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学者姓名：陈淑武
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Abstract ：
In recent years, deep neural networks (DNNs) have made significant advancements in various vision tasks, including image classification and object detection. However, these models remain vulnerable to adversarial examples subtle perturbations that can significantly mislead predictions. In response to this, we propose a novel adversarial attack method called Local Masking and Multi-stage Momentum Optimization (LMMMO). LMMMO integrates a self-adaptive local masking mechanism to focus perturbations on decision-critical regions, improving both attack efficiency and perceptual stealth. Additionally, LMMMO uses a multi-stage momentum optimization strategy, adjusting step sizes dynamically and refining gradients through a coarse-to-fine approach to ensure stable convergence. Compared to existing methods, LMMMO achieves competitive high attack success rates (e.g., 0.989 average ASR) while demonstrating the lowest average L2 distortion (0.234). It also shows favorable perceptual similarity (LPIPS) compared to several iterative baselines like PGD and MIFGSM, offering a promising balance between attack performance and imperceptibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Keyword ：
Deep neural networks Deep neural networks Image classification Image classification Iterative methods Iterative methods Momentum Momentum Object detection Object detection Object recognition Object recognition
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| GB/T 7714 | Lin, Liqiang , Lin, Yu , Kang, Yunyu et al. An Adversarial Attack Method Based on Local Masking and Multi-stage Momentum Optimization [C] . 2026 : 329-341 . |
| MLA | Lin, Liqiang et al. "An Adversarial Attack Method Based on Local Masking and Multi-stage Momentum Optimization" . (2026) : 329-341 . |
| APA | Lin, Liqiang , Lin, Yu , Kang, Yunyu , Chen, Shuwu , Liu, Xiaolong . An Adversarial Attack Method Based on Local Masking and Multi-stage Momentum Optimization . (2026) : 329-341 . |
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本发明公开了一种基于深度强化学习的服务功能链(SFC)动态调整方法，属于网络功能虚拟化(NFV)编排领域，所述基于深度强化学习的SFC动态调整方法包括：步骤1)构建面向数据中心的物理网络模型；步骤2)对数据中心所需要的SFC请求进行建模；步骤3)对SFC动态调整过程建模；步骤4)马尔可夫决策过程(MDP)建模；步骤5)搭建智能SFC动态调整算法(ISFCDAA)模型；步骤6)训练ISFCDAA模型；利用训练好的ISFCDAA进行SFC动态调整。本发明最终可实现数据中心网络中较高服务接受率和长期收益，为网络功能虚拟化编排器(Network Function Virtualization Orchestrator,NFVO)编排器的资源管理提供了有效的解决方案。
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| GB/T 7714 | 舒兆港 , 王媛滔 , 陈淑武 et al. 一种基于深度强化学习的服务功能链资源动态调整方法 : CN202411649701.8[P]. | 2024-11-19 . |
| MLA | 舒兆港 et al. "一种基于深度强化学习的服务功能链资源动态调整方法" : CN202411649701.8. | 2024-11-19 . |
| APA | 舒兆港 , 王媛滔 , 陈淑武 , 谢海辉 . 一种基于深度强化学习的服务功能链资源动态调整方法 : CN202411649701.8. | 2024-11-19 . |
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本发明公开一种基于地质灾害监测的滑坡位移图像分析方法与系统，该方法包括1)获取滑坡区域的图像及水文信息，并对图像进行预处理；2)提取图像中的多尺度特征；3)对图像中每个像素进行分类，生成分割结果；4)比对特征点，获得滑坡位移数据；5)计算滑坡区域的像素面积，并转换为实际面积得到滑坡的总面积；6)计算面积转换规模；7)通过水文信息与治理信息模型判断滑坡可能发生的规模；8)将面积转换规模、水文信息及治理信息模型得到的规模，按权重相加得到滑坡预测规模；9)当滑坡位移数据超出阈值，则发出警报，并生成风险评估报告；本发明通过图像分析能够快速获取滑坡区域的相关信息，加快人员反应速度，减少灾难带来的损失。
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| GB/T 7714 | 陈淑武 , 翁宇宏 . 一种基于地质灾害监测的滑坡位移图像分析方法与系统 : CN202411625249.1[P]. | 2024-11-14 . |
| MLA | 陈淑武 et al. "一种基于地质灾害监测的滑坡位移图像分析方法与系统" : CN202411625249.1. | 2024-11-14 . |
| APA | 陈淑武 , 翁宇宏 . 一种基于地质灾害监测的滑坡位移图像分析方法与系统 : CN202411625249.1. | 2024-11-14 . |
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本发明涉及环境监测技术领域，具体涉及一种基于边缘智能的森林火灾监控方法，其包括如下步骤：S1、部署边缘节点及传感设备；S2、实时采集传感设备获取的数据，并对其进行初步处理；S3、通过边缘节点进行火灾风险智能识别与决策；S4、应急响应与事件管理；S5、用户交互与反馈。本发明通过引入边缘智能技术，优化森林火灾监控系统的架构，实现数据的本地处理与实时分析，从而提升火灾监控的响应速度和检测精度，适宜进一步推广应用。
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| GB/T 7714 | 陈淑武 , 林茁杨 . 一种基于边缘智能的森林火灾监控方法 : CN202510066897.6[P]. | 2025-01-16 . |
| MLA | 陈淑武 et al. "一种基于边缘智能的森林火灾监控方法" : CN202510066897.6. | 2025-01-16 . |
| APA | 陈淑武 , 林茁杨 . 一种基于边缘智能的森林火灾监控方法 : CN202510066897.6. | 2025-01-16 . |
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The widespread convenience of generative models has exacerbated the misuse of attribute-editing-based Deepfake technologies, leading to the proliferation of illegally generated content that severely threatens personal privacy and security. Existing proactive defense strategies mitigate Deepfake attacks by embedding imperceptible adversarial watermarks into the spatial-domain of protected images. However, spatial-domain adversarial watermarks are inherently sensitive to lossy compression operations, which significantly degrades their defense efficacy. To address this limitation, we propose a frequency-domain cross-image adversarial watermark generation scheme to enhance robustness toward JPEG compression. In the proposed method, the adversarial watermark training process is migrated to the frequency domain using a differentiable JPEG module, which explicitly simulates the impact of quantization and compression on perturbation distributions. Furthermore, a fusion module is incorporated to coordinate watermark distributions across images, thereby enhancing the generalization of the defense. Experimental results demonstrate that the generated adversarial watermarks exhibit strong robustness against JPEG compression and effectively disrupt the outputs of Deepfake models. Moreover, the proposed scheme can be directly applied to diverse facial images without retraining, thereby providing reliable protection for real-world image application scenarios.
Keyword ：
Adversarial attack Adversarial attack Cross-image Cross-image Deepfake Deepfake JPEG-resistance JPEG-resistance Proactive defense Proactive defense
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| GB/T 7714 | Lin, Zhiyu , Lin, Hanbin , Lin, Liqiang et al. Robust cross-image adversarial watermark with JPEG resistance for defending against Deepfake models [J]. | COMPUTER VISION AND IMAGE UNDERSTANDING , 2025 , 260 . |
| MLA | Lin, Zhiyu et al. "Robust cross-image adversarial watermark with JPEG resistance for defending against Deepfake models" . | COMPUTER VISION AND IMAGE UNDERSTANDING 260 (2025) . |
| APA | Lin, Zhiyu , Lin, Hanbin , Lin, Liqiang , Chen, Shuwu , Liu, Xiaolong . Robust cross-image adversarial watermark with JPEG resistance for defending against Deepfake models . | COMPUTER VISION AND IMAGE UNDERSTANDING , 2025 , 260 . |
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With the rapid development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) is emerging as a new network architecture for the modern Internet. SDN separates the control plane from the data plane, allowing a central controller, the SDN controller, to quickly direct the routing devices within the topology to forward data packets, thus providing flexible traffic management for communication between information sources. However, traditional Distributed Denial of Service (DDoS) attacks still significantly impact SDN systems. This paper proposes a novel dual-layer strategy capable of detecting and mitigating DDoS attacks in an SDN network environment. The first layer of the strategy enhances security by using blockchain technology to replace the SDN flow table storage container in the northbound interface of the SDN controller. Smart contracts are then used to process the stored flow table information. We employ the time window algorithm and the token bucket algorithm to construct the first layer strategy to defend against obvious DDoS attacks. To detect and mitigate less obvious DDoS attacks, we design a second-layer strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm from unsupervised learning techniques to perform binary classification, thereby identifying abnormal traffic. We conduct experimental validation using the publicly available DDoS dataset CIC-DDoS2019. The results show that using this strategy in the SDN network reduces the average deviation of round-trip time (RTT) by approximately 38.86% compared with the original SDN network without this strategy. Furthermore, the accuracy of DDoS attack detection reaches 97.66% and an F1 score of 92.2%. Compared with other similar methods, under comparable detection accuracy, the deployment of our strategy in small-scale SDN network topologies provides faster detection speeds for DDoS attacks and exhibits less fluctuation in detection time. This indicates that implementing this strategy can effectively identify DDoS attacks without affecting the stability of data transmission in the SDN network environment. © 2025 by the authors.
Keyword ：
Big data Big data Blockchain Blockchain Data communication systems Data communication systems Denial-of-service attack Denial-of-service attack Digital storage Digital storage Distributed computer systems Distributed computer systems Information management Information management Internet of things Internet of things Learning algorithms Learning algorithms Learning systems Learning systems Network architecture Network architecture Network layers Network layers Network security Network security Network topology Network topology Software defined networking Software defined networking Traffic control Traffic control Unsupervised learning Unsupervised learning
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| GB/T 7714 | Peng, Shengmin , Tian, Jialin , Zheng, Xiangyu et al. DDoS Defense Strategy Based on Blockchain and Unsupervised Learning Techniques in SDN [J]. | Future Internet , 2025 , 17 (8) . |
| MLA | Peng, Shengmin et al. "DDoS Defense Strategy Based on Blockchain and Unsupervised Learning Techniques in SDN" . | Future Internet 17 . 8 (2025) . |
| APA | Peng, Shengmin , Tian, Jialin , Zheng, Xiangyu , Chen, Shuwu , Shu, Zhaogang . DDoS Defense Strategy Based on Blockchain and Unsupervised Learning Techniques in SDN . | Future Internet , 2025 , 17 (8) . |
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Traditional image classification often misclassifies unknown samples as known classes during testing, degrading recognition accuracy. Open-set image recognition can simultaneously detect known classes (KCs) and unknown classes (UCs) but still struggles to improve recognition performance caused by open space risk. Therefore, we introduce a cosine distance loss function (CDLoss), which exploits the orthogonality of one-hot encoding vectors to align known samples with their corresponding one-hot encoder directions. This reduces the overlap between the feature spaces of KCs and UCs, mitigating open space risk. CDLoss was incorporated into both Softmax-based and prototype-learning-based frameworks to evaluate its effectiveness. Experimental results show that CDLoss improves AUROC, OSCR, and accuracy across both frameworks and different datasets. Furthermore, various weight combinations of the ARPL and CDLoss were explored, revealing optimal performance with a 1:2 ratio. T-SNE analysis confirms that CDLoss reduces the overlap between the feature spaces of KCs and UCs. These results demonstrate that CDLoss helps mitigate open space risk, enhancing recognition performance in open-set image classification tasks.
Keyword ：
cosine distance loss cosine distance loss one-hot encoding one-hot encoding open-set image classification open-set image classification open space risk open space risk
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| GB/T 7714 | Li, Xiaolin , Chen, Binbin , Li, Jianxiang et al. Cosine Distance Loss for Open-Set Image Recognition [J]. | ELECTRONICS , 2025 , 14 (1) . |
| MLA | Li, Xiaolin et al. "Cosine Distance Loss for Open-Set Image Recognition" . | ELECTRONICS 14 . 1 (2025) . |
| APA | Li, Xiaolin , Chen, Binbin , Li, Jianxiang , Chen, Shuwu , Huang, Shiguo . Cosine Distance Loss for Open-Set Image Recognition . | ELECTRONICS , 2025 , 14 (1) . |
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With the continuous development of Network Function Virtualization (NFV) technology, Virtual Network Function (VNF) migration has become a crucial approach to optimizing network resource utilization, reducing service latency, and improving service quality. However, in dynamic network environments, VNF migration faces challenges such as resource overload, service request prioritization, migration cost optimization, routing overhead, and energy consumption. To address these challenges, this paper proposes a priority-aware and multi-objective optimization-based VNF migration algorithm, namely the Lagrangian Fish Optimization for VNF Migration (LFO-VNM) Algorithm. This algorithm integrates the Lagrangian relaxation method with the Artificial Fish Swarm Algorithm (AFSA) to dynamically adjust resource allocation and migration paths, optimizing migration cost, network performance, and node energy consumption while prioritizing high-priority service requests. First, a Mixed-Integer Linear Programming (MILP) model is established to quantify the impact of VNF migration on network link load, node resource consumption, and service performance. Based on this, a multi-objective optimization model is formulated, considering network bandwidth, latency, migration cost, and energy consumption. This model is decomposed into a series of linear subproblems, which are more efficiently solved using the Lagrangian relaxation method. Finally, leveraging the global search capability of AFSA, an efficient solution algorithm, LFO-VNM, is designed to optimize VNF migration decisions. Experimental results demonstrate that the proposed algorithm not only improves computational efficiency but also effectively reduces total cost and energy consumption, outperforming existing migration algorithms across various network topologies. This study provides an effective solution for VNF migration and resource scheduling in complex network environments.
Keyword ：
Multi-objective optimization Multi-objective optimization Network Function Virtualization Network Function Virtualization Priority awareness Priority awareness VNF migration VNF migration
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| GB/T 7714 | Luo, Yu , Shu, Zhaogang , Chen, Shuwu et al. Multi-objective optimization algorithm for VNF migration with priority awareness in dynamic networks [J]. | COMPUTER NETWORKS , 2025 , 272 . |
| MLA | Luo, Yu et al. "Multi-objective optimization algorithm for VNF migration with priority awareness in dynamic networks" . | COMPUTER NETWORKS 272 (2025) . |
| APA | Luo, Yu , Shu, Zhaogang , Chen, Shuwu , Tu, Qiang , Wu, Xianzhang , Lin, Qingjie . Multi-objective optimization algorithm for VNF migration with priority awareness in dynamic networks . | COMPUTER NETWORKS , 2025 , 272 . |
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This letter considers an extension of multi-access coded caching networks referred to as the multi-access multi-input single-output (MAMISO) networks, where a server with multiple transmit antennas is connected to a number of single receive antenna users over a broadcast link and each user can access multiple neighboring cache nodes in a cyclic wrap-around fashion. For such networks, we propose a novel coded caching scheme based on an effective transform over any regular placement delivery array (PDA). By a delicate design of the cache placement, the resulting scheme can yield a degrees of freedom (DoF) as large as possible. It is shown that our proposed scheme has advantage either in the DoF or the subpacketization size over the existing schemes under some parameters.
Keyword ：
Coded caching Coded caching degrees of freedom degrees of freedom multi-access multi-access multiple antennas multiple antennas subpacketization size subpacketization size
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| GB/T 7714 | Cheng, Minquan , Bai, Wenli , Wu, Xianzhang et al. Design of Coded Caching Scheme for MAMISO Networks With Improved DoF [J]. | IEEE WIRELESS COMMUNICATIONS LETTERS , 2024 , 13 (5) : 1478-1482 . |
| MLA | Cheng, Minquan et al. "Design of Coded Caching Scheme for MAMISO Networks With Improved DoF" . | IEEE WIRELESS COMMUNICATIONS LETTERS 13 . 5 (2024) : 1478-1482 . |
| APA | Cheng, Minquan , Bai, Wenli , Wu, Xianzhang , Chen, Shuwu . Design of Coded Caching Scheme for MAMISO Networks With Improved DoF . | IEEE WIRELESS COMMUNICATIONS LETTERS , 2024 , 13 (5) , 1478-1482 . |
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Large-scale Internet-of- Things (IoT) networks enable intelligent applications and services, such as autonomous deriving. As many users generate various datasets, federated learning in distributed IoT networks emerges from learning from distinct datasets. To realize efficient and reliable communications in distributed networks, we propose a collaborative optimization model for resource-constrained federated learning using a joint design of wireless resource allocation and expected learning losses. Precisely, we start to formulate a learning-oriented power allocation problem. Then, we derive a convergence bound and build the relationship between communications and learning. At last, we perform an optimal algorithm based on majorization-minimization frameworks. Thanks to the high parallelization of the proposed algorithm, extensive experimental results corroborate that optimal power allocation in distributed networks benefits efficient federated learning compared to the state-of-the-art benchmark algorithms. © 2024 IEEE.
Keyword ：
Adversarial machine learning Adversarial machine learning Contrastive Learning Contrastive Learning Federated learning Federated learning
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| GB/T 7714 | Xie, Haihui , Chen, Shuwu , Sun, Teng et al. Collaborative Optimization for Resource-constrained Federated Learning in Large-scale IoT Networks [J]. | International Conference on Communications in China, ICCC Workshops 2024 , 2024 : 341-345 . |
| MLA | Xie, Haihui et al. "Collaborative Optimization for Resource-constrained Federated Learning in Large-scale IoT Networks" . | International Conference on Communications in China, ICCC Workshops 2024 (2024) : 341-345 . |
| APA | Xie, Haihui , Chen, Shuwu , Sun, Teng , Zhao, Junhui , Xia, Minghua . Collaborative Optimization for Resource-constrained Federated Learning in Large-scale IoT Networks . | International Conference on Communications in China, ICCC Workshops 2024 , 2024 , 341-345 . |
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