Coding Algorithm Research Articles

A Novel Steganography Method for Infrared Image Based on Smooth Wavelet Transform and Convolutional Neural Network

Infrared images have been widely used in many research areas, such as target detection and scene monitoring. Therefore, the copyright protection of infrared images is very important. In order to accomplish the goal of image-copyright protection, a large number of image-steganography algorithms have been studied in the last two decades. Most of the existing image-steganography algorithms hide information based on the prediction error of pixels. Consequently, reducing the prediction error of pixels is very important for steganography algorithms. In this paper, we propose a novel framework SSCNNP: a Convolutional Neural-Network Predictor (CNNP) based on Smooth-Wavelet Transform (SWT) and Squeeze-Excitation (SE) attention for infrared image prediction, which combines Convolutional Neural Network (CNN) with SWT. Firstly, the Super-Resolution Convolutional Neural Network (SRCNN) and SWT are used for preprocessing half of the input infrared image. Then, CNNP is applied to predict the other half of the infrared image. To improve the prediction accuracy of CNNP, an attention mechanism is added to the proposed model. The experimental results demonstrate that the proposed algorithm reduces the prediction error of the pixels due to full utilization of the features around the pixel in both the spatial and the frequency domain. Moreover, the proposed model does not require either expensive equipment or a large amount of storage space during the training process. Experimental results show that the proposed algorithm had good performances in terms of imperceptibility and watermarking capacity compared with advanced steganography algorithms. The proposed algorithm improved the PSNR by 0.17 on average with the same watermark capacity.

An adaptive image steganography algorithm based on the use of non-cryptographic hash functions for data extraction

An adaptive steganography algorithm for data hiding in full-color images based on iterative introduction of minor distortions into blocks of container images and the use of high-speed non-cryptographic hash functions for data extraction is considered. Modification of the minimum number of container elements compared to the length of the hidden message is a distinctive feature of the algorithm. This feature allows the hidden throughput to be increased and the visual and statistical visibility of hidden data to be reduced. The algorithm is compared with modern algorithms of adaptive spatial steganography in terms of assessing the level of distorting changes in stego-containers. In addition, a modified version of the algorithm that implements covert channel multiplexing using a common subset of container elements when embedding various messages into them is considered.

Optimization and Improvement of BP Decoding Algorithm for Polar Codes Based on Deep Learning

Abstract In order to solve the high latency problem of polar codes belief propagation decoding algorithm in the 5G and the dimension limitation problem of belief propagation decoding algorithm under deep learning, a multilayer perceptron belief propagation decoding (MLP-BP) algorithm based on partitioning idea is proposed. In this work, polar codes is decoded using neural networks in partitioning, and the right transfer message value of BP decoding algorithm is also set to complete the propagation process. Simulation results show that, compared with BP decoding algorithm, the proposed algorithm has better decoding performance, reducing the decoding latency, and it is also applicable to long polar codes.

Simplified SCL decoding algorithm of polar codes based on critical sets

Simplified SCL decoding algorithm of polar codes based on critical sets

Improve steganography system using agents software based on statistical and classification technique

In digital communications, information security is a paramount necessity. In the hiding algorithm, there are three basic parameters: security, capacity, and imperceptibility. Therefore, there are many ways to design the steganography algorithm, such as least significant bit (LSB), discrete wave transformation (DWT), and discrete cosine transform (DCT). The aim of this paper is to improve agent software design based on a steganography system. It proposed an agent system based on a support vector machine (SVM) classifier to hide a secret message in a certain cover image. The common dataset for steganography uses 80% training and 20% testing to get accurate results. Developing an agent system depends on six statistical parameters such as energy, standard deviation, histogram, variance, mean, and entropy. This resulted in features classified by the SVM classifier to predict the best cover image to be nominated for embedding. Worthy results were obtained in terms of imperceptibility, attack, and cover image prediction by statistical issues.

Secret Communication Using Multi-Image Steganography with Face Recognition and OTP System

Abstract: Our Proposed system is to develop a Web operation for hiding information in any image train to ensure the safety of the exchange of data between different military parties and give better security during communication transmission. We use the LSB fashion. The proposed approach uses a steganography algorithm to bed data in the image lines for military operations. For security purposes, we used modules face Recognition fashion with the AES algorithm for Strong Security purposes. And we use the cover channel fashion as an information-hiding fashion that can be exploited by a process to transfer information in a manner that violates the system security programs. Image steganography is the main aspect of information caching where the ciphertext is bedded into an image called a cover image which is coming to insolvable for the interferers to see with their naked eyes. The retired information can be any textbook, images, audio, or indeed videos inside a cover image. The abstract description ofMulti-image Steganography is that the secret law is divided into multiple corridors and is etched into multiple cover images. So we proposed two image steganography ideas to make it veritably grueling for the hackers to conceal the data. This paper proposes the Least significant bit( LSB) fashion of Steganography and the Advanced Encryption Standard( AES) fashion of Cryptography to make a safe and secure system. Then the sender and the receiver use the same key to encrypt and decipher the data which is popularly nominated a symmetric crucial Keywords AES Algorithm, LSB Algorithm, and Steganography.

Toward secret data location via fuzzy logic and convolutional neural network

Locating hidden data in digital images, otherwise called steganalysis, is a process of identifying the existence of secret messages within digital images. Steganalysis is used to manage digital data transmission by detecting the possible hidden information that can be used to violate the network policy; hence, it helps the development of policies and regulations aimed at strong protection from cyber threats to individuals’ and organizations’ data. The research works in the field of information security commonly focus on developing the locating approaches for non-adaptive steganography, which present a problem of less investigation of the complex challenge of locating the payload embedded with an adaptive steganographic algorithm. In this article, we propose a method to locate hidden data in a digital image in three stages: a) Identification of the modification maps between the carrier and final images. b) Using the modification maps as input to Mamdani fuzzy inference with four input membership functions: covariance map matrix, compass mean matrix, distance vector matrix, and pixel intensity matrix, and one output membership function, notably the fuzzy correlation maps. c) Feeding the fuzzy correlation maps to a convolutional neural network to identify the pixels with confidential data from the innocent pixels. By experimenting with our method against four steganographic algorithms, namely, HILL, HUGO-BD, WOW, and S-UNIWARD, the recall rates for the four algorithms initially increase in a similar range and improve with increasing payload capacity, which justifies the outperformance of the proposed strategy over the existing methods.

Performance Comparison of Modulation Technique and Coding Algorithms

Performance Comparison of Modulation Technique and Coding Algorithms

Human Behavior Recognition via Hierarchical Patches Descriptor and Approximate Locality-Constrained Linear Coding.

Human behavior recognition technology is widely adopted in intelligent surveillance, human-machine interaction, video retrieval, and ambient intelligence applications. To achieve efficient and accurate human behavior recognition, a unique approach based on the hierarchical patches descriptor (HPD) and approximate locality-constrained linear coding (ALLC) algorithm is proposed. The HPD is a detailed local feature description, and ALLC is a fast coding method, which makes it more computationally efficient than some competitive feature-coding methods. Firstly, energy image species were calculated to describe human behavior in a global manner. Secondly, an HPD was constructed to describe human behaviors in detail through the spatial pyramid matching method. Finally, ALLC was employed to encode the patches of each level, and a feature coding with good structural characteristics and local sparsity smoothness was obtained for recognition. The recognition experimental results on both Weizmann and DHA datasets demonstrated that the accuracy of five energy image species combined with HPD and ALLC was relatively high, scoring 100% in motion history image (MHI), 98.77% in motion energy image (MEI), 93.28% in average motion energy image (AMEI), 94.68% in enhanced motion energy image (EMEI), and 95.62% in motion entropy image (MEnI).

Validation of diagnostic coding for bronchiectasis in an electronic health record system in Hong Kong.

Electronic medical record (EMR) databases can facilitate epidemiology research in various diseases including bronchiectasis. Given the diagnostic challenges of bronchiectasis, the validity of the coding in EMR requires clarification. We aimed to assess the validity of International Classification of Diseases, 9th Revision (ICD-9) code algorithms for identifying bronchiectasis in the territory-wide electronic medical health record system of Clinical Data Analysis and Reporting System (CDARS) in Hong Kong. Adult patients who had the diagnosis of bronchiectasis input from Queen Mary Hospital in 2011-2020 were identified using the ICD-9 code of 494 by CDARS. All patients who had high resolution computed tomography (HRCT) were reviewed by respiratory specialists to confirm the presence of bronchiectasis on HRCT. 19 617 patients who had the diagnostic code of bronchiectasis among all public hospitals in Hong Kong and 1866 in Queen Mary Hospital in the same period. 648 cases were randomly selected and validated using medical record and HRCT review by a respiratory specialist. The overall positive predictive value (PPV) was 92.7% (95% CI 90.7-94.7). This was the first ICD-9 coding validation for bronchiectasis in Hong Kong CDARS. Our study demonstrated that using ICD-9 code of 494 was reliable to support utility of CDARS database for further clinical research on bronchiectasis. This article is protected by copyright. All rights reserved.

Non-Gaussian Reconciliation for Continuous-Variable Quantum Key Distribution

Non-Gaussian modulation can improve the performance of continuous-variable quantum key distribution (CV QKD). For Gaussian-modulated coherent-state CV QKD, photon subtraction can realize non-Gaussian modulation, which can be equivalently implemented by non-Gaussian postselection. However, non-Gaussian reconciliation has not been deeply researched, which is one of the key technologies in CV QKD. In this paper, we propose a non-Gaussian reconciliation method to obtain identical keys from non-Gaussian data. Multidimensional reconciliation and multiedge-type low-density parity-check codes (MET LDPC) are used in a non-Gaussian reconciliation scheme, where the layered belief propagation decoding algorithm of MET LDPC codes is used to reduce the decoding complexity. Furthermore, we compare the error-correction performance of Gaussian data and non-Gaussian data. The results show that the error-correction performance of non-Gaussian data is better than Gaussian data, where the frame error rate can be reduced by 50 % for code rate 0.1 at SNR of 0.1554 and the average iteration number can be reduced by 25 %.

Fuzzy linear codes based on nested linear codes

In this paper, we describe a correspondence between a fuzzy linear code and a family of nested linear codes. We also describe the arithmetic of fuzzy linear codes. As a special class of nested linear codes, we consider a family of nested self-orthogonal codes. A linear code is self-orthogonal if it is contained in its dual and self-dual if it is equal to its dual. We introduce a definition of fuzzy self-dual or self-orthogonal codes which include classical self-dual or self-orthogonal codes. As examples, we construct several interesting classes of fuzzy linear codes including fuzzy Hamming codes, fuzzy Golay codes, and fuzzy Reed-Muller codes. We also give a general decoding algorithm for fuzzy linear codes.

On the equivalence of linear cyclic and constacyclic codes

We introduce new sufficient conditions for permutation and monomial equivalence of linear cyclic codes over various finite fields. We recall that monomial equivalence and isometric equivalence are the same relation for linear codes over finite fields. A necessary and sufficient condition for the monomial equivalence of linear cyclic codes through a shift map on their defining set is also given. Finally, we prove that if gcd⁡(3n,ϕ(3n))=1, where ϕ is the Euler's totient function, then all permutation equivalent constacyclic codes of length n over F4 are given by the action of multipliers. The results of this work allow us to prune the search algorithm for new linear codes and discover record-breaking linear and quantum codes.

A Progressive Approach to Arabic Character Recognition Using a Modified Freeman Chain Code Algorithm

Arabic character identification presents a significant obstacle to the comprehension and analysis of Arabic text. This paper presents an improved technique that generates Freeman code from handwritten Arabic characters. This code provides the shortest code length without losing character information, accounting for all handwritten Arabic character variants. We tested this code using a set of Arabic characters in various formats to identify Arabic characters in order to take use of the code generated by our enhanced method. We also performed a comparison between our Freeman code and codes generated in other related research. In light of this, the code that we obtained correctly represents the Arabic letter in all of its variants, including the ones that the algorithms in previous publications did not consider. Consequently, our novel method based on Freeman coding represents a significant advancement in Arabic character recognition. Furthermore, our method provides a successful way of identifying and presenting Arabic characters

Research on the Improvement of LSB-based Image Steganography Algorithm

This study has made an improvement based on the LSB algorithm, which uses a randomly generated sequence for embedding information, with an implementation environment of Python and a combination of OpenCV By analyzing the embedded data with PSNR values and pretzel-plus-noise attacks, the results show that this method has better image quality and stronger resistance to attacks, while the complexity of extracting the embedded information by external users is high. The improved LSB algorithm in this study has great reference value for information covert transmission, and can be used for image copyright protection and privacy information protection in practical applications.

Design of Polar Codes and PAC Codes for SCL Decoding

The performance of a code under the maximum-likelihood (ML) decoder highly depends on the weight enumerating function (WEF). However, how to compute efficiently the WEF of a polar code or a polarization-adjusted convolutional (PAC) code is still an open problem. For the design of stand-alone polar codes, we consider enumerating the number of minimum-weight non-zero codewords of the polar code. The block error rate (BLER) under the ML decoder can be approximated as a function of the minimum weight of non-zero codewords and its multiplicity. On the other hand, for the design of PAC codes, we consider enumerating the WEF averaged over the ensemble of random PAC codes. The ML-BLER upper bound can be represented as a function of the WEF. The bit-channel selection algorithms for polar codes and PAC codes are proposed, which take both utilization of the polarization effect and the ML decoding performance as selection criteria. Simulation results show that the proposed stand-alone polar codes are competitive when the block length gets larger. Also, simulation results show that the proposed PAC codes yield excellent performance for a wide range of code rates and block lengths and outperform the 5G polar codes.

A Rank-Iteration Based Blind Identification Method for Convolutional Codes

A blind identification technology of channel coding has received increasing attentions in recent years. In this letter, we focus on blind identification of convolutional codes without a candidate set. To improve the blind identification accuracy, a novel rank-iteration based blind identification algorithm for convolutional codes is proposed in this paper. Specifically, a weight-dependent threshold for the Binary Symmetric Channel (BSC) channel is firstly proposed to calculate the rank of a received data matrix more accurately. Then, we propose a new iterative operation that repeats the Gauss-Jordan Elimination Through Pivoting (GJETP) algorithm for several times, and select the smallest rank as the rank of the data matrix. Employing this strategy, the rank of a rank-deficient matrix in noisy environments can be close to its noiseless rank. Further, a rank-deficient threshold is introduced to amend the rank of the full-rank matrix after the end of the whole iterative process, which can reduce the rank deviation of the full-rank matrix. Finally, the effectiveness of the proposed method is verified by simulations and comparisons.

Research on Improved DNA Coding and Multidirectional Diffusion Image Encryption Algorithm.

In order to make the security and operating efficiency of an image encryption algorithm coexist, this study proposed a color image encryption algorithm with improved DNA coding and rapid diffusion. During the stage of improving DNA coding, the chaotic sequence was used to form a look-up table to complete the base substitutions. In the replacement process, several encoding methods were combined and interspersed to make the randomness higher, thereby improving the security performance of the algorithm. In the diffusion stage, three-dimensional and six-directional diffusion was performed on the three channels of the color image by taking the matrix and the vector as the diffusion unit successively. This method not only ensures the security performance of the algorithm, but also improves the operating efficiency in the diffusion stage. From the simulation experiments and performance analysis, it was shown that the algorithm has good encryption and decryption effects, large key space, high key sensitivity, and strong security. The algorithm can effectively resist differential attacks and statistical attacks, and has good robustness.

СТЕГАНОГРАФИЧЕСКОЕ ВСТРАИВАНИЕ ИНФОРМАЦИИ В SVG-ИЗОБРАЖЕНИЯ НА ВЕБ-СТРАНИЦЕ

This study suggests a steganographic algorithm for embedding data in SVG images integrated into html code. The algorithm uses a composite key that includes an embed key, an encryption key, and a replacement table. The basic principle of embedding is to add style classes to an SVG image. These classes do not affect the picture in the browser. The message is encoded as the names of the classes to be added. Class names are formed based on a symmetric encryption algorithm and a replacement table. One class name contains 9 bits of information. A style set is generated for each added class. The new styles do not affect the appearance of the picture. Style settings are defined either based on default values or based on existing classes. Embed labels are added to tags with embedded data. The labels are calculated based on the hash functions from the string from the embedding key and the block number. Embedded data is randomly distributed over the code of an html page with SVG images. This arrangement makes it difficult to analyze the page when hacking the system. Embedded information is detected by sequential analysis of page code and search for embed marks. The sender of the message must have a permission to edit the web page. The recipient retrieves the message in page code view. An analysis of possible attacks on the proposed system has been carried out. Hacking of the system is possible only by direct overhauling. The system is resilient with the correct choice of hash function and encryption algorithm. The proposed steganographic embedding method is effective for html pages containing a fairly large number of SVG images.

Text, Image and Audio Steganography

Abstract: Steganography refers to the concealment of confidential data within a common medium, like a text, image, or audio file, without altering its original appearance, to evade detection. This method is used for several purposes, including protecting data privacy, preventing unauthorized access to data, and ensuring safe information transmission over insecure networks. While steganography can be used for legitimate purposes such as by military, law enforcement, and journalism, it can also be employed for malicious activities like cybercrime and terrorism. The least significant bit (LSB) of each letter in text & audio and the LSB of each pixel in images are altered in steganography to make the alterations invisible. Advanced steganography algorithms like Masking & Filtering and Encrypt & Scatter have improved security against steganalysis assaults while producing higher-quality stego images. The method’s capability for concealing text with an unlimited hidden text size in text, audio, and images will be employed in the outcome.