Search Results (87)

To address the issues of image quality degradation and file size expansion encountered during reversible data hiding (RDH) of JPEG images, a JPEG reversible data hiding algorithm based on block smoothness estimation and optimal zero coefficient selection is proposed. Firstly, a block smoothness estimation strategy is designed based on the number of zero coefficients and non-zero quantisation table values within DCT blocks, prioritising DCT blocks with higher smoothness for information embedding. Subsequently, under a given embedding payload, an optimal zero coefficient selection strategy is introduced. Blocks are partitioned into embedding regions and non-embedding regions based on a preset position threshold T. Within embedding regions, the frequency of zero coefficients at different positions across all blocks is statistically analysed, with embedding prioritised at positions exhibiting the highest zero coefficient frequency to enhance embedding efficiency. Concurrently, by setting positive and negative displacement gaps to constrain the modification range of non-zero coefficients, invalid shifts are minimised. This further enhances visual quality while controlling file expansion. Experimental results demonstrate that, compared to existing algorithms, the proposed method achieves a peak signal-to-noise ratio improvement of 0.75 to 3.62 dB under fixed embedding capacity. File expansion is reduced by 1038 to 2243 bits, whilst enabling fully reversible image restoration. Full article

Ice loads continue to pose challenges to the structural safety of offshore wind turbines (OWTs), while the rapid development of offshore wind power in cold regions is enabling the deployment of OWTs in deeper waters. To accurately simulate the dynamic response of an OWT under combined ice–wind loading, this paper proposes a Discrete Element Method–Wind Turbine Integrated Analysis (DEM-WTIA) framework. The framework can synchronously simulate discontinuous ice-crushing processes and aeroelastic–structural dynamic responses through a holistic turbine model that incorporates rotor dynamics and control systems. To address the issue of insufficient prediction accuracy for dynamic responses, we introduced a multivariate time series forecasting method that integrates a secondary decomposition strategy with a hybrid prediction model. First, we developed a parallel signal processing mechanism, termed Adaptive Complete Ensemble Empirical Mode Decomposition with Improved Singular Spectrum Analysis (CEEMDAN-ISSA), which achieves adaptive denoising via permutation entropy-driven dynamic window optimization and multi-feature fusion-based anomaly detection, yielding a noise suppression rate of 76.4%. Furthermore, we propose the F-Transformer prediction model, which incorporates a Frequency-Enhanced Channel Attention Mechanism (FECAM). By integrating the Discrete Cosine Transform (DCT) into the Transformer architecture, the F-Transformer mines hidden features in the frequency domain, capturing potential periodicities in discontinuous data. Experimental results demonstrate that signals processed by ISSA exhibit increased signal-to-noise ratios and enhanced fidelity. The F-Transformer achieves a maximum reduction of 31.86% in mean squared error compared to the standard Transformer and maintains a coefficient of determination (R²) above 0.91 under multi-condition coupled testing. By combining adaptive decomposition and frequency-domain enhancement techniques, this framework provides a precise and highly adaptable ultra-short-term response forecasting tool for the safe operation and maintenance of offshore wind power in cold regions. Full article

In recent years, most proposed digital image encryption algorithms have primarily focused on encrypting raw pixel data, often neglecting the integration with image compression techniques. Image compression algorithms, such as JPEG, are widely utilized in internet applications, highlighting the need for encryption methods that are compatible with compression processes. This study introduces an innovative color image encryption algorithm integrated with JPEG compression, designed to enhance the security of images susceptible to attacks or tampering during prolonged transmission. The research addresses critical challenges in achieving an optimal balance between encryption security and compression efficiency. The proposed encryption algorithm is structured around three key compression phases: Discrete Cosine Transform (DCT), quantization, and entropy coding. At each stage, the algorithm incorporates advanced techniques such as block segmentation, block replacement, DC coefficient confusion, non-zero AC coefficient transformation, and RSV (Run/Size and Value) pair recombination. Extensive simulations and security analyses demonstrate that the proposed algorithm exhibits strong robustness against noise interference and data loss, effectively meeting stringent security performance requirements. Full article

This paper introduces a robust and imperceptible video watermarking framework designed for blind extraction in dynamic video environments. The proposed method operates in the spatial domain and combines multiscale perceptual analysis, adaptive Just Noticeable Difference (JND)-based quantization, and temporal redundancy via multiframe embedding. Watermark bits are embedded selectively in blocks with high perceptual masking using a QIM strategy, and the corresponding DCT coefficients are estimated directly from the spatial domain to reduce complexity. To enhance resilience, each bit is redundantly inserted across multiple keyframes selected based on scene transitions. Extensive simulations over 21 benchmark videos (CIF, 4CIF, HD) validate that the method achieves superior performance in robustness and perceptual quality, with an average Bit Error Rate (BER) of 1.03%, PSNR of 50.1 dB, SSIM of 0.996, and VMAF of 97.3 under compression, noise, cropping, and temporal desynchronization. The system outperforms several recent state-of-the-art techniques in both quality and speed, requiring no access to the original video during extraction. These results confirm the method’s viability for practical applications such as copyright protection and secure video streaming. Full article

Format-compatible encryption can be used to ensure the security and privacy of JPEG images. Recently, a JPEG image encryption method proved to be secure against known plaintext attacks by employing an adaptive encryption key, which depends on the histogram of the number of non-zero alternating current coefficients (ACC) in Discrete Cosine Transform (DCT) blocks. However, this scheme has been demonstrated to be vulnerable to chosen-plaintext attacks (CPA) based on the run consistency of MCUs (RCM) between the original image and the encrypted image. In this paper, an improved CPA scheme is proposed. The method of incrementing run-length values instead of permutation is utilized to satisfy the uniqueness of run sequences of different minimum coded units (MCUs). The experimental results show that the proposed method can successfully recover the outlines of plaintext images from the encrypted images, even with lower-quality factors. Full article

Objectives: Identifying patients’ advantageous radiotherapy modalities prior to CT simulation is challenging. This study aimed to develop a workflow using deep learning (DL)-predicted synthetic CT (sCT) for treatment modality comparison based solely on a diagnostic CT (dCT). Methods: A DL network, U-Net, was trained utilizing 46 thoracic cases from a public database to generate sCT images predicting planning CT (pCT) scans based on the latest dCT, and tested on 15 institutional patients. The sCT accuracy was evaluated against the corresponding pCT and a commercial algorithm deformed CT (MdCT) based on Mean Absolute Error (MAE) and Universal Quality Index (UQI). To determine advantageous treatment modality, clinical dose-volume histogram (DVH) metrics and Normal Tissue Complication Probability (NTCP) differences between proton and photon treatment plans were analyzed on the sCTs via concordance correlation coefficient (CCC). Results: The AI-generated sCTs closely resembled those of the commercial deformation algorithm in the tested cases. The differences in MAE and UQI values between the sCT-vs-pCT and MdCT-vs-pCT were 19.38 HU and 0.06, respectively. The mean absolute NTCP deviation between sCT and pCT was 1.54%, 0.21%, and 2.36% for esophagus perforation, lung pneumonitis, and heart pericarditis, respectively. The CCC between sCT and pCT was 0.90 for DVH metrics and 0.97 for NTCP, indicating moderate agreement for DVH metrics and substantial agreement. Conclusions: Radiation oncologists can potentially utilize this personalized sCT based approach as a clinical support tool to rapidly compare the treatment modality benefit during patient consultation and facilitate in-depth discussion on potential toxicities at a patient-specific level. Full article

With the rapid development of technologies such as vision–language modeling, sharing images with corresponding descriptions has become a common means of information transfer. Studying data-hiding techniques for JPEG images can protect sensitive descriptions, such as personal information associated with them while sharing images. Therefore, research on data-hiding techniques for JPEG images is of significant importance. However, existing methods that modify discrete cosine transform (DCT) coefficients still have room for improvement in increasing their embedding capacity while minimizing file size expansion. To address this issue, this paper proposes a knowledge encapsulation method for JPEG images using a special hexagonal tessellation matrix. First, a special hexagonal tessellation matrix is constructed based on the characteristics of non-zero AC coefficients. Then, non-zero AC coefficients in JPEG images are paired to form coordinate pairs, and the data are embedded by modifying the non-zero AC coefficient pairs into the coordinates corresponding to the secret data. Experimental results demonstrate that, compared to the previously proposed JPEG image data-hiding schemes, the proposed approach achieves a higher embedding capacity, a minimal file size increase (FSI), and an acceptable peak signal-to-noise ratio (PSNR). Full article

Background/Objectives: Cardiac arrhythmia (ARR) and congestive heart failure (CHF) are heart diseases that can cause dysfunction of other body organs and possibly death. This paper describes a fast and accurate detection system to distinguish between ARR and normal sinus (NS), and between CHF and NS. Methods: the proposed automatic detection system uses the higher amplitude coefficients (HAC) of the discrete cosine transform (DCT) of the electrocardiogram (ECG) as discriminant features to distinguish ARR and CHF signals from NS. The approach is tested with three statistical classifiers, of which the k-nearest neighbors (k-NN) algorithm. Results: the DCT provides fast compression of the ECG signal, and statistical tests show that the obtained HACs are different from ARR and NS, and for CHF and NS. The latter achieved highest accuracy under ten-fold cross-validation in comparison to Naïve Bayes (NB) and nonlinear support vector machine (SVM). The kNN yielded 97% accuracy, 99% sensitivity, 90% specificity and 0.63 s processing time when classifying ARR against NS, and it yielded 99% accuracy, 99.7% sensitivity, and 99.2% specificity, and 0.27 seconds processing time when classifying HCF against NS. In addition to a fast response, the DCT-kNN system yields higher accuracy in comparison to recent works. Conclusions: it is concluded that using the DCT based HACs as biomarkers of ARR and CHF can lead an efficient computer aided diagnosis (CAD) system which is fast, accurate and does not require ECG signal pre-processing and segmentation. The proposed system is promising for applications in clinical milieu. Full article

Purpose: Dual-energy computed tomography (DECT) allows for the measurement of iodine concentration, a component for the synthesis of thyroid hormones. DECT can create virtual non-contrast (VNC) images, potentially reducing radiation exposure. This study explores the correlations between thyroid function and iodine concentration, as well as the relationship between thyroid densities in true non-contrast (TNC) and virtual non-contrast (VNC) images and thyroid function. Methods: The study involved 87 patients undergoing 4D-CT imaging with single and dual-energy scans for diagnosing primary hyperparathyroidism. Thyroid densities and iodine concentrations were measured across all scanning phases. These measurements were correlated with thyroid function, indicated by TSH and FT4 levels. Differences in thyroid density between post-contrast phases and TNC phases (ΔHU) were analyzed for correlations with thyroid function and iodine concentrations. Results: Positive correlations between iodine concentrations and TSH were found, with Spearman’s coefficients (R) of 0.414, 0.361, and 0.349 for non-contrast, arterial, and venous phases, respectively. Thyroid density on TNC showed significant positive correlations with TSH levels (R = 0.436), consistently across both single- (R = 0.435) and dual-energy (R = 0.422) scans. Thyroid densities on VNC images did not correlate with TSH or FT4. Differences in density between contrast and non-contrast scans (ΔHU) negatively correlated with TSH (p = 0.002). Conclusions: DECT-derived iodine concentrations and thyroid densities in non-contrast CT scans demonstrated positive correlations with thyroid function, in contrast to thyroid densities on VNC scans. This indicates that VNC images are unsuitable for this purpose. Correlations between ΔHU and TSH suggest a potential link between the thyroid’s structural properties to capture iodine and its hormonal function. This study underscores the potential value of (DE-) CT imaging for evaluating thyroid function as an additional benefit in head and neck scans. Full article

Background/Objectives: 5DCT was first proposed in 2005 as a motion-compensated CT simulation approach for radiotherapy treatment planning to avoid sorting artifacts that arise in 4DCT when patients breathe irregularly. Since March 2019, 5DCT has been clinically implemented for routine use at our institution to leverage this technological advantage. The clinical workflow includes a quality assurance report that describes the output of primary workflow steps. This study reports on the challenges and quality of the clinical 5DCT workflow using these quality assurance reports. Methods: We evaluated all thoracic 5DCT simulation datasets consecutively acquired at our institution between March 2019 and December 2022 for thoracic radiotherapy treatment planning. The 5DCT datasets utilized motion models constructed from 25 fast-helical free-breathing computed tomography (FHFBCTs) with simultaneous respiratory bellows signal monitoring to reconstruct individual, user-specified breathing-phase images (termed 5DCT phase images) for internal target volume contouring. Each 5DCT dataset was accompanied by a structured quality assurance report composed of qualitative and quantitative measures of the breathing pattern, image quality, DIR quality, model fitting accuracy, and a validation process by which the original FHFBCT scans were regenerated with the 5DCT model. Measures of breathing irregularity, image quality, and DIR quality were retrospectively categorized on a grading scale from 1 (regular breathing and accurate registration/modeling) to 4 (irregular breathing and inaccurate registration/modeling). The validation process was graded according to the same scale, and this grade was termed the suitability-for-treatment-planning (STP) grade. We correlated the graded variables to the STP grade. In addition to the quality assurance reports, we reviewed the contour sessions to determine how often 5DCT phase images were used for treatment planning and delivery. Results: There were 169 5DCT simulation datasets available from 156 patients for analysis. The STP was moderately correlated with breathing irregularity, image quality, and DIR quality (Spearman coefficients: 0.26, 0.30, and 0.50, respectively). Multiple linear regression analysis demonstrated that STP was correlated with regular breathing patterns (p = 0.008), image quality (p < 0.001), and better DIR quality (p < 0.001). 5DCT datasets were used for treatment planning in 82% of cases, while in 12% of cases, a backup image process was used. In total, 6% of image datasets were not used for treatment planning due to factors unrelated to the 5DCT workflow quality. Conclusions: The strongest association with STP was with DIR quality grades, as indicated by both Spearman and multiple linear regression analysis, implying that improvements to DIR accuracy and evaluation may be the best route for further improvement to 5DCT. The high rate of 5DCT phase image use for treatment planning showed that the workflow was reliable, and this has encouraged us to continue to develop and improve the workflow steps. Full article

To guarantee the reliability and integrity of audio, data have been focused on as an essential topic as the fast development of generative AI. Significant progress in machine learning and speech synthesis has increased the potential for audio tampering. In this paper, we focus on the digital watermarking method as a promising method to safeguard the authenticity of audio evidence. Due to the integrity of the original data with probative importance, the algorithm requires reversibility, imperceptibility, and reliability. To meet the requirements, we propose a reversible digital watermarking approach that embeds feature data concentrating in high-frequency intDCT coefficients after transforming data from the time domain into the frequency domain. We explored the appropriate hiding locations against spectrum-based attacks with novel proposed methodologies for spectral expansion for embedding. However, the drawback of fixed expansion is that the stego signal is prone to being detected by a spectral analysis. Therefore, this paper proposes two other new expansion methodologies that embed the data into variable locations—random expansion and adaptive expansion with distortion estimation for embedding—which effectively conceal the watermark’s presence while maintaining high perceptual quality with an average segSNR better than 21.363 dB and average MOS value better than 4.085. Our experimental results demonstrate the efficacy of our proposed method in both sound quality preservation and log-likelihood value, indicating the absolute discontinuity of the spectrogram after embedding is proposed to evaluate the effectiveness of the proposed reversible spectral expansion watermarking algorithm. The result of EER indicated that the adaptive hiding performed best against attacks by spectral analysis. Full article

Leveraging data redundancy has long been recognized as an effective approach for concealing large amounts of secret data. In digital images, the 2D-pixel matrix inherently provides opportunities for redundancy, as each pixel is connected to its eight neighbors. Video segments, with their 3D structures, introduce an additional layer of redundancy known as temporal redundancy. Recent video steganography techniques have proposed utilizing this temporal redundancy for data concealment. This paper seeks to fully exploit the redundancy present in video segments by integrating both spatial and temporal redundancy through an Octree segmentation method. The video is divided into homogeneous, variable-sized 3D cubes to enhance redundancy in each region, thereby improving energy compaction in the 3D discrete cosine transform (3D-DCT) domain. Consequently, the hiding capacity is optimized because most of the signal’s energy is concentrated in a few significant 3D-DCT coefficients, leaving a substantial portion of insignificant coefficients. These insignificant coefficients can be replaced with secret data without significantly affecting the quality of the carrier signal. Full article

Achieving high attack success rate (ASR) with minimal perturbed distortion has consistently been a prominent and challenging research topic in the field of adversarial examples. In this paper, a novel method to optimize communication signal adversarial examples is proposed by focusing on low-frequency components of perturbations (LFCP). Observations on model attention towards DCT coefficients reveal the crucial role of LFCP within adversarial examples in altering the model’s predictions. As a result, selectively preserving LFCP is established as the fundamental concept of the optimization strategy. By utilizing the binary search algorithm, which considers the inconsistency in the model’s predictions as a constraint, LFCP can be effectively identified, and the aim of minimizing perturbed distortion while maintaining ASR can be achieved. Experimental results conducted on a publicly available dataset, six adversarial attacks and two DNN models, indicate that the proposed method not only significantly minimizes perturbed distortion for FGSM, BIM, PGD, and MI-FGSM but also achieves a modest improvement in ASR. Notably, even for DeepFool and BS-FGM, which introduce small perturbations and exhibit high ASRs, the proposed method can still deliver feasible performance. Full article

The prediction and modeling of stock price movements have been shown to possess considerable economic significance within the finance sector. Recently, a range of artificial intelligence methodologies, encompassing both traditional machine learning and deep learning approaches, have been introduced for the purpose of forecasting stock price fluctuations, yielding numerous successful outcomes. Nonetheless, the identification of effective features for predicting stock movements is considered a complex challenge, primarily due to the non-linear characteristics, volatility, and inherent noise present in financial data. This study introduces an innovative Deep Convolutional Transformer (DCT) model that amalgamates convolutional neural networks, Transformers, and a multi-head attention mechanism. It features an inception convolutional token embedding architecture alongside separable fully connected layers. Experiments conducted on the NASDAQ, Hang Seng Index (HSI), and Shanghai Stock Exchange Composite (SSEC) employ Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), accuracy, and Matthews Correlation Coefficient (MCC) as evaluation metrics. The findings reveal that the DCT model achieves the highest accuracy of 58.85% on the NASDAQ dataset with a sliding window width of 30 days. In terms of error metrics, it surpasses other models, demonstrating the lowest average prediction error across all datasets for MAE, MSE, and MAPE. Furthermore, the DCT model attains the highest MCC values across all three datasets. These results suggest a promising capability for classifying stock price trends and affirming the DCT model’s superiority in predicting closing prices. Full article

Detecting Traumatic Brain Injuries (TBI) through imaging remains challenging due to limited sensitivity in current methods. This study addresses the gap by proposing a novel approach integrating deep-learning algorithms and advanced image-fusion techniques to enhance detection accuracy. The method combines contextual and visual models to effectively assess injury status. Using a dataset of repeat mild TBI (mTBI) cases, we compared various image-fusion algorithms: PCA (89.5%), SWT (89.69%), DCT (89.08%), HIS (83.3%), and averaging (80.99%). Our proposed hybrid model achieved a significantly higher accuracy of 98.78%, demonstrating superior performance. Metrics including Dice coefficient (98%), sensitivity (97%), and specificity (98%) verified that the strategy is efficient in improving image quality and feature extraction. Additional validations with “entropy”, “average pixel intensity”, “standard deviation”, “correlation coefficient”, and “edge similarity measure” confirmed the robustness of the fused images. The hybrid CNN-ViT model, integrating curvelet transform features, was trained and validated on a comprehensive dataset of 24 types of brain injuries. The overall accuracy was 99.8%, with precision, recall, and F1-score of 99.8%. The “average PSNR” was 39.0 dB, “SSIM” was 0.99, and MI was 1.0. Cross-validation across five folds proved the model’s “dependability” and “generalizability”. In conclusion, this study introduces a promising method for TBI detection, leveraging advanced image-fusion and deep-learning techniques, significantly enhancing medical imaging and diagnostic capabilities for brain injuries. Full article