Article
Computer Science, Information Systems
Yu-Guang Yang, Bao-Pu Wang, Yong-Li Yang, Yi-Hua Zhou, Wei-Min Shi, Xin Liao
Summary: The study introduces a visually meaningful image encryption (VMIE) algorithm based on a universal embedding model, which achieves better visual quality by optimizing the number of embedded bits and flexibly adjusting wavelet transform subbands.
INFORMATION SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
Mohamed G. Abdelfattah, Salem F. Hegazy, Nihal F. F. Areed, Salah S. A. Obayya
Summary: A novel image encryption scheme based on the gyrator transform and the Henon chaotic map is proposed to generate visually meaningful encrypted images (VMEIs). The encryption system uses polarization degree of freedom to reduce complexity and yields VMEIs with the same size as the secret plain image, providing enhanced security in storage and transmission. The proposed system adopts a non-steganographic approach, assigning independent VMEIs without any information related to the original secret image.
OPTICAL AND QUANTUM ELECTRONICS
(2022)
Article
Telecommunications
Xiaoling Huang, Youxia Dong, Guodong Ye, Wun-She Yap, Bok-Min Goi
Summary: Traditional image encryption algorithms transform a plain image into a noise-like image. This paper proposes a visually meaningful encrypted image algorithm that hides a secret image and a digital signature which provides authenticity and confidentiality. The proposed algorithm has high key sensitivity, strong robustness against attacks, and produces visually similar final images.
DIGITAL COMMUNICATIONS AND NETWORKS
(2023)
Article
Engineering, Multidisciplinary
Xiaoling Huang, Youxia Dong, Hongyong Zhu, Guodong Ye
Summary: This paper introduces a new asymmetric image encryption and hiding algorithm based on SHA-3 and compressive sensing, which involves steps such as hashing the plain image, dividing it into blocks, and extracting numbers to achieve encryption and hiding of images.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Computer Science, Information Systems
Yu-Guang Yang, Bao-Pu Wang, Shuai-Kang Pei, Yi-Hua Zhou, Wei-Min Shi, Xin Liao
Summary: A visually meaningful image encryption algorithm based on M-ary decomposition and virtual bits is proposed, which includes a Generalized Embedding Model and strategies for optimal static and dynamic virtual embedded bits. Experimental simulations show improved visual quality of cipher images compared to existing algorithms.
INFORMATION SCIENCES
(2021)
Review
Computer Science, Information Systems
Varsha Himthani, Vijaypal Singh Dhaka, Manjit Kaur, Dilbag Singh, Heung-No Lee
Summary: Due to advancements in technology, digital images are widely used in various applications. Encryption is the most common technique to protect images, but it can make the images look noisy and easily attract attackers' attention. Visually Meaningful Encrypted Image (VMEI) technique provides more security by encrypting the original image and hiding it into a reference image, making the encrypted image look normal.
Article
Physics, Multidisciplinary
Zhaoyang Liu, Ru Xue
Summary: Most image encryption schemes currently used produce ciphertext images without visual significance, making them susceptible to various attacks. To address this, a visually meaningful image encryption algorithm is proposed, which combines reversible data hiding in encrypted images (RDHEI). The algorithm encrypts the secret and source images using different chaotic systems and embeds additional information. The resulting encrypted image is then processed and saved in a cloud database to generate an indexing key for easy recovery of the secret image. Experimental results and security analysis demonstrate the effectiveness and wide applicability of the proposed scheme.
Article
Mathematical & Computational Biology
Xianyi Chen, Mengling Zou, Bin Yang, Zhenli Wang, Nannan Wu, Lili Qi
Summary: The new method utilizes integer wavelet transform and prediction scheme to encrypt images, improving image quality while maintaining good invisibility.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Physics, Multidisciplinary
Hanaa A. Abdallah, Dalia H. ElKamchouchi
Summary: Digital Signature using Self-Image signing is a technique introduced in this paper to verify the integrity and originality of images transmitted over insecure channels. The approach includes subdividing images into four bands using Discrete Wavelet Transform (DWT) and embedding marks from one sub-band to another using Discrete Cosine Transform (DCT). The marked image is encrypted using Double Random Phase Encryption for increased security. By verifying the presence of the mark, the authority of the sender is verified, and authorized and unauthorized users can be distinguished using a threshold.
Article
Computer Science, Artificial Intelligence
Xingyuan Wang, Cheng Liu, Donghua Jiang
Summary: This paper proposes a novel visually meaningful image encryption and adaptive embedding scheme using chaotic cellular neural network (CCNN), parallel compressive sensing (PCS), and least significant bit (LSB) embedding in the transform domain. The scheme involves sparsing the plain image using 2D discrete wavelet transform (DWT), encrypting and measuring the sparse matrix using local binary pattern (LBP) and PCS technology, and analyzing the texture degree of the carrier image for adaptive embedding using information entropy, resulting in visually meaningful cipher image.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Ruoyu Zhao, Yushu Zhang, Yu Nan, Wenying Wen, Xiuli Chai, Rushi Lan
Summary: Traditional image encryption methods transform images into meaningless noise-like ones, while the proposed visually meaningful image encryption (VMIE) method preserves the visual meaning of the encrypted image. To further improve this method, a new paradigm called primitively visually meaningful image encryption is introduced, along with a permutation-diffusion architecture that retains the original visual meaning of the encrypted image. The prototype of this method has been experimentally proven to be superior.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Kunshu Wang, Mengqi Liu, Zehui Zhang, Tiegang Gao
Summary: This paper presents an optimized embedding strategy for visually meaningful embedded image based on compressive sensing, 2D DWT, and SVD, achieving compression and encryption simultaneously.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Ahmed S. Salama, Mohamed Amr Mokhtar, Mazhar B. Tayel, Esraa Eldesouky, Ahmed Ali
Summary: The paper proposes a novel technique called EbHFT that combines DWT and DCT to improve the imperceptibility and security of medical images. By encrypting and fusing images at multiple levels, watermarked images are generated to protect them from illegal replication and unauthorized tampering.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Computer Science, Information Systems
Zeba Shamsi, Dolendro Singh Laiphrakpam
Summary: Advancements in communication technologies have led to the growth of digital data transfer, particularly in the transmission of images. Cryptographic techniques are used to create secure communication channels, but they also attract attackers. To address this issue, a method for encrypting and hiding secret images within audio data is proposed. The Ikeda map is utilised to encrypt the image, which is then concealed within the audio's lifting wavelet transform. Statistical experiments demonstrate that this approach effectively conceals the encrypted image with minimal impact on the audio. The proposed algorithm exhibits robustness against noise addition or random audio crop attacks, as it can retrieve a visually perceivable image even after the attack. In terms of imperceptibility and embedding capacity, the suggested approach outperforms existing algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Xiaoling Huang, Youxia Dong, Guodong Ye, Yang Shi
Summary: This study proposes a new meaningful image encryption algorithm based on compressive sensing and integer wavelet transformation. The algorithm encrypts the initial values of the chaotic system and embeds the secret image in the carrier image. The proposed algorithm can resist known-plaintext attack and chosen-plaintext attack, and has good hiding effect.
FRONTIERS OF COMPUTER SCIENCE
(2023)
Article
Computer Science, Information Systems
Xuan Shao, Ying Shen, Lin Zhang, Shengjie Zhao, Dandan Zhu, Yicong Zhou
Summary: This article introduces a large benchmark dataset called BeVIS for evaluating the performance of SLAM systems for autonomous indoor parking. The dataset includes both raw data and groundtruth trajectories collected from visual, inertial, and surround-view sensors. Additionally, a semantic SLAM framework called VISSLAM-2 is proposed for modeling various semantic objects on the ground. The effectiveness of VISSLAM-2 is demonstrated through experiments on the BeVIS dataset.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Yang Chen, Lin Zhang, Ying Shen, Brian Nlong Zhao, Yicong Zhou
Summary: An SVS consists of four fisheye cameras mounted around the vehicle for sensing the surrounding environment. A top-down surround-view can be synthesized from synchronized camera images, assuming the calibration of intrinsics and extrinsics. We propose a novel extrinsic self-calibration scheme, WESNet, which follows a weakly supervised framework to fill the research gap in extrinsic calibration.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Theory & Methods
Shen Wang, Zhaoyang Zhang, Guopu Zhu, Xinpeng Zhang, Yicong Zhou, Jiwu Huang
Summary: With the widespread use of automated speech recognition systems, attacks against these systems have become popular. Existing black-box attack methods for ASR systems are query-intensive and lack efficiency. This paper proposes a new black-box attack called MGSA, which generates adversarial audio samples with substantially fewer queries.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Artificial Intelligence
Wei Lu, Lingyi Liu, Bolin Zhang, Junwei Luo, Xianfeng Zhao, Yicong Zhou, Jiwu Huang
Summary: With the rapid progress of deepfake techniques, detecting highly deceptive facial video forgery has become a pressing and challenging task. This article proposes a spatial-temporal model that uses a novel long-distance attention mechanism to capture forgery traces in both the spatial and time domains. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in detecting face forgery.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hong Chen, Youcheng Fu, Xue Jiang, Yanhong Chen, Weifu Li, Yicong Zhou, Feng Zheng
Summary: Variable selection methods aim to select key covariates related to the response variable in high-dimensional data. Existing methods depend on chosen parameter function class and cannot handle heavy-tailed or skewed data noise. To overcome these drawbacks, we propose a robust model-free variable selection method called sparse gradient learning with mode-induced loss (SGLML). Theoretical analysis shows that SGLML can estimate gradients and identify informative variables under mild conditions, and experimental analysis demonstrates its competitive performance over previous gradient learning methods (GL).
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Min Shi, Jialin Shen, Qingming Yi, Jian Weng, Zunkai Huang, Aiwen Luo, Yicong Zhou
Summary: This article introduces a lightweight multiscale-feature-fusion network (LMFFNet) that achieves a good balance between accuracy and inference speed in real-time semantic segmentation. The network extracts features with fewer parameters, fuses multiscale semantic features to improve segmentation accuracy, and recovers details of input images through the attention mechanism. Experiments demonstrate that the proposed network is suitable for autonomous driving and robotics.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Min Du, Lianhong Wang, Yicong Zhou
Summary: In this paper, a single mixed-integer linear programming model is presented for high-stealth false data attacks (FDAs) on overloading a set of lines by injecting stealthy false data. The proposed model reveals that an intelligent attacker is able to deliberately construct a valid attack vector to overload multiple transmission lines while hiding it among normal data to evade advanced anomaly detection methods. In addition, the proposed cyber-attack mode can help the attacker optimally select the targeted lines. Simulation results on multiple large-scale test systems validate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Automation & Control Systems
Yinxing Zhang, Zhongyun Hua, Han Bao, Hejiao Huang, Yicong Zhou
Summary: This article proposes a method to generate n-dimensional hyperchaotic maps based on the Gershgorin-type theorem, and demonstrates its feasibility and superiority through theoretical analysis and performance evaluations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Review
Ergonomics
Tianyu Xu, Xiaojing Liu, Zeling Zhang, Yuhan Li
Summary: This study analyzes the relationship between vessel groups (small, medium, and large) and casualty or loss type of UK fishing vehicles based on a summary of information from 2013 to 2020. The study finds that loss of control is the main cause of casualties for all fishing vessels. Flooding/foundering is the main contributor to the loss of fishing vessels smaller than 24 m, while grounding/stranding is the main contributor to the loss of fishing vessels 24 m or longer. Fishing vessels below 15 m in length make up the majority of casualties and losses, while medium-sized vessels contribute the highest average loss per vessel.
INTERNATIONAL JOURNAL OF OCCUPATIONAL SAFETY AND ERGONOMICS
(2023)
Article
Computer Science, Artificial Intelligence
Zheng Zhou, Yue Wu, Yicong Zhou
Summary: This paper introduces a method called Consistent Arbitrary Style Transfer (CAST) that quantifies the consistency of generated images and effectively transfers style patterns while preserving consistency. The proposed CAST method incorporates IoUPC module, SA module, and SILR module to achieve consistent style transfer. Experimental results demonstrate that the CAST framework can effectively transfer style patterns while preserving consistency and achieves state-of-the-art performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yongyong Chen, Xiaojia Zhao, Zheng Zhang, Youfa Liu, Jingyong Su, Yicong Zhou
Summary: Multiview clustering (MVC) has gained attention in recent years for its ability to uncover intrinsic clustering structures in data. However, previous methods only focus on either complete or incomplete multiview, lacking a unified framework to handle both tasks simultaneously. To address this, we propose a unified framework, called TDASC, which integrates tensor learning and dynamic anchor learning for scalable clustering. TDASC efficiently learns smaller view-specific graphs using anchor learning and incorporates multiple graphs into an inter-view low-rank tensor, effectively modeling high-order correlations across views. Experimental results on complete and incomplete multiview datasets demonstrate the effectiveness and efficiency of TDASC compared to state-of-the-art techniques.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Geochemistry & Geophysics
Quanyong Liu, Jiangtao Peng, Yujie Ning, Na Chen, Weiwei Sun, Qian Du, Yicong Zhou
Summary: In this article, a refined prototypical contrastive learning network for few-shot learning is proposed to address problems related to prototype instability and domain shift. By imposing triple constraints on prototypes of the support set, the prototypes are stabilized and refined. Additionally, a fusion training strategy is designed to alleviate the domain shift in few-shot learning.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Information Systems
Shuyi Li, Bob Zhang, Lunke Fei, Shuping Zhao, Yicong Zhou
Summary: Compared with uni-modal biometrics systems, multimodal biometrics systems using multiple sources of information have received considerable attention recently. However, most traditional multimodal biometrics techniques extract features independently without considering the associations between different modalities. This paper proposes a method to learn sparse and discriminative multimodal feature codes for multimodal finger recognition, which takes into account inter-modality and intra-modality information. Experimental results demonstrate the effectiveness and robustness of the proposed framework.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Information Systems
Kuiyuan Zhang, Zhongyun Hua, Yuanman Li, Yongyong Chen, Yicong Zhou
Summary: This paper proposes an adaptive multi-scale image compressive sensing network in the wavelet domain called AMS-Net, which fully exploits the different importance of image low-frequency and high-frequency components, resulting in improved reconstruction quality.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Jie Zhang, Yongshan Zhang, Yicong Zhou
Summary: This study proposes a quantum-inspired spectral-spatial network (QSSN) and a quantum-inspired spectral-spatial pyramid network (QSSPN) for feature extraction and classification of hyperspectral images (HSIs). By dynamically fusing spectral and spatial information and using a quantum representation, joint features are extracted and feature representations are progressively learned for classification. Experimental results demonstrate the superiority of the proposed methods in HSI processing.
2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2023)
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)
