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International Joint Conference on Neural networks (IJCNN)

作者： Xinru Song Feng Wu Zongchao Xie Longxiang Gao Lei Cui Youyang Qu Shujun Gu Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science University of Technology Sydney Australia Faculty of Science University of Melbourne Australia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Federated learning (FL) has been widely applied in medical field, which allows clients to collaboratively train global models without sharing local data. Nevertheless, the diversity and scarcity of samples from rare diseases may result in a decline in the performance of local models on client-side due to using a singular global model. Moreover, direct transmission of local models or parameters will likely lead to user privacy violations. To solve these problems, we propose a Grouped Federated Meta-Learning (GrFML) method to improve the performance of local personalization models while protecting data privacy. Specifically, we first utilize a self-attention mechanism to extract partial features from the client’s local data, which are uploaded to the server (medical data is susceptible to perturbation and data integrity, thus this process does not expose the private data). The server groups clients with similar features based on these extracted features. Then, multiple meta-models are trained on these groups and distributed back to the clients to enhance the performance of the client’s local models. Furthermore, during the FL process, we introduce dynamic perturbation to the uploaded gradients based on the model’s test accuracy to protect their privacy. Typically, the perturbation magnitude is directly proportional to the model’s test accuracy. Extensive experiments shown that the GrFML model significantly improves client personalization model accuracy and achieves a good privacy-utility trade-off.

关键词： Metalearning Privacy Data privacy Accuracy Perturbation methods Feature extraction Data models

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LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks

arXiv

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arXiv 2024年

作者： Wang, Tianyi Huang, Mengxiao Cheng, Harry Zhang, Xiao Shen, Zhiqi Nanyang Technological University Singapore Singapore Qilu University of Technology Jinan China Shandong University Qingdao China Joint NTU-WeBank Research Centre on Fintech Nanyang Technological University Singapore Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology China College of Computing and Data Science Nanyang Technological University Singapore

Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algorithms that have been attempted to thwart malicious Deepfake attacks, they mostly struggle with the generalizability challenge when confronted with hyper-realistic synthetic facial images. To tackle the problem, this paper proposes a proactive Deepfake detection approach by introducing a novel training-free landmark perceptual watermark, LampMark for short. We first analyze the structure-sensitive characteristics of Deepfake manipulations and devise a secure and confidential transformation pipeline from the structural representations, i.e. facial landmarks, to binary landmark perceptual watermarks. Subsequently, we present an end-to-end watermarking framework that imperceptibly and robustly embeds and extracts watermarks concerning the images to be protected. Relying on promising watermark recovery accuracies, Deepfake detection is accomplished by assessing the consistency between the content-matched landmark perceptual watermark and the robustly recovered watermark of the suspect image. Experimental results demonstrate the superior performance of our approach in watermark recovery and Deepfake detection compared to state-of-the-art methods across in-dataset, cross-dataset, and cross-manipulation scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Watermarking

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge computing Environment

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IEEE Transactions on Vehicular technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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Few-Shot Hyperspectral Image Classification with Spectral-Spatial Feature Fusion Based on Fuzzy Broad Learning System

Few-Shot Hyperspectral Image Classification with Spectral-Sp...

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IEEE International Conference on Image Processing

作者： Xiaopei Hu Guixin Zhao Aimei Dong Guohua Lv Yi Zhai Ying Guo Xiangjun Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In the few-shot hyperspectral image (HSI) classification, most current models don't fully utilize the advantage of spectral-spatial feature fusion, resulting in low classification accuracy. Therefore, we propose a few-shot HSI classification model with spectral-spatial feature fusion based on fuzzy broad learning system (FBLS) (FSFBLS). Firstly, we use a Gaussian filter to suppress noise while smoothing spectral features based on spatial information to achieve the first fusion of spectral-spatial features. Secondly, we use FBLS with fuzzy rules to fully model the complex mapping relationship between spectral-spatial features and HSI labels to complete HSI classification. The fuzzy processing can extract rich discriminative features to enhance the recognition of different categories. Finally, the guided filter corrects the misclassified samples of FBLS based on the guided image to achieve the second fusion of spectral-spatial features. Extensive experimental results on three public datasets demonstrate that FSFBLS achieves state-of-the-art classification performance compared to nine popular models.

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A High-Dimensional Temporal Data Publishing Method Based on Dynamic Bayesian networks and Differential Privacy

A High-Dimensional Temporal Data Publishing Method Based on ...

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International Joint Conference on Neural networks (IJCNN)

作者： Yaxin Wang Zhen Zhang Heng Qian Yongchao Gao Qiuyue Wang Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Massive high-dimensional data generated by the Internet typically contains sensitive privacy information. Protecting data privacy while maintaining utility has become a pressing challenge. We propose a novel high-dimensional temporal data publishing method leveraging dynamic Bayesian networks and differential privacy. Initially, a dynamic Bayesian network is constructed, utilizing mutual information filtering of data. Subsequently, we calculate the Coherent Neighborhood Propinquity for each node within the network to determine edge sensitivity and establish a privacy budget. Noise is then strategically added to attribute data in accordance with the sensitivity and privacy budget requirements, ensuring the dataset complies with ε-differential privacy standards. Experimental results demonstrate that the data availability performance of the SMAP dataset (Soil Moisture Active Passive) surpasses that of competing algorithms while providing an equivalent level of privacy protection. Hence, our method significantly enhances data availability without compromising differential privacy protection.

关键词： Differential privacy Privacy Sensitivity Publishing Time series analysis Dynamic scheduling Bayes methods

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A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range

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Applied Sciences 2025年第10期15卷 5628-5628页

作者： Hao Chen Zheng Dang Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an 710048 China School of Computer Science and Engineering Xi’an University of Technology Xi’an 710048 China

The Logical Range is a mission-oriented, reconfigurable environment that integrates testing, training, and simulation by virtually connecting distributed systems. In such environments, task-processing devices often experience highly dynamic workloads due to varying task demands, leading to scheduling inefficiencies and increased latency. To address this, we propose GCSG, a hybrid load forecasting model tailored for Logical Range operations. GCSG transforms time-series device load data into image representations using Gramian Angular Field (GAF) encoding, extracts spatial features via a Convolutional Neural network (CNN) enhanced with a Squeeze-and-Excitation network (SENet), and captures temporal dependencies using a Gated Recurrent Unit (GRU). Through the integration of spatial–temporal features, GCSG enables accurate load forecasting, supporting more efficient resource scheduling. Experiments show that GCSG achieves an R 2 of 0.86, MAE of 4.5, and MSE of 34, outperforming baseline models in terms of both accuracy and generalization.

关键词： logical range time-series data load forecasting

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Incremental Context-free Grammar Inference in Black Box Settings

arXiv

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arXiv 2024年

作者： Li, Feifei Chen, Xiao Xiao, Xi Sun, Xiaoyu Chen, Chuan Wang, Shaohua Han, Jitao Tsinghua Shenzhen International Graduate School China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China The University of Newcastle Australia The Australian National University Australia Central University of Finance and Economics China

Black-box context-free grammar inference presents a significant challenge in many practical settings due to limited access to example programs. The state-of-the-art methods, Arvada and Treevada, employ heuristic approaches to generalize grammar rules, initiating from flat parse trees and exploring diverse generalization sequences. We have observed that these approaches suffer from low quality and readability, primarily because they process entire example strings, adding to the complexity and substantially slowing down computations. To overcome these limitations, we propose a novel method that segments example strings into smaller units and incrementally infers the grammar. Our approach, named Kedavra, has demonstrated superior grammar quality (enhanced precision and recall), faster runtime, and improved readability through empirical comparison. © 2024, CC BY.

关键词： Context free grammars

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Learning Together Securely: Prototype-Based Federated Multi-Modal Hashing for Safe and Efficient Multi-Modal Retrieval 39

Learning Together Securely: Prototype-Based Federated Multi-...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zuo, Ruifan Zheng, Chaoqun Zhu, Lei Lu, Wenpeng Xiang, Yuanyuan Li, Zhao Qu, Xiaofeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China School of Electronic and Information Engineering Tongji University China Shandong Branch of National Computer Network Emergency Response Technical Team Coordination Center (CNCERT/SD) China Evay Info China School of Information Science and Engineering University of Jinan China

ISBN: (纸本)157735897X

With the proliferation of multi-modal data, safe and efficient multi-modal hashing retrieval has become a pressing research challenge, particularly due to concerns over data privacy during centralized processing. To address this, we propose Prototype-based Federated Multi-modal Hashing (PFMH), an innovative framework that seamlessly integrates federated learning with multi-modal hashing techniques. PFMH achieves fine-grained fusion of heterogeneous multi-modal data, enhancing retrieval accuracy while ensuring data privacy through prototype-based communication, thereby reducing communication costs and mitigating risks of data leakage. Furthermore, using a prototype completion strategy, PFMH tackles class imbalance and statistical heterogeneity in multi-modal data, improving model generalization and performance across diverse data distributions. Extensive experiments demonstrate the efficiency and effectiveness of PFMH within the federated learning framework, enabling distributed training for secure and precise multi-modal retrieval in real-world scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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CHECKWHY: Causal Fact Verification via Argument Structure

arXiv

引用

arXiv 2024年

作者： Si, Jiasheng Zhao, Yibo Zhu, Yingjie Zhu, Haiyang Lu, Wenpeng Zhou, Deyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

With the growing complexity of fact verification tasks, the concern with "thoughtful" reasoning capabilities is increasing. However, recent fact verification benchmarks mainly focus on checking a narrow scope of semantic factoids within claims and lack an explicit logical reasoning process. In this paper, we introduce CHECKWHY, a challenging dataset tailored to a novel causal fact verification task: checking the truthfulness of the causal relation within claims through rigorous reasoning steps. CHECKWHY consists of over 19K "why" claim-evidence-argument structure triplets with supports, refutes, and not enough info labels. Each argument structure is composed of connected evidence, representing the reasoning process that begins with foundational evidence and progresses toward claim establishment. Through extensive experiments on state-of-the-art models, we validate the importance of incorporating the argument structure for causal fact verification. Moreover, the automated and human evaluation of argument structure generation reveals the difficulty in producing satisfying argument structure by fine-tuned models or Chain-of-Thought prompted LLMs, leaving considerable room for future improvements1 Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Double-RIS Enabled Physical Layer security for Wireless-Powered Communication Systems Over Rayleigh Fading Channels

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IEEE Transactions on Communications 2025年

作者： Chen, Jingyu Cao, Kunrui Ding, Haiyang Lv, Lu Ye, Yinghui Chi, Haolian Wang, Tao Yang, Liang National University of Defense Technology School of Information and Communications Wuhan430035 China Xidian University School of Telecommunications Engineering Xi'an710071 China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This paper investigates the physical layer security for a double-reconfigurable intelligent surface (DRIS) aided wireless-powered communication (WPC) system in the presence of an eavesdropper, where one RIS (termed as RIS-1) is deployed between power station (PS) and information user (U) while the other RIS (termed as RIS-2) is deployed between U and access point (AP). Moreover, an idle user acts as a cooperative jamming user (J) to emit the artificial noise to improve the transmission security of U. According to different types in energy harvesting (EH) and information transmission (IT)/noise transmission (NT) of U/J, we propose four DRIS enabled wireless-powered cooperative jamming transmission schemes based on jointly enhancing EH and IT of U (namely DRIS-1), jointly enhancing EH of U and NT of J (namely DRIS-2), jointly enhancing EH of J and IT of U (namely DRIS-3), and jointly enhancing EH and NT of J (namely DRIS-4), respectively. We analyze connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) of the proposed four schemes, respectively. The results show that DRIS-1 scheme has the highest diversity gain among the four schemes. Besides, the gain of the number of reflecting elements at RIS-1 has the same order as that at RIS-2 in DRIS-1 scheme and DRIS-4 scheme. In DRIS-2 scheme, the gains of the number of reflecting elements at RIS-1 and RIS-2 are two powers and one power, respectively, while the opposite is true in DRIS-3 scheme. In addition, DRIS-1 scheme achieves the best COP, while DRIS-4 scheme achieves the best SOP. DRIS-1 scheme, DRIS-3 scheme, and DRIS-4 scheme respectively achieve the best EST in the low, middle, and high region of transmission power or number of RIS elements. © 1972-2012 IEEE.

关键词： Inductive power transmission

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