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The rapid evolution of computer technology and social networks has led to massive data generation through interpersonal communications, necessitating improved methods for information mining and relational analysis in areas such as criminal activity. This paper introduces a Social Network Forensic Analysis model that employs network representation learning to identify and analyze key figures within criminal networks, including leadership structures. The model incorporates traditional web forensics and community algorithms, utilizing concepts such as centrality and similarity measures and integrating the Deepwalk, Line, and Node2vec algorithms to map criminal networks into vector spaces. This maintains node features and structural information that are crucial for the relational analysis. The model refines node relationships through modified random walk sampling, using BFS and DFS, and employs a Continuous Bag-of-Words with Hierarchical Softmax for node vectorization, optimizing the value distribution via the Huffman tree. Hierarchical clustering and distance measures (cosine and Euclidean) were used to identify the key nodes and establish a hierarchy of influence. The findings demonstrate the effectiveness of the model in accurately vectorizing nodes, enhancing inter-node relationship precision, and optimizing clustering, thereby advancing the tools for combating complex criminal networks. [ABSTRACT FROM AUTHOR]

The rapid growth on the amount of generated 3D data, particularly in the form of Light Detection And Ranging (LiDAR) point clouds (PCs), poses very significant challenges in terms of data storage, transmission, and processing. Point cloud (PC) representation of 3D visual information has shown to be a very flexible format with many applications ranging from multimedia immersive communication to machine vision tasks in the robotics and autonomous driving domains. In this paper, we investigate the performance of four reference 3D object detection techniques, when the input PCs are compressed with varying levels of degradation. Compression is performed using two MPEG standard coders based on 2D projections and octree decomposition, as well as two coding methods based on Deep Learning (DL). For the DL coding methods, we used a Joint Photographic Experts Group (JPEG) reference PC coder, that we adapted to accept LiDAR PCs in both Cartesian and cylindrical coordinate systems. The detection performance of the four reference 3D object detection methods was evaluated using both pre-trained models and models specifically trained using degraded PCs reconstructed from compressed representations. It is shown that LiDAR PCs can be compressed down to 6 bits per point with no significant degradation on the object detection precision. Furthermore, employing specifically trained detection models improves the detection capabilities even at compression rates as low as 2 bits per point. These results show that LiDAR PCs can be coded to enable efficient storage and transmission, without significant object detection performance loss. [ABSTRACT FROM AUTHOR]

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance. [ABSTRACT FROM AUTHOR]

The recent rise in interest in point clouds as an imaging modality has motivated standardization groups such as JPEG and MPEG to launch activities aiming at developing compression standards for point clouds. Lossy compression usually introduces visual artifacts that negatively impact the perceived quality of media, which can only be reliably measured through subjective visual quality assessment experiments. While MPEG standards have been subjectively evaluated in previous studies on multiple occasions, no work has yet assessed the performance of the recent JPEG Pleno standard in comparison to them. In this study, a comprehensive performance evaluation of JPEG and MPEG standards for point cloud compression is conducted. The impact of different configuration parameters on the performance of the codecs is first analyzed with the help of objective quality metrics. The results from this analysis are used to define three rate allocation strategies for each codec, which are employed to compress a set of point clouds at four target rates. The set of distorted point clouds is then subjectively evaluated following two subjective quality assessment protocols. Finally, the obtained results are used to compare the performance of these compression standards and draw insights about best coding practices. [ABSTRACT FROM AUTHOR]

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency. [ABSTRACT FROM AUTHOR]

Micro-electro-mechanical systems inertial measurement units (MEMS-IMUs) are increasingly being employed for measuring the attitude of bottom hole assemblies (BHAs). However, the reliability and measurement precision of a single MEMS-IMU may not meet drilling's stringent needs. Redundant MEMS-IMU systems can effectively enhance the reliability and precision. This paper proposes a redundant configuration method for MEMS sensors tailored to BHA attitude measurement. Firstly, based on reliability theory and a cost-benefit analysis, considering factors such as cost, size, and reliability, the optimal number of sensors in the redundant system was determined to be six. Considering the structural characteristics of the BHA, a hollow hexagonal prism-shaped redundant configuration scheme was proposed, ensuring the circulation of drilling fluid within the drill pipe. Next, by employing Kalman filtering to integrate the output data from the six sensors, a virtual IMU (VIMU) was formed. Finally, experimental verification was carried out. The results confirmed that, after redundancy implementation, the velocity random walk of the accelerometer decreased by an average of 58% compared to a single MEMS-IMU, and bias instability was reduced by an average of 54%. The angular random walk of the gyroscope decreased by an average of 58%, and bias instability was reduced by an average of 37%. This research provides a theoretical foundation for enhancing the precision and reliability of BHA attitude measurements. [ABSTRACT FROM AUTHOR]

Multi-robot Simultaneous Localization and Mapping (SLAM) systems employing 2D lidar scans are effective for exploration and navigation within GNSS-limited environments. However, scalability concerns arise with larger environments and increased robot numbers, as 2D mapping necessitates substantial processor memory and inter-robot communication bandwidth. Thus, data compression prior to transmission becomes imperative. This study investigates the problem of communication-efficient multi-robot SLAM based on 2D maps and introduces an architecture that enables compressed communication, facilitating the transmission of full maps with significantly reduced bandwidth. We propose a framework employing a lightweight feature extraction Convolutional Neural Network (CNN) for a full map, followed by an encoder combining Huffman and Run-Length Encoding (RLE) algorithms to further compress a full map. Subsequently, a lightweight recovery CNN was designed to restore map features. Experimental validation involves applying our compressed communication framework to a two-robot SLAM system. The results demonstrate that our approach reduces communication overhead by 99% while maintaining map quality. This compressed communication strategy effectively addresses bandwidth constraints in multi-robot SLAM scenarios, offering a practical solution for collaborative SLAM applications. [ABSTRACT FROM AUTHOR]

We consider the problem of constructing prefix-free codes in which a designated symbol, a space, can only appear at the end of codewords. We provide a linear-time algorithm to construct almost-optimal codes with this property, meaning that their average length differs from the minimum possible by at most one. We obtain our results by uncovering a relation between our class of codes and the class of one-to-one codes. Additionally, we derive upper and lower bounds to the average length of optimal prefix-free codes with a space in terms of the source entropy. [ABSTRACT FROM AUTHOR]

This paper presents prefix data augmentation (Prd) as an innovative method for enhancing sentence embedding learning through unsupervised contrastive learning. The framework, dubbed PrdSimCSE, uses Prd to create both positive and negative sample pairs. By appending positive and negative prefixes to a sentence, the basis for contrastive learning is formed, outperforming the baseline unsupervised SimCSE. PrdSimCSE is positioned within a probabilistic framework that expands the semantic similarity event space and generates superior negative samples, contributing to more accurate semantic similarity estimations. The model's efficacy is validated on standard semantic similarity tasks, showing a notable improvement over that of existing unsupervised models, specifically a 1.08% enhancement in performance on BERTbase. Through detailed experiments, the effectiveness of positive and negative prefixes in data augmentation and their impact on the learning model are explored, and the broader implications of prefix data augmentation are discussed for unsupervised sentence embedding learning. [ABSTRACT FROM AUTHOR]

In network security, safeguarding digital cyberspace infrastructure against malicious intrusions remains a paramount concern. This study addresses this imperative by proposing a novel approach that integrates digital watermarking, Huffman coding, and sophisticated attack classification techniques within the Controller Area Network (CAN) protocol framework. The proliferation of interconnected systems and the increasing sophistication of cyber threats necessitate novel strategies to fortify network defenses and ensure resilience in the face of adversarial activities. The research problem revolves around the need to develop an effective method for detecting and categorizing reconnaissance and violent attacks within cyberspace networks, particularly those utilizing the CAN protocol. Despite advancements in security protocols and intrusion detection systems, existing methodologies often fall short in accurately distinguishing between benign and malicious network activities, leaving critical infrastructure vulnerable to exploitation. This research uses digital watermarking to embed metadata within network packets, Huffman coding for efficient data compression, and advanced attack classification algorithms for real-time threat identification. The experimental results demonstrate the efficacy of the proposed approach in accurately differentiating between reconnaissance probes and violent attacks, thereby enabling timely and targeted responses to mitigate security threats. [ABSTRACT FROM AUTHOR]

Cloud computing is a technology that holds great promise and has potential to revolutionize the healthcare sector. Many security and privacy issues are brought up by the cloud's centralization of data for both patients and healthcare professionals. There is a need for maintaining secrecy in communication in exchanging medical data between the sender and the receiver, which can be done by cryptography. This article presents a cryptographic algorithm (encryption and decryption) to have a secure communication of digital health care confidential data using DNA cryptography and Huffman coding. The interesting property is the cipher size obtained from our algorithm is equal to the size of the cipher obtained from the character set of given data. Security analysis is provided to show the security of data when stored and transmitted to the cloud. The cryptographic requirements, key space analysis, key and plain text sensitivity, sensitive score analysis, sensitivity and specificity, optimal threshold, randomness analysis, uniqueness of implementation, entropies of binary bits, DNA bases, DNA bases with Huffman code, Huffman encoded binary bits and cloud service provider's risk are analyzed. The method proposed is compared with other cryptographic methods and results that it is more secure and stronger than other methods. [ABSTRACT FROM AUTHOR]

The deaf society supports Sign Language Recognition (SLR) since it is used to educate individuals in communication, education, and socialization. In this study, the results of using the modified Convolutional Neural Network (CNN) technique to develop a model for real-time Kurdish sign recognition are presented. Recognizing the Kurdish alphabet is the primary focus of this investigation. Using a variety of activation functions over several iterations, the model was trained and then used to make predictions on the KuSL2023 dataset. There are a total of 71,400 pictures in the dataset, drawn from two separate sources, representing the 34 sign languages and alphabets used by the Kurds. A large collection of real user images is used to evaluate the accuracy of the suggested strategy. A novel Kurdish Sign Language (KuSL) model for classification is presented in this research. Furthermore, the hand region must be identified in a picture with a complex backdrop, including lighting, ambience, and image color changes of varying intensities. Using a genuine public dataset, real-time classification, and personal independence while maintaining high classification accuracy, the proposed technique is an improvement over previous research on KuSL detection. The collected findings demonstrate that the performance of the proposed system offers improvements, with an average training accuracy of 99.05% for both classification and prediction models. Compared to earlier research on KuSL, these outcomes indicate very strong performance. [ABSTRACT FROM AUTHOR]

With the rapid development of multimedia technology and the massive accumulation of user data, a huge amount of data is rapidly generated and shared over the network, while the problems of inappropriate data access and abuse persist. Reversible data hiding in encrypted images (RDHEI) is a privacy-preserving method that embeds protected data in an encrypted domain and accurately extracts the embedded data without affecting the original content. However, the amount of embedded data has been one of the major limitations in the performance and application of RDHEI. Currently, the main approaches to improve the capacity of RDHEI are either to increase the overall capacity or to reduce the length of the auxiliary information. In this paper, we propose a novel RDHEI scheme based on multi-prediction and adaptive Huffman encoding. To increase the overall capacity, we propose a multi-prediction, called MED+GAP predictor, to generate the label map data of non-reference pixels prior to image encryption. Then, an adaptive Huffman coding is designed to compress the generated labels in order to reduce the embedding length of the auxiliary information used for the extraction and recovery. Experiments show that the proposed method with MED+GAP predictor and adaptive Huffman coding improves 0.052 bpp, 0.023 bpp, and 0.047 bpp on average over the other state-of-the-art methods on the BOSSBase, BOWS-2, and UCID datasets, respectively, while maintaining security and reversibility. [ABSTRACT FROM AUTHOR]

The attacker in white-box model has full access to software implementation of a cryptographic algorithm and full control over its execution environment. In order to solve the issues of high storage cost and inadequate security about most current white-box cryptographic schemes, WAS, an improved white-box cryptographic algorithm over AS iteration is proposed. This scheme utilizes the AS iterative structure to construct a lookup table with a five-layer ASASA structure, and the maximum distance separable matrix is used as a linear layer to achieve complete diffusion in a small number of rounds. Attackers can be prevented from recovering the key under black-box model. The length of nonlinear layer S and affine layer A in lookup table is 16 bits, which effectively avoids decomposition attack against the ASASA structure and makes the algorithm possess anti-key extraction security under the white-box model, while WAS possesses weak white-box (32 KB, 112)-space hardness to satisfy anti-code lifting security. WAS has provable security and better storage cost than existing schemes, with the same anti-key extraction security and anti-code lifting security, only 128 KB of memory space is required in WAS, which is only 14% of SPACE-16 algorithm and 33% of Yoroi-16 algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a novel hardware-oriented image compression algorithm and its very large-scale integration (VLSI) implementation for wireless sensor networks. The proposed novel image compression algorithm consists of a fuzzy decision, block partition, digital halftoning, and block truncation coding (BTC) techniques. A novel variable-size block partition technique was used in the proposed algorithm to improve image quality and compression performance. In addition, eight different types of blocks were encoded by Huffman coding according to probability to increase the compression ratio further. In order to achieve the low-cost and low-power characteristics, a novel iteration-based BTC training module was created to get representative levels and meet the requirement of wireless sensor networks. A prediction and a modified Golomb-Rice coding modules were designed to encode the information of representative levels to achieve higher compression performance. [ABSTRACT FROM AUTHOR]

The exponential growth of the Internet of Things (IoT) has led to an increased demand for secure and efficient data transmission methods. However, there is a tradeoff in the image quality and hiding capacity in the data hiding methods. Therefore, the maximum amount of data that could be stored in the image media is a difficult challenge while maintaining the image quality. Thus, to make the balance between the quality of the images and the embedding capacity, a novel interpolation-based revisable data hiding (RDH) approach is developed for IoT applications. The proposed interpolation technique takes the average of the root value for the product of two neighboring original pixel values and the third original pixel value. And for the central pixel, it takes an average of two interpolated pixels. By doing so, most of the original pixels are considered and the calculated interpolated pixel is much enhanced as its average value. Furthermore, the data hiding is performed in two stages. In the first stage, RSA is performed on the secret message, and then embedding is done based on which intensity range group. The experimental results indicate that the proposed technique enhanced the embedding capacity by 17.58% and produced 7.80% higher PSNR values for the test images as compared to the baseline methods. [ABSTRACT FROM AUTHOR]

In deep neural network (DNN) accelerators, it is expensive to transfer model parameters from the main memory to the processing elements. Data movement accounts for a large number of the inference latency and energy consumption. In this paper, we present three position-based techniques to compress the DNN model parameters. The techniques could lead to significant energy and performance improvement. The three presented compression techniques are lossless. The first technique takes into consideration the regularly repeat property of the DNN weights to compress them. The second technique saves the relative distance between weights instead of the weights to compress the model. The third technique applies Huffman coding on the relative distance based on the second technique. The proposed techniques are assessed on several DNNs. The results show that, the first technique could decrease 38% of latency and 36% energy, respectively. The second technique could decrease 41% of latency and 39% energy, respectively. The third technique could decrease 45% of latency and 43% energy, respectively. Applying Huffman code could achieve additional 7% reduction in both latency and energy based on the second technique. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing spatial keyword group query problem did not consider the query requirements with exclusion keywords and time attributes, a time-aware group query problem with exclusion keywords (TEGSKQ) is proposed for the first time. To solve this problem effectively, this paper proposes a query method based on the EKTIR-Tree index and dominating group (EKTDG). This method first proposes the EKTIR-tree index, which incorporates Huffman coding and integrates Bloom filters to deal with excluded keywords in order to improve the hit rate of keyword queries, significantly improving the query efficiency and reducing the storage occupancy. Then, the Candidate algorithm is proposed based on the EKTIR-tree index to filter out the spatial–textual objects that meet the query's keywords and time requirements, narrowing the search space for subsequent queries on a large scale. To address the problem of the low efficiency of existing algorithms based on a spatial distance query, a distance-dominating group is defined and a pruning algorithm based on a spatial distance-dominating group is proposed, which is a refining process of query results and greatly improves the search efficiency of the query. Theoretical and experimental studies show that the proposed method can better handle group queries with exclusion keywords based on time awareness. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented—more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems. [ABSTRACT FROM AUTHOR]

In wireless sensor network (WSN), the high volume of observe and transmitted data among sensor nodes make it requires to maintain the security. Even though numerous secure data transmission approaches designed over a network, an inadequate resource and the complex environment cause not able to used in WSNs. Moreover, secure data communication is a big challenging problem in WSNs especially for the military application. This paper proposes a dingo algorithm-based forwarder selection and huffman coding (DAHC) to improve authentication in internet of things (IoT) WSN. Initially, it detects the anomalous nodes by applying support vector machine (SVM) algorithm based on sensor node energy, node selfishness, and signal to noise ratio (SNR). Next, we using the dingo algorithm to select the forwarder node. This dingo algorithm computes the fitness function based on node degreee, node distance and node energy. Finally, the huffman coding to provide end to end authentication established on node energy from sender to receiver. During data transmission, the huffman coding to build the binary hop count value, it improves the authentication in the WSN. Performance results specify that this approach enhances the detection ratio and throughput. [ABSTRACT FROM AUTHOR]

Modern, commonly used cryptosystems based on encryption keys require that the length of the stream of encrypted data is approximately the length of the key or longer. In practice, this approach unnecessarily complicates strong encryption of very short messages commonly used for example in ultra-low-power and resource-constrained wireless network sensor nodes based on microcontrollers (MCUs). In such cases, the data payload can be as short as a few bits of data while the typical length of the key is several hundred bits or more. The article proposes an idea of employing a complex of two algorithms, initially applied for data compression, acting as a standard-length encryption key algorithm to increase the transmission security of very short data sequences, even as short as one or a few bytes. In this article, we present and evaluate an approach that uses LZW and Huffman coding to achieve data transmission obfuscation and a basic level of security. [ABSTRACT FROM AUTHOR]

The widespread availability of digital image-processing software has given rise to various forms of image manipulation and forgery, which can pose a significant challenge in different fields, such as law enforcement, journalism, etc. It can also lead to privacy concerns. We are proposing that a privacy-preserving framework to encrypt images before processing them is vital to maintain the privacy and confidentiality of sensitive images, especially those used for the purpose of investigation. To address these challenges, we propose a novel solution that detects image forgeries while preserving the privacy of the images. Our method proposes a privacy-preserving framework that encrypts the images before processing them, making it difficult for unauthorized individuals to access them. The proposed method utilizes a compression quality analysis in the encrypted domain to detect the presence of forgeries in images by determining if the forged portion (dummy image) has a compression quality different from that of the original image (featured image) in the encrypted domain. This approach effectively localizes the tampered portions of the image, even for small pixel blocks of size 10 × 10 in the encrypted domain. Furthermore, the method identifies the featured image's JPEG quality using the first minima in the energy graph. [ABSTRACT FROM AUTHOR]

In the recent couple of years, due to the accelerated popularity of the internet, various organizations such as government offices, military, private companies, etc. use different transferring methods for exchanging their information. The Internet has various benefits and some demerits, but the primary bad mark is security of information transmission over an unreliable network, and widely uses of images. So, Steganography is the state of the art of implanting a message in the cover objects, that nobody can suspect or identify it. Therefore, in the field of cover steganography, it is very critical to track down a mechanism for concealing data by utilizing different blends of compression strategies. Amplifying the payload limit, and robustness, and working on the visual quality are the vital factors of this research to make a reliable mechanism. Different cover steganography research strategies have been recommended, and each adores its benefits and impediments but there is a need to foster some better cover steganography implements to accomplish dependability between the essential model of cover steganography. To handle these issues, in this paper we proposed a method in view of Huffman code, Least Significant Bits (LSB) based cover steganography utilizing Multi-Level Encryption (MLE) and colorless part (HC-LSBIS-MLE-AC) of the picture. It also used different substitution and flicking concepts, MLE, Magic matrix, and achromatic concepts for proving the proficiency, and significance of the method. The algorithm was also statistically investigated based on some Statistical Assessment Metrics (SAM) such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Normalized Cross Correlation (NCC), Structural Similarity Index Metric (SSIM), etc. and different perspectives. The observational outcomes show the likelihood of the proposed algorithm and the capacity to give unwavering quality between security, payload, perception, computation, and temper protection. [ABSTRACT FROM AUTHOR]

The steganalysis for JPEG image is an important research topic, as the enormous popularity of JPEG image on Internet. However, the stego noise feature extraction process of the existing deep learning‐based steganalytic methods are not adaptive enough to the content of the image, which may lead to suboptimal steganalysis performance. In order to solve this issue, an adaptive stego noise extraction network, named SNENet, for JPEG image steganalysis is proposed. The stego noise extraction module of the network is specifically designed for steganalysis, which consists of parallel dilated convolutional layer and inverted bottleneck layer. This specific design expands the receptive field of the network, which makes the extraction of the stego noise more global and adaptive to the content of the image. The experimental results indicate that proposed network outperforms the state‐of‐the‐art steganalytic method by as much as 6.25% for UED‐JC and 3.35% for J‐UNIWARD. The design of the network is also justified in the extensive ablation experiments. [ABSTRACT FROM AUTHOR]

Lossless compression is still a challenging task in the case of microarray images. This research proposes two algorithms that aim to improve the lossless compression efficiency for high spatial resolution microarray images using general entropy codecs, namely Huffman and arithmetic coders and the image compression standard JPEG 2000. Using the standards ensures that decoders are available to reassess the images for future applications. Typically, microarray images have a bit-depth of 16. In proposed algorithm 1, every image's per bit-plane entropy profile is calculated to automatically determine a better threshold T to split the bit-planes into the foreground and background sub-images. T is initially set to 8. However, in algorithm 1, T is updated, balancing the average value of per bit-plane entropies of the segmented sub-images of an image for improved lossless compression results. Codecs are applied individually to the produced sub-images. Proposed algorithm 2 is designed to increase the lossless compression efficiency of any unmodified JPEG 2000-compliant encoder while reducing side information overhead. In this, pixel intensity reindexing and, thereby, changing the histograms of the same segmented sub-images obtained from algorithm 1 are implemented and confirmed to get better JPEG 2000 results in lossless mode than applying it to the original image. The lossless JPEG 2000 compression performance on microarray images is also compared to JPEG-LS in particular. The experiments are carried out to validate the methods on seven benchmark datasets, namely ApoA1, ISREC, Stanford, MicroZip, GEO, Arizona, and IBB. The average first-order entropy of the datasets above is calculated and compared for codecs and better than competitive efforts in the literature. [ABSTRACT FROM AUTHOR]

Many confidential multimedia, such as personal privacy, commercial, and military secrets, are transmitted on the Internet. To prevent this confidential multimedia from being eavesdropped on by illegal users, information-hiding technology is a leading research topic nowadays. One of the important research topics of information-hiding technology is coverless information hiding, which utilizes the unchanged property of its multimedia carrier to hide secret information. In this paper, we propose two schemes that employ the average pixel value of an image. The first is an extension of the Coverless Information Hiding Based on the Most Significant Bit (CIHMSB) scheme, referred to as E-CIHMSB. In the E-CIHMSB, we build an extended matrix containing the image fragment's average pixel value. The second scheme is a combination theory-based CIHMSB, referred to as CB-CIHMSB. In the CB-CIHMSB, we construct the combined matrix. E-CIHMSB and CB-CIHMSB embed the secret bits by changing the most significant bits of the chosen pixel in the matrix. Experimental results show that our schemes achieved higher hiding capacity than previous related schemes. Moreover, the proposed scheme is more robust against steganalysis tools and image quality attacks such as Additive Gaussian White Noise (AWGN), Salt & Pepper noise, low-pass filtering attacks, and JPEG compression attacks than CIHMSB. [ABSTRACT FROM AUTHOR]

For copyright protection of diffusion-weighted imaging (DWI) images, traditional robust watermarking techniques result in irreversible distortions, while reversible watermarking methods exhibit poor robustness. We propose a two-stage lossless watermarking algorithm based on a Transformer network to solve this problem. The first stage of the algorithm is to train the robust watermarking network, embed the watermark into the cover image in the wavelet domain, and design the frequency information enhancement module to improve the reconstruction quality. In the second stage, based on the pre-trained robust watermarking network, the difference image between the watermarked image and the cover image is reversibly embedded into the watermarked image as the compensation information to losslessly recover the cover image. The difference image is compressed using DCT and Huffman coding to reduce the compensation information. Finally, the watermark extraction network is trained on the second embedding result to avoid weakening the robustness of the first stage caused by the reversible embedding. The experimental results demonstrate that the PSNR of the watermarked image reaches 60.18 dB. Under various types of image attacks, the watermark extraction BER is below 0.003, indicating excellent robustness. The cover image can be recovered losslessly under no attack. [ABSTRACT FROM AUTHOR]

Background: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task. Methods: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the 4 th level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects. Results: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60 ± 6.5 , sensitivities 93.55 ± 4.5 , specificities 94.85 ± 4.2 , precisions 92.50 ± 5.5 , and area under the curve (AUC) 0.93 ± 0.3 using SVM and k-NN machine learning classifiers. Conclusion: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds. [ABSTRACT FROM AUTHOR]

As an interesting technique that allows data extraction and image recovery without any loss, reversible data hiding in encrypted images is an area of great concern in the field of information security. In this paper, a new reversible data hiding method with high embedding capacity is proposed based on hybrid prediction and Huffman coding. The combination of two embedding mechanisms is innovatively designed to improve the embedding capacity according to different parts of the original image, i.e., the most significant bit-plane and the remaining seven bit-planes. In the first part of this method, the prediction value of each pixel is obtained by calculating the average value of its two neighboring pixels, and all of the most significant bits can be vacated to accommodate additional data. In the second part, the prediction value of each pixel is calculated using the median edge detector predictor, on which the tag map is built. Then, Huffman coding is used to compress the tag map so that a large amount of vacant space is obtained. Finally, the secret data can be embedded into the vacated space by directly using bit substitution. Compared with some recently reported methods, experimental results and analysis have demonstrated that an original image with high visual symmetry/quality can be recovered. Also, larger embedding capacity can be achieved, such as 3.3894 bpp and 3.2824 bpp, for BOSSBase and BOWS2 databases, respectively. [ABSTRACT FROM AUTHOR]

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Nowadays, the massive use of multimedia data gives to data compression a fundamental role in reducing the storage requirements and communication bandwidth. Variable-length encoding (VLE) is a relevant data compression method that reduces input data size by assigning shorter codewords to mostly used symbols, and longer codewords to rarely utilized symbols. As it is a common strategy in many compression algorithms, such as the popular Huffman coding, speeding VLE up is essential to accelerate them. For this reason, during the last decade and a half, efficient VLE implementations have been presented in the area of General Purpose Graphics Processing Units (GPGPU). The main performance issues of the state-of-the-art GPU-based implementations of VLE are the following. First, the way in which the codeword look-up table is stored in shared memory is not optimized to reduce the bank conflicts. Second, input/output data are read/written through inefficient strided global memory accesses. Third, the way in which the thread-codes are built is not optimized to reduce the number of executed instructions. Our goal in this work is to significantly speed up the state-of-the-art implementations of VLE by solving their performance issues. To this end, we propose GVLE, a highly optimized implementation of VLE on GPU, which uses the following optimization strategies. First, the caching of the codeword look-up table is done in a way that minimizes the bank conflicts. Second, input data are read by using vectorized loads to exploit fully the available global memory bandwidth. Third, each thread encoding is performed efficiently in the register space with high instruction-level parallelism and lower number of executed instructions. Fourth, a novel inter-block scan method, which outperforms those of state-of-the-art solutions, is used to calculate the bit-positions of the thread-blocks encodings in the output bit-stream. Our proposed mechanism is based on a regular segmented scan performed efficiently on sequences of bit-lengths of 32 consecutive thread-blocks encodings by using global atomic additions. Fifth, output data are written efficiently by executing coalesced global memory stores. An exhaustive experimental evaluation shows that our solution is on average 2.6 × faster than the best state-of-the-art implementation. Additionally, it shows that the scan algorithm is on average 1.62 × faster if it utilizes our inter-block scan method instead of that of the best state-of-the-art VLE solution. Hence, our inter-block scan method offers promising possibilities to accelerate algorithms that require it, such as the scan itself or the stream compaction. [ABSTRACT FROM AUTHOR]

Challenges in cognitive radio and tactical communications include recognizing anonymously received signals and estimating parameters in a blind or semiblind manner. In this paper, we examine this issue for orthogonal frequency division multiplexing (OFDM) signaling. There are several parameters in OFDM signaling, and the blind receiver must extract and consider the synchronization issue. We assume that the blind receiver is aware of modulation type, OFDM, and not aware of chip duration and the length of cyclic prefix. First, we present new criteria based on kurtosis to estimate these parameters and compare their performance at different levels of additive white Gaussian noise with methods based on correlation, kurtosis, maximum likelihood, and matched filter. Then, we perform synchronization and estimate the start time based on these criteria and several new criteria in two steps: fine and coarse synchronization. Finally, in a more practical setup, we present the idea of jointly estimating the mentioned parameters and the signal start time as coarse synchronization. We compare different criteria and show that one of the proposed criteria has the highest efficiency. [ABSTRACT FROM AUTHOR]

We propose a new high-speed secret key distillation system via public discussion based on the common randomness contained in the speech signal of the protocol participants. The proposed system consists of subsystems for quantization, advantage distillation, information reconciliation, an estimator for predicting conditional Renyi entropy, and universal hashing. The parameters of the system are optimized in order to achieve the maximum key distillation rate. By introducing a deep neural block for the prediction of conditional Renyi entropy, the lengths of the distilled secret keys are adaptively determined. The optimized system gives a key rate of over 11% and negligible information leakage to the eavesdropper, while NIST tests show the high cryptographic quality of produced secret keys. For a sampling rate of 16 kHz and quantization of input speech signals with 16 bits per sample, the system provides secret keys at a rate of 28 kb/s. This speed opens the possibility of wider application of this technology in the field of contemporary information security. [ABSTRACT FROM AUTHOR]

Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android smartphones. The researchers have conducted numerous studies on the detection of Android malware, but the majority of the works are based on the detection of generic Android malware. The detection based on malware categories will provide more insights about the malicious patterns of the malware. Therefore, this paper presents a detection solution for different Android malware categories, including adware, banking, SMS malware, and riskware. In this paper, a novel Huffman encoding-based feature vector generation technique is proposed. The experiments have proved that this novel approach significantly improves the efficiency of the detection model. This method makes use of system call frequencies as features to extract malware's dynamic behavior patterns. The proposed model was evaluated using machine learning and deep learning methods. The results show that the proposed model with the Random Forest classifier outperforms some existing methodologies with a detection accuracy of 98.70%. [ABSTRACT FROM AUTHOR]

JPEG steganography has become a research hotspot in the field of information hiding. However, the capacity of conventional JPEG steganography methods is hard to meet the requirements in high-capacity application scenarios and also can not extract secret messages accurately after JPEG compression. To mitigate these problems, we propose a high-capacity and robust JPEG steganography based on adversarial training called HRJS, which implements an end-to-end framework in the JPEG domain for the first time. The encoder is responsible for embedding the secret message while the decoder can reconstruct the original secret message. To enhance robustness, an attack module forces the neural network to automatically learn how to correctly recover the secret message after an attack. Experimental results show that our method achieves near 100 % decoding accuracy against JPEG_50 compression at 1/3 bits per channel (bpc) payload while preserving the imperceptibility of the stego image. Compared with conventional JPEG steganography methods, the proposed method is feasible with high capacity (e.g., 1 bpc) and has an obvious advantage in terms of robustness against JPEG compression at the same time. [ABSTRACT FROM AUTHOR]

As the rapid development of third-party storage and homomorphic encryption have profoundly stimulated the desire for secure communication, reversible data hiding in encrypted images has received widespread attention, since it allows lossless data conveying and perfect image recovery. In order to obtain secure reversible data hiding with high embedding capacity, a novel block embedding method is proposed, based on an adaptive recognition strategy for combined blocks in the binary image, with which the adjacent identical blocks can be integrated into a combination to reserve more spare bits for data accommodation. Furthermore, a fully reversible data hiding method for grayscale images in the encryption domain is designed. The secret data is hidden into lower bit-planes of the image while the original bits of those embedded lower pixels are recorded into the vacated space of higher bit-planes. The original image can be reconstructed flawlessly as well as the secret data being extracted without errors. To reinforce security, the original image and the secret data are encrypted and scrambled based on sequences generated with the high-dimension chaotic system. Due to its high sensitivity of initial values, the performance such as security and robustness is guaranteed. By comparing the PSNR value of the marked decrypted image and evaluating the quality of the extracted secret image, experimental results demonstrate that the proposed method can obtain higher embedding capacity, achieving 0.2700–0.3924 bpp increment over the state-of-the-art methods, and recover the marked decrypted image with high visual symmetry/quality, and efficiently resist against potential attacks, such as the histogram analysis, differential, brute-force, JPEG attacks, and so on. [ABSTRACT FROM AUTHOR]

Internet security has become a major concern with the growing use of the Internet of Things (IoT) and edge computing technologies. Even though data processing is handled by the edge server, sensitive data is generated and stored by the IoT devices, which are subject to attack. Since most IoT devices have limited resources, standard security algorithms such as AES, DES, and RSA hamper their ability to run properly. In this paper, a lightweight symmetric key cipher termed randomized butterfly architecture of fast Fourier transform for key (RBFK) cipher is proposed for resource-constrained IoT devices in the edge computing environment. The butterfly architecture is used in the key scheduling system to produce strong round keys for five rounds of the encryption method. The RBFK cipher has two key sizes: 64 and 128 bits, with a block size of 64 bits. The RBFK ciphers have a larger avalanche effect due to the butterfly architecture ensuring strong security. The proposed cipher satisfies the Shannon characteristics of confusion and diffusion. The memory usage and execution cycle of the RBFK cipher are assessed using the fair evaluation of the lightweight cryptographic systems (FELICS) tool. The proposed ciphers were also implemented using MATLAB 2021a to test key sensitivity by analyzing the histogram, correlation graph, and entropy of encrypted and decrypted images. Since the RBFK ciphers with minimal computational complexity provide better security than recently proposed competing ciphers, these are suitable for IoT devices in an edge computing environment. [ABSTRACT FROM AUTHOR]

Today's digital world necessitates the adoption of encryption techniques to ensure secure peer-to-peer communication. The sole purpose of this paper is to conglomerate the fundamentals of Blockchain, AI (Artificial Intelligence) and DNA (Deoxyribonucleic Acid) encryption into one proposed scheme, KryptosChain, which is capable of providing a secure information exchange between a sender and his intended receiver. The scheme firstly suggests a DNA-based Huffman coding scheme, which alternatively allocates purines—Adenine (A) and Guanine (G), and pyrimidines—Thymine (T) and Cytosine (C) values, while following the complementary rule to higher and lower branches of the resultant Huffman tree. Inculcation of DNA concepts makes the Huffman coding scheme eight times stronger than the traditional counterpart based on binary—0 and 1 values. After the ciphertext is obtained, the proposed methodology next provides a Blockchain-inspired message exchange scheme that achieves all the principles of security and proves to be immune to common cryptographic attacks even without the deployment of any smart contract, or possessing any cryptocurrency or arriving at any consensus. Lastly, different classifiers were engaged to check the intrusion detection capability of KryptosChain on the NSL-KDD dataset and AI fundamentals. The detailed analysis of the proposed KryptosChain validates its capacity to fulfill its security goals and stands immune to cryptographic attacks. The intrusion possibility curbing concludes that the J84 classifier provides the highest accuracy of 95.84% among several others as discussed in the paper. [ABSTRACT FROM AUTHOR]

A reconstruction algorithm is proposed, based on multi-dictionary learning (MDL), to improve the reconstruction quality of acoustic tomography for complex temperature fields. Its aim is to improve the under-determination of the inverse problem by the sparse representation of the sound slowness signal (i.e., reciprocal of sound velocity). In the MDL algorithm, the K-SVD dictionary learning algorithm is used to construct corresponding sparse dictionaries for sound slowness signals of different types of temperature fields; the KNN peak-type classifier is employed for the joint use of multiple dictionaries; the orthogonal matching pursuit (OMP) algorithm is used to obtain the sparse representation of sound slowness signal in the sparse domain; then, the temperature distribution is obtained by using the relationship between sound slowness and temperature. Simulation and actual temperature distribution reconstruction experiments show that the MDL algorithm has smaller reconstruction errors and provides more accurate information about the temperature field, compared with the compressed sensing and improved orthogonal matching pursuit (CS-IMOMP) algorithm, which is an algorithm based on compressed sensing and improved orthogonal matching pursuit (in the CS-IMOMP, DFT dictionary is used), the least square algorithm (LSA) and the simultaneous iterative reconstruction technique (SIRT). [ABSTRACT FROM AUTHOR]

Data compression is essential to reduce high storage and communication costs for a wide range of systems and applications. Canonical Huffman coding plays a pivotal role for several compression standards. This paper presents bit-parallel static and dynamic canonical Huffman decoder implementations using an optimized lookup table approach on a fine-grain many-core processor array and an Intel FPGA. The decoder implementation results are compared with an Intel i7-4850HQ and a massively parallel Nvidia GT 750M GPU executing the corpus benchmarks: Calgary, Canterbury, Artificial, and Large. The many-core implementations achieve a scaled throughput per chip area that is 891× and 7× greater on average than the i7 and GT 750M respectively. Moreover, the many-core implementations result in a scaled energy efficiency (compressed bits decoded per energy) that is 149.5×, 3.9×, and 2.5× greater on average than the i7, GT 750M, and Intel FPGA respectively. In addition, the optimized lookup-table-based static canonical Huffman decoder on the Intel FPGA yields performance and energy efficiency improvements of 2.1× and 3.68× respectively on average compared to a dynamic canonical Huffman decoder at a 17% cost in compression ratio. • The optimized look-up table approach speeds up the canonical Huffman decoding. • Static decoder executes faster than the dynamic decoder at the cost of compression ratio. • Many-core array implementation outperforms GPU, CPU, and FPGA in terms of area and energy efficiency. [ABSTRACT FROM AUTHOR]

Phishing is the most common and effective sort of attack employed by cybercriminals to deceive and steal sensitive information from innocent Web users. Researchers have developed major solutions to deal with this problem in recent years, but there are still a number of open challenges due to the ever-changing nature of phishing attacks. To discriminate between benign and phishing URLs, this paper proposes a static method based on lexical and string complexity analysis and distinguishing URL features. Proposed approach has been evaluated on the basis of two state of the art online learning classifiers. The confidence weighted learning classifier achieved a significant phishing URL detection accuracy of 98.35 %, error-rate of 1.65%, FPR of 0.026 and FNR of 0.005. Also, adaptive regularization of weight classifier achieved accuracy of 97.28%, error-rate of 2.72%, FPR of 0.000 and FNR of 0.052. Similar approach shows the improvement in the detection of the phishing web pages. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) end devices have major limitations related to hardware and energy autonomy. Generally, the highest energy consumption is related to communication, which accounts for up to 60% of consumption depending on the application. Among the strategies to optimize the energy consumed by communication, data compression methods are one of the most promising. However, most data compression algorithms are designed for personal computers and need to be adapted to the IoT context. This study aims to adapt classical algorithms, such as LZ77, LZ78, LZW, Huffman, and Arithmetic coding, and to analyse their performance and energy metrics in IoT end devices. The evaluation is performed in a device with an ESP32 processor and LoRa modulation. The study makes use of real datasets derived from two IoT applications. The results show compression rates close to 70%, a three-fold increase in the number of messages sent, and a reduction in energy consumption of 22%. An analytical model was also developed to estimate the gain in the battery life of the device using the adapted algorithms. [ABSTRACT FROM AUTHOR]

Data compression combined with effective encryption is a common requirement of data storage and transmission. Low cost of these operations is often a high priority in order to increase transmission speed and reduce power usage. This requirement is crucial for battery-powered devices with limited resources, such as autonomous remote sensors or implants. Well-known and popular encryption techniques are frequently too expensive. This problem is on the increase as machine-to-machine communication and the Internet of Things are becoming a reality. Therefore, there is growing demand for finding trade-offs between security, cost and performance in lightweight cryptography. This article discusses asymmetric numeral systems— an innovative approach to entropy coding which can be used for compression with encryption. It provides a compression ratio comparable with arithmetic coding at a similar speed as Huffman coding; hence, this coding is starting to replace them in new compressors. Additionally, by perturbing its coding tables, the asymmetric numeral system makes it possible to simultaneously encrypt the encoded message at nearly no additional cost. The article introduces this approach and analyzes its security level. The basic application is reducing the number of rounds of some cipher used on ANS-compressed data, or completely removing an additional encryption layer when reaching a satisfactory protection level. [ABSTRACT FROM AUTHOR]

Frame memory compression (FMC) saves power by reducing the data bandwidth for accessing reference frames of video coding standards in the external memory. However, since the continuously accumulated data loss degrades the performance of video coding standards, the quality of the reconstructed reference frame must be improved. Previous lossy frame memory compression methods focused on processing speed or simplicity, but their performance remains limited in terms of the quality of the reconstructed frame. In this study, a Huffman coding with a fixed codebook designed to considerably reduce the latency caused by arithmetic coding in lossless compression schemes is proposed. A novel lossy frame memory compression technique based on the Hadamard transform that does not require additional memory access using a lookup table is also proposed. The result of experiments on several reconstructed videos preprocessed with HEVC show that the proposed method improves BDPSNR by 10.7 dB and BDBR by 40.4% compared with the DWT+SPIHT‐based compression technique which is widely used for lossy frame memory compression. [ABSTRACT FROM AUTHOR]

Satellite data transmission is usually limited between hundreds of kilobits-per-second (kb/s) and several megabitsper-second (Mb/s) while the space-to-ground data volume is becoming larger as the resolution of the instruments increases while the bandwidth remains limited, typically. The Surface Water and Ocean Topography (SWOT) altimetry mission is a partnership between the National Aeronautics and Space Administration (NASA) and the Centre National des Études Spatiales (CNES) which uses the innovative KaRin instrument, a Ka band (35.75 GHz) synthetic aperture radar combined with an interforemeter. Its launch is expected for 2022 for oceanographic and hydrological levels measurement and it will generate 7 TeraBytes-per-day, for a lifetime total of 20 PetaBytes. That is why data compression needs to be implemented at both ends of satellite communications. This study compares the compression results obtained with 672 algorithms, mostly based on the Huffman coding approach which constitute the state-of-the-art for scientific data manipulation, including Computational Fluid Dynamics (CFD). We also have incorporated data preprocessing such as shuffle and bitshuffle, and a novel algorithm known as SL6. [ABSTRACT FROM AUTHOR]

In this paper, we propose improved maximum access delay time, noise variance, and power delay profile (PDP) estimation schemes for orthogonal frequency division multiplexing (OFDM) system in multipath fading channels. To this end, we adopt the approximate maximum likelihood (ML) estimation strategy. For the first step, the log-likelihood function (LLF) of the received OFDM symbols is derived by utilizing only the cyclic redundancy induced by cyclic prefix (CP) without additional information. Then, the set of the initial path powers is sub-optimally obtained to maximize the derived LLF. In the second step, we can select a subset of the initial path power set, i.e. the maximum access delay time, so as to maximize the modified LLF. Through numerical simulations, the benefit of the proposed method is verified by comparison with the existing methods in terms of normalized mean square error, erroneous detection, and good detection probabilities. [ABSTRACT FROM AUTHOR]