Coding Algorithm Research Articles

A novel cancellable biometric recognition system based on Rubik’s cube technique for cyber-security applications

ObjectiveTen years ago, biometrics became an efficient tool of security and authentication enforcement. In a classical access system, a user can easily change password if it is compromised. However, user’s biometrics are limited and unique, and if a user biometric trait is compromised, it will be impossible to change it. Hence, cancellable biometric systems depend on the transformation of data or a biometric feature into a new format, so that users can replace their biometric templates in the same or a different system. In this paper, an efficient cancellable biometric recognition system is presented. In this system, both signal-and feature-level transformations are used to generate the cancellable templates MethodsA novel hybrid encryption framework based on the Rubik’s cube technique is presented to allow efficient encryption of face, iris, and fingerprint biometric templates. This framework begins with extraction of biometric features using the Scale-Invariant Feature Transform (SIFT). After that, the resulting biometric features are encrypted with the optical Double Random Phase Encoding (DRPE) algorithm. Then, the resulting encrypted biometric features are further encrypted with the chaotic Baker map permutation, Advanced Encryption Standard (AES), or Ron's Code (RC6) algorithm in the Cipher Feed Back (CFB) mode of operation. Finally, the resulting encrypted biometrics are additionally encrypted with the Rubik’s cube technique. The chaotic, RC6, or AES encrypted biometrics are utilized on the faces of the Rubik’s cube. From the concepts of image encryption, the DRPE, RC6, and the AES algorithms introduce a degree of diffusion, whilst the chaotic Baker map adds a degree of permutation. Moreover, the Rubik’s cube technique adds more permutation to the encrypted biometrics, simultaneously. The quality evaluation of the proposed cancellable biometric system reveals good performance. ResultsThe simulation results prove that the suggested hybrid encryption framework is reliable, and it presents recommended security and robustness levels for its utilization for building efficient cancellable biometric systems compared to the traditional techniques.

Hybrid Cryptography: Secure File Storage on using AES, RC6 and Blow Fish Algorithm

In this era, cloud computing is used in various fields like industry, military, college, etc. for various services and storage of huge amount of data. Data stored in this cloud can be accessed or retrieved on the users request without direct access to the server computer. But the major concern regarding storage of data online that is on the cloud is the Security. This Security concern can be solved using various ways, the most commonly used techniques are cryptography and steganography. But sometimes a single technique or algorithm alone cannot provide high-level security. So we have introduces a new security mechanism that uses a combination of multiple cryptographic algorithms of symmetric key and steganography. In this proposed system, RC6 (Rivest Cipher 6), AES (Advanced Encryption Standard) and Blowfish algorithms are used to provide security to data. All the algorithms use 128-bit keys. LSB steganography technique is used to securely store the key information. Key information will contain the information regarding the encrypted part of the file, the algorithm and the key for the algorithm. File during encryption is split into three parts. These individual parts of the file will be encrypted using different encryption algorithm simultaneously with the help of multithreading technique. The key information is inserted into an image using the LSB technique. Our methodology guarantees better security and protection of customer data by storing encrypted data on a single cloud server, using AES, RC6 and Blowfish algorithm.

FPGA image encryption-steganography using a novel chaotic system with line equilibria

The wide dissemination of images makes its protection problems urgent. Chaos theory is widely applied since it provides better solutions to image protection. This paper introduces a novel chaotic system that has line equilibrium points. Chaotic signals generated by chaotic systems are used in encryption and steganography to enhance security. Besides, the scrambling-diffusion encryption system is combined with the least significant bit (LSB)-pixel value differencing (PVD) steganography algorithm. The proposed algorithm process is reversible and the extracted image is lossless. And the whole system is designed and implemented on a field programmable gate array (FPGA), which improves the embedding efficiency and achieves high throughput while ensuring good image quality.

Wavelet -Based Modified Intermediate Significant Bit Insertion for Text Steganography

In this paper, we propose and investigate the use of a modifiedversion of Intermediate Significant Bit (ISB) insertion algorithm, so-called here as MISB algorithm for secret text steganography. The secret text is passed through a sequence of ciphering techniques. A Modified Intermediate Significant Bit MISB insertion algorithm is proposed for hiding the bit sequences of the cipher text into the time-frequency domain using wavelet transform of the image pixels, in order to improve the robustness of the Steganography system. Results demonstrate the effectiveness of wavelet Modified Intermediate Significant Bit WMISB while evaluating its performance using Peak Signal to Noise Ratio PSNR measure. Moreover, additional experiments are evaluated for noising, and image compression for cover images to compare their Peak Signal to Noise Ratio PSNR. The drawn results confirm the effectiveness of the proposed Wavelet-based Modified Intermediate Significant Bit Insertion WMISB, Also, Bit Error Rate BER, is a key parameter that is used in assessing systems that transmit digital data from one location to another.

Integrated Infectious Disease and Substance Use Disorder Care for the Treatment of Injection Drug Use-Associated Infections: A Prospective Cohort Study With Historical Control.

To address the infectious disease (ID) and substance use disorder (SUD) syndemic, we developed an integrated ID/SUD clinical team rooted in harm reduction at a county hospital in Miami, Florida. The Severe Injection-Related Infection (SIRI) team treats people who inject drugs (PWID) and provides medical care, SUD treatment, and patient navigation during hospitalization and after hospital discharge. We assessed the impact of the SIRI team on ID and SUD treatment and healthcare utilization outcomes. We prospectively collected data on patients seen by the SIRI team. A diagnostic code algorithm confirmed by chart review was used to identify a historical control group of patients with SIRI hospitalizations in the year preceding implementation of the SIRI team. The primary outcome was death or readmission within 90 days post-hospital discharge. Secondary outcomes included initiation of medications for opioid use disorder (MOUD) and antibiotic course completion. There were 129 patients included in the study: 59 in the SIRI team intervention and 70 in the pre-SIRI team control group. SIRI team patients had a 45% risk reduction (aRR, 0.55 [95% confidence interval CI, .32-.95]; 24% vs 44%) of being readmitted in 90 days or dying compared to pre-SIRI historical controls. SIRI team patients were more likely to initiate MOUD in the hospital (93% vs 33%, P < .01), complete antibiotic treatment (90% vs 60%, P < .01), and less likely to have patient-directed discharge (17% vs 37%, P = .02). An integrated ID/SUD team was associated with improvements in healthcare utilization, MOUD initiation, and antibiotic completion for PWID with infections.

Software Implementation of Modified LSB Algorithm with Shamir`s Secret Sharing

Today, it is often necessary to transmit a confidential message of a small volume, while the use of complex cryptographic systems is difficult for some reasons. One of these reasons is the impossibility of using reliable products, which, as a rule, are commercial and unavailable to the average computer user. In modern information society, many services are provided with the help of computer networks and information technologies. Information presented in digital form must be reliably protected from many threats: unauthorized access and use, destruction, forgery, leakage, violation of license agreements, disclaimer of authorship, etc. Information protection is extremely important in both commercial and government spheres. The issues of developing effective methods of protecting digital information, in particular methods of computer steganography and steganalysis, are relevant and important for the state and society. To achieve the goal, it is necessary to propose a method of increasing stego-resistance, determine the effectiveness of the created solution and analyze the obtained results. The object of research is the process of protecting information embedded in a graphic e-container. The subject of research is methods and algorithms of computer steganography and steganalysis for images. The research methods used in this work are based on steganographic algorithms.

A Fast CU Partition Algorithm Based on Gradient Structural Similarity and Texture Features

The H.266/Versatile Video Coding (VVC) standard poses a great challenge for encoder design due to its high computational complexity and long encoding time. In this paper, the fast partitioning decision of coding blocks is investigated to reduce the computational complexity and save the coding time of VVC intra-frame predictive coding. A fast partitioning algorithm of VVC intra-frame coding blocks based on gradient structure similarity and directional features is proposed. First, the average gradient structure similarity of four sub-coding blocks under the current coding block is calculated, and two thresholds are set to determine whether the current coding block terminates the partitioning early or performs quadtree partitioning. Then, for the coding blocks that do not satisfy the above thresholds, the standard deviation of the vertical and horizontal directions of the current coding block is calculated to determine the texture direction and skip unnecessary partitioning to reduce computational complexity. Based on the VTM10.0 platform, this paper evaluates the performance of the designed fast algorithm for partitioning within the VVC coding unit. Compared with VTM10.0, the encoding rate is improved by 1.38% on average, and the encoder execution time is reduced by 49.32%. The overall algorithm achieves a better optimization of the existing VVC intra-frame coding technique.

Performance Analysis of Different Flexible Decoding Algorithms for NR-LDPC Codes

Channel coding technique is a fundamental building block in any modern communication system to realize reliable, fast, and secure data transmission. At the same time, it is a challenging and crucial task, as the data transmission happens in a channel where noise, fading, and other impairments are present. The Low-Density Parity-Check (LDPC) codes give substantial results close to the Shannon limit when the complexity and processing delay time are unlimited. In this paper, the performance of the LDPC decoding with four algorithms was investigated. The investigated four algorithms were Belief Propagation (BP), Layered Belief Propagation (LBP), Normalized min-sum (NMS), and Offset min-sum (OMS). These algorithms were examined for code rates ranging from 1/3 to 9/10 and message block lengths (64, 512, 1024, and 5120) bits. The simulation results revealed the flexibility of these decoders in supporting these code rates and block lengths, which enables their usage in a wide range of applications and scenarios for fifth-generation (5G) wireless communication. In addition, the effect of the maximum number of decoding iterations on the error correction performance was investigated, and a gain of 5.6 dB can be obtained by using 32 decoding iterations at BER=2*10-3 instead of one decoding iteration. The results showed that the decoders performed better for longer message blocks than for short message blocks, and less power was required for transmitting longer messages. Finally, the comparison results of their performance in terms of bit error rate (BER) under the same conditions showed a gain of 0.8 dB using LBP at BER= 10-5 compared with the NMS decoding algorithm.

An Efficient and Reliable Data Transmission Service using Network Coding Algorithms in Peer-to-Peer Network

Network coding is a progressive enhancement in natural network routing that increases throughput and reliability for unicast, multicast, and even broadcast transmissions. P2P networks are ideal for implementing network coding algorithms for two reasons: I. A P2P network's topology isn't predetermined. As a result, designing a topology that is compatible with the network coding algorithm is much easier. II. Every peer is an end host in this network. As a result, instead of saving and sending the message, complex network coding operations, such as encoding and decoding, are now easier to perform. The objective of this work is to use the best features of network coding algorithms and properly apply them to P2P networks to create an efficient and reliable data transmission service. The goal of the network coding algorithm is to make better use of network resources and thus increase P2P network capacity. An encoding algorithm that enables an intermediate peer to produce output messages by encoding (that is, computing a function of the data it receives. The decoder's role is to obtain enough encoded packets so that the original information can be recovered. This research work has measured an amount of hypothetical and applied consequences in which the network coding procedure or a variation of it is used to improve performance parameters such as throughput and reliability in P2P network data transmission based on network coding. The comparison of data transmission between network routing and network coding algorithms was the main focus of this paper. According to our simulations, the new network coding systems can reach 15% to 20% upper throughput than supplementary P2P network routing systems.

Increasing the Huffman generation code algorithm to equalize compression ratio and time in lossless 16-bit data archiving

Increasing the Huffman generation code algorithm to equalize compression ratio and time in lossless 16-bit data archiving

Securing data using deep hiding selected least significant bit and adaptive swarm algorithm

The emphasis on data protection is improved in particular with respect to the transmission protocols utilized. Different research on numerous data protection areas such as authentication, encryption, hiding of data and validation were performed. In addition, a cybersecurity standard, such as IP-SEC, and secure sockets layer (SSL), were introduced to solve privacy infringement problems by applying encryption, authorization and protection to data exchanged and data stored in the cloud. This study suggests a new steganography algorithm, a data protection tool used to conceal massive amounts of data from graphic and statistic attacks in color images. The proposed algorithm is a multi-level steganography modified deep hiding/extracting technique (MDHET), which implements a selected least signified bit (SLSB) for color picture dispersal of the information. In addition, an accurate pixel location randomization feature has been applied. After MDHET, the predicted results will effectively conceal data up to 6 bpp (bit per pixel) with high safety levels by improving the quality of images. In addition, MDHET can be useful for encoding a deep series of images into one in which the testing procedure is carried out using regular reference images used in color image processing and compression analysis from different institutions.

A New Decoding Method for Reed–Solomon Codes Based on FFT and Modular Approach

Decoding algorithms for Reed--Solomon (RS) codes are of great interest for both practical and theoretical reasons. In this paper, an efficient algorithm, called the modular approach (MA), is devised for solving the Welch--Berlekamp (WB) key equation. By taking the MA as the key equation solver, we propose a new decoding algorithm for systematic RS codes. For $(n,k)$ RS codes, where $n$ is the code length and $k$ is the code dimension, the proposed decoding algorithm has both the best asymptotic computational complexity $O(n\log(n-k) + (n-k)\log^2(n-k))$ and the smallest constant factor achieved to date. By comparing the number of field operations required, we show that when decoding practical RS codes, the new algorithm is significantly superior to the existing methods in terms of computational complexity. When decoding the $(4096, 3584)$ RS code defined over $\mathbb{F}_{2^{12}}$, the new algorithm is 10 times faster than a conventional syndrome-based method. Furthermore, the new algorithm has a regular architecture and is thus suitable for hardware implementation.

Implementation of Postprocessing Procedure of a Rapid Algorithm of Geometric Coding of Digital Images Using CUDA Architecture

Implementation of Postprocessing Procedure of a Rapid Algorithm of Geometric Coding of Digital Images Using CUDA Architecture

S45 Cancers Associated With Inflammatory Bowel Disease: A Population-Based Case-Control Study

Background: Individuals with inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), are known to have a higher risk of digestive tract cancers as compared to the general population. As the IBD population has gotten older over time, age-related cancers may be more commonly diagnosed. We conducted a population-based study to assess whether incident cancers were being diagnosed more commonly overtime and whether those with IBD were more likely to be diagnosed with cancer. Methods: A population-based surveillance case-control study was conducted in in Alberta, Canada from April 1, 2002 to March 31, 2018. A validated coding algorithm identified all cases of IBD stratified by type of IBD (CD, UC). Each case was age and sex matched to 10 non-IBD cases from the general population, and then linked to the Alberta provincial cancer registry to extract pathology-confirmed incident cancer. Cancer diagnoses were classified: colorectal, small bowel, pancreatic, biliary and liver, breast, cervical, endometrial, thyroid, hematological, lung, neurological, melanoma, non-melanoma skin cancer, prostate, renal, and bladder. Odds ratios (OR), with 95% confidence intervals (CI), compared IBD cases to matched controls using conditional logistic regression. The annual incidence of cancer diagnosis among the at-risk prevalent IBD population was determined with the average annual percentage change (AAPC) calculated using log binomial regression. Results: Overall, 3,695 incident cancers were diagnosed among 35,763 individuals with IBD as compared to 31,365 cancers among 362,248 controls (IBD: OR = 1.22; 95% CI: 1.18, 1.27; CD: OR = 1.42, 95% CI: 1.34, 1.49; UC: OR = 1.13 95% CI: 1.06, 1.20). Annual incident cancers in those with IBD steadily increased by 3.4% per year (AAPC = 3.44%; 95% CI: 2.71%, 4.18%). A higher odds of cancer was observed across digestive tract cancers for those with IBD: biliary and liver (IBD: OR = 7.76; 95% CI: 5.89, 10.22); colorectal cancer (IBD: OR = 1.88; 95% CI: 1.67, 2.15); small bowel (CD: OR = 10.81; 95% CI: 6.89, 16.95; UC: OR = 1.74; 0.73, 4.16); and pancreas (IBD: OR = 8.01; 95% CI: 5.72, 11.20). Outside of the digestive tract the following cancers were more common in those in IBD: hematological (IBD: OR = 1.47, 95% CI: 1.29, 1.67); lung (IBD: OR = 4.16; 95% CI: 3.59, 4.81); neurological (IBD: OR = 4.73; 95% CI: 3.12, 7.18; and renal (IBD: OR = 2.18; 95% CI: 1.77, 2.68). Non-melanoma skin cancers were associated with IBD (OR = 1.11; 95% CI: 1.05, 1.18), but not melanoma (OR = 0.98; 95% CI: 0.82, 1.18). Other solid-organ cancers were not associated or occurred less commonly in those with IBD: prostate (OR = 0.68; 95% CI: 0.60, 0.77); bladder (OR = 0.73; 95% CI: 0.57, 0.92), cervix (OR = 0.83; 95% CI: 0.73, 0.93), endometrium (OR = 0.50; 95% CI: 0.36, 0.70), and thyroid (OR = 0.84; 95% CI: 0.65, 1.10). Conclusion(s): Over time, persons with IBD are being more commonly diagnosed with cancer. Individuals with IBD are more likely to be diagnosed with cancer as compared to the general population with cancers of the digestive tract driving this association. Healthcare providers should be aware of higher occurrence of hematological, neurological, lung and renal cancers in those with IBD.

Secret Communication Using Multi-Image Steganography and Face Recognition

Abstract: Our Proposed system is to develop a Web Application for hiding information in any image file to ensure the safety of the exchange of data between different military parties and provide better security during message transmission. The scope of the project is the implementation of steganography tools for hiding information including any type of information file and image file and the path where the user wants to save the image and extruded file. We use the LSB technique. The proposed approach is to use a steganography algorithm for embedding data in the image files for military applications. For security purposes we used modules face Recognition technique with AES algorithm for Strong Security purpose. And we use the cover channel technique as an information hiding technique that can be exploited by a process to transfer information in a manner that violates the system security policies. And we use copyright marking techniques. In short, Cover Channels transfer information using nonstandard methods against the system design.d. Deep learning techniques used for image steganography can be broadly divided into three categories - traditional methods, Convolutional Neural Network-based and General Adversarial Network-based methods. Along with the methodology, an elaborate summary on the datasets used, experimental set-ups considered and the evaluation metrics commonly used are described in this paper. A table summarizing all the details are also provided for easy reference. This paper aims to help the fellow researchers by compiling the current trends, challenges and some future direction in this field.

A Survey of Video Steganography Algorithms

The need for data encryption has become more important than ever since the appearance of the covid-19 virus which has led to the rate at which cybercrimes are committed to triple. Data is generally in the form of text, audio, video, and image. Over the years researchers have found a way to hide specific information to intended destinations in these data forms. The is called steganography and hiding secret information in video is known as video steganography. In this paper, a survey on some video steganography algorithms have been presented. Recommendations and direction for future works are also discussed in this paper. Keywords: Steganography, cover image, secret data, steganalysis, least significant bit (LSB), motion picture expart group (MPEG), high embedding efficiency, embedding payload

Extensible Steganalysis via Continual Learning

To realize secure communication, steganography is usually implemented by embedding secret information into an image selected from a natural image dataset, in which the fractal images have occupied a considerable proportion. To detect those stego-images generated by existing steganographic algorithms, recent steganalysis models usually train a Convolutional Neural Network (CNN) on the dataset consisting of paired cover/stego-images. However, it is inefficient and impractical for those steganalysis models to completely retrain the CNN model to make it effective for detecting a new emerging steganographic algorithm while maintaining the ability to detect the existing steganographic algorithms. Thus, those steganalysis models usually lack dynamic extensibility for new steganographic algorithms, which limits their application in real-world scenarios. To address this issue, we propose an accurate parameter importance estimation (APIE)-based continual learning scheme for steganalysis. In this scheme, when a steganalysis model is trained on a new image dataset generated by a new emerging steganographic algorithm, its network parameters are effectively and efficiently updated with sufficient consideration of their importance evaluated in the previous training process. This scheme can guide the steganalysis model to learn the patterns of the new steganographic algorithm without significantly degrading the detectability against the previous steganographic algorithms. Experimental results demonstrate the proposed scheme has promising extensibility for new emerging steganographic algorithms.

Comparative study of metaheuristic optimization algorithms for image steganography based on discrete Fourier transform domain

Image steganography and watermarking have demonstrated their efficiency in creating a covert communication channel and in digital image authentication respectively. The importance of cybersecurity tasks sets high efficiency requirements for information embedding methods. Many studies in this area focus on finding new ways to improve the embedding quality. One of them is to use metaheuristic optimization algorithms. The use of classical metaheuristics is common enough for a data hiding area. However, a lot of new metaheuristics have been proposed over the past few years. Their applicability has not yet been evaluated in the field of steganography and digital watermarking. In this paper, we present a comparative study on the efficiency of seven metaheuristic optimization algorithms for finding the best embedding options in the phase spectrum of the Discrete Fourier Transform (DFT). One of the contributions of our research is an improved DFT-based data hiding algorithm, which is a development of the error-free embedding algorithm we obtained earlier. The new algorithm also provides error-free extraction of embedded data, and it is distinguished by the high invisibility of embedding through the use of metaheuristic optimization. The use of metaheuristic optimization led to an increase in the PSNR value by an average of 2%–6% and an increase in the capacity value by an average of 16%–25%. Another important contribution of our research is the original formulation of the optimization problem for different classes of metaheuristics. The experimental results demonstrate the efficiency of metaheuristic optimization for solving data hiding problem. The Differential Evolution and the Particle Swarm Optimization showed the best values of embedding indicators among the classical metaheuristic optimization algorithms. The Gradient-Based Optimizer showed the highest efficiency among modern algorithms. Thus, our research indicates the relevance of further studies of other modern optimization algorithms for a data hiding area.

A Study on Fast and Low-Complexity Algorithms for Versatile Video Coding.

Versatile Video Coding (VVC)/H.266, completed in 2020, provides half the bitrate of the previous video coding standard (i.e., High-Efficiency Video Coding (HEVC)/H.265) while maintaining the same visual quality. The primary goal of VVC/H.266 is to achieve a compression capability that is noticeably better than that of HEVC/H.265, as well as the functionality to support a variety of applications with a single profile. Although VVC/H.266 has improved its coding performance by incorporating new advanced technologies with flexible partitioning, the increased encoding complexity has become a challenging issue in practical market usage. To address the complexity issue of VVC/H.266, significant efforts have been expended to develop practical methods for reducing the encoding and decoding processes of VVC/H.266. In this study, we provide an overview of the VVC/H.266 standard, and compared with previous video coding standards, examine a key challenge to VVC/H.266 coding. Furthermore, we survey and present recent technical advances in fast and low-complexity VVC/H.266, focusing on key technical areas.

Validating administrative data to identify complex surgical site infections following cardiac implantable electronic device implantation: a comparison of traditional methods and machine learning

BackgroundCardiac implantable electronic device (CIED) surgical site infections (SSIs) have been outpacing the increases in implantation of these devices. While traditional surveillance of these SSIs by infection prevention and control would likely be the most accurate, this is not practical in many centers where resources are constrained. Therefore, we explored the validity of administrative data at identifying these SSIs.MethodsWe used a cohort of all patients with CIED implantation in Calgary, Alberta where traditional surveillance was done for infections from Jan 1, 2013 to December 31, 2019. We used this infection subgroup as our “gold standard” and then utilized various combinations of administrative data to determine which best optimized the sensitivity and specificity at identifying infection. We evaluated six approaches to identifying CIED infection using administrative data, which included four algorithms using International Classification of Diseases codes and/or Canadian Classification of Health Intervention codes, and two machine learning models. A secondary objective of our study was to assess if machine learning techniques with training of logistic regression models would outperform our pre-selected codes.ResultsWe determined that all of the pre-selected algorithms performed well at identifying CIED infections but the machine learning model was able to produce the optimal method of identification with an area under the receiver operating characteristic curve (AUC) of 96.8%. The best performing pre-selected algorithm yielded an AUC of 94.6%.ConclusionsOur findings suggest that administrative data can be used to effectively identify CIED infections. While machine learning performed the most optimally, in centers with limited analytic capabilities a simpler algorithm of pre-selected codes also has excellent yield. This can be valuable for centers without traditional surveillance to follow trends in SSIs over time and identify when rates of infection are increasing. This can lead to enhanced interventions for prevention of SSIs.