Data Embedding Research Articles

Advanced Reversible Data Hiding Techniques in JPEG Images: Methods, Applications, and Future Perspectives

This paper provides an extensive review of recent advancements in reversible information hiding methods tailored for JPEG images, a predominant compression standard crucial for data security and digital copyright protection. It starts by outlining fundamental concepts related to JPEG image structure and reversible data hiding techniques. The analysis then delves into three primary methods: modifications to JPEG quantization tables, adjustments in Huffman tables, and corrections of Discrete Cosine Transform (DCT) coefficients, detailing how each technique contributes to effective and secure data embedding. Furthermore, the exploration of potential applications in critical areas highlights the versatile utility of these techniques in safeguarding digital content. Concluding with a discussion on future research directions, this paper emphasizes the significant potential for growth in this field, particularly in enhancing security measures and improving efficiency. Such advancements promise to extend the applicability of reversible data hiding, ensuring robust copyright protection and information security in the digital age.

Enhancing Data Security through Digital Image Steganography: An Implementation of the Two Least Significant Bits (2LSB) Method

The rapid advancement of digital communication technologies has heightened the demand for robust data security methods, particularly for safeguarding sensitive information against cyber threats. Steganography, the art of concealing data within digital media, offers an effective means of securing communication by masking the very existence of hidden messages. This study evaluates the Two Least Significant Bits (2LSB) method as an enhancement to the conventional Least Significant Bit (LSB) technique, focusing on improving storage capacity and resistance to detection. The research employs a quantitative methodology, conducting experiments to analyze the impact of data embedding on image quality using metrics such as Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). Data embedding was tested on images of varying sizes and resolutions, with results compared before and after embedding. The study also evaluates the system’s tolerance to compression and robustness against steganalysis attacks, such as histogram analysis and noise detection. The results demonstrate that the 2LSB method significantly enhances storage capacity while maintaining high image quality. For instance, PSNR values remained above 40 dB across all tested images, indicating minimal visual distortion. The method also showed resilience to lossless compression formats such as PNG, with minimal impact on data integrity. However, lossy compression formats like JPEG posed challenges in maintaining the embedded data. The MSE values before and after embedding were minor, ensuring that the hidden data remained undetectable under standard analysis. This research contributes to developing more efficient and secure steganography techniques, providing practical applications in protecting sensitive data during transmission. The findings underline the 2LSB method's potential as a viable solution for digital security, balancing enhanced storage capacity with robust resistance to detection. Future work should further explore integrating advanced encryption with 2LSB to strengthen data protection in diverse digital environments

Enhanced reversible data hiding using difference expansion and modulus function with selective bit blocks in images

The rapid growth of Information and communication technology not only has positive impacts but also unveils opportunities for data and information security threats. In recent years, many researchers have worked on developing methods to enhance data security, particularly through data hiding techniques aimed at safeguarding communications by concealing their existence. With the same objective in mind, this study introduces a novel method for Reversible Data Hiding (RDH) based on a combination of difference expansion (DE) and a modulus function. Our method enables the embedding of 3-bit data into 2-bit Least Significant Bit (LSB) difference values of pixel pairs formed in rectangular blocks. Based on the experimental results, the payload capacity of our method can reach 0.3953 bpp with a PSNR of 53.5900 dB on common images and 0.5764 bpp with a PSNR of 52.9234 dB on medical images. Our method consistently achieves high payload capacity with good visual quality, and our method surpasses previous approaches in terms of performance, payload capacity, and visual quality.

The role of data embedding in equivariant quantum convolutional neural networks

Geometric deep learning refers to the scenario in which the symmetries of a dataset are used to constrain the parameter space of a neural network and thus, improve their trainability and generalization. Recently, this idea has been incorporated into the field of quantum machine learning, which has given rise to equivariant quantum neural networks (EQNNs). In this work, we investigate the role of classical-to-quantum embedding on the performance of equivariant quantum convolutional neural networks (EQCNNs) for the classification of images. We discuss the connection between the data embedding method and the resulting representation of a symmetry group and analyze how changing representation affects the expressibility of an EQCNN. We numerically compare the classification accuracy of EQCNNs with three different basis-permuted amplitude embeddings to the one obtained from a non-equivariant quantum convolutional neural network (QCNN). Our results show a clear dependence of classification accuracy on the underlying embedding, especially for initial training iterations. The improvement in classification accuracy of EQCNN over non-equivariant QCNN may be present or absent depending on the particular embedding and dataset used. The noisy simulation using simple noise models shows that certain EQCNNs are more robust to noise than non-equivariant QCNNs. It is expected that the results of this work can be useful to the community for a better understanding of the importance of data embedding choice in the context of geometric quantum machine learning.

Between innovative ambitions and erratic everyday practices: urban planners’ ambivalences towards digital transformation

Great expectations are being projected onto digital transformation as a solution to planning under uncertainty and complexity, while demanding new skills from and posing challenges to professionals. We address the ambivalent inclusion of information and communication technologies (ICTs) in everyday planning practices. How do ambivalences towards digitalisation as either a transformative process or as an intrusion upon everyday routines play out in urban planning as a situated knowledge practice? We focus on the erratic embedding of digital technologies and data into planning practices. We gather insights from interviews with planners in small and medium-sized cities in Germany, Austria and Switzerland. The analytical focus on knowing in practice and situated knowledge allows for insights on how digital transformations shape planning from professionals’ perspectives. Our research shows that digital transformation requires infrastructure and routines to bring forward innovative ambitions, which in many contexts are disrupted by limited resources and hierarchical decision-making procedures.

Automated mapping of electronic data capture fields to SDTM.

The goal of this work is to reduce the amount of manual work required to go from data capture to regulatory submission. It will be shown that the use of Siamese networks will allow for the generation of embeddings that can be used by traditional machine learning classifiers to perform the classification at much higher levels of accuracy than standard approaches. Siamese networks are a method for training data embeddings such that data within the same class are closer with respect to a given distance metric than they are to data points in another class. Because they are designed to learn similarity within pairs of data points, they work well in situations where the number of classes is relatively large compared to the number of training samples. In this work, we will show that embeddings generated via a Siamese network from metadata associated with electronic data capture forms can be used to predict the associated SDTM field. With a relatively simple network coupled with a basic classification algorithm, the proposed method can achieve accuracies greater than 90%, which is significantly higher than what has been achieved with traditional methods, with many of the inaccurate mappings due to a lack of training data. In many cases, there is a 15% increase in accuracy vs. more traditional methods. Leveraging Siamese networks, it is possible to generate embeddings that efficiently represent data fields in a lower dimensional space. This allows the creation of a system that can automatically map between data schemas at high levels of accuracy. Such systems represent the first step in automating one of the many labor-intensive data management tasks associated with clinical trials.

Reversible data hiding in encrypted images using Pixel Shifting Approach (PSA)

This study proposes a novel technique for reversibly embedding data within encrypted images, specifically utilizing a Pixel Shifting Approach (PSA). Traditional data hiding methods commonly employ the modification of the least significant bits (LSBs) of pixels in the original image. In contrast, methods using encrypted cover images offer a wider range of data hiding techniques that are not constrained by LSB modifications. The proposed data hiding method embeds data into an encrypted cover image by dividing it into non-overlapping blocks and then permuting pixels within each block according to a defined rule, rather than modifying the LSBs of pixels. The entropy of the image remains preserved after data embedding, making it difficult to detect the location of hidden data. To extract the hidden data, the encrypted image is decrypted, and the smoothness of each block is measured. The block with the lowest smoothness is identified as the one containing the hidden data, which is also the original block. This is because the pixels within each block have different statistical properties compared to other blocks, allowing for data recovery. The proposed method offers the advantage of embedding 4 bits of data per block, surpassing the performance of the existing Modified Pixel Shifting Approach (MPSA). Moreover, the entropy preservation of the stego-image is comparable to MPSA, while the algorithm’s execution time is more efficient. The simpler data hiding rule compared to MPSA enables faster encoding and decoding. Reversibility is guaranteed by preserving the correlation among pixels within each block when extracting data from the stego-image and restoring the original image. Experimental results demonstrate that the proposed method significantly outperforms existing techniques in terms of data hiding efficiency and security.

Dual-image reversible data hiding based on pixel value parity and multiple embedding strategy

Dual-image reversible data hiding (DI-RDH) has attracted a lot of attention for its excellent embedding capability. However, the stego image after data embedding is degraded, which limits its practical application. This paper proposes an innovative DI-RDH method based on pixel value parity (PVP), which prevents influences from the image texture features and ensures that the pixel value deviation between the temporary stego image and the original image is −1, 0, or 1, as it can partially offset the difference introduced when generating the cover image. Such characteristics enable us to establish a multiple embedding strategy (MES), which applies the difference image to embed the secret message, decreasing the number of invalid shifting pixels in the difference histogram to enhance embedding capacity and image quality. The average maximum embedding rate of our method combining PVP and two phases of MES is determined to be 1.37 bpp, corresponding to the theoretical results. The average peak signal-to-noise ratio of our method on the UCID database is increased by at least 2.19 dB for a given ER of 0.5 bpp compared with several state-of-the-art methods.

High Capacity Adaptive Block-Based Color Image Steganography and Human Perception-Guided Steganalysis

In the realm of image steganography, where the delicate balance between secrecy and visibility is paramount, our proposed method represents a significant advancement. We leverage the human eye's heightened sensitivity to subtle changes in smoother image regions, introducing a novel approach to embedding secret data. Unlike conventional methods, our approach delves deep into the intricacies of each RGB plane in color images, optimizing data embedding while preserving visual integrity. Through a sophisticated block complexity measurement and selection process, we harness image redundancy efficiently using the Hilbert scan pattern. What distinguishes our method is its simplicity and self-sufficiency; no additional complexity is required for data extraction. Our experiments on benchmark datasets validate the superior stego image quality and precise data embedding capabilities of our approach. Moreover, our innovative human perception-based validation approach accounts for the complexities of the human visual system and subjective perceptions, enhancing the reliability of our method. This research not only advances the field of steganography but also lays the groundwork for more effective data hiding applications in color imagery.

F.Y.B.Tech Students’ Engineering Design and Innovation (EDAI 2) Project Paper, SEM 2 A.Y. 2023-24 Vishwakarma Institute of Technology, Pune, INDIA

: In the digital age, the need for secure communication has become paramount, leading to the development of various techniques for data protection and privacy. Image steganography, the practice of concealing information within digital images, has emerged as a crucial method for covert communication. This paper delves into the application of the Least Significant Bit (LSB) insertion technique for image steganography. LSB insertion is a straightforward yet powerful approach that involves modifying the least significant bits of pixel values in an image to embed secret data. This method leverages the human eye's insensitivity to minor changes in color values, ensuring that the alterations remain imperceptible to observers. We begin by exploring the theoretical underpinnings of LSB insertion, including the principles of digital image representation and the rationale behind the selection of least significant bits for data embedding. Following this, we present a detailed implementation framework for LSB insertion, Outlining the Algorithmic steps involved in both embedding and extraction processes. The paper evaluates the effectiveness of LSB insertion in concealing data without compromising the visual quality of the carrier image. Moreover, we address potential vulnerabilities inherent in LSB-based steganography, such as Challenge of ensuring robustness against common image manipulations like compression and cropping. To mitigate these vulnerabilities, we propose enhancements and countermeasures, including the use of cryptographic techniques and adaptive LSB insertion methods. Through extensive experimental results, we demonstrate the capacity of LSB insertion to securely embed and retrieve information, emphasizing its practicality and efficiency for realworld applications. The findings indicate that LSB insertion can be effectively used for secure communication, provided that certain precautions are taken to enhance its robustness. This research contributes to the field of secure communication by presenting a comprehensive analysis of LSB insertion and its potential for enhancing data privacy in digital communications.

Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules

Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules

Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy

This study explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms; to assess performance enhancements and computational implications across a spectrum of models. We explored various classical-to-quantum mapping methods; ranging from basis encoding and angle encoding to amplitude encoding; for encoding classical data. We conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead.This study underscores the potential of quantum data embedding to enhance classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research may involve refining quantum encoding processes to optimize computational efficiency and explore scalability for real-world applications. Our work contributes to the growing body of knowledge on the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.

Dynamic Spatial-Temporal Embedding via Neural Conditional Random Field for Multivariate Time Series Forecasting

How to capture dynamic spatial-temporal dependencies remains an open question in multivariate time series (MTS) forecasting. Although recent advanced spatial-temporal graph neural networks (STGNNs) achieve superior forecasting performance, they either consider pre-defined spatial correlations or simply learn static graphs. Some research has tried to learn many adjacent matrices to reveal time-varying spatial correlations, but they generate discrete graphs which cannot encode evolutionary information and also face computational complexity problem. In this article, we propose two significant plugins to help automatically learn enhanced dynamic spatial-temporal embedding of MTS data: (1) a novel neural conditional random field (CRF) layer. We find that the implicit time-varying spatial dependencies are reflected by the explicit changeable links between edges, and we propose the neural CRF to encode such pairwise changeable evolutionary inter-dependencies; (2) a structure adaptive graph convolution (SAGC) that does not require pre-defined graphs to capture semantically richer spatial correlations. Then, we integrate the neural CRF, SAGC with recurrent neural network to develop a new STGNN paradigm termed Adaptive Spatial-Temporal graph neural network with Conditional Random Field (ASTCRF), which can be trained in an end-to-end fashion. We validate the effectiveness, efficiency, and scalability of ASTCRF on five public benchmark MTS datasets.

PSO-based Quaternion Fourier Transform steganography: Enhancing imperceptibility and robustness through multi-dimensional frequency embedding

This paper presents a novel steganography technique using the Quaternion Fourier Transform (QFT) in the 4D frequency domain to enhance imperceptibility and robustness in digital image embedding. Steganography, the art of hiding information within media, faces challenges in balancing security, imperceptibility, and robustness. To address this, we leverage the multi-dimensional properties of quaternions, enabling the embedding of secret data in both grayscale and color images. For grayscale images, two quaternion dimensions are utilized for intensity and secret data, while for color images, all four dimensions are employed with one reserved for metadata. The research question centers on how to maximize spatial dispersion and color similarity while maintaining high imperceptibility and robustness against attacks. Experimental results show that the proposed method improves visual imperceptibility by more than 4 % and exhibits a 13 % increase in robustness against common steganalysis attacks compared to the best state-of-the-art existing technique. These advancements highlight the potential of this method for applications in secure communication, digital watermarking, and copyright protection. By combining the quaternion mathematical framework with a novel optimization strategy, this approach significantly improves upon traditional steganography methods.

Unraveling the developmental heterogeneity within the human retina to reconstruct the continuity of retinal ganglion cell maturation and stage-specific intrinsic and extrinsic factors.

Tissue development is a complex spatiotemporal process with multiple interdependent components. Anatomical, histological, sequencing, and evolutional strategies can be used to profile and explain tissue development from different perspectives. The introduction of scRNAseq methods and the computational tools allows to deconvolute developmental heterogeneity and draw a decomposed uniform map. In this manuscript, we decomposed the development of a human retina with a focus on the retinal ganglion cells (RGC). To increase the temporal resolution of retinal cell classes maturation state we assumed the working hypothesis that that maturation of retinal ganglion cells is a continuous, non-discrete process. We have assembled the scRNAseq atlas of human fetal retina from fetal week 8 to week 27 and applied the computational methods to unravel maturation heterogeneity into a uniform maturation track. We align RGC transcriptomes in pseudotime to map RGC developmental fate trajectories against the broader timeline of retinal development. Through this analysis, we identified the continuous maturation track of RGC and described the cell-intrinsic (DEGs, maturation gene profiles, regulons, transcriptional motifs) and -extrinsic profiles (neurotrophic receptors across maturation, cell-cell interactions) of different RGC maturation states. We described the genes involved in the retina and RGC maturation, including de novo RGC maturation drivers. We demonstrate the application of the human fetal retina atlas as a reference tool, allowing automated annotation and universal embedding of scRNAseq data. Altogether, our findings deepen the current knowledge of the retina and RGC maturation by bringing in the maturation dimension for the cell class vs. state analysis. We show how the pseudotime application contributes to developmental-oriented analyses, allowing to order the cells by their maturation state. This approach not only improves the downstream computational analysis but also provides a true maturation track transcriptomics profile.

Imputing spatial transcriptomics through gene network constructed from protein language model.

Image-based spatial transcriptomic sequencing technologies have enabled the measurement of gene expression at single-cell resolution, but with a limited number of genes. Current computational approaches attempt to overcome these limitations by imputing missing genes, but face challenges regarding prediction accuracy and identification of cell populations due to the neglect of gene-gene relationships. In this context, we present stImpute, a method to impute spatial transcriptomics according to reference scRNA-seq data based on the gene network constructed from the protein language model ESM-2. Specifically, stImpute employs an autoencoder to create gene expression embeddings for both spatial transcriptomics and scRNA-seq data, which are used to identify the nearest neighboring cells between scRNA-seq and spatial transcriptomics datasets. According to the neighbored cells, the gene expressions of spatial transcriptomics cells are imputed through a graph neural network, where nodes are genes, and edges are based on cosine similarity between the ESM-2 embeddings of the gene-encoding proteins. The gene prediction uncertainty is further measured through a deep learning model. stImpute was shown to consistently outperform state-of-the-art methods across multiple datasets concerning imputation and clustering. stImpute also demonstrates robustness in producing consistent results that are insensitive to model parameters.

The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense.

A recent paper claimed that t-SNE and UMAP embeddings of single-cell datasets are "specious" and fail to capture true biological structure. The authors argued that such embeddings are as arbitrary and as misleading as forcing the data into an elephant shape. Here we show that this conclusion was based on inadequate and limited metrics of embedding quality. More appropriate metrics quantifying neighborhood and class preservation reveal the elephant in the room: while t-SNE and UMAP embeddings of single-cell data do not preserve high-dimensional distances, they can nevertheless provide biologically relevant information.

Forensic Investigation and Legal Issues: Hiding Information through Steganography

Abstract: This paper presents information hiding, also known as data embedding, for transmitting secret information through a network. Data security is the prime aspect of conveying information through communication channels. The data hiding and information procedure can be done using several methods, i.e., cryptography, steganography, and digital watermarking. Most people need clarification about cryptography and steganography because both methods are used for hiding information, but there is a minor difference between them. In cryptography, the secret message is hidden, known as “Secret Writing”; in steganography, secret communication is known as “covered Writing”. This paper mainly focuses on steganography for data and information hiding. Steganography is used primarily on terrorism, where the terrorists secretly transmit their data to others globally. The existence of the steganography evaluation model is dominant for stronger development of preferable techniques and the way to cover the extant ones. Despite the ordinary parameters, they are generally acknowledged for the comprehensive assessment of the steganography technique, which is deficient. Nowadays there are many forensic investigations are ongoing on terrorist attacks, which are done through secret transmission of data or information. Many challenges are faced by steganography forensics during the analysis of embedded data. This paper urges digital forensics to be more forward than criminals who use data-hiding techniques that can take advantage of and obscure their criminal activities. This paper concludes the need to develop more robust tools and steganalysis techniques.

Information hiding using approximate POSIT representation

AbstractPOSIT is a numerical system consists of sign, regime, exponential, and fraction bits to overcome some limitations of the IEEE‐754 floating point (FP) representation. This work proposes a technique to hide critical information in the least significant bits (LSBs) of the POSIT FP representation by exploiting approximate computing (AC). The proposed technique, called information hiding using POSIT and LSB (IHUPL), explores the opportunities offered by both the POSIT representation and the AC to hide information with a minimum loss in accuracy and other performance metrics. IHUPL offers two options for hiding information: either by embedding one digit of each character into each pixel or by embedding all the digits of the character at the same time into each pixel of the alpha‐red, green, blue, or black/white image. Experimental results are evaluated for benchmark images and showed that IHUPL enhanced the accuracy of embedding data into LSB of POSIT FP by an average of 5%, the image quality improvement rates of IHUPL are 19% and 16% for options 1 and 2, respectively. Besides the encoding method using IHUPL, the paper outlines an extraction decoding technique that saves the original replacement bits in a key‐image to recover the hidden security message.

A best-match approach for gene set analyses in embedding spaces.

Embedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine-learning tasks. Much less has been done to examine the embeddings directly, especially analyses of gene sets in embedding spaces. Here, we propose an Algorithm for Network Data Embedding and Similarity (ANDES), a novel best-match approach that can be used with existing gene embeddings to compare gene sets while reconciling gene set diversity. This intuitive method has important downstream implications for improving the utility of embedding spaces for various tasks. Specifically, we show how ANDES, when applied to different gene embeddings encoding protein-protein interactions, can be used as a novel overrepresentation- and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multiorganism joint gene embeddings to facilitate functional knowledge transfer across organisms, allowing for phenotype mapping across model systems. Our flexible, straightforward best-match methodology can be extended to other embedding spaces with diverse community structures between set elements.