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Varimax factor rotations, while popular among practitioners in psychology and statistics since being introduced by Kaiser (1958) , have historically been viewed with skepticism and suspicion by some theoreticians and mathematical statisticians. Now, work by Rohe & Zeng (2023) provides new, fundamental insight: varimax rotations provably perform statistical estimation in certain classes of latent variable models when paired with spectral-based matrix truncations for dimensionality reduction. We build on this new-found understanding of varimax rotations by developing further connections to network analysis and spectral methods rooted in entrywise matrix perturbation analysis. Concretely, this paper establishes the asymptotic multivariate normality of vectors in varimax-transformed Euclidean point clouds that represent low-dimensional node embeddings in certain latent space random graph models. We address related concepts including network sparsity, data denoising and the role of matrix rank in latent variable parameterizations. Collectively, these findings, at the confluence of classical and contemporary multivariate analysis, reinforce methodology and inference procedures grounded in matrix factorization-based techniques. Numerical examples illustrate our findings and supplement our discussion. [ABSTRACT FROM AUTHOR]

Information sharing mechanisms are becoming quicker and more efficient owing to improvements in computer and internet technologies. Data protection is an important consideration when exchanging and communicating data. Digital image encryption has rapidly evolved as an efficient scheme for ensuring data security. Data hiding technology can be used in multidisciplinary fields like healthcare, copyright protection, defence, etc. It is further important to note that the hidden data must be encrypted before transmission, and encrypted data must be secured over the transmission medium. In this paper, a detailed mechanism is proposed for data hiding, encryption, and security. First of all, a multilayer data hiding mechanism that considers interpolation of pixels and histogram shifting for data embedding is considered. Thereafter, for encryption of images, the Arnold transform is used, and finally, for security of encrypted data, the blockchain is used. The results of various state-of-the-art approaches are detailed, and it is shown that both data embedding and encryption algorithms perform better than recently published works. [ABSTRACT FROM AUTHOR]

Reversible data hiding has emerged as a crucial and dynamic area of research, driven by the proliferation of online data transfers involving images, videos, and audio files over the internet. As these digital assets traverse various platforms and networks, the need to embed additional data within them while maintaining their integrity becomes paramount. However, the process of embedding data into images is inherently challenging, as it often leads to distortions and quality degradation, impacting the overall user experience. To address this challenge and minimize the distortion caused by data embedding, guided filtering, a powerful image enhancement technique is harnessed to achieve this edge-preserving smoothing. By adopting this approach, the deleterious effects of data embedding on image quality can be successfully mitigated. In addition to the image enhancement aspect, ensuring the security of the embedded data is important. Blockchain technology has been integrated into the proposed system. This incorporation of blockchain adds an extra layer of protection to the encrypted image data, preventing unauthorized access, tampering, or data breaches. The efficacy of the proposed RDH system is rigorously evaluated using a suite of metrics. Peak Signal-to-Noise Ratio and Structural Similarity Image Metrics are employed to measure image quality. Furthermore, the system's performance is assessed in terms of its embedding capacity, which indicates the amount of data that can be seamlessly embedded per unit of image space. The embedding capacity of 1 bit per pixel is easily achieved with PSNR greater than 35 dB. For encryption evaluation, parameters like the Number of Changing Pixel Rate and Unified Averaged Altered Intensity are employed for the assessment the encryption strength and resilience against attacks. [ABSTRACT FROM AUTHOR]

In this paper, initially, message intensities are used to create two encoding tables: an index and a code sequence. The table is then updated with |$n$| secret data encoded into it. If the code sequence of the second encoding table matches the code sequence of the preceding or succeeding half of the two encoding tables, the second encoding table is folded. The folding is done when maximum intensity occurs in the utmost succeeding part of the two encoding tables. Finally, using the combination orientation approach, the encoded indices are embedded in the stego images. In the extraction phase, using the two stego images and the encoding with the combination orientation method, the encoded indices are obtained. The decoding is performed to extract the secret message as well as the cover image and there is no occurrence of overflow and underflow problems. It has a high visual quality and a high embedding capacity. The proposed technique achieved a maximum |$\mathrm{PSNR}$| of |$49.49$| dB and a |$648\ 242$| -bit embedding capacity. When compared to traditional techniques, the experimental results show that the proposed method outperforms them. [ABSTRACT FROM AUTHOR]

Data embedding into DNA is a conventional encryption technology since it gives the system security and consumes less energy. This work proposes a model that uses the silent mutation method to apply data embedding into DNA within an encryption model. To produce the key, hash functions are applied first to the original image and its metadata followed by the Arnold Transform technique. The key is then sent to LLCS, which uses it to produce a series of coding rules, and transmitted to HCS, which uses it to generate three chaotic sequences. Five chaotic maps are then used to diffuse the output image of the Arnold Transform. DNA operations are applied to the final image using the chaotic HCS sequences followed by the coding rules sequence. After the image has been decoded and silently mutated into a gene sequence, the altered gene is then obtained. Seven evaluation measures are used on ten grayscale images incorporated with two DNA sequences to assess the proposed model's performance. Results indicate a positive improvement in performance as a result of the model's increased security. [ABSTRACT FROM AUTHOR]

After the outbreak of COVID-19, the interaction of infectious disease systems and social systems has challenged traditional infectious disease modeling methods. Starting from the research purpose and data, researchers improved the structure and data of the compartment model or used agents and artificial intelligence based models to solve epidemiological problems. In terms of modeling methods, the researchers use compartment subdivision, dynamic parameters, agent-based model methods, and artificial intelligence related methods. In terms of factors studied, the researchers studied 6 categories: human mobility, nonpharmaceutical interventions (NPIs), ages, medical resources, human response, and vaccine. The researchers completed the study of factors through modeling methods to quantitatively analyze the impact of social systems and put forward their suggestions for the future transmission status of infectious diseases and prevention and control strategies. This review started with a research structure of research purpose, factor, data, model, and conclusion. Focusing on the post-COVID-19 infectious disease prediction simulation research, this study summarized various improvement methods and analyzes matching improvements for various specific research purposes.

Under the background of the development of network technology, this paper aims to promote rural English teaching and constructs an English teaching model that combines English recognition technology and rural teaching. The main process of speech recognition is examined by analyzing different speech recognition technologies. Using a deep learning network, an English speech recognition model has been established. Combined with the English acoustic features in the network data, fluency of English speech is evaluated. Data embedding is performed on the English sequences in the network, combined with the sequence probability in the English data, so as to determine whether the English speech is correct or not. The Eval value for the English recognition model based on deep learning is 5.49%, while the test value is 5.89%, as per the results. As the English dataset increases, so does the English recognition technique proposed in this paper, and the accuracy remains above 0.6, and when the dataset is 500, the speech recognition accuracy is 0.8. The teaching model that combines speech recognition techniques with English teaching improves students’ English to a certain extent.

Recently, advancements in multimedia have made a huge impact on watermarking technologies. The digital video watermarking is the process of embedding the data in the video. One of the major concerns in digital video watermarking is maintaining the quality of video besides preserving the privacy of the data. The aim of the research is to develop a reversible medical video watermarking using Sine Cosine Bird Swarm Algorithm-based Deep Convolutional Neural Network (SCBSA-based Deep CNN) for embedding the secret message in video frames. The development methodology is explained as follows. The SCBSA is developed by integrating Sine Cosine Algorithm (SCA) with the Bird Swarm Algorithm (BSA). The key frames are extracted from the input video using Minkowski distance and Wavelet distance. The features, like Neighborhood-based features, Convolutional Neural Network (CNN) features, Local Optimal Oriented Pattern (LOOP), and histogram features are obtained from the key frames. The interesting region is identified using DCNN, which is trained using the developed SCBSA. The secret message is embedded in the video in the embedding phase, whereas the embedded secret message is extracted in the extraction phase. The embedding and extracting process are carried out through two level decompositions using wavelet transform and inverse wavelet transform. The developed SCBSA-based Deep CNN uses the metrics, such as correlation coefficient, Mean Square Error and Peak signal-to-noise ratio (PSNR) for evaluating the performance. The developed SCBSA method is evaluated using the Mean Square Error, Correlation Coefficient and PSNR with Gaussian noise, Impulse noise, Salt and Pepper noise and in the absence of noise. The developed SCBSA method obtained a maximum correlation coefficient of 0.8990, minimum Mean Square Error of 0.0215 and maximum PSNR of 35.19, respectively while comparing with the existing reversible medical video watermarking methods. [ABSTRACT FROM AUTHOR]

The sentiment analysis relying on the aspect of online reviews is utilized for identifying the polarity of the given review. Nowadays, many methods are introduced for aspect-based sentiment analysis (ABSA) using neural networks, and many methods failed to consider contextual information exploitation to make the performance more accurate. Hence, this research proposed an optimized deep learning method for the detection of the aspect and to identify the polarity. Hence, in this research, an optimized deep learning technique for the ABSA is introduced by considering the online reviews, in which the deep learning classifiers are trained with the proposed Fabricius ringlet optimization (FRO) algorithm to reduce the loss that helps to enhance the accuracy of sentiment polarity prediction. The proposed FRO is developed by the hybridization of the behavioral nature of the Fabricius and the ringlet in feeding for the determination of the global best solution. The tuning of the weights and biases of the classifier enhance the performance of the classifier. The objective behind the tuning is to minimize the loss function while training and to enhance the accuracy of aspect extraction and polarity prediction of sentiment. Based on a study of the existing approach, the suggested FRO-based hybrid deep learning method is significantly improved; its accuracy, sensitivity, and specificity are 87.06%, 90.83%, and 79.37%, respectively, with a training percentage of 40%. The accuracy, sensitivity, and specificity of the existing technique have also been enhanced for aspect restaurant values, which are 87.53%, 96.06%, and 79.88% with a 60% training percentage. Similar to that, Twitter values for accuracy, sensitivity, and specificity are reported to be 89.08%, 99.35%, and 79.70%, respectively, with an 80% training percentage. The proposed method obtained the 90.13%, 99.35%, and 81.10% accuracy, sensitivity, and specificity from the assessment of the FRO-based hybrid deep learning. [ABSTRACT FROM AUTHOR]

In this paper, we propose to exploit the unused configurations of a printable encoding such as Base41, Base45 or Base85 to create a side channel that can store extra data such as error detection or correction codes, integrity verification and authentication information or application defined data. After introducing the encoding of binary octet strings in printable form, we present some case studies that show possible applications of the unused configurations. [ABSTRACT FROM AUTHOR]

The preprocessing of a digital image affects the statistical properties of image pixels and steganographic security. Modification of digital media is quite a trivial task nowadays, and it is required intentionally for various reasons. Therefore, it is significant to discuss the usage of the preprocessed image in steganography and its impact on the security of embedded secret information. In this work, we attempt to estimate the impact of image operations, namely scaling, cropping, and contrast enhancement over LSB steganography. We first attack the original image dataset to produce a cover image dataset, then we apply steganography methods and measure their detection accuracy concerning the original image dataset. Steganalysis is conducted by utilizing the higher-order Markov features and newly introduced Joint Entropy-based features with SVM and ensemble classifiers. Experimental results show that the detection rate of embedding degrades by 20% to 40% due to preprocessing of cover images. Results also indicate that the Markov-based SPAM and Joint Entropy-based features work well for steganalysis. [ABSTRACT FROM AUTHOR]

Traditional embedding methodologies, also known as dimensionality reduction techniques, assume the availability of exact pairwise distances between the high-dimensional objects that will be embedded in a lower dimensionality. In this article, we propose an embedding that overcomes this limitation and can operate on pairwise distances that are represented as a range of lower and upper bounds. Such bounds are typically estimated when objects are compressed in a lossy manner, so our approach is highly applicable in the case of big compressed datasets. Our methodology can preserve multiple aspects of the original data relationships: distances, correlations, and object scores/ranks, whereas existing techniques typically preserve only distances. Comparative experiments with prevalent embedding methodologies (ISOMAP, t-SNE, MDS, UMAP) illustrate that our approach can provide fidelitous preservation of multiple object relationships, even in the presence of inexact distance information. Our visualization method is also easily interpretable. [ABSTRACT FROM AUTHOR]

This paper proposed an innovative approach for reversible data hiding (RDH) within images in such a way that the image can be accurately recovered after extracting the data. The work presented a challenging task due to the need to compromise between the quality of the stego-image and the capacity of the hidden data. It is used in applications that need to reconstruct the original images without any distortion, such as medical diagnosis, remote sensing images and military maps. The aim of this paper is to study improving RDH methods using interpolation-based scheme. The proposed interpolation-based RDH (IRDH) scheme improves the embedding capacity and security over state-of-the-art schemes. We investigated the interpolation techniques improving the parabolic interpolation (PI) method to scale-up the original image, then embedding within it the secret data using newly proposed embedding technique. The experimental results demonstrate that the proposed quadratic Bezier interpolation (QBI) technique reduced the computational complexity of PI with same quality of the produced image. Additionally, the proposed cubic interpolation techniques improved the image quality of PI but with more computational complexity. On the other hand, the proposed circle interpolation (CIRI) achieved less PSNR value than PI showing interesting remarks. The general steps of this scheme are generation of the interpolated image, data embedding, data extraction and image recovery showing attractive promising research contribution.

The paper presents a novel data-embedding method based on the Periodic Haar Piecewise-Linear (PHL) transform. The theoretical background behind the PHL transform concept is introduced. The proposed watermarking method assumes embedding hidden information in the PHL transform domain using the luminance channel of the original image. The watermark is embedded by modifying the coefficients with relatively low values. The proposed method was verified based on the measurement of the visual quality of an image with a watermark with respect to the length of the embedded information. In addition, the bit error rate (BER) is also considered for different sizes of a watermark. Furthermore, a method for the detection of image manipulation is presented. The elaborated technique seems to be suitable for applications in digital signal and image processing where high imperceptibility and low BER are required, and information security is of high importance. In particular, this method can be applied in systems where the sensitive data is transmitted or stored and needs to be protected appropriately (e.g., in medical image processing). [ABSTRACT FROM AUTHOR]

A high capacity message embedding in encrypted HEVC video is proposed in this paper. The challenges addressed in this paper include keeping the encrypted video compliant with standardized decoders, correctly decrypting the video and finally, correctly extracting the message bits. The message embedding is achieved by altering the values of reference picture indices and motion vectors which results in scrambled video. Sixteen picture references are used in this work and therefore, combined with alteration of motion vectors, a maximum of six message bits can be embedded per coding unit. Motion vectors are altered by swapping their $x$ and $y$ components and/or changing their signs. This is achieved with full compliance with the HEVC video syntax. To extract message bits, an authorized decoder builds a classification model per video sequence and uses it for predicting the true values of the reference indices and motion vectors. As such, message bits are extracted and the video is correctly reconstructed to its unscrambled state. Coding units that result in misclassification are identified at the encoder and excluded from message embedding. This results in slightly lower embedding rates but ensures accurate video reconstruction. Using nine video sequences of various resolutions that are compressed using four different quantization parameters, the experimental results revealed that the true average message embedding rate is 2.7 bits per coding unit or 173 kbit/s. This is achieved with accurate video reconstruction at the expense of increasing the bitrate of the encoder by 3%. Comparison with existing work shows that the proposed solution is superior in terms of embedding capacity whilst reducing the excessive bitrate of the encoder.

Evaluating group separability is fundamental to pattern recognition. A plethora of dimension reduction (DR) algorithms has been developed to reveal the emergence of geometrical patterns in a low-dimensional space, where high-dimensional sample similarities are approximated by geometrical distances. However, statistical measures to evaluate the group separability attained by DR representations are missing. Traditional cluster validity indices (CVIs) might be applied in this context, but they present multiple limitations because they are not specifically tailored for DR. Here, we introduce a new rationale called projection separability (PS), which provides a methodology expressly designed to assess the group separability of data samples in a DR geometrical space. Using this rationale, we implemented a new class of indices named projection separability indices (PSIs) based on four statistical measures: Mann-Whitney U-test p-value, Area Under the ROC-Curve, Area Under the Precision-Recall Curve, and Matthews Correlation Coefficient. The PSIs were compared to six representative cluster validity indices and one geometrical separability index using seven nonlinear datasets and six different DR algorithms. The results provide evidence that the implemented statistical-based measures designed on the basis of the PS rationale are more accurate than the other indices and can be adopted not only for evaluating and comparing group separability of DR results but also for fine-tuning DR algorithms’ hyperparameters. Finally, we introduce a second methodological innovation termed trustworthiness, a statistical evaluation that accounts for separability uncertainty and associates to the measure of each index a p-value that expresses the significance level in comparison to a null model.

Introduction: Multi-omics data integration facilitates collecting richer understanding and perceptions than separate omics data. Various promising integrative approaches have been utilized to analyze multi-omics data for biomedical applications, including disease prediction and disease subtypes, biomarker prediction, and others. Methods: In this paper, we introduce a multi-omics data integration method that is constructed using the combination of gene similarity network (GSN) based on uniform manifold approximation and projection (UMAP) and convolutional neural networks (CNNs). The method utilizes UMAP to embed gene expression, DNA methylation, and copy number alteration (CNA) to a lower dimension creating two-dimensional RGB images. Gene expression is used as a reference to construct the GSN and then integrate other omics data with the gene expression for better prediction. We used CNNs to predict the Gleason score levels of prostate cancer patients and the tumor stage in breast cancer patients. Results: The model proposed near perfection with accuracy above 99% with all other performance measurements at the same level. The proposed model outperformed the state-of-art iSOM-GSN model that constructs the GSN map based on the self-organizing map. Conclusion: The results show that UMAP as an embedding technique can better integrate multi-omics maps into the prediction model than SOM. The proposed model can also be applied to build a multi-omics prediction model for other types of cancer. [ABSTRACT FROM AUTHOR]

In this paper, we propose to exploit the unused configurations of a printable encoding such as Base41, Base45 or Base85 to create a side channel that can store extra data such as error detection or correction codes, integrity verification and authentication information or application defined data. After introducing the encoding of binary octet strings in printable form, we present some case studies that show possible applications of the unused configurations.

Data embedding in videos has several important applications including Digital Rights Management, preserving confidentiality of content, authentication and tampering detection. This paper proposes a novel data embedding solution in scrambled videos by rotating motion vectors of predicted macroblocks. The rotation of motion vectors and the propagation of motion compensation error serve another purpose, which is video scrambling. A compliant decoder uses machine learning to counter-rotate the motion vectors and extract embedded message bits. To achieve this, the decoder uses a sequence-dependent approach to train a classifier to distinguish between macroblocks reconstructed using rotated and un-rotated motion vectors. In the testing phase, motion vectors belonging to a classified macroblock are compared against the reviewed rotated motion vectors and the message bits are extracted. Furthermore, to guarantee accurate classification at the decoder, a constrained encoding approach is proposed in which data embedding is restricted to motion vectors that can be correctly counter-rotated at the decoder. The proposed solution is referred to as Classifying Rotated Vectors or CRVs for short. Experimental results revealed that scrambled videos can be reconstructed correctly without quality loss with a bitrate increase at the encoder of around 6% and an average data embedding rate of 1.68 bits per MB. [ABSTRACT FROM AUTHOR]

It is well known that the world is becoming more interconnected with the help of World Wide Web in distributed environment. Persons and Organizations want to share their valuable information over World Wide Web through internet in reliable and secure manner. The Steganography is an elegant strategy to pass on the private data to an approved beneficiary with the most solid wellbeing measure which prompts maintaining a strategic distance from the breaks of information security. These days the meaning of taking solid assurance measures in information correspondence medium has a difficult undertaking on account of security issues created by unapproved intercession. This introduction expects to provide another methodology dependent on a combination of Cryptography and Steganography system to hide the secret information into a cover object (Image, Text, Video etc.) with an improved level of security. In proposed methodology, the Elliptic Curve Cryptography (ECC) is used to encode the secret data and LSB Inversion method for embedding the scrambled information into a cover object. Proposed methodology based on two different techniques has achieved some fundamental characteristics known as information classification, respectability check, limit and heartiness which are the proof of successful execution of this proposed methodology. This new methodology strongly tried through a few steganalysis assaults like investigation of Chi-square, Visual and Histogram. Results show that stego picture has strong resistance power against all assaults. [ABSTRACT FROM AUTHOR]

This paper proposed an innovative approach for reversible data hiding (RDH) within images in such a way that the image can be accurately recovered after extracting the data. The work presented a challenging task due to the need to compromise between the quality of the stego-image and the capacity of the hidden data. It is used in applications that need to reconstruct the original images without any distortion, such as medical diagnosis, remote sensing images and military maps. The aim of this paper is to study improving RDH methods using interpolation-based scheme. The proposed interpolation-based RDH (IRDH) scheme improves the embedding capacity and security over state-of-the-art schemes. We investigated the interpolation techniques improving the parabolic interpolation (PI) method to scale-up the original image, then embedding within it the secret data using newly proposed embedding technique. The experimental results demonstrate that the proposed quadratic Bezier interpolation (QBI) technique reduced the computational complexity of PI with same quality of the produced image. Additionally, the proposed cubic interpolation techniques improved the image quality of PI but with more computational complexity. On the other hand, the proposed circle interpolation (CIRI) achieved less PSNR value than PI showing interesting remarks. The general steps of this scheme are generation of the interpolated image, data embedding, data extraction and image recovery showing attractive promising research contribution. [ABSTRACT FROM AUTHOR]

Abstract Background Matrix factorization methods are linear models, with limited capability to model complex relations. In our work, we use tropical semiring to introduce non-linearity into matrix factorization models. We propose a method called Sparse Tropical Matrix Factorization (STMF) for the estimation of missing (unknown) values in sparse data. Results We evaluate the efficiency of the STMF method on both synthetic data and biological data in the form of gene expression measurements downloaded from The Cancer Genome Atlas (TCGA) database. Tests on unique synthetic data showed that STMF approximation achieves a higher correlation than non-negative matrix factorization (NMF), which is unable to recover patterns effectively. On real data, STMF outperforms NMF on six out of nine gene expression datasets. While NMF assumes normal distribution and tends toward the mean value, STMF can better fit to extreme values and distributions. Conclusion STMF is the first work that uses tropical semiring on sparse data. We show that in certain cases semirings are useful because they consider the structure, which is different and simpler to understand than it is with standard linear algebra.

With the proliferation of cloud computing and the Internet of Things(IoT), ensuring privacy and robust data protection has become increasingly critical. Reversible data hiding in encrypted images (RDHEI) methods emerge as a promising solution to address these challenges. In this paper, we propose an asymmetric CNN-based predictor (ACNNP) that significantly outperforms other predictors in terms of prediction accuracy. Then, an adaptive mean predictor is proposed to cooperate with ACNNP. According to these two predictors, a two-stage prediction and embedding model is designed to further enhance the embedding capacity and make data embedding more flexible. Experimental results demonstrate that the proposed method exhibits a high embedding capacity and reversibility when compared to state-of-the-art methods. On the BOWS-2, BOSSBase, and UCID datasets, it obtains average embedding rates of 3.302, 3.461, and 2.69 bpp, respectively. Therefore, this method provides an effective solution for applications such as data integrity verification, privacy protection, and copyright preservation. • Apply convolutional neural network to reversible data hiding in encrypted images • Design a novel asymmetric CNN-based predictor • Design an adaptive mean predictor • Propose a two-stage prediction and embedding model. [ABSTRACT FROM AUTHOR]

Content-adaptive steganography intends to hide data in the complex texture content of the image. Recently, some secure steganography methods have been proposed to identify the textural complexity of an image. However, most of the techniques do not take into account the information of pixel variation around the central pixel in all possible directions and therefore they are unable to accurately analyse the texture complexity. This work offers a quality-enhanced and secure method of content-adaptive image steganography. The proposed method is divided into three sequential steps: image segmentation, pixel complexity identification, and data embedding. An input cover image is initially divided into small local regions and the pixel-complexity is identified based on the proposed Complex Block Prior (CBP) criterion. In a local block, a high pass filter (HPF) bank is applied and eight residual responses are obtained. Following the CBP criterion, a complexity level out of nine levels is assigned to an individualized pixel block. The pixels are then arranged in the priority of complexity from highest to lowest. Data embedding for the corresponding complexity level then takes place using the proposed adaptive embedding algorithm. Experimental results verify the preservation of visual quality of stego images produced by the proposed method. Three image datasets: Standard test images, BOWS2 and BOSS-base are used for the experimentation and comparison with prior state-of-art methods. Highest values of the IQ (image quality) parameters e.g., SSIM and WPSNR show the effectiveness of the proposed method.

This paper presents an image Steganography algorithm that can work for cover images of multiple formats. Having a single algorithm for multiple image types provides several advantages. For example, we can apply uniform security policies across all image formats, we can adaptively select the most suitable cover image based on data length, network bandwidth and allowable distortions, etc. We present our algorithm based on the abstract concept of image components that can be adapted for JPEG, Bitmap, TIFF and PNG cover images. To the best of our knowledge, the proposed algorithm is the first Steganography algorithm that can work for multiple cover image formats. In addition, we have utilized concepts like capacity pre-estimation, adaptive partition schemes and data spreading to embed secret data with enhanced security. The proposed method is tested for robustness against Steganalysis with favorable results. Moreover, comparative results for the proposed algorithm are very promising for three different cover image formats.

The paper presents a novel data-embedding method based on the Periodic Haar Piecewise-Linear (PHL) transform. The theoretical background behind the PHL transform concept is introduced. The proposed watermarking method assumes embedding hidden information in the PHL transform domain using the luminance channel of the original image. The watermark is embedded by modifying the coefficients with relatively low values. The proposed method was verified based on the measurement of the visual quality of an image with a watermark with respect to the length of the embedded information. In addition, the bit error rate (BER) is also considered for different sizes of a watermark. Furthermore, a method for the detection of image manipulation is presented. The elaborated technique seems to be suitable for applications in digital signal and image processing where high imperceptibility and low BER are required, and information security is of high importance. In particular, this method can be applied in systems where the sensitive data is transmitted or stored and needs to be protected appropriately (e.g., in medical image processing).

This paper proposed three feature extraction solutions suitable for detecting data embedding in motion vectors (MVs) of coded HEVC videos. In the first feature extraction solution, videos under consideration are reencoded and features are extracted from both videos from Coding Units (CUs). The difference between the two feature sets form a feature vector at a CU level. The CU level feature vectors are then summarized by computing the average and standard deviation of individual features. This summarization is computed at a frame-level and at a video sequence level. Detection models are then used to detect MV data embedding. To generate the detection models, HEVC videos are reencoded whilst employing two different data embedding solutions. Feature variables are then computed and the detection models at frame and sequence levels are generated. The second solution is an extension of an existing work that uses the concept of MV consistency for computing feature variables. In this work, we extent the MV consistency concept to HEVC coded videos by grouping sub CUs based on their coding depths. One set of features is computed by finding the joint probability that a CU has a given coding depth and the bitrate of the MV differences of the sub CUs are smaller than or equal to the between-CU MV differences. Another set of features is computed by finding a similar probability but for sub CUs with MV differences greater than the between-CU MV differences. The third solution combines the features of all of the aforementioned solutions resulting in a set of 16 feature variables. The feature variables are visualized by projecting them using spectral regression where it is shown that the third solution results in separable features. In comparison to existing work, experimental results show excellent classification accuracies for HEVC videos coded at different spatio-temporal resolutions and different bitrates. [ABSTRACT FROM AUTHOR]

With the increasing number of video applications, it is essential to resolve issues such as ineffective search of video content, tampered/forged video content, packet loss, to name a few. Data embedding is typically utilized as one of the solutions to address the aforementioned issues. One of the important requirements of data embedding is to maximize embedding capacity with minimal bit rate overhead while ensuring imperceptibility of the inserted data. However, embedding capacity varies depending on the video content and increasing the embedding capacity usually leads to video quality degradation. In this work, a threshold-controlled block splitting technique is proposed for embedding data into SHVC video. Specifically, the embedding capacity can be increased by coding the host video by using more small blocks, which can be achieved by tuning a threshold-controlled parameter in the rate distortion optimization process. Subsequently, the predictive syntax elements in both intra and inter-coded blocks are jointly utilized to embed data, which ensures that data can be embedded regardless of the prediction mode used in coding a block. Results suggest that the proposed method can achieve a trade-off between the increase in embedding capacity and bit rate overhead while maintaining video quality. In the best case scenario, the sequence PartyScene can embed 516.9 kbps with an average bit rate overhead of + 7.0 % for the Low Delay P configuration, while the same video can embed 1578.6 kbps with an average bit rate overhead of + 2.9 % for the All Intra configuration. [ABSTRACT FROM AUTHOR]

Recently, learning-based methods have developed rapidly, with promising results in many areas. And deep learning models are expected to be the next-generation optimal image compression solutions. This paper proposes a new image compression and encryption framework that integrates encryption algorithms with a deep-learning compression network. Our work employs Auto-Encoder (AE) based compression network as the backbone. After extracting semantic features from the learning network, encryption layers are added to introduce end-to-end learning to image encryption. And for a higher level of visual security, the parameters of the synthesis network are replaced by a new parameter matrix based on a logistic map controlled by a secret key. The encryption key of the system is derived from the image content, which will be embedded in the deep feature vectors to save the cost of sending the key for different images. Another secret key, which will be shared among other images, is used for controlling the embedding process. And to learn the entropy model from the scrambled feature maps, an attention scheme is exploited in estimating parameters to achieve more effective compression. Experimental results and the security analysis using Kodak dataset show that the proposed scheme can achieve a sufficiently high-security level with high compression efficiency. [ABSTRACT FROM AUTHOR]

With the rapid advancement in digital technologies, video rises to become one of the most effective communication tools that continues to gain popularity and importance. As a result, various proposals are put forward to manage videos, and one of them is data embedding. Essentially, data embedding inserts data into the video to serve a specific purpose, including proof of ownership via watermark, covert communication in steganography, and authentication via fragile watermark. However, most conventional methods embed data by using only one type of syntax element defined in the video coding standard, which may suffer from large bit rate overhead, quality degradation, or low payload. Therefore, this work aims to explore the combined use of multiple prediction syntax elements in SHVC for the purpose of data embedding. Specifically, the intra prediction mode, motion vector predictor, motion vector difference, merge mode and coding block structure are collectively manipulated to embed data. The experimental results demonstrate that, in comparison to the conventional single-venue data embedding methods, the combined use of prediction syntax elements can achieve higher payload while preserving the perceptual quality with minimal bit rate variation. In the best case scenario, a total of 556.1 kbps is embedded into the video sequence PartyScene with a drop of 0.15 dB in PSNR while experiencing a bit rate overhead of 7.4% when all prediction syntax elements are utilized altogether. A recommendation is then put forward to choose specific types of syntax element for data embedding based on the characteristics of the video. [ABSTRACT FROM AUTHOR]

This paper proposes a dual encoding approach with sequence folding for reversible data hiding in dual stego images. This method initially encodes the secret data by creating two encoding tables that contain index as well as code sequence based on message intensities and the two encoding tables are updated. The code sequence in the second encoding table is folded if the preceding half or succeeding half of the two encoding tables gets matched. The folding is also done if the maximum intensity in the code of two encoding tables lies in the most succeeding end. The same encoding process is repeated for all message intensities. After encoding the message intensities, the encoded indices are embedded on the cover image to obtain the dual stego images. In the data extraction phase, the encoded indices are initially extracted and is decoded by using the dual decoding table. The behavior of the proposed data hiding is evaluated using the metrics such as embedding capacity, embedding rate, PSNR, and SSIM using the standard test images. Experimental verification reveals that the proposed data hiding algorithm shows outstanding performance when compared to the traditional dual stego based data hiding methods. [ABSTRACT FROM AUTHOR]