Coding Algorithm Research Articles

A Magnetic Flux Leakage Detector for Ferromagnetic Pipeline Welds with a Magnetization Direction Perpendicular to the Direction of Travel.

For the sake of realizing the safety detection of natural gas and petroleum pipeline welds, this paper designs a ferromagnetic pipeline weld magnetic flux leakage detector based on the calculation of the magnetic circuit of the detection probe, with the magnetization direction perpendicular to the traveling direction. The traditional pipeline magnetic flux leakage detection device uses a detection system mode in which the magnetization direction is parallel to the direction of travel. However, due to the structural characteristics of the weld, the traditional detection system mode is not applicable. Since the weld magnetic flux leakage detector needs to travel along the direction of the weld, the detector designed in this paper rotates the magnetizer 90 degrees along the direction of the weld seam so that the magnetization direction is perpendicular to the direction of travel, breaking through the technical barrier that make traditional magnetic flux leakage detection devices unsuitable for weld detection. The detection device includes a magnetizing structure, a data sampling device, and a driving and traveling device. The magnetic flux leakage signal collected by the detector is converted into a digital image in the form of a grayscale matrix. Using mathematical morphology and chain code algorithms in image processing technology, a pipeline weld defect inversion software system is developed, and a preliminary quantitative analysis of pipeline weld defects is achieved. The application of this technology enables the inspection and protection of oil and gas pipeline welds throughout their life cycle, broadens the scope of existing inspection objects, and is of great safety significance for ensuring national public security.

Adaptive 3D Mesh Steganography Based on Feature-Preserving Distortion.

Current 3D mesh steganography algorithms relying on geometric modification are prone to detection by steganalyzers. In traditional steganography, adaptive steganography has proven to be an efficient means of enhancing steganography security. Taking inspiration from this, we propose a highly adaptive embedding algorithm, guided by the principle of minimizing a carefully crafted distortion through efficient steganography codes. Specifically, we tailor a payload-limited embedding optimization problem for 3D settings and devise a feature-preserving distortion (FPD) to measure the impact of message embedding. The distortion takes on an additive form and is defined as a weighted difference of the effective steganalytic subfeatures utilized by the current 3D steganalyzers. With practicality in mind, we refine the distortion to enhance robustness and computational efficiency. By minimizing the FPD, our algorithm can preserve mesh features to a considerable extent, including steganalytic and geometric features, while achieving a high embedding capacity. During the practical embedding phase, we employ the Q-layered syndrome trellis code (STC). However, calculating the bit modification probability (BMP) for each layer of the Q-layered STC, given the variation of Q, can be cumbersome. To address this issue, we design a universal and automatic approach for the BMP calculation. The experimental results demonstrate that our algorithm achieves state-of-the-art performance in countering 3D steganalysis.

Linguistic steganalysis via multi-task with crossing generative-natural domain

In the real world, the sources of text often transcend the boundaries of generative and natural domains, which introduces challenges for existing linguistic steganalysis methods. The typical problem arises from the sample selection bias caused by training solely on a single-source domain, rendering the model incapable of inferencing across the entire generative-natural (GN) space. Moreover, there exists the problem of overlooking the sensitive discrepancies between generative text and natural text, which hampers model fitting. In this paper, we model steganalysis in a brand-new perspective by employing multi-task learning to build the main task and auxiliary tasks in the cross GN domain. The proposed Cross Generative-Natural Domain Multi-task Model (CG-NDMM) can concurrently address the two aforementioned issues through i) modeling steganalysis across the entire GN space, incorporating two auxiliary tasks alongside a main task, and ii) utilizing a feature representation transfer learning strategy to harmonize two sub-networks. Furthermore, we employ diverse steganography algorithms to construct the datasets, which comprise four types of texts (generative-cover, generative-steganographic, natural-cover, and natural-steganographic) derived from two public datasets, Movie and Twitter. The experiments on these datasets demonstrate the effectiveness of the proposed approach, showcasing its substantially superior performance over the comparative baseline methods.

Explorer: efficient DNA coding by De Bruijn graph toward arbitrary local and global biochemical constraints.

With the exponential growth of digital data, there is a pressing need for innovative storage media and techniques. DNA molecules, due to their stability, storage capacity, and density, offer a promising solution for information storage. However, DNA storage also faces numerous challenges, such as complex biochemical constraints and encoding efficiency. This paper presents Explorer, a high-efficiency DNA coding algorithm based on the De Bruijn graph, which leverages its capability to characterize local sequences. Explorer enables coding under various biochemical constraints, such as homopolymers, GC content, and undesired motifs. This paper also introduces Codeformer, a fast decoding algorithm based on the transformer architecture, to further enhance decoding efficiency. Numerical experiments indicate that, compared with other advanced algorithms, Explorer not only achieves stable encoding and decoding under various biochemical constraints but also increases the encoding efficiency and bit rate by ¿10%. Additionally, Codeformer demonstrates the ability to efficiently decode large quantities of DNA sequences. Under different parameter settings, its decoding efficiency exceeds that of traditional algorithms by more than two-fold. When Codeformer is combined with Reed-Solomon code, its decoding accuracy exceeds 99%, making it a good choice for high-speed decoding applications. These advancements are expected to contribute to the development of DNA-based data storage systems and the broader exploration of DNA as a novel information storage medium.

Disguising Text Using Caesar Cipher, Reverse Cipher and Least Significant Bit (LSB) Algorithms in Video

In communication, there is a process of transferring information from the sender to the recipient. The information sent must be the same as the information received. If there are differences, it means that there has been a data change process carried out by irresponsible parties. One technique for changing the content of information is man in the middle. The data changer will receive information from the sender, then change it and forward it to the recipient, so that the changed information appears to have come from the sender.To protect information, this can be done by utilizing the science of cryptography and steganography which aims to protect information by changing it to another form or by inserting the information into other media. In this research, to protect information the Caesar Cipher Algorithm is used, this algorithm will change the letters in plaintext to another letter (ciphertext) by using an alphabetical shift according to the number in the form of the key used, namely > 1 and < 26, then the Reverse Cipher algorithm is carried out, namely changing the position of the letters of the plaintext from the first order to the last order and so on. The encrypted information will then be inserted into a video using the Steganography Algorithm, namely Least Significant Bit (LSB). Before being inserted, the video will first be converted into several image frames, then in one frame the information will be inserted. This can be done because the frame is a collection of RGB arrays which have values 0-255 or 0 and 1. So the insertion is done in bit form. Frames containing information will then be converted back into a video.On the receiving side, the video will be converted into a frame, next is the process of retrieving the information that was previously inserted. The information that has been taken is then reversed in order and then shifted using the Caesar chipper algorithm according to the key used by the sender, then the first letter of each word is changed to capital, so that the information sent is the same as that received. The implication of this research is that it is a way to combine cryptography with steganography as an information security technique.

Incoherent frequency-selective multiplexing holographic encryption by using a triple-beam setup

Inspired by the wavefront masking of the scattering medium, we proposed a multiplexed coded aperture holographic encryption method. The incoherent multiplexed phase mask encryption experiments involved in the method are realized for what we believe to be the first time. From the holograms, we extracted three images using the frequency-selective phase iterative coding algorithm we purposely put forward. Through the theoretical analysis of frequency-selective phase coding, the constructed linear system can recover the original image well. We verified the security of the cryptography using structural similarity index measurements, key space, key sensitivity, and axial multiplexing. Through the theoretical analysis of frequency-selective phase coding, we think the present triple-multiplexed cryptography can be directly extended to multiple-multiplexed cases and applied to multi-image encryption. It may cause a paradigm shift in applications such as incoherent image encryption, wavefront acquisition in scattering, information hiding, and fast target identification.

USteg-DSE: Universal quantitative Steganalysis framework using Densenet merged with Squeeze & Excitation net

Carrying concealed communication via media is termed as steganography and unraveling details of such covert transmission is known as steganalysis. Extracting details of hidden message like length, position, embedding algorithm etc. forms part of forensic steganalysis. Predicting length of payload in camouflaged interchange is termed as quantitative steganalysis and is an indispensable tool for forensic investigators. When payload length is estimated without any prior knowledge about cover media or used steganography algorithm, it is termed as universal quantitative steganalysis.Most of existing frameworks on quantitative steganalysis available in literature, work for a specific embedding algorithm or are domain specific. In this paper we propose and present USteg-DSE, a deep learning framework for performing universal quantitative image steganalysis using DenseNet with Squeeze & Excitation module (SEM). In deep learning techniques, deeper networks easily capture complex statistical properties. But as depth increases, networks suffer from vanishing gradient problem. In classic architectures, all channels are equally weighted to produce feature maps. Presented USteg-DSE framework overcomes these problems by using DenseNet and SEM. In DenseNet, each layer is directly connected with every other layer. DenseNet makes information and gradient flow easier with fewer feature maps. SEM incorporates content aware mechanism to adaptively regulate weight for every feature map. Presented framework has been compared with existing state-of-the-art techniques for spatial domain as well as transform domain and show better results in terms of Mean Absolute Error (MAE) and Mean Square Error (MSE).

An Efficient Privacy and Anonymity Setup on Hyperledger Fabric for Blockchain-Enabled Internet of Things (IoT) Devices

The rise in IoT (Internet of Things) devices poses a significant security challenge. Maintaining privacy and ensuring anonymity within the system is a sought-after feature with inevitable trade-offs, such as scalability and increased complexity, making it incredibly challenging to handle. To tackle this, we introduce our proposed work for managing IoT devices using Hyperledger Fabric. We integrated our system on the blockchain with a closed-circuit television (CCTV) security camera fixed at a rental property. The CCTV security camera redirects its feed whenever a new renter walks in. We have introduced the web token for authentication from the renter to the owner. Our contributions include an efficient framework architecture using key invalidation scenarios and token authentication, a novel chain code algorithm, and stealth addresses with modified ring signatures. We performed different analyses to show the system’s throughput and latency through stress testing. We have shown the significant advantages of the proposed architectures by comparing similar existing schemes. Our proposed scheme enhances the security of blockchain-enabled IoT devices and mitigates the single point of failure issue in the existing scheme, providing a robust and reliable solution. Our future work includes scaling it up to cater to the needs of the healthcare system.

Errorless robust JPEG steganography using steganographic polar codes

Recently, a robust steganographic algorithm that achieves errorless robustness against JPEG recompression has been proposed. The method employs a lattice embedding scheme and utilizes the syndrome-trellis code (STC) for practical embedding. However, we have noticed that errorless robust embedding with STC may encounter failures due to modifications on wet coefficients, especially when a high quality factor is used by the compression channel. To solve this problem, we have discovered that using steganographic polar code (SPC) for embedding has better performance in avoiding modifications on wet coefficients. In this paper, we conduct theoretical analysis to prove the better performance of SPC in wet paper embedding. We establish the condition of avoiding modifications on wet coefficients, followed by presenting a recursive calculation method for determining the distribution of columns in the generator matrix of SPC. The findings reveal that SPC can avoid modifications on wet coefficients under a larger number of wet coefficients compared with STC, and therefore we propose a better errorless robust embedding method employing SPC. The experimental results demonstrate that under close security performance, the proposed method achieves a higher success rate compared with embedding with STC. Specifically, when the quality factor of the compressor is 95 and the payload size is 0.4 bpnzac, our method achieves a success rate of 99.85%, surpassing the 91.95% success rate of the embedding with STC.

Decoder-ROI based Versatile Video Coding for Multi-Object Tracking Vision Task

The video encoding standards High Efficiency Video Coding (HEVC) and, more recently, Versatile Video Coding (VVC) have introduced significant advancements in multimedia communication applications, such as video conferencing, broadcasting, and notably, E-learning. However, recent developments in artificial intelligence (AI) and big data have given rise to an urgent need for a specialized video encoding model designed specifically for image and video analysis applications using machine vision. In this paper, we propose a novel video encoding approach that effectively combines the ROI Coding algorithm and the VVC encoding model. The proposed method identifies regions of interest within video frames through fundamental and deep features. Based on this, we propose an adaptive compression method for each frame block, ensuring both the execution performance of machine learning applications and minimal data encoding requirements. To achieve new coding scheme without adding bitrate, New feature extraction approach are utilizing only decoded information (Decoder-ROI). The results demonstrate that the Decoder-ROI achieved significant compression rate improvement when compared to standard and relevant VCM schemes. Furthermore, ROI exploitation contributes to a 3.25\% reduction in encoding time compared to the baseline VVC encoding standard.

Clustering-Based Dual-Population Co-Evolutionary Algorithm for 2D Segmentation Coding in NAND Flash Memory

Clustering-Based Dual-Population Co-Evolutionary Algorithm for 2D Segmentation Coding in NAND Flash Memory

Video saliency detection using modified high efficiency video coding and background modelling

Video saliency has a profound effect on our lives with its compression efficiency and precision. There have been several types of research done on image saliency but not on video saliency. This paper proposes a modified high efficiency video coding (HEVC) algorithm with background modelling and the implication of classification into coding blocks. This solution first employs the G-picture in the fourth frame as a long-term reference and then it is quantized based on the algorithm that segregates using the background features of the image. Then coding blocks are introduced to decrease the complexity of the HEVC code, reduce time consumption and overall speed up the process of saliency. The solution is experimented upon with the dynamic human fixation 1K (DHF1K) dataset and compared with several other state-of-the-art saliency methods to showcase the reliability and efficiency of the proposed solution.

The EEG signals steganography based on wavelet packet transform-singular value decomposition-logistic

Brain-computer interface (BCI) technology is widely used in online medicine for the diagnosis and treatment of brain diseases. However, during brain-computer interaction, Electroencephalogram (EEG) signals and private information may be leaked when transmitted through unsecured Internet channels. To protect private information and EEG signal security, this paper proposes a steganography algorithm based on Wavelet Packet Transform-Singular Value Decomposition-Logistic (WPT-SVD-Logistic). The algorithm utilized wavelet packet transform (WPT) to conceal more private information while maintaining better perceptual fidelity. After two-level WPT processing, the EEG signal is decomposed into 4 sub-band signals, and then the singular value decomposition (SVD) method is used to embed private information into these sub-band signals. Additionally, the algorithm employed the Logistic map to confuse private information further and enhance its security. Experimental results show that WPT is more suitable for information hiding in EEG signals. The average peak signal-to-noise ratio of the algorithm on four different datasets is 96.5 dB, indicating that adding private information has a weak impact on the EEG signal. Compared with similar methods, this algorithm has smaller errors and stronger robustness, so it has more potential to become the main means of EEG signal steganography.

Алгоритм стеганографической защиты информации в видеофайлах на основе диффузионно-вероятностной модели с шумоподавлением

The results of a study are presented, the purpose of which was to develop a steganography algorithm for hiding text messages in video files. The algorithm is based on the use of a diffusion-probability model with noise reduction, which is implemented by a deep artificial neural network. The algorithm consists of two parts – for the parties sending and receiving the message. On the transmitting side, the following is carried out: synthesis of handwritten images of symbols (signatures) of the line of the hidden message, alignment of their frequency; applying direct diffusion to signatures, resulting in the generation of a noisy image that is deposited into a video stego container. At the receiving end, signatures are extracted from the video content, back diffusion is performed to obtain signatures of handwritten string characters, which are recognized using a convolutional neural network. The novelty of the research lies in the original developed algorithm for steganographic information protection in video files, as well as in a modified method of signature deposition based on the method of replacing the least significant bits. The method consists of bitwise embedding of bytes characterizing the pixel brightness level in the signature into the same blue brightness digits in a sequence of 8 frames of a video stego container. This method made it possible to significantly reduce the visible changes made to the video content when replacing not the least significant bits, but the middle significant bits in the stego container. This, in turn, provides greater resistance to compression attacks when transmitting information over the stegochannel. The practical significance of the research results lies in the developed software, with the help of which the algorithm for steganographic information protection in video files was tested, which showed high values of the peak signal-to-noise ratio and the index of structural similarity of images when embedding information in the middle bits of the bytes that set the brightness of the pixels of the stego container.

T-SMILES: a fragment-based molecular representation framework for de novo ligand design

Effective representation of molecules is a crucial factor affecting the performance of artificial intelligence models. This study introduces a flexible, fragment-based, multiscale molecular representation framework called t-SMILES (tree-based SMILES) with three code algorithms: TSSA (t-SMILES with shared atom), TSDY (t-SMILES with dummy atom but without ID) and TSID (t-SMILES with ID and dummy atom). It describes molecules using SMILES-type strings obtained by performing a breadth-first search on a full binary tree formed from a fragmented molecular graph. Systematic evaluations using JTVAE, BRICS, MMPA, and Scaffold show the feasibility of constructing a multi-code molecular description system, where various descriptions complement each other, enhancing the overall performance. In addition, it can avoid overfitting and achieve higher novelty scores while maintaining reasonable similarity on labeled low-resource datasets, regardless of whether the model is original, data-augmented, or pre-trained then fine-tuned. Furthermore, it significantly outperforms classical SMILES, DeepSMILES, SELFIES and baseline models in goal-directed tasks. And it surpasses state-of-the-art fragment, graph and SMILES based approaches on ChEMBL, Zinc, and QM9.

Efficient DNA Coding Algorithm for Polymerase Chain Reaction Amplification Information Retrieval

Polymerase Chain Reaction (PCR) amplification is widely used for retrieving information from DNA storage. During the PCR amplification process, nonspecific pairing between the 3’ end of the primer and the DNA sequence can cause cross-talk in the amplification reaction, leading to the generation of interfering sequences and reduced amplification accuracy. To address this issue, we propose an efficient coding algorithm for PCR amplification information retrieval (ECA-PCRAIR). This algorithm employs variable-length scanning and pruning optimization to construct a codebook that maximizes storage density while satisfying traditional biological constraints. Subsequently, a codeword search tree is constructed based on the primer library to optimize the codebook, and a variable-length interleaver is used for constraint detection and correction, thereby minimizing the likelihood of nonspecific pairing. Experimental results demonstrate that ECA-PCRAIR can reduce the probability of nonspecific pairing between the 3’ end of the primer and the DNA sequence to 2–25%, enhancing the robustness of the DNA sequences. Additionally, ECA-PCRAIR achieves a storage density of 2.14–3.67 bits per nucleotide (bits/nt), significantly improving storage capacity.

Improved Decoding Algorithms for Convolutional Codes

Improved Decoding Algorithms for Convolutional Codes

Quantum differential evolutionary algorithm with quantum-adaptive mutation strategy and population state evaluation framework for high-dimensional problems

Differential Evolution (DE) has been found to be inefficient and inaccurate in addressing high-dimensional complex problems. The Quantum-inspired Differential Evolution algorithm (QDE), endowed with quantum computing characteristics, efficiently manages high-dimensional problems but suffers from excessive mutation and poor convergence performance. Therefore, a new quantum differential evolutionary algorithm with quantum-adaptive mutation strategy and population state evaluation framework, namely PSEQADE is proposed. In PSEQADE, the quantum adaptive mutation strategy is employed to address the issue of excessive mutation in QDE, which adaptively reduces the degree of mutation, taking full advantage of the exceptional performance of quantum computing to enhance convergence accuracy. The quantum adaptive PSE framework is introduced to monitor the unstable mutation trends within the population, evaluate the population’s state, and intervene accordingly, thereby significantly improving the convergence performance and stability of the quantum differential evolution algorithm. 20 well-known functions from CEC2017 were selected for comparison with EPSDE, SADE, SHADE, JADE, CODE algorithms in dimensions of 500, 1000 and 3000. Additionally, comparisons were conducted with MLSHADE-SPA, SHADE-ILS, CCPSO2, NFDDE, DBO, and RIME algorithms in the dimension of 3000. Experimental results demonstrate that PSEQADE exhibits excellent convergence performance, high convergence accuracy, and exceptional stability in solving high-dimensional complex problems.

A Robust Coverless Image Steganography Algorithm Based on Image Retrieval with SURF Features

With the advancement of image steganography, coverless image steganography has gained widespread attention due to its ability to hide information without modifying the carrier of images. However, existing coverless image steganography methods often require both communicating parties to transmit an amount of additional information including image blocks’ locations or a large number of parameters, which will raise a serious suspicion. In light of this issue, we propose a robust coverless image steganography algorithm based on Speeded-Up Robust Features (SURF). Firstly, the proposed method allows both communicating parties to independently create multiple coverless image datasets (CIDs) using random seeds. Then, a mapping rule is designed for creating one-to-one correspondence between hash sequences and images in CIDs. Finally, the secret information will be carried by the images whose hash sequences are equal to the secret segments. At the receiver side, the robust SURF of images is utilized to retrieve the secret information. Experimental results demonstrate that the proposed algorithm outperforms other methods in terms of capacity, robustness, and security. Furthermore, it is worth noting that the proposed method eliminates the need to transmit a large amount of additional information, which is a significant security issue in existing coverless image steganography algorithms.

Risk Analysis of Respiratory Syncytial Virus Among Infants in the United States by Birth Month.

Respiratory syncytial virus (RSV) is a major cause of morbidity and mortality among U.S. infants. A child's calendar birth month determines their age at first exposure(s) to RSV. We estimated birth month-specific risk of medically attended (MA) RSV lower respiratory tract infection (LRTI) among infants during their first RSV season and first year of life. We analyzed infants born in the USA between July 2016 and February 2020 using three insurance claims databases (two commercial, one Medicaid). We classified infants' first MA RSV LRTI episode by highest level of care incurred (outpatient, emergency department, or inpatient), employing specific and sensitive diagnostic coding algorithms to define index RSV diagnoses. In our main analysis we focused on infants' first RSV season. In our secondary analysis we compared the risk of MA RSV LRTI during infants' first RSV season to that of their first year of life. Infants born from May through September generally had the highest risk of first-season MA RSV LRTI-approximately 6%-10% under the specific RSV index diagnosis definition and 16%-26% under the sensitive. Infants born between October and December had the highest risk of RSV-related hospitalization during their first season. The proportion of MA RSV LRTI events classified as inpatient ranged from 9%-54% (specific) and 5%-33% (sensitive) across birth month and comorbidity group. Through the first year of life, the overall risk of MA RSV LRTI is comparable across birth months within each claims database (6%-11% under the specific definition, 17%-30% under the sensitive), with additional cases progressing to care at outpatient or ED settings. Our data support recent national recommendations for the use of nirsevimab in the USA. For infants born at the tail end of an RSV season who do not receive nirsevimab, a dose administered prior to the onset of their second RSV season could reduce the incidence of outpatient and ED-related events.