Difference Histogram Research Articles

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.

Statistical features of the response of a superconducting hot-electron bolometer to extremely weak terahertz pulses of picosecond and nanosecond duration

We study the statistical distributions of the output signals of a superconducting hot-electron bolometer (HEB) detector exposed to weak photon pulses of 1 THz (terahertz) frequency. Pulses with variable photon numbers were generated through strongly non-degenerate parametric downconversion (PDC) in a LiNbO3 crystal at 4.8 K. Fundamental differences in histograms are found between two PDC pumping modes, with pulse widths of 28 ps and 10 ns. HEB response to a picosecond THz pulse was detected in the form of a single elementary electrical pulse (SEP), with the average amplitude proportional to radiation intensity and dispersion relative to the bolometer's dark current. HEB responses to extremely weak radiation intensities have been recorded using nanosecond pulsed illumination. We found that nanosecond histograms are asymmetrical and broaden as radiation intensity increases, indicating that the nanosecond response consists of several SEPs. Poisson–Gauss approximation of histograms indicates that not only the average number of SEPs increase but also the average amplitude of a SEP increases with increasing power of incident THz radiation.

Detection of Abnormal Blood Flow Region Based on Near Infrared Correlation Spectroscopy

Blood flow measurement of microvessels in human tissues is of vital importance for the diagnosis and treatment of many diseases. In this paper, the detection method of abnormal blood flow regions based on near-infrared correlation spectroscopy is studied. We used the NL-Bregman-TV imaging algorithm to realize Blood flow imaging. However, due to the limitation of the number and distribution of detectors, the pixels obtained from images are extremely low, which cannot meet the practical requirements of the visual and the abnormal blood flow range measurement. In this paper, the bicubic interpolation method is used to improve the resolution of low-pixel blood flow images. The parameter index of the normalized similarity was proposed to help judge the effect of the interpolation method on the resolution of this kind of image. Aiming at the extraction of abnormal regions, a threshold segmentation algorithm based on the histogram difference method and a morphological processing algorithm is proposed to extract the contour of abnormal blood flow. The method proposed in this paper can be used to accurately locate and extract the clear and smooth contour of abnormal blood flow.

Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.

Canopy imaging offers a non-destructive, efficient way to objectively measure canopy size, detect stress symptoms, and assess pigment concentrations. While it is faster and easier than traditional destructive methods, manual image analysis, including segmentation and evaluation, can be time-consuming. To make imaging more widely accessible, it's essential to reduce the cost of imaging systems and automate the analysis process. We developed a low-cost imaging system with automated analysis using an embedded microcomputer equipped with a monochrome camera and a filter for a total hardware cost of ~USD 500. Our imaging system takes images under blue, green, red, and infrared light, as well as chlorophyll fluorescence. The system uses a Python-based program to collect and analyze images automatically. The multi-spectral imaging system separates plants from the background using a chlorophyll fluorescence image, which is also used to quantify canopy size. The system then generates normalized difference vegetation index (NDVI, "greenness") images and histograms, providing quantitative, spatially resolved information. We verified that these indices correlate with leaf chlorophyll content and can easily add other indices by installing light sources with the desired spectrums. The low cost of the system can make this imaging technology widely available.

Evaluation of clear-sky surface downwelling shortwave fluxes computed by three atmospheric radiative transfer models

In this study the clear-sky total, direct, and diffuse shortwave (SW) fluxes at the surface, have been calculated by three radiation transfer models (RTMs) – MODTRAN6.0 (M6.0), Canadian Centre for Climate Modelling and Analysis (CCCma), and Langley-modified Fu-Liou (NASA CERES). These calculations have been evaluated by surface measurements collected from seven sites that represent different climatological regimes with various surface scene types including ocean, grassland/continental, desert, and snow/sea ice. For pristine atmospheric conditions, SW fluxes predicted by CCCma and M6.0 shows little variation, which lays a baseline for further analysis. Note that computing time required by CCCma is ∼1000 times smaller than M6.0. Based on all samples collected from seven sites, mean differences of total, direct, and diffuse fluxes between surface measurements and CCCma / M6.0 / Fu-Liou are [5.3 / 2.4 / 0.9], [-2.2 / -5.1 / -13.7], and [7.5 / 7.5 / 14.6] W m-2, respectively. Histograms of differences between the three RTM calculations and surface measurements show that CCCma computed direct and diffuse fluxes have the smallest biases with standard deviations similar to those for M6.0, while Fu-Liou values have the largest biases and standard deviations. While Fu-Liou outperforms for total flux, especially for desert conditions, it is hampered by large biases for direct and diffuse across all scene types. The three RTMs are consistent with showing the least error for total flux and the largest in diffuse based on bias, correlation, and root mean square error.

Robust Clustering-Based Automated Video Shot Boundary Detection Using Handcrafted and Deep Feature Fusion

Video shot boundary detection (VSBD) plays a key role in analyzing, summarizing, indexing and retrieving content-based data from videos. Many artificial intelligence (AI)-based techniques have recently been introduced to detect the gradual transition from video frames. However, those techniques fail to detect the presence of inter-classes like fade-in, fade-out and dissolve from gradual transition video frames. In addition, the existing techniques face high computational complexity during detecting transitions from the video frames. This research brings novel clustering-based techniques to classify the inter-classes like fade-in, fade-out and dissolve from gradual transition video frames. At the initial stage, color histogram differences (CHD) technique is introduced to detect the abrupt transition from the video frames. The identified abrupt transitions are completely removed from the video frames. Then, segmentation is done to segment the gradual transitions from the video frames. The segmented gradual transition frames are then given to extract the handcrafted and deep features from the video frames. The deep features are extracted from the segments using the Morlet Wavelet-assisted modified stacked autoencoder (MW-MSAE) technique. The extracted handcrafted features and deep features are then concatenated together, and finally, fused feature vectors are obtained. The fused features are then fed to the robust deep [Formula: see text]-means map clustering method (RDKMM) to aggregate based on similar features. For calculating the similar features, a similarity-based correlation calculation (SBCC) is done in adjacent frames to determine gradual shot transitions like fade-in, fade-out and dissolve. The dataset used in this research is the TREC Video Retrieval Evaluation (TRECVID) 2021 dataset. In the experimental scenario, an accuracy of 95.5%, a sensitivity of 95.3%, a specificity of 96.9%, a precision of 93.9%, an F-measure of 94.6% and Mathew’s correlation coefficient (MCC) of 92.4% are obtained.

Insulator Extraction from UAV LiDAR Point Cloud Based on Multi-Type and Multi-Scale Feature Histogram

Insulators are key components to ensure the normal operation of power facilities in transmission corridors. Existing insulator identification methods mainly use image data and lack the acquisition of three-dimensional information. This paper proposes an efficient insulator extraction method based on UAV (unmanned aerial vehicle) LiDAR (light detection and ranging) point cloud, using five histogram features: horizontal density (HD), horizontal void (HV), horizontal width (HW), vertical width (VW) and vertical void (VV). Firstly, a voxel-based method is employed to roughly extract power lines and pylons from the original point cloud. Secondly, the VV histogram is used to categorize the pylons into suspension and tension types, and the HD histogram is used to locate the tower crossarm and further refine the roughly extracted powerlines. Then, for the suspension tower, insulators are segmented based on the HV histogram and HD difference histogram. For the tension tower, the HW histogram is used to recognize the jumper conductor (JC) and transmission conductor (TC) from the power line. The HW histogram and VW histogram are used to extract the tension insulator in the TC and suspension insulator in the JC, respectively. Finally, considering the problem of setting a suitable grid width when constructing the feature histogram, an adaptive method of multi-scale histograms is proposed to refine the extraction result. Two 220 kV long transmission lines are used for the validation, and the overall object-based accuracy for suspension and tension towers are 100% and 97.3%, respectively. Compared with the point feature-based method, the mean F1 score of the proposed method improved by 0.3, and the runtime for each tower is within 2 s.

Satellite-based atmospheric characterization for sites of interest in millimeter and sub-millimeter astronomy

Context. Water vapor is the main source of atmospheric opacity for millimeter and sub-millimeter astronomy. Hence, several studies seek to effectively characterize it for site-testing purposes. In this task, reanalysis databases are quickly becoming a popular alternative to on-site measurements due to easy accessibility and the versatility of the data they provide. Aims. In the framework of validating the use of reanalysis data as a site-testing oriented tool, we perform a statistical comparison of atmospheric water vapor values obtainable from the MERRA-2 database with ground-based microwave radiometer measurements taken at two astronomical sites in Chile: Llano de Chajnantor, Atacama, and Cerro Paranal, Antofagasta. Methods. The MERRA-2 data were interpolated both vertically (across pressure levels) and geographically (latitude-longitude). For each site, different plots were generated: a direct temporal variation plot (to visually compare the data variation over time between both sources); a PWV versus PWV plot, fitting a linear fit through robust linear regression and calculating both the Pearson (r) and Spearman (ρ) correlation coefficients in order to look for correlations between both data sources; a histogram showing the distribution of the differences between the MERRA-2 data and the water vapor measurements (defined as APWV = PWVMERRA-2 − PWVsite), along with its standard deviation (σ), mean (µ), and median values, with the aim of better appreciating the similarities of the data sources over time; and a CDF plot to compare both data distributions disregarding time stamps. Finally, millimeter and sub-millimeter transmittance curves were created through the am atmospheric modeling software, which uses ozone and temperature data along with the verified water vapor data for the two studied sites as well as three other sites of interest for the next-generation Event Horizon Telescope: Las Campanas Observatory near La Serena, Chile; Valle Nevado, located near Santiago, Chile; and the General Bernardo O’Higgins base, located in Antarctica. Results. The interpolated MERRA-2 PWV values are highly correlated with the ground-based PWV values, with a Pearson coefficient greater than 0.9 and a Spearman coefficient higher than 0.85. However, their dependence is not linear, as PWVAPEX = m * PWV, with m being higher than 0.9 in both cases. The difference histograms show an almost zero-centered distribution for Llano de Chajnantor, with a µ value of −0.021 and a median value of −0.007. On the other hand, in Cerro Paranal, the difference histogram is slightly offset toward positive values, with µ value of 0.171 and a median value of 0.256. This offset is most likely due to the strong winds present in the site’s location, close to the Pacific Ocean. The transmittance curves show different performances depending on the site studied, with Cerro Chajnantor being the highest overall transmittance and Antarctica the lowest. Additionally, the transmittance profiles estimated for Cerro Chajnantor and Cerro Paranal were scaled using the PWV measurements, providing differences of less than 12% to the model data profiles. Results obtained at the Valle Nevado site suggest promising atmospheric conditions for stronomic observations in the millimeter and sub-millimeter range. Conclusions. The results we obtained show that the atmospheric water vapor estimation using MERRA-2 data can be used for site testing of new sites by evaluating the millimeter–sub-millimeter transmittance profile through vertical pressure correction and averaging the closest grid points to the site. This new method opens the door for future site-testing studies using MERRA-2 and potentially other reanalysis databases (e.g., ERA5) as reliable sources of information.

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

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

Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy

AimsAccurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) algorithm for fast and accurate dose prediction of cervical cancer VMAT in multicenter datasets and then explore the feasibility of the DL algorithm to endometrial cancer VMAT with different prescriptions. Materials and methodsWe proposed the AtTranNet algorithm for three-dimensional dose prediction. A total of 367 cervical patients were enrolled in this study. Three hundred twenty-two cervical patients from 3 centers were randomly divided into 70%, 10%, and 20% as training, validation, and testing sets, respectively. Forty-five cervical patients from another center were selected for external testing. Moreover, 70 patients of endometrial cancer with different prescriptions were further selected to test the model. Prediction precision was evaluated by dosimetric difference, dose map, and dose-volume histogram metrics. ResultsThe prediction results were all clinically acceptable. The mean absolute error within the body in internal testing was 0.66 ± 0.63%. The maximum |δD| for planning target volume was observed in D98, which is 1.24 ± 2.73 Gy. The maximum |δD| for organs at risk was observed in Dmean of bladder, which is 4.79 ± 3.14 Gy. The maximum |δV| were observed in V40 of pelvic bones, which is 4.77 ± 4.48%. ConclusionAtTranNet showed the feasibility and reasonable accuracy in the dose prediction for cervical cancer in multiple centers. The model can also be generalized for endometrial cancer with different prescriptions without any transfer learning.

Blind Steganalysis Method Using Image Spectral Density and Differential Histogram Correlative Power Spectral Density

Recent research has demonstrated the success of employing neural networks for the purpose of detecting image tampering. Nevertheless, the utilization of reference-free steganalysis has become increasingly popular as a result of the challenges associated with obtaining an annotated dataset. This dataset is crucial for the classification process using neural networks, which aims to detect and identify instances of tampering. This paper introduces a robust approach to blind steganalysis, utilizing image spectral density and differential histogram correlative power spectral density. The proposed method employed two distinct forms of image data, namely a gray-scale image and true-color image data. The results indicate that the proposed methodology successfully achieved the anticipated outcomes in identifying manipulated images as evidenced by its successful application on the two distinct datasets. In the experiment results, the proposed technique succeeded quite well in terms of accuracy at low embedding ratios. Also, it successfully recognized sequential and random least significant bit steganography.

Optimized steganography techniques based on PVDS and genetic algorithm

Due to the rapid development of confidential digital communication over international networks, security is the most significant factor. Steganography is the science of concealed communication and is used to conceal the presence of secret information among the various methods of hiding data in communications. Steganography techniques are usually implemented either in spatial or frequency domains. Some spatial domain methods fundamentally utilize the absolute difference value between neighbor pixels to encode the secret message, categorized as Pixel-Value Differencing Steganography methods (PVDS). In PVDS, the hiding capacity of the adjacent pixels depends on their difference value. Consequently, the secret data will be embedded with a high visual quality of the stego-image. Indicator-based PVDS (IPVDS) technique is derived from the PVDS method. It depends on an Indicator pixel (IP) to extract the secret data correctly. The Genetic Algorithm (GA) approach to optimization depends on the concept of survival of the fittest. This paper introduces two proposals, GA-based steganography schemes (GA-IPVD) and (GA-IPVDM). These new techniques encoded secret data using the IPVDS method. Before embedding data in the cover image, the order of the pixels corresponding to the secret data is modified and rearranged. GA controls the operation of rearranging and modifying the order of pixels and all parameters. The current paper addresses steganography as an optimization problem and seeks to determine the optimal order of the pixels that enhance the matching between the cover image and stego-image to reduce distortion despite high embedded capacity. In the first proposed technique GA-IPVD, GA concentrates on the imperceptibility of embedded data and maximizes the quality of the stego-image according to the given secret data. In contrast, the GA maximizes the capacity of embedded data while maintaining an acceptable amount of stego-image quality in the second proposed technique GA-IPVDM. The proposed systems have embedding key that increase the security of proposed schemes. The experimental results demonstrate that the proposed methods can embed a large quantity of secret data while maintaining an acceptable visual quality of the stego-images. The proposed technique has also demonstrated excellent resistance to a variety of stego-attacks, including the pixel difference histogram (PDH) as well as regular and singular (RS) analyses. Additionally, the out-of-boundary pixel issue (BI), which endures in the majority of modern data hiding algorithms, has been successfully resolved.

Diagnostic Value of Apparent Diffusion Coefficient Statistics in Differentiating the Glioblastoma Tumors with an Oligodendroglioma and Glioblastomas Without Such Moiety

Background: Differentiating types of brain tumors using magnetic resonance imaging (MRI) as a non-invasive element is essential. Objectives: The study aimed at evaluating the difference in the first-order histogram (FOH) features obtained from apparent diffusion coefficients (ADCs) between glioblastoma (GBM) tumors with or without oligodendroglial (ODG) components. Methods: MRIs were taken before surgical resection of the brain tumors. The brain lesions were analyzed and ADC maps were plotted to calculate FOH. Brain lesions were surgically removed, diagnosed using pathological methods, and categorized as GBM with an ODG (GBM-ODG) or without ODG. A comparison of ADC values was performed between GBM-ODG and GBM. Receiver-operator characteristic (ROC) curves were used to determine the cut-off sensitivity and specificity values. Results: In the GBM group, 26 patients were included; 20 cases were male and 6 were female; the mean ± SD of age was 54.1 ± 1.2 and 55.2 ± 1.6 years, respectively. In the GBM-ODG group, 31 patients were included; 22 cases were male and 9 cases were female; the mean ± SD of 52.1 ± 0.8 and 55.3 ± 1.5 years, respectively. The results of peritumoral edema had several significant FOHs. In the MRIs of tumor and T1W groups, the third moment and minimum-ADC had significant results, respectively. Cut-off values of the FOHs were statistically significant outcomes in the peritumoral edema region when compared between GBM-ODG and GBM categories: Median > 1.3516, normal mean > 0.6671, third moment ≤ -0.0001, 25 percentile > 0.5929, 75 percentile > 0.7336, and 95 percentile > 0.8542. The highest sensitivity and specificity values for these FOHs were higher than 80% and sometimes near 99.9%. Conclusions: The results showed that ADC values from peritumoral edema regions differ between these two types of tumors. Then, an MRI from the edematous areas is suggested when evaluating the patient's status and deciding to make interventions.

An Image Steganographic Scheme Based on Edge Detection and Least Significant Bit Substitution

<p>This work presents a steganographic scheme based on Laplacian-of-Gaussian (LoG) edge detection and least significant bit (LSB) substitution. The cover image is first divided into continuous and non-overlapping 4×4-pixel blocks. The pixel at the top left corner of each block (first pixel) is defined as the reference pixel. After the LoG edge detection, the blocks are classified as edge or non-edge blocks, and this information is embedded in the reference pixels. Each non-reference pixel is then embedded with 5 bits and 4 bits of cipher text if it belongs to an edge block or non-edge block, respectively. Compared to the method of Tseng and Leng, Bai et al., and Ghosal et al., proposed method increased the capacity by 39.6%, 7.3%, and 42.7%, respectively, in the “Lena” cover image. To test the generalizability of our method, an embedding capacity and image quality test were conducted using 10,000 512 × 512 sized greyscale images from the BOSSBase dataset. Compared to the aforementioned previous methods, our method improved the capacity by 33.9%, 2.7%, and 36.1%, respectively, while maintaining an acceptable stego-image quality. Finally, proposed method can resist the detection of RS, pixel difference histogram analysis and second order SPAM features.</p> <p> </p>

Comparison of the AIRS, IASI, and CrIS Infrared Sounders Using Simultaneous Nadir Overpasses: Novel Methods Applied to Data From 1 October 2019 to 1 October 2020

AbstractHigh spectral resolution infrared sounders are an integral component of the global observing system and are used in a broad range of applications. This is enabled by their high accuracies which are ensured by rigorous calibration/validation activities. One of these activities is the post‐launch intercomparison with other high spectral resolution infrared sounders using simultaneous nadir overpasses (SNOs). This paper introduces a novel application of the previously developed SNO methodology by including time difference histogram symmetrization and a spatial sampling uncertainty. Where possible, radiometric measurement uncertainties are included and propagated through the statistics. Comparisons of Atmospheric Infrared Sounder (AIRS), METOP‐A/B/C Infrared Atmospheric Sounding Interferometer (IASI), and Cross‐track Infrared Sounder (CrIS) from 1 October 2019, to 1 October 2020, are analyzed. Results show AIRS and IASI differences as well as CrIS and AIRS differences are generally less than 0.4 K across the spectrum, and CrIS and IASI differences are generally less than 0.25 K. Comparison of the Suomi National Polar‐Orbiting Partnership and NOAA‐20 CrIS instruments via IASI and AIRS shows differences are generally less than 0.1 K across all bands and that the two CrIS instruments statistically agree within their radiometric uncertainties except for the narrow 2,370 cm−1 region where artifacts due to brightness temperature conversion are prone to occur.

High-dimensional CFD optimization of a low-flow coefficient S–CO2 centrifugal compressor for enhanced oil recovery systems

The design of low-flow coefficient (∼0.01) centrifugal compressors with supercritical CO2 as working fluid is still a challenge for engineers due to its increased friction losses at the impeller. However, the reinjection pressure required for Enhanced Oil Recovery (EOR) systems is achieved by compression trains with stages of high-Pressure Ratios (PR > 3) which can only be obtained by lowering the flow coefficient of the equipment. The carbon dioxide mitigation, due to the reinjection process, also increases oil productivity and extraction lifetime. A four-staged compression system was considered and the preliminary geometry of its last stage was considered herein after a 1D optimization that decreased the total required power of the system. In order to further increase the systems' performance, a CFD model was developed and submitted to Sensitivity Analysis (SA) and parametric optimization procedure, considering polar angles, meridional profile and vaneless diffuser passage (25 variables). The assessment of the sequential SA using, Morris' screening method Design of Experiment (DoE) and SS-ANOVA for variable ranking and response surface training, has exposed the method's limitation in recognizing interaction between variables since low-quality Response Surfaces (RS) were trained. However, the Incremental Space Filler (ISF) sampling has complemented the sample space screening, guarantying adequate RS at a low computational cost. This indirect optimization strategy that increased the equipment's polytropic efficiency by 1.19%, diminishing total entropy generation by 8.5% can deliver important cost reductions to the operation of EOR compression systems. The ‘entropy-guided’ phenomenology analysis strategy combined with SA results, identified that the narrowing of the vaneless diffuser has extinguished the recirculation present in the original geometry's impeller/diffuser interface region, which was the largest difference in the entropy histogram. Moreover, the enlargement of the impeller's meridional profile has smoothed the fluid flow change of direction (from axial to radial) and displaced the swirl structures that restricted the fluid flow in the main passage.

Capacity-Raising Reversible Data Hiding Using Empirical Plus–Minus One in Dual Images

Electronic records of a patient’s health history are often shared among healthcare providers, and patient data must be kept secure to maintain the privacy of patients. One way of doing this is through data hiding, and this paper demonstrates a scheme to achieve this. This paper proposes a capacity-raising reversible data-hiding scheme using an empirical rules table in dual images. The aim of this research is to avoid drawing awareness to the transmission of information by providing a steganographic technique capable of embedding high-capacity data into an image while maintaining the good quality of the image. To hide the secret message(s), a rules table containing 13 entries is presented. This rules table is extendable to a table of up to 262,133 entries (with each entry containing one distinct character) that are related to the 13 entries in terms of the rules. The rules of this table are used during the embedding and extraction procedures. In the proposed method, 512 × 512 images are divided into 1 × 2 blocks where adjacent pixels are represented using x and y for both embedding and extraction, respectively. Recovery of the cover image from the stego image is also achievable during the extraction process. Conducted experiments show that the proposed method has an average pixel-to-signal noise ratio of 52.65 dB, which is higher than that achieved with the methods discussed in this paper. Additionally, the proposed method can embed a wider range of characters (depending on the image size) as compared to the rest of the methods, hence its high embedding capacity of 4.25 bpp. The proposed method can also withstand security attacks such as RS, pixel value difference, entropy, and chi-square attacks. The proposed method is also undetectable under visual attack analyses such as the difference histogram, pixel difference histogram, and visual inspection. Based on the higher embedding capacity, pixel-to-signal noise ratio, the ability of this method to be undetected under visual attack analysis, and the ability of this method to withstand security attacks, it can be concluded that the proposed method is superior to the other methods.

A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method

In digital image processing and steganography, images are often described using edges and local binary pattern (LBP) codes. By combining these two properties, a novel hybrid image steganography method of secret embedding is proposed in this paper. This method only employs edge pixels that influence how well the novel approach embeds data. To increase the quantity of computed edge pixels, several edge detectors are applied and hybridized using a logical OR operation. A morphological dilation procedure in the hybridized edge image is employed to this purpose. The least significant bits (LSB) and all LBP codes are calculated for edge pixels. Afterward, these LBP codes, LSBs, and secret bits using an exclusive-OR operation are merged. These resulting implanted bits are delivered to edge pixels’ LSBs. The experimental results show that the suggested approach outperforms current strategies in terms of measuring perceptual transparency, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI). The embedding capacity per tempered pixel in the proposed approach is also substantial. Its embedding guidelines protect the privacy of implanted data. The entropy, correlation coefficient, cosine similarity, and pixel difference histogram data show that our proposed method is more resistant to various types of cyber-attacks.

Sign2Pose: A Pose-Based Approach for Gloss Prediction Using a Transformer Model.

Word-level sign language recognition (WSLR) is the backbone for continuous sign language recognition (CSLR) that infers glosses from sign videos. Finding the relevant gloss from the sign sequence and detecting explicit boundaries of the glosses from sign videos is a persistent challenge. In this paper, we propose a systematic approach for gloss prediction in WLSR using the Sign2Pose Gloss prediction transformer model. The primary goal of this work is to enhance WLSR's gloss prediction accuracy with reduced time and computational overhead. The proposed approach uses hand-crafted features rather than automated feature extraction, which is computationally expensive and less accurate. A modified key frame extraction technique is proposed that uses histogram difference and Euclidean distance metrics to select and drop redundant frames. To enhance the model's generalization ability, pose vector augmentation using perspective transformation along with joint angle rotation is performed. Further, for normalization, we employed YOLOv3 (You Only Look Once) to detect the signing space and track the hand gestures of the signers in the frames. The proposed model experiments on WLASL datasets achieved the top 1% recognition accuracy of 80.9% in WLASL100 and 64.21% in WLASL300. The performance of the proposed model surpasses state-of-the-art approaches. The integration of key frame extraction, augmentation, and pose estimation improved the performance of the proposed gloss prediction model by increasing the model's precision in locating minor variations in their body posture. We observed that introducing YOLOv3 improved gloss prediction accuracy and helped prevent model overfitting. Overall, the proposed model showed 17% improved performance in the WLASL 100 dataset.

Ink Tone Analysis of Printed Character Images towards Identification of Medieval Korean Printing Technique: The Song of Enlightenment (1239), the Jikji (1377), and the Gutenberg Bible (~1455)

To identify printing techniques for medieval Korean books, ink tone analysis of printed characters is proposed. Ink tones of printed character images in two ancient books, The Song of Enlightenment with Commentaries by Buddhist Monk Nammyeong Cheon (南明泉和尙頌證道歌), designated as Korean treasures in 1984 and 2012, were compared and analyzed. Both books have been misidentified and disclosed by the Cultural Heritage Administration of Korea as woodblock-printed versions from the Goryeo dynasty of Korea in the 13th century. Ink tone analysis showed clear differences in brightness histograms between printed characters on the two books, suggesting printing technique differences. Statistical ink tone analysis of printed characters in the two books revealed totally different brightness (or darkness) histograms of pixels, within inked areas, suggesting differences in printing techniques and materials used for the two books. Ink tone analysis was performed for the Jikji (直指: metal type printed in Korea in 1337) and the Gutenberg Bible (metal type printed in Europe around 1455) for comparisons. As additional references, the ink tone analysis was conducted for two sets of old Korean books titled Myeongeuirok (明義錄), printed in 1777, and Sok-Myeongeuirok (續明義錄), printed in 1778, using both metal type and re-carved woodblocks. The Gongin version of The Song of Enlightenment, designated as a Korean treasure in 2012, showed very similar distribution and average brightness of ink with the metal-type-printed books from Korea and Europe from the 14th to 18th centuries. All metal-type-printed books from Korea and the Gutenberg Bible showed spotty prints with lighter ink tones and more symmetrical histograms compared with woodblock-printed Korean books from the 14th to 18th centuries. Ink tone analysis of printed character images can provide additional insights into a printing technique identification method. It is additional evidence for metal type printing of the Gongin version (空印本) of The Song of Enlightenment, designated as a Korean treasure in 2012. The version of interest is the world’s oldest extant book, printed using metal type in Korea in September 1239, as indicated in the imprint. This predates Jikji (1377) by 138 years and the 42-line Gutenberg Bible (~1455) by 216 years.