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

作者： Jin, Haiyan Wang, Jing Zuo, Fengyuan Su, Haonan Xiao, Zhaolin Wang, Bin Zhang, Yuanlin Xi'an University of Technology 710048 China Shaanxi Key Laboratory for Network Computing and Security Technology 710048 China

ISBN: (纸本)9798350368741

Low-light image enhancement methods based on diffusion models, though effective in improving image quality, often overrely on noise sensitivity and neglect the reconstruction deviations due to the naive up- and down-sampling operations. To address this issue, we propose a novel diffusion model, MWT-Diff, which utilizes multi-layer wavelet transforms to replace up- and down-sampling based on convolutions for extracting high-order features of different scales while mitigating representation degradations. Specifically, MWT-Diff is based on the U-Net architecture;it encodes four local features after the frequency-based down-sampling at each layer and fuses the enhanced four components during the up-sampling process. Additionally, we incorporate global refinement branches to mitigate information loss and employ efficient soft gate aggregation for feature fusion and reconstruction. Extensive quantitative and qualitative experiments demonstrate that our model achieves state-of-the-art performance on benchmark datasets. Code is available at: https://***/lalalulao/MWT-Diff. © 2025 IEEE.

关键词： Diffusion Model Low-light Image Enhancement Multi-layer Wavelet Transform Soft Gate Mechanism

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Textual Data De-Privatization Scheme Based on Generative Adversarial networks 24th

Textual Data De-Privatization Scheme Based on Generative Adv...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Du, Yanning Xu, Jinnan Zhang, Yaling Wang, Yichuan Wang, Zhoukai School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9789819615506

In many fields, such as healthcare, finance, and scientific research, data sharing and collaboration are critical to achieving better outcomes. However, the sharing of personal data often involves privacy risks, so privacy-preserving techniques are needed to ensure data security and privacy. The superior performance and flexibility of generative models in data representation have led to significant progress in the development of data privacy. This paper proposes a textual data de-privatization scheme based on generative adversarial networks that well combines generative adversarial networks with privacy protection. The generated de-privacy data retains the statistical properties of the original data and effectively removes sensitive information. Moreover, feature sorting and reshaping modules are introduced to enable the generator to better capture the relationships between features, thus improving the quality of synthetic data. In this paper, the utility of synthetic data is evaluated from three aspects, including similarity assessment, privacy assessment, and model utility assessment. Experimental results show that this method achieves a good trade-off in terms of textual data privacy protection and data quality maintenance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

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Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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Hedonic Coalition Formation Game and Contract-Based Federated Learning in AAV-Assisted Internet of Things

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IEEE Internet of Things Journal 2025年第9期12卷 11258-11272页

作者： Liu, Jia Li, Xiaoxu Xu, Yang Lyu, Chen Wang, Yichuan Liu, Xiaoying Xidian University School of Computer Science and Technology Xi’an710071 China Shanghai University of Finance and Economics School of Computing and Artificial Intelligence MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai200433 China Xi’an University of Technology Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an710071 China Zhejiang University of Technology School of Computer Science and Technology Hangzhou310023 China

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Things (IoT) components enable effective artificial intelligence (AI)-based models to be built. Beyond ground data sources, autonomous aerial vehicles (AAVs)-based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), have become increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impede data sharing across independently owned AAVs. To this end, we propose in this article a federated learning (FL)-based architecture that enables privacy-preserving collaborative machine learning across a federation of independent DaaS providers for the development of IoT applications. Specifically, this work introduces a novel incentive mechanism based on the hedonic coalition formation game to enhance the sustainable efficiency and stability of the FL system. By establishing tailored operational rules and functions, the proposed mechanism enables IoT sensing nodes to autonomously form optimal coalitions with AAVs, thereby ensuring robust collaboration. To deal with incentive mismatches and information asymmetry, we leverage the contract theory and propose a self-disclosure mechanism that guarantees truthful reporting of AAV capabilities while optimizing the global model owner’s profits. The performance-based AAV type is also defined to offer a practical measure for heterogeneous AAVs and serve as the foundation for fair and effective contract design. Simulation results validate the superiority of the proposed approach, demonstrating significant improvements in utility optimization and system stability compared to existing benchmarks. © 2014 IEEE.

关键词： Federated learning

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1+1>2: A Dual-Function Defense Framework for Adversarial Example Mitigation

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IEEE Transactions on Information Forensics and security 2025年 20卷 4121-4136页

作者： Yang, Rui Sun, Qindong Cao, Han Shen, Chao Cai, Jiaming Rong, Dongzhu Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China Xi’an University of Technology Shaanxi Key Laboratory of Network Computing and Security Xi’an710049 China Xi’an Jiaotong University School of Software Engineering Xi’an710049 China

Current state-of-the-art plug-and-play countermeasures for mitigating adversarial examples (i.e., purification and detection) exhibit several fatal limitations, impeding their deployment in safety-critical real-world applications. These limitations include susceptibility to adaptive attacks, adverse impact on benign samples, high time consumption for conducting a complete defense cycle, etc. To bridge the gap, developing more advanced plug-and-play countermeasures is urgently needed to safeguard these applications. Specifically, this paper first proposes a novel method named Gaussian-augmented GAN-based Adversarial Purification (GA-GAP). Unlike previous methods, GA-GAP enhances the density of the training data in low-robustness regions by using random Gaussian noise. Moreover, GA-GAP incorporates a pre-trained deep learning classifier into the training architecture and integrates its classification loss into the training loss function. Then, following the development of GA-GAP, this paper innovatively proposes a dual-function defense framework named Adversarial Detection on Purification (ADoP) to mitigate adversarial examples further. In ADoP, purification and detection complement each other, achieving the effect of 1+1 >2 , which can more efficiently avoid adaptive attacks. Extensive experiments on ImageNet demonstrate that ADoP outperforms other countermeasures in multiple aspects. These aspects include superior generalization capability in purifying and detecting various adversarial examples, less adverse impact on benign samples, and practical time consumption for conducting a complete defense cycle. © 2005-2012 IEEE.

关键词： Adversarial machine learning

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Multiple Edge Data Integrity Verification With Multi-Vendors and Multi-Servers in Mobile Edge computing

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IEEE Transactions on Mobile computing 2025年第6期24卷 4668-4683页

作者： Islam, Md Rashedul Xiang, Yong Uddin, Md Palash Zhao, Yao Kua, Jonathan Gao, Longxiang Deakin University School of Information Technology GeelongVIC3220 Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

Ensuring Edge Data Integrity (EDI) is imperative in providing reliable and low-latency services in mobile edge computing. Existing EDI schemes typically address single-vendor (App Vendor, AV) single-server (Edge Server, ES), single-vendor multi-server, and multi-vendor multi-server scenarios, which consider a single data replica cached by an ES from the AVs. However, the most practical scenario of Multi-Vendors and Multi-Servers with Multiple Data (MVMS-MD) cached by an ES from different AVs remains unexplored. Current solutions struggle when applied to this scenario due to increased computation and communication costs in the verification process across all ESs using the classical challenge-response per-data multi-round strategy. To tackle this issue, we propose a Multiple EDI-Verification (MEDI-V) approach in this paper. In particular, our MEDI-V utilizes an adaptive Merkle Hash Tree (ad-MHT) to efficiently generate a tree of multiple data replicas within each AV. Next, the dynamic mechanism computes minimal verification information using ad-MHT to create a challenge for individual ESs to produce EDI proofs. The ES then leverages its ad-MHT and the ES’s proof to send the reconstructed ad-MHT root to the AV for verification. Theoretical insights into MEDI-V’s correctness, efficiency, security, and comprehensive evaluations demonstrate its superiority in addressing MEDI issues in the MVMS-MD scenario. © 2002-2012 IEEE.

关键词： Data integrity

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Edge-Cloud Cooperation-Driven Intelligent Sustainability Evaluation Strategy Based on IoT and CPS for Energy-Intensive Manufacturing Industries

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IEEE Internet of Things Journal 2025年第9期12卷 12287-12297页

作者： Ma, Shuaiyin Huang, Yuming Chen, Yanping Xiao, Qinge Xu, Jun Leng, Jiewu Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing School of Computer Science and Technology Xi’an710121 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Xidian University Advanced Manufacturing Technology Innovation Center Guangzhou Institute of Technology Guangzhou510555 China Guangdong University of Technology Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangzhou510006 China

The advancement of the Industry 5.0 in information technology has led to increased interest in integrating edge-cloud cooperation with Internet of Things (IoT) and cyber-physical system (CPS) designs. This integration effectively reduces service delays and provides real-time analysis feedback to physical spaces, attracting attention from both academia and industry. These advanced technologies enhance production system intelligence, their alignment with circular economy principles for promoting sustainability has been overlooked. To address this gap, this article proposes an intelligent sustainability evaluation strategy driven by edge-cloud cooperation, IoT, and CPS. The proposed approach aims to enhance production sustainability and intelligence through circular economy perspectives. It introduces improved gray relation analysis and deep clustering network techniques to extract meaningful insights from diverse indicators within the evaluation system. By analyzing relationships between different equipment and workshops, it provides an analytical method that enhances production efficiency while reducing energy consumption and resource waste. To further validate the proposed method, an illustrative example using a partner company’s production data demonstrates its accuracy. © 2014 IEEE.

关键词： Circular economy

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k-TEVS: A k-Times E-Voting Scheme on Blockchain With Supervision

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IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 2326-2337页

作者： Liu, Yang He, Debiao Luo, Min Wang, Lianhai Peng, Cong Wuhan University School of Cyber Science and Engineering Wuhan430072 China Shenzhen Institute of Information Technology Institute of Information Technology Shenzhen518172 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Matrix Elements Technologies Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Shanghai200232 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

Thee-vote is regarded as a waytoexpress the opinion that the voters ask for. Actually, the e-vote could be applied wildly likequestionnaire,***,thecoexistences of efficiency and security as well as transparency and privacy could be considered as building blocks in the e-vote system. The blockchain could provide a public access board to reduce the storagecostsforthefieldconsistedofthevotegroupmanager(GM) with its vote assistants (VA). Particularly the k-times anonymous authentication (k-TAA) could also be a practical approach to pre serve voters’ privacy and reduce the computation costs during the ***,thee-voteschemewithpurek-TAAstrategy could damage either the supervision of voting or the efficiency and consistency of authentication process. What’s more, the impacts of dishonest voters couldn’t be stopped until the vote end. To tackle these problems, we apply the accumulator technology to add or revoke the voters at any time and extend the framework of k-TAA with the update process for the e-vote on blockchain for supervision (k-TEVS). In our scheme, the voter updates his membership witness and proves the fact that he is still a valid member with respective VA under the latest accumulator value. What’s more, this witness update operation is not contained in the authentication process, which means that the authentication process is still constant and efficient. Moreover, our add or delete update process with signature of knowledge needs only one pairing operation. For the security, we prove that the relaxed anonymity still holds in the k-TEVS framework. Finally, We implement k TEVS scheme, the Emura’s work [1] and the Huang’s work [2] for comparison. Then we make time cost and communication cost experiments, which present the feasibility and practicality of this scheme. © 2004-2012 IEEE.

关键词： Efficiency

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A Method for Removing Reflections from Water Surface Images Based on Pre-trained Image Restoration

A Method for Removing Reflections from Water Surface Images ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Minghua Zhao Rui Zhi Shuangli Du Jing Hu Cheng Shi Lin Wang Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Reflections on the water surface hinder the extraction of valuable information from water surface images. To remove reflections from water surface images, we construct a synthetic dataset and propose a multi-task network for water surface reflection detection and removal. Specifically, we first use a U-Net-based reflection detection module to generate a reflection mask, followed by a GAN-based network to remove the reflection. To extract multi-level features from the images, we design a color feature extraction network and a detail feature extraction network. Finally, to enhance the model's ability to remove large-area reflections, we pre-train the reflection removal network on an image restoration dataset. Experimental results on the proposed synthetic dataset and real water surface reflection images from the Internet show that our method significantly outperforms other methods in water surface reflection detection and removal.

关键词： Image color analysis Signal processing Feature extraction Multitasking Reflection Image restoration Speech processing Surface treatment Synthetic data Sports

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Diffusion Model with Multi-layer Wavelet Transform for Low-Light Image Enhancement

Diffusion Model with Multi-layer Wavelet Transform for Low-L...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haiyan Jin Jing Wang Fengyuan Zuo Haonan Su Zhaolin Xiao Bin Wang Yuanlin Zhang Xi’an University of Technology China Shaanxi Key Laboratory for Network Computing and Security Technology China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light image enhancement methods based on diffusion models, though effective in improving image quality, often overrely on noise sensitivity and neglect the reconstruction deviations due to the naive up- and down-sampling operations. To address this issue, we propose a novel diffusion model, MWT-Diff, which utilizes multi-layer wavelet transforms to replace up-and down-sampling based on convolutions for extracting high-order features of different scales while mitigating representation degradations. Specifically, MWT-Diff is based on the U-Net architecture; it encodes four local features after the frequency-based down-sampling at each layer and fuses the enhanced four components during the up-sampling process. Additionally, we incorporate global refinement branches to mitigate information loss and employ efficient soft gate aggregation for feature fusion and reconstruction. Extensive quantitative and qualitative experiments demonstrate that our model achieves state-of-the-art performance on benchmark datasets. Code is available at: https://***/lalalulao/MWT-Diff.

关键词： Wavelet transforms Sensitivity Noise Logic gates Speech enhancement Feature extraction Diffusion models Image enhancement Image reconstruction Optimization

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