography are two major forms of enforcing data privacy, with the former being prevalent. For small payloads, Steganography is preferred, as the data remains concealed and abstracted. The process involves embedding bits onto a carrier image. However, the disadvantage of steganography is that the carrier image gets distorted, which is a major concern when it comes to pixel sensitive images, such as forensic and medical images that aremission critical. Furthermore, due to modifications in the pixel values, the images get distorted, which can be irrecoverable. Hence, the technique of Reversible Data Hiding(RDH) can be used as a solution. Usually two methods are used under RDH, namely Histogram Shifting (HS) and Difference Expansion (DE). Histogram Shifting method shifts all pixel values between pairs of peak and zero points, which allows data to be embedded in the intermediate points. However, this method also depends on frequencies of pixel values on the image, andthe embedding capacity varies greatly on the image. In Difference Expansion Method, the difference between a pixel and its adjacent pixels is considered to embed a single bit intoone of the pixels. Here, a scheme to embed multiple bits in a pixel is proposed, which greatly enhances the embedding capacity. Initially, three horizontally adjacent pixels are takenand the difference between the rightmost and leftmost pixel iscalculated. This difference is used to classify the centre pixel as embeddable or non-embeddable. If the pixel is embeddable, anew pixel value is calculated, which comprises of the secret bits, offset bits and flag bits, without the use of any difference expansion or histogram shifting process. The secret bits and the original pixel value can be easily recovered from the modified value using the offset and flag bits. This scheme has higher embedding capacity, and lower complexity in terms of computations, when comparedwith other RDH schemes.

Penyisipan Data Secara Reversible Pada Gambar Medis Menggunakan Metode Difference Expansion of Quad dan Fungsi Modulus

The rapid advancement of the Internet of Things (IoT) brings convenience across various domains, but raises its own challenges, particularly concerning data security and privacy. Cryptography and steganography are two techniques used to secure data. Cryptography protects information by encrypting messages, while steganography hides the existence of data in other media. The embedding of data into other media like medical images necessitates specialized approaches to prevent potential distortion that may compromise their integrity. Distortion in medical images could lead to erroneous diagnoses. In this study, we introduce a reversible data embedding technique capable of returning storage media to its original state without distortion, utilizing the Difference Expansion of Quad and Modulus Function. This approach employs 2×2 pixel blocks to embed 3-bits of data. Our experiments on six medical images demonstrate that the proposed method offers substantial embedding capacity while maintaining high visual quality. The embedding capacity can reach 0.4834 bits per pixel (bpp) with a PSNR of 49.4330 dB. Experimental findings indicate that the proposed method outperforms previous techniques.

Effectiveness of the Spatial Domain Techniques in Digital Image Steganography

Digital steganography is a new and extremely demanding method for transmitting information securely over the internet while employing a covert device. Since its inception in the 1990s till the present, digital steganography has a lengthy history. Early steganography focused primarily on imperceptibility, security and embedding capacity. In addition to using statistics as a foundation, convolution neural networks (CNN), generative adversarial networks (GAN), coverless approaches, and machine learning are all used to construct steganographic methods. Robustness is becoming a crucial component of many innovative techniques. Spatial, Transform, and Adaptive domains serve as the understructure of those novel methods. This broadens the range of steganographic technique development and often concentrates the implementation of adaptive techniques. As a result, this study helps to analyze the fundamentals of image steganography, a comparative review on the spatial domain algorithms. As using evaluation tools is strongly tied to the effectiveness of steganography, this study also goes into great detail about its application. The purpose of this research is to determine the best and most effective algorithm among the three competitive spatial domain algorithms, which are Least Significant Bit (LSB), Optimum Pixel Adjustment Procedure (OPAP), and Pixel Value Differencing (PVD) which in regard demonstrated the efficacy of spatial domain algorithms.

High-capacity data hiding for medical images based on the mask-RCNN model

This study introduces a novel approach for integrating sensitive patient information within medical images with minimal impact on their diagnostic quality. Utilizing the mask region-based convolutional neural network for identifying regions of minimal medical significance, the method embeds information using discrete cosine transform-based steganography. The focus is on embedding within “insignificant areas”, determined by deep learning models, to ensure image quality and confidentiality are maintained. The methodology comprises three main steps: neural network training for area identification, an embedding process for data concealment, and an extraction process for retrieving embedded information. Experimental evaluations on the CHAOS dataset demonstrate the method’s effectiveness, with the model achieving an average intersection over union score of 0.9146, indicating accurate segmentation. Imperceptibility metrics, including peak signal-to-noise ratio, were employed to assess the quality of stego images, with results showing high capacity embedding with minimal distortion. Furthermore, the embedding capacity and payload analysis reveal the method’s high capacity for data concealment. The proposed method outperforms existing techniques by offering superior image quality, as evidenced by higher peak signal-to-noise ratio values, and efficient concealment capacity, making it a promising solution for secure medical image handling.

StegaStyleGAN: Towards Generic and Practical Generative Image Steganography

The recent advances in generative image steganography have drawn increasing attention due to their potential for provable security and bulk embedding capacity. However, existing generative steganographic schemes are usually tailored for specific tasks and are hardly applied to applications with practical constraints. To address this issue, this paper proposes a generic generative image steganography scheme called Steganography StyleGAN (StegaStyleGAN) that meets the practical objectives of security, capacity, and robustness within the same framework. In StegaStyleGAN, a novel Distribution-Preserving Secret Data Modulator (DP-SDM) is used to achieve provably secure generative image steganography by preserving the data distribution of the model inputs. Additionally, a generic and efficient Secret Data Extractor (SDE) is invented for accurate secret data extraction. By choosing whether to incorporate the Image Attack Simulator (IAS) during the training process, one can obtain two models with different parameters but the same structure (both generator and extractor) for lossless and lossy channel covert communication, namely StegaStyleGAN-Ls and StegaStyleGAN-Ly. Furthermore, by mating with GAN inversion, conditional generative steganography can be achieved as well. Experimental results demonstrate that, whether for lossless or lossy communication channels, the proposed StegaStyleGAN can significantly outperform the corresponding state-of-the-art schemes.

On the security of JPEG image encryption with RS pairs permutation

Recently, a JPEG encryption scheme with RS (run/size) pairs multi-permutation and block permutation has been proposed, which mainly improves security by extracting the expected number of RS pairs for global permutations that need overflow processing. However, it still has the risk of information leakage under the chosen plaintext attack. This is because the limited embedding capacity in overflow processing makes the overflow blocks (OBs) less. On the one hand, a few OBs make the sketch generated by non-zero coefficients count after RS pair multi-permutation visually indistinguishable from the original sketch. On the other hand, a few OBs limit the number of extracted RS pairs that can participate in global permutation, so there are many partially extracted blocks (PEBs) containing unextracted RS pairs. More PEBs make the accuracy of the estimated block permutation sequence high, because the unique plaintext block can be constructed as long as there is an unextracted RS pair in an encrypted block. Simulation results show that the correct ratio of block permutation sequence is not less than 60 %. The highest and average PSNRs between the attacked sketch and the original one reach up to 47.07 dB and 27.42 dB, respectively.

Parallel BFS through pennant data structure with reducer hyper‐object based data hiding for 3D mesh images

AbstractData hiding refers to the technique employed to conceal sensitive data within various forms of multimedia, such as video, audio, text, 2D images, 3D images, and more, while also being able to successfully extract said information from the multimedia file. Although numerous data hiding techniques exist for 2D images, research on data hiding in 3D images is still in its early stages. Current 3D image steganography methods suffer from limitations in terms of embedding capacity and time complexity. To overcome these difficulties, we propose a novel 3D image steganographic technique using Parallel Breadth First Search (PBFS) with hyper‐object. Our approach utilizes the PBFS searching strategy, along with layer synchronization, to embed private information within the vertices of the 3D mesh images. To maximize the effectiveness of the process and minimize time, cost, complexity, and execution, we involved the data structure “bag” to parallelize the BFS. The implementation of bags is based on the pennant data structure. This methodology obtained an impressive Embedding Capacity (EC) of 9.00 bits per vertex (bpv) while maintaining superior visual quality and time complexity. Consequently, the proposed methodology holds great potential for widespread utilization across numerous sectors, including private and government organizations such as intelligence agencies, intellectual property rights management, cloud data security, defense, covert communication, medical imagery, and more.

VLSI Implementation of Speech Steganography with Advanced Wavelet Transform

In the realm of secure communication systems, the integration of advanced wavelet transforms into speech steganography systems has emerged as a promising path. Secure transmission of sensitive information in military communications, confidential exchange of data in corporate environments, and protection of personal information in telecommunication networks. However, the current system employs conventional methods of speech steganography, relying on basic encryption techniques and simplistic embedding algorithms. While functional, these approaches lack robustness and fail to adequately conceal secret data within speech signals, leaving them vulnerable to detection and interception. So, the proposed system leverages advanced wavelet transforms to enhance the security and efficiency of speech steganography. By exploiting the multi-resolution analysis capabilities of wavelets, the system achieves improved embedding capacity while maintaining perceptual transparency. Additionally, the use of dynamic embedding algorithms based on wavelet coefficients ensures adaptability to varying signal characteristics, enhancing robustness against attacks and noise interference. The VLSI implementation of this system optimizes resource utilization and computational efficiency, making it suitable for real-time applications in communication systems.

Hybrid Edge Detection Methods in Image Steganography for High Embedding Capacity

Abstract In this research, we propose two new image steganography techniques focusing on increasing image-embedding capacity. The methods will encrypt and hide secret information in the edge area. We utilized two hybrid methods for the edge detection of the images. The first method combines the Laplacian of Gaussian (LoG) with the wavelet transform algorithm and the second method mixes the LOG and Canny. The Combining was performed using addWeighted. The text message will be encrypted using the GIFT cipher method for further security and low computation. For the effectiveness evaluation of the proposed method, various evaluation metrics were used such as embedding capacity, PSNR, MSE, and SSIM. The obtained results indicate that the proposed method has a greater embedding capacity in comparison with other methods, while still maintaining high levels of imperceptibility in the cover image.

Arnold’s cat map secure multiple-layer reversible watermarking

Reversible watermarking is a novel approach to digital copyright protection that allows the embedding of watermarks into digital data using multiple layers while retaining the ability to recover the original content without data loss. This method provides a unique solution for securing digital data while maintaining the integrity and quality of the content. Nonetheless, new challenges have emerged with the increase in attacks on this method, as reversible watermarking methods lack security keys, making it easy to extract and modify hidden data. In this paper, we present a method for multiple-layer reversible watermarking with security keys, with the goal of addressing the challenges posed by attacks and improving data protection within embedded content. The method uses arnold’s cat map to scramble images, and data embedding in predetermined iterations serves as the methods security key. We put the method through its paces with six grayscale images. With this method, the embedding capacity can reach 2.999 bpp across four layers of embedding, while the visual image quality can reach 22.01 dB. The outcomes from this approach are that the security of multiple-layer reversible watermarking can be enhanced while preserving the capacity to embed data in each layer.

Image Steganography and Style Transformation Based on Generative Adversarial Network

Traditional image steganography conceals secret messages in unprocessed natural images by modifying the pixel value, causing the obtained stego to be different from the original image in terms of the statistical distribution; thereby, it can be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial-Intelligence-Generated Content (AIGC) becoming popular on social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image-style-transformation neural network with a steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder–decoder model, and finally, generates a styled image containing the secret messages at the same time. An adversarial training technique was applied to enhance the imperceptibility of the generated art-style stego image from plain-style-transferred images. The lack of the original cover image makes it difficult for the opponent learning steganalyzer to identify the stego. The proposed approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of three bits per pixel for a color image, according to the experimental results.

MIWET: Medical image watermarking using encryption and fusion technique

Digital augmentation of contemporary healthcare has directed the expansion of ICT-based systems. At this stage, a variety of telemedical support has been implemented to ensure the provision of medical utilities. Data Privacy, reliability, and credibility are the three security prerequisites that demand constant monitoring and corrective measures. Watermarking and cryptographic techniques offer a potentially viable solution for effectively addressing the aforementioned challenges. This article presents a robust and secure watermarking procedure that effectively safeguards the privacy and accuracy of digital medical data. This work uses a hybridization of NSST and DTCWT to implement a multimodality image fusion method that generates the watermark image. Further, a combination of three transforms namely, NSST, BEMD, and MSVD is used to conceal the generated mark in the carrier image. Also, the weighting factor is computed using HOG-based optimization to achieve a balance between visual quality and robustness. Finally, the marked carrier image is encrypted with a suitable encryption technique for better security. The proposed framework’s versatility, embedding capacity, robustness, and imperceptibility are evaluated, with the results justifying its efficacy. The study demonstrates a significant improvement of 47.65 % over the baseline, thus highlighting the framework’s superiority.

High-Payload Data-Hiding Scheme Based on Interpolation and Histogram Shifting

The whole world is quickly transforming into an information world with the setup of smart cities, among other things. Information and other forms of privacy are therefore at risk due to the growing intelligence of hackers. Therefore, to enhance the security of information, a capacity-raising interpolation method based on histogram shifting is proposed. According to the experimental results, the capacity-raising interpolation method based on histogram shifting produces high-quality images with a significantly high embedding capacity, outperforming other methods. Apart from the already-mentioned advantages, the proposed method is reversible and can recover images in a lossless manner.

A reversible fragile watermarking technique using fourier transform and Fibonacci Q-matrix for medical image authentication

Image authentication techniques are crucial for a multitude of multimedia applications. When images are transmitted through non-secure channels like the internet, they are vulnerable to unauthorized alterations and manipulations. Therefore, it is essential in sensitive fields, like the medical industry, to employ image authentication techniques to guarantee the integrity and authenticity of transmitted images. In this paper, to address these security issues and to ensure the authenticity of medical images, we present a reversible fragile watermarking model where the watermark is first generated from the cover image using DCT and then encrypted by the Fibonacci Q-matrix technique, enhancing the model’s security. Subsequently, DFT is performed on the host image and the obtained sub-band is segmented into 2 × 2 blocks, the watermark is then embedded within the frequency coefficients using a new embedding technique. Experimental results indicates that the presented model produces a great watermarked image quality, with a PSNR superior to 117 dB, while maintaining an acceptable embedding capacity of 0.25 BPP and high sensitivity to various attacks, as it was tested against 16 different attacks and could detect tampering in all of them. Furthermore, the presented scheme offers nearly perfect reversibility with a PSNR of 324 dB, which is very useful in the medical field where we need images with as little distortion as possible.

Leveraging coverless image steganography to hide secret information by generating anime characters using GAN

There has been a growing need for secure communication in recent years, particularly in the digital realm. Researchers have developed various text-hiding methods to address this need, but these methods often suffer from limitations such as low embedding capacity or high embedding distortion. However, current methods for information hiding require a large image database or have a low capacity for hiding information, which makes them impractical. To address these issues, we propose a coverless information-hiding method that generates anime characters through generative adversarial networks (GANs). This method converts secret information into an attribute label set for anime characters and uses the label set to generate anime characters directly. The quality of the generated anime characters was improved by the super-resolution GAN (SRGAN) model. The resulting images were used to communicate secret information on the digital channel. Numerous experiments were conducted to evaluate the performance of the proposed framework, including hiding capacity, image clarity, robustness and security. Results showed that the proposed framework outperforms existing text-hiding methods in terms of hiding capacity and robustness while maintaining image clarity and computation time. In conclusion, our proposed framework provides a secure and efficient solution for text encryption and decryption in feasible computation time using anime characters generated by a GAN. The framework has a high embedding capacity and low embedding distortion, making it a promising solution for secure communication in the digital world.

Bi-directional Block Encoding for Reversible Data Hiding over Encrypted Images

Reversible data hiding over encrypted images (RDH-EI) technology is a viable solution for privacy-preserving cloud storage, as it enables the reversible embedding of additional data into images while maintaining image confidentiality. Since the data hiders, e.g., cloud servers, are willing to embed as much data as possible for storage, management, or other processing purposes, a large embedding capacity is desirable in an RDH-EI scheme. In this article, we introduce a novel bi-directional block encoding (BDBE) method, which, for the first time, encodes the distances of values in a binary sequence from both ends. This approach allows for encoding images with smaller sizes compared to traditional and state-of-the-art encoding methods. Leveraging the BDBE technique, we propose a high-capacity RDH-EI scheme. In this scheme, the content owner initially predicts the image pixels and then employs BDBE to encode the prediction errors, creating space for data embedding. The resulting encoded data are subsequently encrypted using a secure stream cipher, such as the Advanced Encryption Standard, before being transmitted to a data hider. The data hider can embed confidential information within the encrypted image for the purposes of storage, management, or other processing. Upon receiving the data, an authorized receiver can accurately recover the original image and the embedded data without any loss. Experimental results demonstrate that our RDH-EI scheme achieves a significantly larger embedding capacity compared to several state-of-the-art schemes.