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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

Computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yihuan Li, Yuanzhe Ren, Yanzhen Tu, Weiping Yang, Yuhong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education China School of Cyber Science and Engineering Wuhan University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China

ISBN: (纸本)9798350368741

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds. © 2025 IEEE.

关键词： Adversarial Attack Adversarial Example Detection Speaker Recognition

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LMR-CBT: learning modality-fused representations with CB-Transformer for multimodal emotion recognition from unaligned multimodal sequences

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Frontiers of Computer Science 2024年第4期18卷 39-47页

作者： Ziwang FU Feng LIU Qing XU Xiangling FU Jiayin QI School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Beijing 100876China Shanghai International School of Chief Technology Officer East China Normal UniversityShanghai 200062China School of Computer Science and Technology East China Normal UniversityShanghai 200062China School of Cyberspace Security Guangzhou UniversityGuangdong 510006China

Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion *** approaches use directional pairwise attention or a message hub to fuse language,visual,and audio ***,these fusion methods are often quadratic in complexity with respect to the modal sequence length,bring redundant information and are not *** this paper,we propose an efficient neural network to learn modality-fused representations with CB-Transformer(LMR-CBT)for multimodal emotion recognition from unaligned multi-modal ***,we first perform feature extraction for the three modalities respectively to obtain the local structure of the ***,we design an innovative asymmetric transformer with cross-modal blocks(CB-Transformer)that enables complementary learning of different modalities,mainly divided into local temporal learning,cross-modal feature fusion and global self-attention *** addition,we splice the fused features with the original features to classify the emotions of the ***,we conduct word-aligned and unaligned experiments on three challenging datasets,IEMOCAP,CMU-MOSI,and *** experimental results show the superiority and efficiency of our proposed method in both *** with the mainstream methods,our approach reaches the state-of-the-art with a minimum number of parameters.

关键词： modality-fused representations cross-model blocks multimodal emotion recognition unaligned multimodal sequences computational affection

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Hybrid Platoon Control Based on Driving Characteristics 19th

Hybrid Platoon Control Based on Driving Characteristics

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Hu, Jingpeng Xiong, Zhiguo School of Computing Beijing Institute of Technology Zhuhai519000 China Software Engineering Technology Research Center Zhuhai519000 China School of Aviation Beijing Institute of Technology Zhuhai519000 China

ISBN: (纸本)9783031651229

Research is being conducted on the problem of nonlinear cooperative formation control of unmanned vehicles, considering sensor failures. Firstly, a nonlinear cooperative control system model that incorporates sensor failures is being established. Secondly, a model predictive controller is being designed based on this model using the Lyapunov method and linear matrix inequalities (LMIs) to optimize the control performance metrics of the vehicle fleet. This approach ensures both the asymptotic stability and queue stability of the fleet while further enhancing its control performance. Lastly, the effectiveness and practicality of the algorithm are being validated through an experimental fleet constructed with Arduino serving as the control unit. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Lyapunov methods

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MFN: Explainable DNA triple helixes Stabilized Design based on mCGR and flow network

MFN: Explainable DNA triple helixes Stabilized Design based ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wen, Xiaoru Sun, Lijun Xie, Lei Zheng, Yanfen Cao, Ben Wang, Bin Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350386226

DNA triple helix structure, as a highly specific gene targeting tool, enable gene regulation by precisely identifying and binding to target DNA sequences. However, the limits of design quality and efficiency affect their wide application in gene therapy. Therefore, in this paper, we propose an antiparallel DNA triple helixes design method-MFN based on matrix chaotic game representation (mCGR) and flow network. Leveraging the structural characteristics of DNA sequences, this method employs the mCGR algorithm to construct an initial matrix, generating a set of DNA sequences that conform to foundational constraints. Then, these sequences are mapped as stream network nodes to screen crosstalk structures by path search, and the whole process is observable and interpretable. Experimental results show that MFN significantly improves the design efficiency of triple helix structure and reduces crosstalk phenomenon. Wet experiments further verify the effectiveness of the method. In summary, MFN achieves an efficient and high-quality design of DNA triple helixes and provides a new idea for targeted gene therapy. © 2024 IEEE.

关键词： Crosstalk

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Tu, Longchuan Meng, Shunmei Liu, Xiao Liu, Guanfeng Beheshti, Amin Zhang, Xuyun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjin China State Key Laboratory for Novel Software Technology Nanjing University Nanjin China School of Computing Macquarie University Sydney Australia

ISBN: (纸本)9789819605699

Recently, graph neural networks(GNNs) have played a key crucial in many recommendation situations. In particular, contrastive learning-based hypergraph neural networks (HGNNs) are gradually becoming a research focus for addressing issues of data sparsity and noise. Despite many studies proving their outstanding performance, there are still some shortcomings: i) Most contrastive learning-based HGNNs primarily rely on cross-view contrastive learning, while neglecting contrastive learning on the interaction graph. ii) Utilizing node embedding for hypergraph structure learning is susceptible to the influence of low-quality representations, thereby constraining the learning capability of HGNNs. To address these issues, we offer a Semantic Similarity-based Graph Contrastive Learning framework (SSGCL), which aims to jointly learn representations with rich semantic information through within-view and cross-view contrastive learning. Specifically, we first introduce consistency contrastive learning, which captures self-supervised signals through semantic similarity between nodes and their neighborhoods. It better captures the unique features of nodes through the connections between nodes and their neighborhoods. Then, we utilize interaction graph learning hypergraph structure, promoting it to extract potential node connections and thus improving its ability to describe homogeneous node relationships. Experimental evaluations on three actual datasets show that SSGCL performs much better than the current baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350302493

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5. © 2023 IEEE.

关键词： Graphic methods

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Mobile-YOLO-SDD: A Lightweight YOLO for Real-time Steel Defect Detection 18

Mobile-YOLO-SDD: A Lightweight YOLO for Real-time Steel Defe...

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18th CIRP Conference on Computer Aided Tolerancing, CAT 2024

作者： Luo, Shen Xu, Yuanping Zhu, Ming Zhang, Chaolong Kong, Chao Jin, Jin Li, Tukun Jiang, Xiangqian Guo, Benjun School of Software Engineering Chengdu University of Information Technology Chengdu China School of Automation Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom

Defect detection is essential in the steel production process. Recent years have seen significant advancements in steel surface defect detection based on deep learning methods, notably exemplified by the YOLO series models capable of precise and rapid detection. However, challenges arise due to the high complexity of surface textures on steel and the low recognition rates for minor defects, making real-time and accurate detection difficult. This study introduces Mobile-YOLO-SDD (Steel Defect Detection), a lightweight YOLO-based model designed with high accuracy for real-time steel defect detection. Firstly, based on the effective YOLOv5 algorithm for steel defect detection, the backbone network is replaced with MobileNetV2 to reduce the model size and computational complexity. Then, the ECA (Efficient Channel Attention) module was integrated into the C3 module to reduce the number of parameters further while maintaining the defect detection rate in complex backgrounds. Finally, the K-Means++ algorithm regenerates anchor boxes and determines optimal sizes, enhancing their adaptability to actual targets. Experimental results on NEU-DET data demonstrate that the improved algorithm achieves a 60.6% reduction in model size, a 60.8% reduction in FLOPs, and a 1.8% improvement in mAP compared to YOLOv5s. These results confirm the effectiveness of Mobile-YOLO-SDD and lay the foundation for subsequent lightweight deployment of steel defect detection models. © 2024 The Authors. Published by Elsevier B.V.

关键词： Deep learning

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An Irregularly Dropped Garbage Detection Method Based on Improved YOLOv5s 22

An Irregularly Dropped Garbage Detection Method Based on Imp...

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4th International Symposium on Signal Processing Systems, SSPS 2022

作者： Zhan, Yi Xu, Yuanping Zhang, Chaolong Xu, Zhijie Guo, Benjun School of Software Engineering Chengdu University of Information Technology China School of Computing and Engineering University of Huddersfield United Kingdom

ISBN: (纸本)9781450396103

Waste sorting and recycling play a significant role in carbon neutrality, and the government has promoted waste sorting stations in various cities while the stations have limited efficiency due to the absence of intelligent surveillance systems to monitor and analyze the scene in waste stations, especially to detect the irregularly dropped garbage. To take the most advantage of these stations, this study proposes an improved YOLO (You Only Look Once) v5s detector named YOLOv5s-Garbage to monitor waste sorting stations in real-time. This study enhances its ability to detect garbage by introducing CBAM (Convolutional Block Attention Module) and using EIoU (Efficient Intersection over Union) to accelerate the convergence of the bonding box loss. According to experiments, the mAP of YOLOv5s-Garbage on the waste sorting dataset reaches 89.7%, which is 3.3% higher than the classical YOLOv5s. This study then combines the DeepSort tracking algorithm and re-filter process to filter the target garbage to distinguish the irregularly dropped garbage and normal one, which reduces the false alarm significantly. © 2022 ACM.

关键词： Object detection

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Accurate Reliability Analysis Methods for Approximate computing Circuits

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Tsinghua Science and Technology 2022年第4期27卷 729-740页

作者： Zhen Wang Guofa Zhang Jing Ye Jianhui Jiang Fengyong Li Yong Wang School of the Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Software Engineering Tongji UniversityShanghai 200092China

In recent years,Approximate computing Circuits(ACCs)have been widely used in applications with intrinsic tolerance to *** the increased availability of approximate computing circuit approaches,reliability analysis methods for assessing their fault vulnerability have become highly *** this study,two accurate reliability evaluation methods for approximate computing circuits are *** reliability of approximate computing circuits is calculated on the basis of the iterative Probabilistic Transfer Matrix(PTM)*** the calculation,the correlation coefficients are derived and combined to deal with the correlation problem caused by fanout *** accuracy and scalability of the two methods are verified using three sets of approximate computing circuit instances and more circuits in Evo Approx8 b,which is an approximate computing circuit open source *** results show that relative to the Monte Carlo simulation,the two methods achieve average error rates of 0.46%and 1.29%and time overheads of 0.002%and 0.1%.Different from the existing approaches to reliability estimation for approximate computing circuits based on the original PTM model,the proposed methods reduce the space overheads by nearly 50%and achieve time overheads of 1.78%and 2.19%.

关键词： Approximate computing Circuit(ACC) correlation coefficient iterative Probabilistic Transfer Matrix(PTM) reliability

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