School of Computer Science and Informatics

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Now showing 1 - 20 of 3627
  • ItemOpen Access
    Evolutionary multi/many-objective optimisation via bilevel decomposition
    (IEEE Press, 2024-09) Jiang, Shouyong; Guo, Jinglei; Wang, Yong; Yang, Shengxiang
    Decomposition of a complex multi-objective optimisation problem (MOP) to multiple simple subMOPs, known as M2M for short, is an effective approach to multi-objective optimisation. However, M2M facilitates little communication/collaboration between subMOPs, which limits its use in complex optimisation scenarios. This paper extends the M2M framework to develop a unified algorithm for both multi-objective and many-objective optimisation. Through bilevel decomposition, an MOP is divided into multiple subMOPs at upper level, each of which is further divided into a number of single-objective subproblems at lower level. Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another, and eventually to all the subMOPs. The bilevel decomposition is readily combined with some new mating selection and population update strategies, leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multi- and many-objective optimisation. Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.
  • ItemOpen Access
    Intelligent Optimization: Principles, Algorithms and Applications
    (Springer, 2024-07-10) Li, Changhe; Han, Shoufei; Zeng, Sanyou; Yang, Shengxiang
    This textbook comprehensively explores the foundational principles, algorithms, and applications of intelligent optimization, making it an ideal resource for both undergraduate and postgraduate artificial intelligence courses. It remains equally valuable for active researchers and individuals engaged in self-study. Serving as a significant reference, it delves into advanced topics within the evolutionary computation field, including multi-objective optimization, dynamic optimization, constrained optimization, robust optimization, expensive optimization, and other pivotal scientific studies related to optimization. Designed to be approachable and inclusive, this textbook equips readers with the essential mathematical background necessary for understanding intelligent optimization. It employs an accessible writing style, complemented by extensive pseudo-code and diagrams that vividly illustrate the mechanisms, principles, and algorithms of optimization. With a focus on practicality, this textbook provides diverse real-world application examples spanning engineering, games, logistics, and other domains, enabling readers to confidently apply intelligent techniques to actual optimization problems. Recognizing the importance of hands-on experience, the textbook introduces the Open-source Framework for Evolutionary Computation platform (OFEC) as a user-friendly tool. This platform serves as a comprehensive toolkit for implementing, evaluating, visualizing, and benchmarking various optimization algorithms. The book guides readers on maximizing the utility of OFEC for conducting experiments and analyses in the field of evolutionary computation, facilitating a deeper understanding of intelligent optimization through practical application.
  • ItemOpen Access
    The Effect of Directional Tactile Memory on the Back of the User on Reaction Time and Accuracy
    (MDPI, 2024-06-25) Elshafei, Ali; Romano, Daniela; Fahim, Irene S.
    Tactile memory is the cognitive process of storing and recalling information that has been perceived through the sense of touch. Directional tactile memory involves the encoding and retrieval of sensory data associated with a tactile experience, allowing individuals to remember and recognize directional information encoded through the sense of touch. A new method for providing directional tactile feedback, at the back of the user, has been developed to investigate the efficacy of directional tactile memory, its decay over time, and its impact during a concurrent cognitive task. Two experiments were presented. In the first experiment, tactile memory deterioration, with a visual or a tactile cue, was tested with different action-cue latencies (10 s and 20 s). In the second experiment, we considered tactile memory deterioration when there was an increased cognitive load as the participants played Tetris. Forty volunteers participated in the two experiments using purpose-built tactile seats with nine motors controlled by an Arduino. The performance data (error and reaction times) were analyzed statistically, and a NASA task load index (NASA-TLX) questionnaire was administered to measure the subjective workload after each of the two experiments. The findings highlighted that the directional tactile memory of the back can guide individuals to the correct point on the screen and that it can be maintained for at least 20 s. There was no statistically significant difference in the number of errors or reaction time with a visual or tactile action cue. However, being involved in a concurrent cognitive task (playing Tetris) adversely affected the reaction time, the number of errors, and the directional tactile memory, which degraded as the time between the directional cue and the action cue increased. Participants perceived the performance while playing Tetris as significantly more mentally and perceptually demanding, requiring more mental and physical effort and being more frustrating. These trials revealed a new potential for a human–machine interface system, leveraging directional tactile memory, which might be utilized to increase the safety of autonomous vehicles.
  • ItemEmbargo
    Continuous variation operator configuration for decomposition-based evolutionary multi-objective optimization
    (Elsevier, 2024-07-10) Liu, Yuan; Li, Jiazheng; Zou, Juan; Hou, Zhanglu; Yang, Shengxiang; Zheng, Jinhua
    There are various multi-objective evolutionary algorithms (MOEAs) for solving multi-objective optimization problems (MOPs), and the significant difference between them lies in the way they generate offspring, which are the so-called variation operators. Since different variation operators have their own characteristics, it is often tedious to select a suitable EA for a given MOP. Even if the optimal operator is assigned, the fixed operator and hyper-parameters make it difficult to balance exploration and exploitation during the evolutionary process. It is imperative to configure variation operators and hyper-parameters automatically during the evolutionary process, which can improve the efficiency of algorithm search. However, numerous configurations only consider operators or discretize hyper-parameters, making it difficult to achieve satisfactory results. In this paper, we formulate the operator configuration as a continuous Markov Decision Process (MDP) and use a suitable Reinforcement Learning (RL) paradigm to realize the online configuration of EAs. To simplify the deployment of MDP, we adopt a decomposition-based framework and use a one-dimensional vector with a combination of weights and objectives as state spaces. In addition, we take the selection of crossover and mutation operators and the fine-tuning of their hyper-parameters as joint action spaces. With an RL technique, we expect to achieve maximum improvement in the performance of offspring on each preference by selecting an action in a given state. We further explore the effectiveness of the proposed methodology on different characteristic MOPs. Experimental results show that our method is more competitive than other configurations and state-of-the-art EAs.
  • ItemEmbargo
    Uncertainty-Aware Reservoir Permeability Prediction using Gaussian Processes Regression and NMR Measurements
    (ACM, 2025-03-01) Lawal, Ahmad; Yang, Yingjie; Baisa, Nathanael L.; He, Hongmei
    This study investigates the challenges of permeability prediction in reservoir engineering, focusing on addressing uncertainties inherent in the data and modelling process, and leveraging Nuclear Magnetic Resonance (NMR) log data from the Northern Sea Volve field. The study uses a probabilistic machine learning method called Gaussian Process Regression (GPR) with different kernels, such as Matern52, Matern32, and Radial Basis Function (RBF). LSboost, K-nearest neighbour (KNN), and XGBoost are some of the existing models that are used for comparison. Performance metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination ($R^{2}$) are utilized for assessment. Additionally, the uncertainty associated with different GPR kernels is analyzed, and confidence intervals are generated to provide insights into model behaviour. The inclusion of confidence intervals enhances interpretability by quantifying the range within which the true permeability value is likely to fall with a specified level of confidence, offering valuable information for decision-making processes in reservoir engineering applications. Findings demonstrate the effectiveness of GPR with Matern52 and Matern32 kernels in permeability prediction, offering competitive performance and robust uncertainty quantification. This research contributes to advancing reservoir engineering by providing a comprehensive and uncertainty-aware approach to permeability prediction.
  • ItemEmbargo
    A novel preference-driven dynamic multi-objective evolutionary algorithm for solving dynamic multi-objective problems
    (Elsevier, 2024-06-30) Wang, Xueqing; Zheng, Jinhua; Hou, Zhanglu; Liu, Yuan; Zou, Juan; Xia, Yizhang; Yang, Shengxiang
    Most studies in dynamic multi-objective optimization have predominantly focused on rapidly and accurately tracking changes in the Pareto optimal front (POF) and Pareto optimal set (POS) when the environment undergoes changes. However, there are real-world scenarios where it is necessary to simultaneously solve changing objective functions and satisfy the preference of Decision Makers (DMs). In particular, the DMs may be only interested in a partial region of the POF, known as the region of interest (ROI), rather than requiring the entire POF. To meet the challenge of simultaneously predicting a changing POF and/or POS and dynamic ROI, this paper proposes a new dynamic multi-objective evolutionary algorithm (DMOEAs) based on the preference. The proposed algorithm consists of three key components: an evolutionary direction adjustment strategy based on changing reference points to accommodate shifts in preferences, an angle-based search strategy for tracking the varying ROI, and a hybrid prediction strategy that combines linear prediction models and population manifold estimation within the ROI to ensure convergence and distribution in scenarios where preferences remain unchanged. Experimental studies conducted on 30 widely used benchmark problems in which it outperforms contrasting algorithms on 71% of test suits. Empirical results demonstrate the significant advantages of the proposed algorithm over existing state-of-the-art DMOEAs.
  • ItemEmbargo
    Dynamic constrained multi-objective optimization algorithm based on co-evolution and diversity enhancement
    (Elsevier, 2024-06-27) Che, Wang; Zheng, Jinhua; Hu, Yaru; Zou, Juan; Yang, Shengxiang
    Dynamic constrained multi-objective optimization problems (DCMOPs) involve objectives, constraints, and parameters that change over time. This kind of problem presents a greater challenge for evolutionary algorithms because it requires the population to quickly track the changing pareto-optimal set (PS) under constrained conditions while maintaining the feasibility and good distribution of the population. To address these challenges, this paper proposes a dynamic constrained multi-objective optimization algorithm based on co-evolution and diversity enhancement (CEDE), in which we have made improvements to both the static optimization and dynamic response parts, innovatively utilizing the valuable information latent in the optimization process to help the population evolve more comprehensively. The static optimization involves the co-evolution of three populations, through which their mutual synergy can more comprehensively identify potential true PS and provide more useful historical information for dynamic response. Additionally, to prevent the elimination of potentially valuable infeasible individuals (i.e., individuals that are not dominated by feasible individuals) due to pareto domination, we employ an archive set to store and update these individuals. When the environment changes, to effectively enhance population diversity under complex dynamic constraints and help the population to respond quickly to changes, we propose a diversity enhancement strategy, which includes a diversity maintenance strategy and a center point-based exploration strategy. This strategy effectively enhances population diversity in complex and changing environments, helping the population respond quickly to changes. The effectiveness of the algorithm is validated through two test sets. The experimental results show that CEDE can effectively use valuable information to cope with complex dynamic constraint environments. Compared with several of the most advanced algorithms, it is superior in 94% of the test problems, demonstrating strong competitiveness in handling DCMOPs.
  • ItemEmbargo
    A similarity-detection-based evolutionary algorithm for large-scale multimodal multi-objective optimization
    (Elsevier, 2024-04-09) Long, Si; Zheng, Jinhua; Deng, Qi; Liu, Yuan; Zou, Juan; Yang, Shengxiang
    In recent years, there has been a surge in the development of evolutionary algorithms tailored for multimodal multi-objective optimization problems (MMOPs). These algorithms aim to find multiple equivalent Pareto optimal solution sets (PSs). However, little work has been done on MMOPs with large-scale decision variables, especially when the Pareto optimal solutions are sparse. These problems pose significant challenges due to the dimension curse, the unknown sparsity, and the unknown number of equivalent PSs. In this paper, we propose an evolutionary algorithm based on similarity detection called SD-MMEA to solve large-scale MMOPs with sparse Pareto-optimal solutions. Specifically, it employs a multi-population independent evolution to explore multiple PSs and distinguishes different PSs by double detection of the similarity between subpopulations. Simultaneously, develop online scoring mechanisms for decision variables to guide the subpopulations to explore in different directions. In addition, during the latter stage of evolution, the decision variables of individuals are further optimized by a double-layer grouping process. The proposed algorithm is compared with six state-of-the-art algorithms. Experimental results show that SD-MMEA has significant advantages in solving large-scale MMOPs with sparse solutions.
  • ItemEmbargo
    Local-Aware Global Attention Network for Person Re-Identification Based on Body and Hand Images
    (Elsevier, 2024-06-27) Baisa, Nathanael L.
    Learning representative, robust and discriminative information from images is essential for effective person re-identification (Re-Id). In this paper, we propose a compound approach for end-to-end discriminative deep feature learning for person Re-Id based on both body and hand images. We carefully design the Local-Aware Global Attention Network (LAGA-Net), a multi-branch deep network architecture consisting of one branch for spatial attention, one branch for channel attention, one branch for global feature representations and another branch for local feature representations. The attention branches focus on the relevant features of the image while suppressing the irrelevant backgrounds. The global and local branches intends to capture global context and fine-grained information, respectively. A set of ablation study shows that each component contributes to the increased performance of the LAGA-Net. Extensive evaluations on four popular body-based person Re-Id benchmarks and two publicly available hand datasets demonstrate that our proposed method consistently outperforms existing state-of-the-art methods.
  • ItemEmbargo
    Joint Person Identity, Gender and Age Estimation from Hand Images using Deep Multi-Task Representation Learning
    (IEEE, 2024-07-03) Baisa, Nathanael L.
    In this paper, we propose a multi-task representation learning framework to jointly estimate the identity, gender and age of individuals from their hand images for the purpose of criminal investigations since the hand images are often the only available information in cases of serious crime such as sexual abuse. We investigate different up-to-date deep learning architectures and compare their performance for joint estimation of identity, gender and age from hand images of perpetrators of serious crime. To simplify the age prediction, we create age groups for the age estimation. We make extensive evaluations and comparisons of both convolution-based and transformer-based deep learning architectures on a publicly available 11k hands dataset. Our experimental analysis shows that it is possible to efficiently estimate not only identity but also other attributes such as gender and age of suspects jointly from hand images for criminal investigations, which is crucial in assisting international police forces in the court to identify and convict abusers. The source code is available at
  • ItemOpen Access
    A tolerance index based non-cooperative behaviour managing method with minimum cost in social network group decision making
    (Elsevier, 2024-06-26) Sun, Qi; Wu, Jian; Chiclana, Francisco; Ji, Feixia
    This paper introduces a novel consensus theoretical framework designed to effectively manage non-cooperative behavior in social network group decision making (SNGDM). It addresses the challenge by considering both individuals’ willingness to adjust preferences and the associated costs of achieving consensus. To deal with this issue, the personalized individual semantics (PIS) model is employed to handle original evaluation matrices by converting linguistic terms into numerical values based on experts’ personalized opinions. Subsequently, a tolerance index (TI) is defined to reflect the willingness of experts to adjust their preferences. An improved minimum cost (MC) feedback model based on TI is established. The novelty of the proposed approach is that its integration of individual preference adjustment willingness and consensus efficiency, effectively preventing groupthink. In addition, a maximum group consensus degree optimisation model is proposed to detect non-cooperative behaviour of experts. To ensure an optimal solution for the minimum cost feedback model, a weight update method is proposed, considering the trust relationship between experts. A detailed analysis regarding the selection of tolerance thresholds to prevent over-penalisation of weights of non-collaborators is reported. Finally, comprehensive numerical and comparative analyses are presented to validate the proposed method.
  • ItemEmbargo
    Interactive dynamic trust network for consensus reaching in social network analysis based Large-Scale decision making
    (Elsevier, 2024-06-23) Guo, Sijia; Ding, Ru-Xi; Li, Meng-Nan; Shi, Zijian; Wang, Xueqing; Chiclana, Francisco
    With the advances in social media and e-democracy technologies, large-scale decision making (LSDM) problems with trust relationships demands effective consensus-reaching processes. Existing literature has identified a need for improvement in the updating method of trust relationships and the recommendation-feedback mechanism, while the overall effect of trust relationships on consensus reaching remains unexplored and not quantitatively examined. This study proposes a novel interactive dynamic trust-lead consensus reaching process (IDT-CRP)-based model to address these challenges. The model integrates a dynamic Trust network updating (DTNU) process and a trust-lead and efficiency-driven recommendation-feedback (TERF) process. In the DTNU process, secondary trust is defined to represent the trust derived from decision makers’ modification behaviors in each iteration. In the TERF process, a novel trust-lead and efficiency-driven recommendation-feedback mechanism is proposed, in which two different decision scenarios are provided. Furthermore, the role of trust relationships in enhancing group consensus level is analyzed, revealing its significant impact while identifying an upper limit. Novelties of this paper include 1) the improved trust updating algorithm which derived secondary trust from modification behaviors instead of opinion similarities; 2) the improved recommendation-feedback mechanism with different trust utilization scenarios while retaining minority opinions; 3) the novel IDT-CRP model based on the interactive mechanism between trust relationship and opinion evolution. A series of experiments are implemented, validating that the proposed model is of good feasibility, effectiveness and robustness, and can be applied in future LSDM research.
  • ItemOpen Access
    Design of a thermal store and heat pump system with hybrid photovoltaic-thermal solar charging for a low energy house in England
    (Bulgarian Academy of Sciences, 2024-05-14) Wright, A. J.; Khattak, S. H.
    Low carbon domestic heating is a major challenge for cold climates such as the UK, where most homes still use fossil gas boilers. Heat pumps have lower carbon emissions. Use of ground or water instead of air as a heat source allows the option of thermal storage from solar or other sources and can improve the efficiency of the system. This paper considers the options for a large, detached house to be built in south-west England, including comparison of a ground and water storage, use of photovoltaic-thermal panels for heat and electricity, design of the house to minimise heat loss, and timescales of thermal storage.
  • ItemOpen Access
    A two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization
    (Elsevier, 2024-05-17) Zou, Juan; Tang, Li; Liu, Yuan; Yang, Shengxiang; Wang. Shiting
    Large-scale multiobjective optimization problems (LSMOPs) have exponential growth in the search space as the decision variables increase, and the vast search space poses a challenge to the performance of multiobjective evolutionary algorithms (MOEAs). Many current large-scale MOEAs need to consume a large amount of computational resources to get good performance. This paper proposes a two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization (LMOEA-S2D) to balance the performance and computational resource overhead. The algorithm exploits the Pareto-optimality property of domination and the diversity-preserving property of decomposition to optimize the performance in the two stages, respectively, and designs a corresponding direction-guided mechanism to improve search efficiency. LMOEA-S2D designs global direction search and local direction search in the domination-based stage for efficient exploitation to accelerate population convergence. To promote greater population diversity, a hybrid direction search was devised to aid diversity exploration in the decomposition-based stage, and this facilitates even distribution of candidate solutions across the Pareto optimal frontier. LMOEA-S2D is compared with five state-of-the-art large-scale MOEAs on some large-scale multiobjective test suites with 100 to 5,000 decision variables. The experimental results show that LMOEA-S2D significantly outperformed all compared algorithms under limited computational resources.
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    Fast Coding Mode Prediction for Intra Prediction in VVC SCC
    (2024 IEEE International Conference on Image Processing (ICIP 2024), 2024-06-06) Wang, Dayong; Yu, Junyi; Lu, Xin; Dufaux, Frederic; Guo, Hongwei; Guo, Hui; Zhu, Ce
    Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by 34.95% on average while maintaining almost the same coding efficiency.
  • ItemOpen Access
    An efficient deep learning model for brain tumour detection with privacy preservation
    (CAAI Transactions on Intelligence Technology, 2023-07-01) Rehman, Mujeeb Ur; Shafique, Arslan; Khan, Imdad Ullah; Ghadi, Yazeed Yasin; Ahmad, Jawad; Alshehri, Mohammed S.; Al Qathrady, Mimonah; Alhaisoni, Majed; Zayyan, Muhammad H.
    Internet of medical things (IoMT) is becoming more prevalent in healthcare applicationsas a result of current AI advancements, helping to improve our quality of life and ensure asustainable health system. IoMT systems with cutting‐edge scientific capabilities arecapable of detecting, transmitting, learning and reasoning. As a result, these systemsproved tremendously useful in a range of healthcare applications, including brain tumourdetection. A deep learning‐based approach for identifying MRI images of brain tumourpatients and normal patients is suggested. The morphological‐based segmentationmethod is applied in this approach to separate tumour areas in MRI images. Convolu-tional neural networks, such as LeNET, MobileNetV2, Densenet and ResNet, are testedto be the most efficient ones in terms of detection performance. The suggested approachis applied to a dataset gathered from several hospitals. The effectiveness of the proposedapproach is assessed using a variety of metrics, including accuracy, specificity, sensitivity,recall and F‐score. According to the performance evaluation, the accuracy of LeNET,MobileNetV2, Densenet, ResNet and EfficientNet is 98.7%, 93.6%, 92.8%, 91.6% and91.9%, respectively. When compared to the existing approaches, LeNET has the bestperformance, with an average of 98.7% accuracy.
  • ItemOpen Access
    A Novel Chaos-based Privacy-Preserving Deep Learning Model for Cancer Diagnosis
    (IEEE, 2022-08-17) Rehman, Mujeeb Ur; Shafique, Arslan; Ghadi, Yazeed Yasin; Boulila, Wadii; Jan, Sana Ullah; Gadekallu, Thippa Reddy; Driss, Maha
    Early cancer identification is regarded as a challenging problem in cancer prevention for the healthcare community. In addition, ensuring privacy-preserving healthcare data becomes more difficult with the growing demand for sharing these data. This study proposes a novel privacy-preserving non-invasive cancer detection method using Deep Learning (DL). Initially, the clinical data is collected over the Internet via wireless channels for diagnostic purposes. It is paramount to secure personal clinical data against eavesdropping by unauthorized users that may exploit it for personalized interests. Therefore, the collected data is encrypted before transmission over the channel to prevent data theft. Various security measures, including correlation, entropy, contrast, structural content, and energy, are used to assess the proposed encryption method’s efficiency. In this paper, we proposed using the Convolutional Neural Network (CNN)-based model and Magnetic Resonance Imaging (MRI) with different techniques, including transfer learning, fine-tuning, and K-fold analysis cancer detection. Extensive experiments are carried out to evaluate the performance of the proposed DL techniques with regard to traditional machine learning approaches such as Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). Results show that the CNN-based model has achieved an accuracy of 98.9% and outperforms conventional ML algorithms. Further experiments demonstrate the efficiency of both encryption schemes, achieving entropy, contrast, and energy of 7.9999, 10.9687, and 0.0151, respectively.
  • ItemOpen Access
    A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks
    (Springer, 2024-04-04) Rehman, Mujeeb Ur; Shafique, Arslan; Mehmood, Abid; Alawida, Moatsum; Elhadef, Mourad
    Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in real-time applications.
  • ItemOpen Access
    Innovative Cybersecurity for Enhanced Data Protection: An Extended Bit-Plane Extraction and Chaotic Permutation-Diffusion Approach in Information Security
    (IEEE, 2023-12-14) Rehman, Mujeeb Ur; Shafique, Arslan; Khan, Kashif Hesham; Altamimi, Saad Nasser; Qasem, Sultan Noman; Al-Sarem, Mohammed
    In the era of big data, protecting digital images from cyberattacks during network transmission is of utmost importance. While various image encryption algorithms have been developed, some remain vulnerable to specific cyber threats. This paper presents an enhanced version of the image encryption algorithm based on bit-plane extraction (BPCPD) to address its vulnerability to chosen-plaintext attacks. The proposed cryptographic system encompasses three primary phases. The initial phase involves bit-plane extraction from the plaintext image and the generation of random sequences and a random image using multiple chaotic maps, such as the chaotic Arnold map and the chaotic CAT map. The second phase is dedicated to permutation operations, which comprise three sub-phases: multi-layer permutation, multi-round permutation, and recursive permutation. In the third phase, diffusion is introduced to the permuted image through pixel substitution, coupled with XOR operations performed on the respective bit-planes of the random image. To gauge the efficiency of the proposed encryption scheme, a range of experimental analyses are conducted, including histogram analysis, contrast assessment, entropy measurement, correlation analysis, encryption quality assessment, and investigations into noise attacks and occlusion attacks. The results of these experimental analyses, in comparison to an existing encryption scheme, demonstrate that the proposed framework exceeds both BPCPD and other existing encryption schemes in various aspects of performance.
  • ItemOpen Access
    Efficient and Secure Image Encryption Using Key Substitution Process with Discrete Wavelet Transform
    (Elsevier, 2023-06-15) Rehman, Mujeeb Ur; Shafique, Arslan; Khan, Kashif Hesham; Hazzazi, Mohammad Mazyad
    Over the past few years, there has been a rise in the utilization of chaotic encryption algorithms for securing images. The majority of chaos-based encryption algorithms adhere to the conventional model of confusion and diffusion, which typically involves either implementing multiple encryption rounds or employing a single round of intricate encryption to guarantee robust security. However, such kind of approaches reduces the computational efficiency of the encryption process but compromises security. There is a trade-off between security and computational efficiency. Prioritizing security may require high computational processes. To overcome this issue, a key substitution encryption process with discrete wavelet transform (KSP-DWT) is developed in the proposed image encryption technique (IET). Based on KSP-DWT and IET, the abbreviation of the proposed work is used in this paper as KSP-DWT-IET. The proposed KSP-DWT algorithm employs a key scheming technique to update the initial keys and uses a novel substitution method to encrypt digital images of different sizes. Additionally, the integration of DWT can result in the compression of frequency sub-bands of the source image, leading to lower computational overheads without compromising the security of the encryption. The KSP-DWT-IET performs a single encryption round and is highly secure and efficient. The simulation results and security analysis conducted on KSP-DWT-IET confirm its effectiveness in ensuring high-security image encryption while minimizing computational overhead. The proposed encryption technique undergoes various security analyses, including entropy, contrast, correlation, energy, NPCR (Number of Pixel Changes Rate), UACI (Unified Average Change Intensity) and computational complexity. The statistical values obtained for such parameters are 7.9991, 10.9889, 0.0001, 0.0152, 33.6767, and 33.6899, respectively, which indicate that the encryption technique performs very well in terms of security and computational efficiency. The proposed encryption scheme is also analyzed for its computational time in addition to its security. The analysis shows that the scheme can efficiently encrypt images of varying sizes with a high level of security in a short amount of time (i.e., 2 ms). Therefore, it is feasible to use this encryption scheme in realtime applications without causing any significant delays. Moreover, the key space of the proposed encryption scheme is large enough (i.e. Keyspace > 2100) to resist the brute force attack.