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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Yang, Ching-Nung Lu, Yu-Quan Liu, Shu-Yu Xiong, Lizhi Dept. of Computer Sci. and Inf. Engineering National Dong Hwa University Hualien Taiwan School of Computer and Software Nanjing University of Inf. Sci. & Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Reversible data hiding (RDH) is one special type of data hiding, and is widely used for many intended applications. Moreover, to protect the cover image, RDH in encrypted image (RDHEI) is accordingly proposed. With the reversibility and hiding data in encrypted image, RDHEI has potential utility in fields such as military and medical image transmission, and forensic analysis. Recently, Panchikkil et al. propose a RDHEI by using pixel swapping (RDHE-PSW) to swap some pixels in a block. The pixel swapping does not change pixel values, and thus Panchikkil et al.’s RDHEI-PSW can retain entropy and histogram. The smoothness of a block is calculated to extract the hidden bits and meantime recover the cover image. Panchikkil et al.’s RDHEI-PSW only hides two secret bits in every block. Recently, Kim et al. use pixel shifting (PSH), a circular right shift on a block, instead of pixel swapping to propose a RDHEI-PSH. In this paper, we still adopt PSH approach but use it in a different way (a modified PSH) to propose an enhanced RDHEI-PSH with a higher embedding capacity than Kim et al.’s RDHEI-PSH. Our enhanced RDHEI-PSH double the patterns on a block, on which we can embed more secret bits. Experimental results demonstrate that our enhanced RDHEI-PSH surpasses Panchikkil et al.’s RDHEI-PSW and Kim et al.’s RDHEI-PSH. © 2025 SPIE.

关键词： Military photography

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How to Securely Outsource the Multiple Kernel Fuzzy Clustering Task in Edge Computing

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IEEE Internet of Things Journal 2025年第11期12卷 16451-16466页

作者： Sun, Xinrong Tao, Yunting Kong, Fanyu Yu, Qian Ge, Chunpeng Xu, Qiuliang Zhang, Hanlin Binzhou Polytechnic College of Information Engineering Shandong Binzhou256603 China Shandong University School of Software Jinan250101 China Shandong Sansec Information Technology Co. Ltd Jinan250101 China Singapore Singapore Qingdao University College of Computer Science and Technology Qingdao266071 China

For the huge amount of data from the Internet of Things (IoT) devices, multiple kernel learning is a widely concerned issue in data analyzing, among which the multiple kernel fuzzy clustering (MKFC) algorithm is an effective approach for extracting linear features in high-dimensional space. For a time-consuming multiple kernel clustering task, it's meaningful to find a secure and efficient outsourcing scheme in the edge-end collaborative architecture, which utilizes edge computing resources while resisting untrusted edge servers. However, existing secure outsourcing schemes cannot align well with the distributed and real-Time characteristics of edge computing due to their complex encryption processes. In this paper, we propose a secure multiple kernel fuzzy clustering outsourcing scheme based on a novel matrix blinding method. The proposed novel matrix blinding method conducts two related encryption operations with disturbance terms, which avoids specific disturbance elimination computations, to reduce the computational burdens in the decryption phase. Additionally, we introduce a sampling verification method to detect the server's deceptive behaviors. The theoretical analysis demonstrates that our scheme guarantees data privacy and has the capability to verify incorrect results. The experimental results indicate that our scheme is 6.73% superior to other schemes on average when conducting matrix outsourcing computation and enhances the efficiency of conducting the MKFC algorithm by 10.44% to 55.70% on different datasets. © 2014 IEEE.

关键词： Differential privacy

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Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

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Few-shot Bearing Fault Diagnosis using Adaptive Detail Convolution and Global KAN-Transformer with Mahalanobis Distance

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Journal of Vibration engineering and Technologies 2025年第5期13卷 1-16页

作者： Zhao, Xinyu Zhao, Zhanshan Cui, Xiubin Yin, Jiao Cao, Jinli Wang, Hua School of Computer Science and Technology Tiangong University Tianjin 300387 China School of Software Engineering Tiangong University Tianjin 300387 China School of Computer Science China University of Geosciences Wuhan 430078 China Institute for Sustainable Industries Liveable Cities Victoria University Melbourne 3011 VIC Australia Department of Computer Science and Information Technology La Trobe University Melbourne 3086 VIC Australia

Background: In bearing failure diagnosis, accurate fault detection is essential for ensuring the safe operation of machinery. However, acquiring enough labeled fault data for training is expensive and challenging. Objective: This paper aims to address key challenges in few-shot fault diagnosis by proposing a novel framework incorporating an Adaptive Detail Convolution and a dual-branch architecture. Methods: The proposed framework consists of three main components: an Adaptive Detail Convolution module for enhanced feature extraction, a Global KAN-Transformer learning branch to model long-range dependencies between global features, and an adaptive-regularized Mahalanobis distance module to measure the similarity of local features between support and query samples. Results: Experimental results on the CWRU dataset show that the proposed framework significantly improves classification performance in terms of accuracy, robustness, and efficiency. Conclusion: The proposed solution offers an effective approach for few-shot bearing fault diagnosis and outperforms existing methods in both accuracy and computational efficiency. © Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive Detail Convolution Bearing Fault Diagnosis Few-shot Learning Global KAN-Transformer Mahalanobis Distance

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SG-LSBA: Semantic Latent Space Backdoor Attack on Stable Diffusion 3rd

SG-LSBA: Semantic Latent Space Backdoor Attack on Stable Di...

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3rd International Conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Pan, Yu Chen, Jiahao Wang, Lin Dai, Bingrong School of Computer and Information Engineering Shanghai Polytechnic University Shanghai 201209 China Shanghai Development Center of Computer Software Technology Shanghai 201112 China

ISBN: (纸本)9789819645084

Stable Diffusion (SD) models have achieved remarkable success in text-to-image synthesis, but their security vulnerabilities remain largely unexplored. In this paper, we introduce a novel semantic-guided latent space backdoor attack (SG-LSBA) that leverages the semantic information in the text input to inject stealthy and semantically coherent backdoors into SD models. Our approach outperforms existing methods by crafting context-aware semantic triggers, identifying target visual features in the latent space, and employing an adversarial optimization framework. Extensive evaluations demonstrate the high success rates, strong semantic relevance, and exceptional stealthiness of SG-LSBA. Our findings highlight the urgent need for considering the complex interplay between semantics and latent representations in developing robust defenses against back-door attacks in SD models. We make our code and datasets publicly available to facilitate further research and development of secure and reliable text-to-image synthesis models. The code is available at https://***/paoche11/SG-LSBA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Latent space backdoor attack Semantic guidance Semantic triggers Stable Diffusion

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Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

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Energy Oriented Three-Tier Computation Offloading Scheme in Maritime Edge Computing Network

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8126-8140页

作者： Zhang, Hongxia Xi, Shiyu Shang, Bodong Zhang, Peiying Wu, Sheng Jiang, Chunxiao Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Eastern Institute of Technology Eastern Institute for Advanced Study Ningbo315200 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

With the development of maritime continuing to grow, large-scale maritime wireless devices (MWDs) are being deployed for various maritime applications. The rapid development of maritime Internet of Things (IoT) and growing demand for these applications have led to an increasing number of computation-intensive and resource-sensitive tasks. Considering the limited computing capacity and energy of MWDs, it is challenging to transmit and process massive maritime computing tasks in an energy efficient manner. In this paper, we propose a three-tier maritime edge computing system in which low earth orbit (LEO) satellite and offshore base station (OBS) support to provide communication and computing services for MWDs. The system optimizes association variable, task partitioning, transmission power, and computing resource allocation to reduce system energy consumption under latency constraints. The optimization problem is formulated as a mixed integer non-linear programming (MINLP) optimization problem. We decompose the original problem into four subproblems and design algorithms to solve them. Firstly, the association variable optimization subproblem is solved by slack variable method. Then, we adopt quadratic transformation and design a difference of convex algorithm to solve transmission power optimization subproblem. Further, task partitioning subproblem is solved by obtaining upper and lower bounds of offloading task size by strict deduction, and then solved by the standard convex method. Finally, Lagrangian dual method is utilized to deal with joint computing resource optimization subproblem of LEO satellite and OBS. In simulation results, the proposed algorithm saves 39.3% of system energy consumption compared to benchmark schemes. © 1967-2012 IEEE.

关键词： Computation offloading

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A Novel Redundant Service Caching and Task Offloading Method in Mobile Edge Computing 31st

A Novel Redundant Service Caching and Task Offloading Metho...

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31st International Conference on Web Services, ICWS 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Ouyang, Zhaobin Xia, Yunni Li, Jingpeng Feng, Jiafeng Yu, Yang Zhang, Ke Xu, Xifeng Ma, Yong Chen, Peng Li, Xiaobo School of Computer Chongqing University Chongqing400030 China China Huadian Corporation Guangdong Branch Guangdong China Nanjing Nanzi Huadun Digital Technology Co. Ltd. Nanjing China School of Computer Science and Technology Beijing Institute of Technology Beijing100083 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330000 China School of Computer and Software Engineering Xihua University Chengdu China Chongqing Animal Husbandry Techniques Extension Center Chongqing China

ISBN: (纸本)9783031770715

Edge caching is a promising technologies, which enhances content delivery and reduces service latency by caching potentially interested content and services on edge nodes near users. Due to constraints such as limited edge server capacities and unstable communications in a highly dynamic MEC environment, caching and content delivery are prone to failures and inconsistencies. Consequently, redundant caching mechanisms are in high need as counter measures in this paper, we proposes a novel redundant service caching and task offloading Decision (FT-STD) method. Specifically, we utilize the primary backup (PB) approach for offloading fault tolerance and employ a learning-based algorithm for yielding redundant service caching schedules in MEC. Simulation results demonstrate thatclearly outperforms its peers across multiple performance metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mobile edge computing

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Education and Teaching Research of IoT Embedded Subject under the National Vocational and Technical Skills Standards 6

Education and Teaching Research of IoT Embedded Subject unde...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Yanglu Wang, Xingchen Yadav, Amit Shah, Abhishek Pratap College of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University Darwin Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

This research study analyzes six key factors in the education and teaching of IoT embedded direction: training objectives (which direction to teach), curriculum system (what to teach), teaching organization (how to teach), faculty team (who teaches), and teaching evaluation (how well the teaching is) under the current status of embedded teaching on the Internet of Things (IoT) engineering discipline. Guided by the latest National Occupational Technical Skill Standards for IoT engineering professionals and in accordance with the educational philosophy of Chengdu Neusoft University, here we propose a design for teaching system for IoT embedded talent courses, clarified the key elements and formulated new ideas for the current development of talents in this direction to assist improvement and perfection of related curriculum systems, and to promote the establishment and operation of practical and technological activity platforms. It aims to provide practical references for the construction of professionalism in education and teaching standards of IoT. © 2025 IEEE.

关键词： Curricula

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Interpretable CNN-Based Lithographic Hotspot Detection Through Error Marker Learning

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1031-1044页

作者： Sun, Haoyang Jiang, Cong Ye, Xun Feng, Dan Tan, Benjamin Ma, Yuzhe Liu, Kang Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China University of Calgary Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada With Microelectronics Thrust Guangzhou510530 China

As the technology node develops toward its physical limit, lithographic hotspot detection has become increasingly important and ever-challenging in the computer-aided design (CAD) flow. In recent years, convolutional neural networks (CNNs) have achieved great success in hotspot detection. However, the interpretability of their hotspot prediction has yet to be considered. Compared with conventional lithography simulation and pattern matching-based methods, the black-box nature of CNNs wavers their practical applications with confidence. In this article, we propose the first interpretable CNN-based hotspot detector capable of providing high-detection accuracy and reliable explanations for hotspot identification. Specifically, we augment the training dataset with expanded error markers obtained and preprocessed from lithography simulation, which are then learned by an encoder-decoder architecture as intermediate features. We additionally introduce coordinate attention in the encoder to facilitate better-feature extraction. By learning these error markers and part of their surrounding metals as root cause hotspot features, our architecture achieves the highest-hotspot accuracy of 99.78% and the lowest-false positive rate of 5.29% compared to all prior work. Moreover, our method demonstrates the best visual and quantitative interpretability results when applying CNN interpretation methods. © 2024 IEEE.

关键词： Pattern matching

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