Article
Engineering, Electrical & Electronic
Ying Huang, Hu Guan, Jie Liu, Shuwu Zhang, Baoning Niu, Guixuan Zhang
Summary: This research proposes a texture-aware local adaptive watermarking algorithm that enhances the visual quality of images by embedding watermarks into textured regions and utilizes texture value to identify these regions. Simulation experiments demonstrate that this algorithm outperforms others in terms of imperceptibility, robustness, and adaptability.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Mathematics
Yimeng Zhao, Chengyou Wang, Xiao Zhou, Zhiliang Qin
Summary: In this paper, a deep learning and attention network framework (DARI-Mark) is proposed for robust image watermarking. The attention network is used to enhance the imperceptibility of the watermark, while the embedding and extraction networks improve the robustness. Experimental results show that DARI-Mark achieves better robustness compared to other state-of-the-art watermarking methods.
Article
Computer Science, Information Systems
Jay Patel, Dev Tailor, Kevin Panchal, Samir Patel, Rajeev Gupta, Manan Shah
Summary: The field of digital image watermarking has attracted significant attention and offers challenges and opportunities for technological advancements. Technological advancements such as transform domain techniques, steganography, and robust watermark extraction can address issues with multimedia access control. This can enhance technology in areas such as intellectual property protection and data integrity and security.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Tanya Koohpayeh Araghi, David Megias
Summary: This paper analyzes the hybrid Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for image watermarking, focusing on the effect of a deeper level of SVD on imperceptibility and robustness against signal processing and geometric attacks. Two hybrid watermarking schemes are designed and compared, with the second scheme showing significant improvement in both imperceptibility and robustness compared to the first scheme. The experimental results demonstrate that the SVD2 scheme offers high imperceptibility in the LL sub-band and strong robustness against various attacks in the HH sub-band, making it a good candidate for content protection, especially in medical images.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Modigari Narendra, M. L. Valarmathi, L. Jani Anbarasi
Summary: This study provides an in-depth review of different types of 3D model representations, watermarking techniques, and common attacks. It highlights the significance of digital watermarking in protecting integrity and authentication of 3D models, and suggests further research on related issues and challenges.
MULTIMEDIA SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Sourabh Sharma, Harish Sharma, Janki Ballabh Sharma, Ramesh Chandra Poonia
Summary: This article reports a robust color image watermarking technique in the transform domain using artificial intelligence, which embeds a color watermark in a color image to enhance its security. The watermark information is scrambled and extracted using a secret key, providing ownership protection. Experimental results demonstrate the proposed algorithm's robustness and security.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Green & Sustainable Science & Technology
Shahid Rahman, Jamal Uddin, Hameed Hussain, Salman Jan, Inayat Khan, Muhammad Shabir, Shahrulniza Musa
Summary: The development of the Internet and Big Data has increased the need for storage to hold and share information. Ensuring privacy and security when transmitting data is crucial. This paper analyzes the best image file type for image steganography and proposes a single algorithm that provides advantages for various types of images. The experimental results demonstrate the importance and potential of these image formats.
Article
Computer Science, Information Systems
Ahmed Khan
Summary: This article proposes a new image watermarking technique, utilizing the 2D Otsu algorithm to embed grayscale images onto colored images, to address issues of robustness and imperceptibility found in past schemes.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Sourabh Sharma, Harish Sharma, Janki Ballabh Sharma
Summary: This work proposes a perceptually tuned blind digital watermarking method for color images in a hybrid lifting wavelet transform and discrete cosine transform domain. The watermarked color image is encrypted using Arnold transform and embedded into the host image through quantization, DCT decomposition, and perceptually tuned dynamic embedding. The robustness and imperceptibility of the watermark are optimized using Artificial bee colony (ABC) optimization algorithm, showing effectiveness against image processing and manipulation attacks when compared with other methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Review
Computer Science, Information Systems
Said Boujerfaoui, Rabia Riad, Hassan Douzi, Frederic Ros, Rachid Harba
Summary: Currently, with the rise of technology, most transactions and exchanges occur online, posing risks of information falsification and distortion. Image watermarking has emerged as a critical field for preventing such attacks and enhancing durability. While there is no integrated technology capable of repelling all attacks, there are opportunities for contribution to the development of this field. Recently, the image watermarking field has benefited from the popularity of deep learning, leading to the evolution from traditional technology to intelligent techniques.
Article
Computer Science, Information Systems
Ferda Ernawan, Dhani Ariatmanto
Summary: This paper discusses the existing scaling factor and adaptive scaling factor in image watermarking schemes, covering issues such as robustness, imperceptibility, and computational time for embedding a watermark. It also explores the general concept of image watermarking, transform methods, embedding regions, and security in existing schemes. The recent use of watermarking techniques, potential issues, and available solutions in adaptive watermarking schemes are also examined. Additionally, the performance of state-of-the-art embedding techniques is summarized for future research. This literature review is valuable for researchers to understand the current challenges in embedding a watermark image, and can aid in designing efficient embedding techniques for copyright protection.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Manish Rai, Sachin Goyal, Mahesh Pawar
Summary: The active use of the Internet and multimedia content has led to a rise in copyright violations, especially in regards to digital images and videos. This paper proposes an Enhanced Chimp Optimization algorithm based on Deep Fusion Convolutional Neural Network (ECO-DFCNN) for robust watermarking. The proposed method is tested against various attacks and compared with existing watermarking techniques, showing its effectiveness and robustness.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Information Systems
Ferda Ernawan, Dhani Ariatmanto
Summary: This paper proposed a watermarking technique that generates scaling factors by considering the image content, in order to improve the imperceptibility and robustness of the watermarked image. The embedding regions are determined based on variance pixels and the watermark image pixels are scrambled using an Arnold cat map. Experimental results showed that this method outperforms other watermarking schemes in terms of invisibility and robustness.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Ramen Pal, Somnath Mukhopadhyay, Debasish Chakraborty, Ponnuthurai Nagaratnam Suganthan
Summary: This study proposes a pixel-level Multi-Spectral very high resolution image segmentation algorithm based on variable-length multiobjective genetic clustering to address the issues in satellite image segmentation. The algorithm maintains the variable length property throughout the optimization process and produces a set of near Pareto-optimal solutions. Additionally, the proposed algorithm is applied to large-scale change detection.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Wenbo Wan, Jun Wang, Yunming Zhang, Jing Li, Hui Yu, Jiande Sun
Summary: With the rapid development and popularity of the Internet, multimedia security has become a general essential concern. Digital watermarking has made significant contributions to image content security and has attracted increasing attention. This paper provides a comprehensive review on digital image watermarking methods and introduces conventional schemes and emerging approaches in different domains.
Article
Automation & Control Systems
Kamal Mammadov, Cheng-Chew Lim, Peng Shi
Summary: In this manuscript, we formulate the general Target-Attacker-Defender differential game in both continuous-time and discrete-time turn-based variants in n-dimensional Euclidean space. The objective of the Attackers is to get as close as possible to the Target before collision with the Defender, while the Target and Defender coordinate to achieve the opposite. We consider the most general setting for this zero-sum differential game, where the agents can move at different speeds, and prove the Nash equilibrium strategies in the discrete-time turn-based variant.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Automation & Control Systems
Ting Shi, Peng Shi, Liping Zhang
Summary: This paper investigates the leader-following consensus problem for general linear multi-agent systems under external disturbances. The communication topologies are time-varying and switched from a finite set. A switched control system is introduced to model these topologies, and the weighted L-2 - L-infinity performance is analyzed. A topology-dependent controller is designed based on local information from the neighbors. Conditions are developed for the existence of a control protocol that achieves the leader-following consensus with a certain level of weighted L-2 - L-infinity performance. The design algorithm is formulated as a set of linear matrix inequalities (LMIs), and a numerical example is provided to demonstrate the effectiveness of the proposed consensus algorithm.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Automation & Control Systems
Renjie Ma, Peng Shi
Summary: This paper presents defense strategies based on switched counteraction principle to protect the secure state estimation (SSE) of Cyber-Physical Systems (CPSs) from sparse data injection (DI) attacks. The physical layer is modeled using a hybrid mechanism and malicious injections are excluded through adaptively switched counteraction searching. The proposed design methods are demonstrated to be effective and promising through numerical examples.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Qian Liu, Lifan Long, Qian Yang, Hong Peng, Jun Wang, Xiaohui Luo
Summary: The SNP systems are a class of neural-like membrane computing models abstracted by applying the mechanisms of spiking neurons. The LSTM-SNP model is a novel variant of long short-term memory model developed based on a parameterised nonlinear SNP system, which shows effectiveness in time series forecasting. Comparison results with state-of-the-art prediction models and baseline prediction models further confirm the effectiveness of the proposed LSTM-SNP model.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Nan Zhou, Hong Peng, Jun Wang, Qian Yang, Xiaohui Luo
Summary: This paper discusses the computational completeness of SNP-IR systems as language generating devices and explores the relationship between the languages generated by SNP-IR systems and regular languages.
THEORETICAL COMPUTER SCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Jiahao Yan, Li Zhang, Xiaohui Luo, Hong Peng, Jun Wang
Summary: This paper proposes a neural-like computing model called ODTNP systems to address the common shortcomings of existing edge detection methods. By integrating pulse, dynamic threshold, gradient magnitude, and gradient direction information, a novel edge detector based on ODTNP systems is developed and its effectiveness and availability are demonstrated through experiments.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Lifan Long, Rikong Lugu, Xin Xiong, Qian Liu, Hong Peng, Jun Wang, David Orellana-Martin, Mario J. Perez-Jimenez
Summary: Nonlinear spiking neural P (NSNP) systems are distributed parallel neural-like computing models that abstract the nonlinear spiking mechanisms of biological neurons. Inspired by the structure of echo state network (ESN), this study proposes a new variant of NSNP systems called echo spiking neural P (ESNP) systems. The experimental results demonstrate the effectiveness of the proposed ESNP model for time-series forecasting.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yanping Huang, Hong Peng, Qian Liu, Qian Yang, Jun Wang, David Orellana-Martin, Mario J. Perez-Jimenez
Summary: The study combines a modified GSNP with attention mechanism to develop a novel model called attention-enabled GSNP model or AGSNP model for sentiment classification. The AGSNP model has two channels for processing content words and aspect items, using modified GSNPs to capture dependencies between words. It also incorporates attention components to establish semantic correlation. Comparative experiments demonstrate the effectiveness of the AGSNP model for aspect-level sentiment classification tasks.
Article
Computer Science, Information Systems
Yuxiang Feng, Yao Huang, Bing Li, Hong Peng, Jian Wang, Weikai Zhou
Summary: In this paper, a QL-mRSU series artificial intelligence energy saving method is proposed to optimize the energy consumption of parked electric vehicles during communication. The method dynamically clusters electric vehicles parked in parking lots and selects suitable vehicles as mobile roadside units based on reinforcement learning. The method achieves self-learning and energy saving effects.
Article
Computer Science, Theory & Methods
Qian Yang, Xin Xiong, Hong Peng, Jun Wang, Xiaoxiao Song
Summary: This paper investigates a new variant of spiking neural P systems (SN P systems), called nonlinear spiking neural P systems with multiple channels (NSN PMC systems). In this variant, each neuron can use its multiple channels to connect different successor neurons, and nonlinear spiking rules are introduced to control the spiking of neurons. The computational power of NSN P-MC systems is discussed, showing the Turing universality as number generating/accepting devices. Additionally, small universal NSN P-MC systems with 54 and 63 neurons are constructed to compute any Turing computable function and generate numbers.
THEORETICAL COMPUTER SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Qian Liu, Lifan Long, Hong Peng, Jun Wang, Qian Yang, Xiaoxiao Song, Agustin Riscos-Nunez, Mario J. Perez-Jimenez
Summary: This article proposes a new variant of SNP systems, called GSNP systems, which are composed of gated neurons and introduce two gated mechanisms to control the updating of states in neurons. The GSNP model based on gated neurons is developed for time series prediction and is evaluated against benchmark models, demonstrating its availability and effectiveness.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Qian Liu, Hong Peng, Lifan Long, Jun Wang, Qian Yang, Mario J. Perez-Jimenez, David Orellana-Martin
Summary: SNP systems are neural-like computing models that are inspired by spiking neurons and have applications in chaotic time series forecasting. Nonlinear SNP systems with autapses (NSNP-AU systems) are proposed in this study, which have nonlinearity in spike consumption, generation, and gate functions. Based on NSNP-AU systems, a recurrent-type prediction model for chaotic time series, called the NSNP-AU model, is developed and implemented using a deep learning framework. Experimental results show the superiority of the NSNP-AU model in chaotic time series forecasting.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Engineering, Electrical & Electronic
Huiyan Zhang, Hao Sun, Peng Shi, Luis Ismael Minchala
Summary: This article proposes a novel chip detection method that combines attentional feature fusion and cosine nonlocal attention to effectively handle chip images with multiple classes or complex backgrounds. Experimental results demonstrate that the proposed method outperforms the benchmark method on a medium-scale dataset.
Article
Computer Science, Theory & Methods
Shuwei Zhao, Li Zhang, Zhicai Liu, Hong Peng, Jun Wang
Summary: Inspired by spiking mechanisms in spiking neural P (SNP) systems, this paper proposes a new type of neurons, termed as SNP-like neurons. Based on SNP-like neurons, a new class of deep learning models called ConvSNP models are developed. Five ConvSNP models are designed by referring to the structures of existing convolutional neural networks (CNNs). The evaluation results on three benchmark data sets demonstrate the availability and effectiveness of ConvSNP models for classical classification tasks.
JOURNAL OF MEMBRANE COMPUTING
(2022)
Article
Remote Sensing
Yang Fei, Yuan Sun, Peng Shi
Summary: In this study, a hierarchical formation control strategy is used to address the robust formation control problem for a group of UAVs with system uncertainty. A sliding mode neural-based observer is constructed to estimate the nonlinear uncertainty in the UAV model, and sliding mode controllers and differentiators are designed to alleviate chattering in the control input. The proposed control scheme's effectiveness is validated through Lyapunov stability theory and numerical simulations on a multiple-UAV system.
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
