Embedding Phase Research Articles

Review on the Phase Change Materials in Wood for Thermal Regulative Wood-Based Products

Wood is an excellent building material or component that has been used all over the world. The rise in energy consumption worldwide, particularly in the building sector, has led to the development of diverse methods to overcome this problem. Embedding phase change material, PCM, into the wood has been researched as one of the most effective alternatives of controlling the thermal loads of wood, as it can store and release latent heat energy at a specific temperature range. Due to increasing interest, this article reviews the PCM in wood, including some research on the recent efforts that has been made by other researchers regarding this topic. This article also provides insight into problems associated with the wood and wood-based products incorporated with PCM. From the three groups of PCM, namely organic, inorganic, and eutectic mixture, the organic and eutectic mixture were most commonly chosen and successfully impregnated into wood structure. Carbonization and delignification can help to increase the stabilization of the PCM in wood. Adding PCM to wood sometimes decreases its strength, thus balancing between the thermal regulative performance and other industrial requirements needed to fabricate a fully functional thermal-regulative wood.

A Neural Network Model Secret-Sharing Scheme with Multiple Weights for Progressive Recovery

With the widespread use of deep-learning models in production environments, the value of deep-learning models has become more prominent. The key issues are the rights of the model trainers and the security of the specific scenarios using the models. In the commercial domain, consumers pay different fees and have access to different levels of services. Therefore, dividing the model into several shadow models with multiple weights is necessary. When holders want to use the model, they can recover the model whose performance corresponds to the number and weights of the collected shadow models so that access to the model can be controlled progressively, i.e., progressive recovery is significant. This paper proposes a neural network model secret sharing scheme (NNSS) with multiple weights for progressive recovery. The scheme uses Shamir’s polynomial to control model parameters’ sharing and embedding phase, which in turn enables hierarchical performance control in the secret model recovery phase. First, the important model parameters are extracted. Then, effective shadow parameters are assigned based on the holders’ weights in the sharing phase, and t shadow models are generated. The holders can obtain a sufficient number of shadow parameters for recovering the secret parameters with a certain probability during the recovery phase. As the number of shadow models obtained increases, the probability becomes larger, while the performance of the extracted models is related to the participants’ weights in the recovery phase. The probability is proportional to the number and weights of the shadow models obtained in the recovery phase, and the probability of the successful recovery of the shadow parameters is 1 when all t shadow models are obtained, i.e., the performance of the reconstruction model can reach the performance of the secret model. A series of experiments conducted on VGG19 verify the effectiveness of the scheme.

Twin K-Shuffle Based Audio Steganography

Secure communication is most effective when it is covert. In the realm of covert communication, steganography conceals secret message within a cover medium. This ensures that adversaries who have access to this carrier medium are unaware of the existence of the secret message. This paper proposes a novel twin K-Shuffling and embedding technique that scrambles and hides secret message inside audio samples. The scrambling phase of the proposed technique consists of bit and character shuffling. The bit-shuffling scrambles the bit-string of each character in the secret message into cipher-text via K-Shuffle. The characters of the resulting cipher-text are then shuffled by another K-Shuffle technique to yield chaotic cipher-text. At the embedding phase, the scrambled cipher-text is randomly planted into the carrier audio samples. The novelty in this proposed technique is the provision of a three-layer protection for secret messages; bit, character, and encoding layers. Results and analyses show that this technique satisfied both embedding and encryption requirements of steganographic systems.

A Visually Secure Image Encryption Based on the Fractional Lorenz System and Compressive Sensing

Recently, generating visually secure cipher images by compressive sensing (CS) techniques has drawn much attention among researchers. However, most of these algorithms generate cipher images based on direct bit substitution and the underlying relationship between the hidden and modified data is not considered, which reduces the visual security of cipher images. In addition, performing CS on plain images directly is inefficient, and CS decryption quality is not high enough. Thus, we design a novel cryptosystem by introducing vector quantization (VQ) into CS-based encryption based on a 3D fractional Lorenz chaotic system. In our work, CS compresses only the sparser error matrix generated from the plain and VQ images in the secret generation phase, which improves CS compression performance and the quality of decrypted images. In addition, a smooth function is used in the embedding phase to find the underlying relationship and determine relatively suitable modifiable values for the carrier image. All the secret streams are produced by updating the initial values and control parameters from the fractional chaotic system, and then utilized in CS, diffusion, and embedding. Simulation results demonstrate the effectiveness of the proposed method.

An Enhanced Term Weighted Question Embedding for Visual Question Answering

Visual Question Answering (VQA) is a multi-modal AI-complete task of answering natural language questions about images. Literature solved VQA with a three-phase pipeline: image and question featurisation, multi-modal feature fusion and answer generation or prediction. Most of the works have given attention to the second phase, where multi-modal features get combined ignoring the effect of individual input features. This work investigates VQA’s natural language question embedding phase by proposing a new question featurisation framework based on Supervised Term Weighting (STW) schemes. In addition, two new STW schemes integrating text semantics, qf.cos and tf.rf.sim, have been introduced to boost the framework’s performance. A series of tests on the DAQUAR VQA dataset is used to compare the new system to conventional pre-trained word embedding. Over the past few years, STW schemes have been commonly used in text classification research. In light of this, tests are carried out to verify the effectiveness of the two newly proposed STW schemes in the general text classification task.

Year-long energy analysis of building brick filled with phase change materials

Recently, embedding phase change materials (PCMs) within building walls have attracted significant attention as an energy-saving technique. However, year-long analysis requires further investigation. Therefore, this study proposes two different models, that is, simulated and validated with experimental results using data from the literature. Analysis for one year is conducted to estimate the energy-saving using PCM in building brick. Energy assessment of a common brick filled with PCM is investigated. The effect of using different PCM types and arrangements is evaluated. The results show that integrating PCM to the block enhances the thermal properties of building materials by modifying the thermal storage ability of the wall materials and decreasing and shifting the peak load on the building. Using n-Eicosane as a PCM shows the lowest maximum temperature of 37.86 °C with the peak indoor heat flux decreased by 29% and shifted by 9.28%. Moreover, one-year simulations reduce the average indoor surface temperature by 1.5% using PCM with an 8% reduction in total energy transfer. Additionally, n-Eicosane gives the highest energy saving of 18.69% compared to other types of PCM and configurations. Also, results show that horizontal wall orientation received the highest value of heat gain during the day.

Separable High Capacity Reversible Data Hiding Algorithm for Encrypted Images

This paper presents a separable Reversible Data Hiding Algorithm for Encrypted Images (RDHEI) that consists of three phases. The encryption phase in the algorithm circularly shifts the columns in the image by a random amount, blocks the image into equal and regular blocks and maps them to irregular blocks generated based on Hilbert filling curve, and finally complements a random subset of the blocks. The embedding phase is essentially an adapted version of the modification of the prediction errors algorithm that is applied to each block in the encrypted image independently. In the decryption phase, and since the algorithm is separable, the user can extract the data only, decrypt the image only, or can perform both actions depending on the type of keys he has. When compared to a very similar and recent algorithm, performance evaluation proved the ability of the proposed algorithm in increasing the embedding capacity with reasonable quality of the directly decrypted image. In terms of the security, the analytical and quantitative assessment showed the superiority of the proposed algorithm in protecting the encrypted image.

Data Hiding in AMBTC Images Using Selective XOR Hiding Scheme

Nowadays since the Internet is ubiquitous, the frequency of data transfer through the public network is increasing. Hiding secure data in these transmitted data has emerged broad security issue, such as authentication and copyright protection. On the other hand, considering the transmission efficiency issue, image transmission usually involves image compression in Internet-based applications. To address both issues, this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding (AMBTC). First, an image is divided into non-overlapping blocks through AMBTC compression, the blocks are classified four types, namely smooth, semi-smooth, semi-complex, and complex. The secret data are embedded into the smooth blocks by using a simple replacement strategy. The proposed method respectively embeds nine bits (and five bits) of secret data into the bitmap of the semi-smooth blocks (and semi-complex blocks) through the exclusive-or (XOR) operation. The secret data are embedded into the complex blocks by using a hidden function. After the embedding phase, the direct binary search (DBS) method is performed to improve the image quality without damaging the secret data. The experimental results demonstrate that the proposed method yields higher quality and hiding capacity than other reference methods.

Contourlet and Gould Transforms for Hybrid Image Watermarking in RGB Color Images

The major intention of this work is to introduce a novel hybrid image watermarking technique for RGB color images. This hybrid watermarking algorithm uses two transforms such as Contourlet and Gould transform. The Contourlet transform is used as first stage while the Gould transform is used as second stage. In the watermark embedding phase, the R, G and B channels are transformed using Contourlet transform. The bandpass directional sub band coefficients of Contourlet transformed image are then divided into sub-blocks. The sub-blocks are then transformed using Gould transform and the watermark information is embedded on the initial coefficients of each sub-blocks. After the embedding of watermark, inverse of Gould and Contourlet are taken to obtain the watermarked image. The reverse operation is performed to extract the watermark. Results obtained from attack-based experimentation for checking robustness implicate the superiority of this method in comparison with conventional methods. Better values are obtained for Embedding Rate, Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR).

Paediatric ultrasound-guided vascular access: Experiences and outcomes from an emergency department educational intervention.

This quality assurance project aims to describe the provision of an ultrasound-guided vascular access education package to paediatric emergency department staff. It subsequently aims to measure clinician and departmental responses to this educational intervention to support future effective education provision. Participants were opt-in emergency department staff. Staff were required to be approved to insert intravenous cannulae in the department. A minimum of 50% were non-rotational staff. The educational package consisted of a theory phase (pre-learning video, information document), a practical phase (intensive 90-120 minute individualised session using a mix of live subjects/training equipment), and an embedding phase (education group available for procedural supervision). Data collection was via de-identified, encoded self-reported survey data and logbooks. Twenty-three staff were enrolled for training. Sixteen (69.9%) were non-rotational. Prior to the education intervention, 18 trainees (78.3%) had placed no successful ultrasound-guided peripherally inserted venous cannulae. By 15 weeks following training, six participants (28.6%) had achieved a predetermined competency benchmark; 61.9% had placed at least one successful ultrasound-guided cannula. Difficult intravenous (IV) access predictors were present in 46.3% of patients throughout the data collection period, with infants overrepresented in this group (64.9% with difficult IV access predictors). IV access attempts by staff with prior ultrasound experience increased from 11.0 to 81.8% post-education intervention. A low-resource brief educational intervention around ultrasound-guided vascular access is achievable. Several barriers to education uptake were presented. Targeting the group of trainees with a high degree of motivation led to the highest yield of benchmark competency acquisition.

Optimal Deep Neural Network-Based Model for Answering Visual Medical Question

Over the last few years, the amount of available information has increased exponentially in all professional fields, including the medical field. Modern-day patients have access to a wealth of medical information, whether it be from brochures, newspapers, television campaigns, or internet documents. To facilitate and accelerate the search for medical information, more precise systems have been implemented, such as visual question-and-answer systems. A visual question-and-answer system is designed to provide direct and precise answers to questions asked in natural language. In this context, we propose an optimal deep neural network model based on an adaptive optimization algorithm, which takes medical images and natural language questions as input, then provides precise answers as output. Our model starts by classifying medical questions following an embedding phase. We then use a deep learning model for visual and textual feature extraction and emergence. In this paper, we aim to maximize the accuracy rate and minimize the number of epochs in order to accelerate the process. This is a multi-objective optimization problem. The selection of deep learning model parameters, such as epoch number and batch size, is an essential step in improving the model, thus, we use an adaptive genetic algorithm to determine the optimal deep learning parameters. Finally, we propose a dense layer for answer retrieval. To evaluate our model, we used the ImageCLEF 2019 VQA data set. Our model outperforms existing visual question-and-answer systems and offers a significantly higher retrieval accuracy rate.

Backward movement oriented shark smell optimization-based audio steganography using encryption and compression strategies

Audio steganography is the process of hiding secret information such as images, text, or video into the audio file. Data hiding in audio signals has been utilized in diverse applications like secret communication, hiding of data that may affect the safety and security of personnel and governments. Most of the recently introduced audio steganography approaches are content non-adaptive that may suffer low embedding capability and poor security. This paper proposes the audio steganography model for secure audio transmission during communication based on the meta-heuristic concept. In the embedding phase, the optimized instant selection is made for embedding the secret message (image) into the input audio. Further, the secret message to be embedded is encrypted using the optimized 2D-Logistic Chaotic Map. Here, the development of optimal instant selection and the optimized 2D-Logistic Chaotic Map are performed by the improved Shark Smell Optimization (SSO) called Backward Movement oriented SSO (BM-SSO). Once the secret message is encrypted, the compression of the message is done by Modified Huffman Encoding (MHE). Finally, the extraction phase uses the optimized 2D-Logistic Chaotic Map-based decryption. The proposed audio steganography by optimized instant selection for audio embedding minimizes the distortion error between the stego audio and original audio in terms of Mean Squared Error (MSE), and optimized 2D-Logistic Chaotic Map-based encryption aims to maximize the entropy of cipher image. The performance of the developed model has been estimated extensively against attacks, and simulation results are presented to prove the robustness of the proposed algorithm.

LRW-CRDB: Lossless Robust Watermarking Scheme for Categorical Relational Databases

In 2002, Agrawal and Kiernan defined six basic requirements, including preventing illegal watermark embedding and authentication, reversibility, robustness, and others, which must be satisfied when a reversible watermark is designed for relational databases. To meet these requirements, in this paper, a lossless watermarking scheme for a categorical relational database called LRW-CRDB (lossless robust watermarking for categorical relational databases) is proposed. In our LRW-CRDB scheme, the database owner needs to generate two secret embedding keys, K1 and K2, in advance. Then, two reference sets are generated based on two different secret embedding keys and a symmetry-based data hiding strategy, and then these are used for the watermark embedding phases. Experimental results confirmed that our LRW-CRDB scheme successfully detects 100% of hidden watermarks, even when more than 95% of the watermarked relational database has been deleted. In other words, the robustness of our proposed LRW-CRDB scheme outperforms other existing schemes under a variety of possible attacks, such as alteration, sorting, deletion, and mix-match attacks.

A Reframed Watermark Extraction Approach Using the ABC Algorithm

The fast development and the rapid spread of information technology have raised the issue of digital-image copyright protection, which was mostly addressed through robust watermarking. To ensure the survival of the watermark after attacks, most works have been making use of an additional step at the embedding phase, and by so making the scheme unsuitable for real-time implementation. However, to be effective and secure, it is recommended to combine the watermarking algorithm and the capture sensor in one device. In this paper, we brought the extra stage to the detection side. We firstly studied the effect of some signal processing operations on a watermarked image, then we proposed a general model for watermark extraction whose parameters were determined using the artificial bee colony. The efficacy of the proposed method was validated by comparing its robustness with two state-of-art schemes focusing on the watermark extraction.

ROI-based medical image watermarking for accurate tamper detection, localisation and recovery.

Smart healthcare systems play a vital role in the current era of Internet of Things (IoT) and Cyber-Physical Systems (CPS); i.e. Industry 4.0. Medical data security has become the integral part of smart hospital applications to ensure data privacy and patient data security. Usually, patient medical reports and diagnostic images are transferred to the specialist physician in other hospitals for effective diagnostics. Therefore, the transmission of medical data over the internet has attained significant interest among many researchers. The three main challenges associated with the e-healthcare systems are the following: (1) ensuring authentication of medical information; (2) transmission of medical image and patient health record (PHR) should not cause data mismatch/detachment; and (3) medical image should not be modified accidentally or intentionally as they are transmitted over the insecure medium. Thus, it is highly essential to ensure the integrity of the medical image, especially the region of interest (ROI) before taking any diagnostic decisions. Watermarking is a well-known technique used to overcome these challenges. The current research work has developed a watermarking algorithm to ensure integrity and authentication of the medical data and image. In this paper, a novel watermarking algorithm is designed based on Integer Wavelet Transform (IWT), combined chaotic map, recovery bit generation and SHA-256 to address the objective as mentioned earlier. The paper's significant contribution is divided into four phases, namely, watermark generation and data embedding phase, authentication phase, tamper detection phase and localisation and lossless recovery phase. Experiments are carried out to prove that the developed IWT-based data embedding scheme offers high robustness to the data embedded in region of non-interest (RONI), detects and localises the tampered blocks inside ROI with 100% accuracy and recovers the tampered segments of ROI with zero MSE. Further, a comparison is made with the state-of-art schemes to verify the sternness of the developed system.

Quadruple histogram shifting-based reversible information hiding approach for digital images

Nowadays, data hiding techniques are one of the most growing researching areas. This paper introduces a quadruple histogram shifting-based reversible data embedding approach for digital images. Pixel differences distribution is utilized for maximizing the hiding payload capacity while preserving minimum degradations for stego-images. The improved histogram-based reversible data embedding algorithm is designed with the potential of maximizing the hiding payload capacity while keeping a perfect stego-image quality with data lossless. The proposed algorithm utilizes a 3 × 3 -box predictive filter and can hide 2-bits per pixel to increase the payload capacity. Firstly, an embedding phase has been performed by applying a numerical ordering strategy of the pixels to be predicted by the 3-by-3 box filter. The proposed algorithm is performing this ordering strategy in four stages to avoid affecting the follow-up prediction of all other pixels. In the other side, an extracting phase has been performed by reversing the four stages to generate the embedded data. The comparison with state-of-the-art schemes is performed to validate and guarantee the prevalence of the improved histogram-based reversible data embedding approach. The obtained test results demonstrated and confirmed that the proposed improved histogram-based reversible data embedding approach not only can improve the payload capacity but also can save the stego-image quality.

Performance analysis of SMRT-based color image watermarking in different color spaces

ABSTRACT Watermarking is a generic strategy for overcoming numerous issues associated with multimedia security and digital rights management. The color image digital watermarking has not been given considerable attention, and there exists many applications that require them for copyright protection. An image-watermarking scheme based on Sequency based MRT (SMRT) for color image is proposed in this paper to achieve this goal. Choosing a color space for watermarking has always been a big question, as there are a number of color spaces. Primary objective is to find a better color space under the same method from among the frequently used color spaces. In the embedding phase, grayscale image watermark is embedded in the SMRT of R component of the cover image. Scaling factor is varied from 0.01 to 0.5 for evaluating performance of the methodology using PSNR, MSE, SSIM, IEM and NC. Performance of color image watermarking technique using SMRT in nine color spaces is analyzed. It is observed that embedding watermark in Cb channel of YCbCr color space is more imperceptible and robust compared to other color spaces considered. Simulation results show significant improvement in terms of imperceptibility. The robustness is high against GN attack as compared to other attacks.

Multiplex Network Embedding Model with High-Order Node Dependence

Multiplex networks have been widely used in information diffusion, social networks, transport, and biology multiomics. They contain multiple types of relations between nodes, in which each type of the relation is intuitively modeled as one layer. In the real world, the formation of a type of relations may only depend on some attribute elements of nodes. Most existing multiplex network embedding methods only focus on intralayer and interlayer structural information while neglecting this dependence between node attributes and the topology of each layer. Attributes that are irrelevant to the network structure could affect the embedding quality of multiplex networks. To address this problem, we propose a novel multiplex network embedding model with high-order node dependence, called HMNE. HMNE simultaneously considers three properties: (1) intralayer high-order proximity of nodes, (2) interlayer dependence in respect of nodes, and (3) the dependence between node attributes and the topology of each layer. In the intralayer embedding phase, we present a symmetric graph convolution-deconvolution model to embed high-order proximity information as the intralayer embedding of nodes in an unsupervised manner. In the interlayer embedding phase, we estimate the local structural complementarity of nodes as an embedding constraint of interlayer dependence. Through these two phases, we can achieve the disentangled representation of node attributes, which can be treated as fined-grained semantic dependence on the topology of each layer. In the restructure phase of node attributes, we perform a linear fusion of attribute disentangled representations for each node as a reconstruction of original attributes. Extensive experiments have been conducted on six real-world networks. The experimental results demonstrate that the proposed model outperforms the state-of-the-art methods in cross-domain link prediction and shared community detection tasks.

A novel audio watermarking scheme based on fuzzy inference system in DCT domain

Digital watermarking technology provides an effective potential solution to copyright protection and authentication of digital media. In this article, a new design of audio watermarking is introduced which is able to make compromise between transparency, robustness, and capacity by means of the synergy between fuzzy inference system, Singular Value Decomposition (SVD), and Fibonacci’s sequence in Discrete Cosine Transform (DCT). In the embedding phase, the proposed method, first of all, finds proper segments for inserting watermark data by helping fuzzy inference system with energy, zero-crossing rate (ZCR), and music edge features. Then, the watermark bits are embedded in the suitable segments based on collaboration of SVD technique and Fibonacci sequence in DCT domain. The proposed watermark extraction is performed in blind manner. The results on five Blues, Electronic, Classic, Jazz and Rock audio files show that the proposed method has high transparency (in average SNR = 49.80 dB) with payload of 598.34 bps. Moreover, robustness tests against Stirmark attacks show that the average of error rate is 1.3644, which means the proposed scheme has high stability in the digital signal processing attacks.

A New Method of Coding for Steganography Based on LSB Matching Revisited

LSB matching revisited is an LSB-based approach for image steganography. This method is a type of coding to increase the capacity of steganography. In this method, two bits of the secret message are hidden in two pixels with only one change. But this method provides no idea for hiding a message with a large number of bits. In other words, this method works only for n = 2 , where n is the number of bits in a block of the secret message. In this paper, we propose an improved version of the LSB matching revisited approach, which works for n > 2 . The proposed scheme contains two phases including embedding and extracting the message. In the embedding phase, we first convert the secret message into a bit-stream, and then the bit-stream is divided into a set of blocks including n bits in each block. Then we choose 2 n − 1 pixels for hiding such n bits of the secret message. In the next step, we choose the operations needed to generate such a message. Finally, we perform the obtained operations over the coefficients to hide the secret message. The proposed approach needs fewer changes than LSB MR when n > 2 . The capacity of the proposed approach is 2 n − 1 / 2 n − 1 − 1 × 100 % higher than the F5 method where this value for n > 2 is bigger than 75%. For example, the capacity of our scheme is 75% higher than the capacity of F5 for n = 3 . The proposed method can be used in the first step of every steganography method to reduce the change in the stego image. Therefore, this method is a new coding method for steganography. Our experimental results using steganalysis show that using our method provides around 10% higher detection error for SRNet over two steganography schemes.