Search Results (55)

In this paper, a security-enhanced three-dimensional (3D) image hiding and encryption method is proposed by combining a layer-based phase-only hologram (POH) under structured light illumination with chaotic encryption and digital image watermarking technology. In the proposed method, the original 3D plaintext image is firstly encoded into a layer-based POH and then further encrypted into an encrypted phase with the help of a chaotic random phase mask (CRPM). Subsequently, the encrypted phase is embedded into a visible ciphertext image by using a digital image watermarking technology based on discrete wavelet transform (DWT) and singular value decomposition (SVD), leading to a 3D image hiding with high security and concealment. The encoding of POH and the utilization of CRPM can substantially enhance the level of security, and the DWT-SVD-based digital image watermarking can effectively hide the information of the 3D plaintext image in a visible ciphertext image, thus improving the imperceptibility of valid information. It is worth noting that the adopted structured light during the POH encoding possesses many optical parameters, which are all served as the supplementary keys, bringing about a great expansion of key space; meanwhile, the sensitivities of the wavelength key and singular matrix keys are also substantially enhanced thanks to the introduction of structured light, contributing to a significant enhancement of security. Numerical simulations are performed to demonstrate the feasibility of the proposed 3D image hiding method, and the simulation results show that the proposed method exhibits high feasibility and apparent security-enhanced effect as well as strong robustness. Full article

With the increasing integration of augmented reality (AR) in various applications, ensuring secure access and content authenticity has become a critical challenge. This paper proposes an innovative and robust authentication framework for protecting AR multimedia content through a hash-based data-hiding technique. Leveraging the Discrete Wavelet Transform (DWT) in the YCbCr color space, the method embeds multiple cryptographic hash signatures directly into the AR visual data. This design not only utilizes the symmetric property between two consecutive AR contents but also allows users to verify the connectivity between two AR digital contents by checking the embedded hash values. These embedded signatures support hierarchical, multi-level authentication, verifying not only the integrity and authenticity of individual AR objects but also their contextual relationships within the AR environment. The proposed system exhibits exceptional resilience to tampering, effectively identifying whether two consecutive e-pages in the AR content have been altered, while preserving high perceptual quality with PSNR values above 45 dB and SSIM scores consistently exceeding 0.98. This work presents a practical, real-time solution for enhancing AR content security, contributing significantly to the advancement of secure multimedia systems in next-generation interactive platforms. Full article

At present, the monitoring technology of the growth status of sika deer antlers faces many challenges in a complex breeding environment (such as light change, object occlusion, etc.). More importantly, an effective method for the segmentation of sika deer antlers is still lacking, which hinders the development of subsequent quality classification of sika deer antlers. In order to fill the research gap and lay a foundation for future sika deer antler quality classification, this paper proposed an improved semantic segmentation model based on U-Net, named SDAS-Net. In order to improve the segmentation accuracy and generalization ability of the model in a complex environment, we introduced a two-dimensional discrete wavelet transform module (2D-DWT) in the encoder head to reduce noise interference and enhance the ability to capture features. In order to compensate for the loss of feature information caused by 2D-DWT, we embedded the Star Blocks module in the encoder. In addition, the efficient mixed channel attention (EMCA) module was introduced to adaptively enhance key feature channels in the decoder, and the dual cross-attention mechanism (DCA) module was used to fuse high-dimensional features in skip connections. To verify the validity of the model, we constructed a 1055-image sika deer antler dataset (SDR). The experimental results show that compared with the baseline model, the performance of the SDAS-Net model is significantly improved, reaching 92.12% in MIoU and 93.63% in the PA index, and the number of parameters is only increased by 6.9%. The results show that the SDAS-Net model can effectively deal with the task of sika deer antler segmentation in a complex breeding environment while maintaining high precision. Full article

This paper presents a novel forensic watermarking method for digital evidence distribution in non-cloud environments. The approach addresses the critical need for the secure sharing of Joint Photographic Experts Group (JPEG) images in forensic investigations. The method utilises an adaptive Discrete Cosine Transform–Discrete Wavelet Transform (DCT-DWT) domain technique to embed a 64-bit watermark in both stand-alone JPEGs and those within forensic disk images. This occurs without alterations to disk structure or complications to the chain of custody. The system implements uniform secure randomisation and recipient-specific watermarks to balance security with forensic workflow efficiency. This work presents the first implementation of forensic watermarking at the disk image level that preserves structural integrity and enables precise leak source attribution. It addresses a critical gap in secure evidence distribution methodologies. The evaluation occurred on extensive datasets: 1124 JPEGs in a forensic disk image, 10,000 each of BOSSBase 256 × 256 and 512 × 512 greyscale images, and 10,000 COCO2017 coloured images. The results demonstrate high imperceptibility with average Peak Signal-to-Noise Ratio (PSNR) values ranging from 46.13 dB to 49.37 dB across datasets. The method exhibits robust performance against geometric attacks with perfect watermark recovery (Bit Error Rate (BER) = 0) for rotations up to 90° and scaling factors between 0.6 and 1.5. The approach maintains compatibility with forensic tools like Forensic Toolkit FTK and Autopsy. It performs effectively under attacks including JPEG compression (QF ≥ 60), filtering, and noise addition. The technique achieves high feature match ratios between 0.684 and 0.690 for a threshold of 0.70, with efficient processing times (embedding: 0.0347 s to 0.1187 s; extraction: 0.0077 s to 0.0366 s). This watermarking technique improves forensic investigation processes, particularly those that involve sensitive JPEG files. It supports leak source attribution, preserves evidence integrity, and provides traceability throughout forensic procedures. Full article

The resource limitations of Low-Power Wireless Networks (LP-WNs), such as Wireless Sensor Networks (WSNs), Wireless Actuator/Sensor Networks (WA/SNs), and Internet of Things (IoT) outdoor applications, restrict the utilization of the error-performance-enhancing techniques and the use of the powerful and robust security tools. Therefore, these LP-WN applications require special techniques to satisfy the requirements of a low data loss rate and satisfy the security requirements while considering the accepted level of complexity and power efficiency of these techniques. This paper focuses on proposing a power-efficient, robust cryptographic algorithm for the WA/SNs. The lower-complexity cryptographic algorithm is proposed, based on merging the data composition tools utilizing data transforms and chaos mapping techniques. The decomposing tool is performed by the various data transforms: Discrete Cosine Transform (DCT), Discrete Cosine Wavelet (DWT), Fast Fourier Transform (FFT), and Walsh Hadamard Transform (WHT); the DWT performs better with efficient complexity. It is utilized to separate the plaintext into the main portion and side information portions to reduce more than 50% of complexity. The main plaintext portion is ciphered in the series of cryptography to reduce the complexity and increase the security capabilities of the proposed algorithm by two chaos mappings. The process of reduction saves complexity and is employed to feed the series of chaos cryptography without increasing the complexity. The two chaos mappings are used, and two-dimensional (2D) chaos logistic maps are used due to their high sensitivity to noise and attacks. The chaos 2D baker map is utilized due to its high secret key managing flexibility and high sensitivity to initial conditions and plaintext dimensions. Several computer experiments are demonstrated to evaluate the robustness, reliability, and applicability of the proposed complexity-efficient crypto-system algorithm in the presence of various attacks. The results prove the high suitability of the proposed lower-complexity crypto-system for WASN and LP-WN applications due to its robustness in the presence of attacks and its power efficiency. Full article

Recent advancements in speech enhancement (SE) have leveraged deep neural networks with multi-domain features to improve noise suppression. This study introduces a wavelet-enhanced adaptive FullSubNet (WA-FSN) framework that replaces traditional short-time Fourier transform (STFT)-based complex spectrograms with discrete wavelet transform (DWT) sub-band features while retaining magnitude spectrogram inputs. Evaluated on the VoiceBank-DEMAND dataset, WA-FSN with one-level DWT features achieves a PESQ score of 2.8889 (+3.6% vs. baseline A-FSN’s 2.7885) and SI-SNR of 18.55 dB (+3% vs. 18.02 dB), while two-level DWT extensions reach 2.8937 PESQ (+3.8%) and 18.83 dB SI-SNR (+4.5%). The framework maintains computational efficiency through LSTM-based fusion models, requiring only six additional convolution operations for DWT feature extraction. Quantitative analysis reveals that low-frequency sub-bands contribute most to PESQ improvements (2.8937 for the lowest three sub-bands), while high-frequency sub-bands enhance SI-SNR (18.83 dB for the highest two sub-bands). These results demonstrate that wavelet-derived features complement STFT magnitude spectra effectively, providing richer time-frequency representations for complex ideal ratio mask estimation in challenging noise conditions. Full article

Achieving both accurate and real-time monitoring heartbeat signals by non-invasive sensing techniques is challenging due to various noise interferences. In this paper, we propose an enhanced discrete wavelet transform (DWT) method that incorporates objective denoising quality assessment metrics to determine accurate thresholds and adaptive threshold functions. Our approach begins by denoising ECG signals from various databases, introducing several types of typical noise, including additive white Gaussian (AWG) noise, baseline wandering noise, electrode motion noise, and muscle artifacts. The results show that for Gaussian white noise denoising, the enhanced DWT can achieve 1–5 dB SNR improvement compared to the traditional DWT method, while for real noise denoising, our proposed method improves the SNR tens or even hundreds of times that of the state-of-the-art denoising techniques. Furthermore, we validate the effectiveness of the enhanced DWT method by visualizing and comparing the denoising results of heartbeat signals monitored by fiber-optic micro-vibration sensors against those obtained using other denoising methods. The improved DWT enhances the quality of heartbeat signals from non-invasive sensors, thereby increasing the accuracy of cardiovascular disease diagnosis. Full article

A multi-level 2D Discrete wavelet transform (DWT) architecture for JPEG2000 is proposed, enhancing speed through parallel processing multiple tile blocks. Based on the lifting scheme, folded architecture and unfolded architecture achieving critical path delay with only one multiplier are designed to increase throughput rate. Connecting the folded and unfolded architecture through a pipeline architecture ensures uniform throughput rates across all DWT levels within a singular clock domain. Computational resource consumption is reduced by adjusting the timing to allow one folded architecture to process three tile blocks of three to five levels of DWT, and a transposing module requiring merely six registers is devised to decrease storage resource consumption. The quantization module, crucial for code-word control in JPEG2000, is integrated into the scaling module with minimal additional resource expenditure. Compared to the existing architecture, the analysis demonstrates that the proposed architecture exhibits enhanced hardware efficiency, with a reduction in transistor-delay-product (TDP) of no less than 14.69%. Synthesis results further reveal an area reduction of at least 26.64%, and a decrease in area-delay-product (ADP) by a minimum of 29.89%. Results from FPGA implementation indicate a significant decrease in resource utilization. Full article

Low probability of intercept (LPI) radar signal recognition under low signal-to-noise ratio (SNR) is a challenging task within electronic reconnaissance systems, particularly when faced with scarce labeled data and limited resources. In this paper, we introduce an LPI radar signal recognition method based on a semi-supervised Support Vector Machine (SVM). First, we utilize the Multi-Synchrosqueezing Transform (MSST) to obtain the time–frequency images of radar signals and undergo the necessary preprocessing operations. Then, the image features are extracted via Discrete Wavelet Transform (DWT), and the feature dimension is reduced by the principal component analysis (PCA). Finally, the dimensionality reduction features are input into the semi-supervised SVM to complete the classification and recognition of LPI radar signals. The experimental results demonstrate that the proposed method achieves high recognition accuracy at low SNR. When the SNR is −6 dB, its recognition accuracy reaches almost 100%. Full article

Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are very weak and susceptible to noise interference, leading to inaccurate diagnostic results. In this paper, a combined enhanced discrete wavelet transform (DWT) and non-local mean estimation (NLM) denoising method is proposed to remove noise from heartbeat signals, which adaptively determines the filtering parameters of the DWT-NLM composite method using objective noise reduction quality assessment metrics by denoising different ECG signals from multiple databases with the addition of additive Gaussian white noise (AGW) with different signal-to-noise ratios (SNRs). The noise reduction results are compared with those of NLM, enhanced DWT, and conventional DWT combined with NLM method. The results show that the output SNR of the proposed method is significantly higher than the other methods compared in the range of −5 to 25 dB input SNR. Further, the proposed method is employed for noise reduction of heartbeat signals measured by fiber optic microvibration sensing. It is worth mentioning that the proposed method does not need to obtain the exact noise level, but only the adaptive filtering parameters based on the autocorrelation nature of the denoised signal. This work greatly improves the signal quality of the multimode fiber microvibration sensing system and helps to improve the diagnostic accuracy. Full article

In the field of image watermarking technology, it is very important to balance imperceptibility, robustness and embedding capacity. In order to solve this key problem, this paper proposes a new color image adaptive watermarking scheme based on discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD). In order to improve the security of the watermark, we use Lorenz hyperchaotic mapping to encrypt the watermark image. We adaptively determine the embedding factor by calculating the Bhattacharyya distance between the cover image and the watermark image, and combine the Alpha blending technique to embed the watermark image into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. The experimental results show that the average PSNR of our scheme is 45.9382 dB, and the SSIM is 0.9986. Through a large number of experimental results and comparative analysis, it shows that the scheme has good imperceptibility and robustness, indicating that we have achieved a good balance between imperceptibility, robustness and embedding capacity. Full article

Software-defined vehicles (SDVs) make automotive systems more intelligent and adaptable, and this transformation relies on hybrid automotive in-vehicle networks that refer to multiple protocols using automotive Ethernet (AE) or a controller area network (CAN). Numerous researchers have developed specific intrusion-detection systems (IDSs) based on ResNet18, VGG16, and Inception for AE or CANs, to improve confidentiality and integrity. Although these IDSs can be extended to hybrid automotive in-vehicle networks, these methods often overlook the requirements of real-time processing and minimizing of the false positive rate (FPR), which can lead to safety and reliability issues. Therefore, we introduced an IDS based on the Swin Transformer to bolster hybrid automotive in-vehicle network reliability and security. First, multiple messages from the traffic assembly are transformed into images and compressed via two-dimensional wavelet discrete transform (2D DWT) to minimize parameters. Second, the Swin Transformer is deployed to extract spatial and sequential features to identify anomalous patterns with its attention mechanism. To compare fairly, we re-implemented up-to-date conventional network models, including ResNet18, VGG16, and Inception. The results showed that our method could detect attacks with 99.82% accuracy and 0 FPR, which saved 14.32% in time costs and improved the accuracy by 1.60% compared to VGG16 when processing 512 messages. Full article

This paper designs a multi-variable hybrid islanding-detection method (HIDM) using signal-processing techniques. The signals of current captured on a test system where the renewable energy (RE) penetration level is between 50% and 100% are processed by the application of the Stockwell transform (ST) to compute the Stockwell islanding-detection factor (SIDF) and the co-variance islanding-detection factor (CIDF). The signals of current are processed by the application of the Hilbert transform (HT), and the Hilbert islanding-detection factor (HIDF) is computed. The signals of current are also processed by the application of the Alienation Coefficient (ALC), and the Alienation Islanding Detection Factor (AIDF) is computed. A hybrid islanding-detection indicator (HIDI) is derived by multiplying the SIDF, CIDF, AIDF, and an islanding weight factor (IWF) element by element. Two thresholds, designated as the hybrid islanding-detection indicator threshold (HIDIT) and the hybrid islanding-detection indicator fault threshold (HIDIFT), are selected to detect events of islanding and also to discriminate such events from fault events and operational events. The HIDM is effectively tested using an IEEE-13 bus power network, where solar generation plants (SGPs) and wind generation plants (WGPs) are integrated. The HIDM effectively identified and discriminated against events such as islanding, faults, and operational. The HIDM is also effective at identifying islanding events on a real-time distribution feeder. The HIDM is also effective at detecting islanding events in the scenario of a 20 dB signal-to-noise ratio (SNR). It is established that the HIDM has a small non-detection zone (NDZ). The effectiveness of the HIDM is better relative to the islanding-detection method (IDM) supported by the discrete wavelet transform (DWT), an IDM using a hybridization of the slantlet transform, and the Ridgelet probabilistic neural network (RPNN). An IDM using wavelet transform multi-resolution (WT-MRA)-based image data and an IDM based on the use of a deep neural network (DNN) were used. The study was performed using the MATLAB software (2017a) and validated in real-time using the data collected from a practical distribution power system network. Full article

In this study, carbon steel was examined under different corrosive conditions using electrochemical noise (EN) as the primary method of investigation. The corroded carbon steel surfaces were examined using 3D profilometry to gather information about localized defects (pits). A post-EN analysis approach was used using the discrete wavelet transform (DWT) method, which emphasizes the necessity of employing wavelet analysis as a quantitative analysis approach for electrochemical noise. A well-established approach to extract features from wavelet scalogram images, based on the concept of local binary patterns (LBPs), was used to extract features from these wavelet images. The results demonstrated that electrochemical noise associated with wavelet transform analysis, particularly wavelet scalograms, is an effective tool for monitoring the localized corrosion of carbon steel. Full article

Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, the present study proposes the use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding time compared to binary Huffman codes. For image compression, discrete wavelet transform (DWT) and CHC with principal component analysis (PCA) were combined. The lossy method was introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed images have a better peak signal-to-noise ratio (PSNR). In this study, we also developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for the efficient use of storage space. The proposed technique achieved up to 60% compression while maintaining high visual quality. This method also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach was tested on various color images, including Peppers 512 × 512 × 3 and Couple 256 × 256 × 3, showing improvements by 17 dB and 22 dB, respectively, while reducing the bpp by 0.56 and 0.10, respectively. For grayscale images as well, i.e., Lena 512 × 512 and Boat 256 × 256, the proposed method showed improvements by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases. Full article