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学者姓名：王美清
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Node classification tasks have seen considerable progress with the use of Graph Neural Networks (GNNs). However, they cannot work well on imbalanced node classification and tend to prioritize the majority classes with more labeled instances while overlooking the minority classes with fewer labeled instances. Existing solutions focus on generating new nodes to augment the training set, which may disrupt the original topological structure of the graph, so GNNs may not achieve optimal classification results. To address this issue, we introduce GraphMMC, a reliable and flexible strategy to generate pseudo-labels that can be easily integrated with various GNNs, which will augment the imbalance training set to a class-balanced set without generating new nodes. We use the similarity between the unlabeled nodes and the minority classes to correction the low-confidence pseudo-labels generated by GNNs to obtain reliable pseudo-labels. Our experiments demonstrate that the proposed method outperforms state-of-the-art baselines on several class-imbalanced datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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| GB/T 7714 | Ma, J. , Chen, F. , Jiang, F. et al. GraphMMC: Class-Balanced Pseudo-Labels Generation for Graph Node Classification [未知]. |
| MLA | Ma, J. et al. "GraphMMC: Class-Balanced Pseudo-Labels Generation for Graph Node Classification" [未知]. |
| APA | Ma, J. , Chen, F. , Jiang, F. , Cheng, H. , Wang, M. . GraphMMC: Class-Balanced Pseudo-Labels Generation for Graph Node Classification [未知]. |
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This paper utilizes a coupled model of the lattice Boltzmann method and discrete element method (LBM-DEM) to simulate the flow field and particle motion in a three-dimensional porous medium. It investigates the impact of different microstructural characteristics of filter media on filtration performance under the same porosity conditions. The model not only considers the mechanisms of inertial deposition and electrostatic forces but also incorporates the effects of Brownian diffusion and dielectrophoresis, providing a preliminary comparison of various filtration mechanisms. The research results indicate that: (1) under identical porosity conditions, the filtration efficiency of unidirectional interlaced fibers significantly surpasses that of other fiber models with different microstructures; (2) the model integrating four filtration mechanisms demonstrates higher filtration efficiency. © Published under licence by IOP Publishing Ltd.
Keyword ：
Computational fluid dynamics Computational fluid dynamics High modulus textile fibers High modulus textile fibers Hydraulics Hydraulics Infiltration Infiltration Kinetic theory Kinetic theory Microfiltration Microfiltration Micropores Micropores Porosity Porosity Porous materials Porous materials
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| GB/T 7714 | Chen, Xueyan , Wang, Jingyue , Wang, Meiqing . Numerical simulation of different microstructures on fiber filtration efficiency [C] . 2025 . |
| MLA | Chen, Xueyan et al. "Numerical simulation of different microstructures on fiber filtration efficiency" . (2025) . |
| APA | Chen, Xueyan , Wang, Jingyue , Wang, Meiqing . Numerical simulation of different microstructures on fiber filtration efficiency . (2025) . |
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Data confidentiality, a fundamental security element for dependable cloud storage, has been drawing widespread concern. Public-key encryption with keyword search (PEKS) has emerged as a promising approach for privacy protection while enabling efficient retrieval of encrypted data. One of the typical applications of PEKS is searching sensitive electronic medical records (EMR) in healthcare clouds. However, many traditional countermeasures fall short of balancing privacy protection with search efficiency, and they often fail to support multi-user EMR sharing. To resolve these challenges, we propose a novel lightweight multi-user public-key authenticated encryption scheme with keyword search (LM-PAEKS). Our design effectively counters the inside keyword guessing attack (IKGA) while maintaining the sizes of ciphertext and trapdoor constant in multi-user scenarios. The novelty of our approach relies on introducing a dedicated receiver server that skillfully transforms the complex many-to-many relationship between senders and receivers into a streamlined one-to-one relationship. This transformation prevents the sizes of ciphertext and trapdoor from scaling linearly with the number of participants. Our approach ensures ciphertext indistinguishability and trapdoor privacy while avoiding bilinear pairing operations on the client side. Comparative performance analysis demonstrates that LM-PAEKS features significant computational efficiency while meeting higher security requirements, positioning it as a robust alternative to existing solutions.
Keyword ：
Cloud computing Cloud computing Encryption Encryption Hospitals Hospitals Indexes Indexes inside keyword guessing attack inside keyword guessing attack Keyword search Keyword search lightweight cryptography lightweight cryptography multi-user healthcare cloud multi-user healthcare cloud Privacy Privacy Public key Public key public-key authenticated encryption public-key authenticated encryption Receivers Receivers Searchable encryption Searchable encryption Security Security Servers Servers
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| GB/T 7714 | Xu, Yongliang , Cheng, Hang , Li, Jiguo et al. Lightweight Multi-User Public-Key Authenticated Encryption With Keyword Search [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2025 , 20 : 3234-3246 . |
| MLA | Xu, Yongliang et al. "Lightweight Multi-User Public-Key Authenticated Encryption With Keyword Search" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 20 (2025) : 3234-3246 . |
| APA | Xu, Yongliang , Cheng, Hang , Li, Jiguo , Liu, Ximeng , Zhang, Xinpeng , Wang, Meiqing . Lightweight Multi-User Public-Key Authenticated Encryption With Keyword Search . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2025 , 20 , 3234-3246 . |
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Collaborative searchable encryption for group data sharing enables a consortium of authorized users to collectively generate trapdoors and decrypt search results. However, existing countermeasures may be vulnerable to a keyword guessing attack (KGA) initiated by malicious insiders, compromising the confidentiality of keywords. Simultaneously, these solutions often fail to guard against hostile manufacturers embedding backdoors, leading to potential information leakage. To address these challenges, we propose a novel privacy-preserving collaborative searchable encryption (PCSE) scheme tailored for group data sharing. This scheme introduces a dedicated keyword server to export server-derived keywords, thereby withstanding KGA attempts. Based on this, PCSE deploys cryptographic reverse firewalls to thwart subversion attacks. To overcome the single point of failure inherent in a single keyword server, the export of server-derived keywords is collaboratively performed by multiple keyword servers. Furthermore, PCSE extends its capabilities to support efficient multi-keyword searches and result verification and incorporates a rate-limiting mechanism to effectively slow down adversaries' online KGA attempts. Security analysis demonstrates that our scheme can resist KGA and subversion attack. Theoretical analyses and experimental results show that PCSE is significantly more practical for group data sharing systems compared with state-of-the-art works.
Keyword ：
cloud computing cloud computing Cloud computing Cloud computing Collaboration Collaboration cryptographic reverse firewall cryptographic reverse firewall Cryptography Cryptography Encryption Encryption keyword guessing attack keyword guessing attack Mobile computing Mobile computing Protection Protection Protocols Protocols Public key Public key searchable encryption searchable encryption Security Security Servers Servers Threshold access control Threshold access control
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| GB/T 7714 | Xu, Yongliang , Cheng, Hang , Liu, Ximeng et al. PCSE: Privacy-Preserving Collaborative Searchable Encryption for Group Data Sharing in Cloud Computing [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2025 , 24 (5) : 4558-4572 . |
| MLA | Xu, Yongliang et al. "PCSE: Privacy-Preserving Collaborative Searchable Encryption for Group Data Sharing in Cloud Computing" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 24 . 5 (2025) : 4558-4572 . |
| APA | Xu, Yongliang , Cheng, Hang , Liu, Ximeng , Jiang, Changsong , Zhang, Xinpeng , Wang, Meiqing . PCSE: Privacy-Preserving Collaborative Searchable Encryption for Group Data Sharing in Cloud Computing . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2025 , 24 (5) , 4558-4572 . |
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Image manipulation has sparked widespread concern due to its potential security threats on the Internet. The boundary between the authentic and manipulated region exhibits artifacts in image manipulation localization (IML). These artifacts are more pronounced in heterogeneous image splicing and homogeneous image copy-move manipulation, while they are more subtle in removal and inpainting manipulated images. However, existing methods for image manipulation detection tend to capture boundary artifacts via explicit edge features and have limitations in effectively addressing subtle artifacts. Besides, feature redundancy caused by the powerful feature extraction capability of large models may prevent accurate identification of manipulated artifacts, exhibiting a high false-positive rate. To solve these problems, we propose a novel edge-aware network (EAN) to capture boundary artifacts effectively. This network treats the image manipulation localization problem as a segmentation problem inside and outside the boundary. In EAN, we develop an edge-aware mechanism to refine implicit and explicit edge features by the interaction of adjacent features. This approach directs the encoder to prioritize the desired edge information. Also, we design a multi-feature fusion strategy combined with an improved attention mechanism to enhance key feature representation significantly for mitigating the effects of feature redundancy. We perform thorough experiments on diverse datasets, and the outcomes confirm the efficacy of the suggested approach, surpassing leading manipulation localization techniques in the majority of scenarios.
Keyword ：
attention mechanism attention mechanism Attention mechanisms Attention mechanisms convolutional neural network convolutional neural network Discrete wavelet transforms Discrete wavelet transforms Feature extraction Feature extraction feature fusion feature fusion Image edge detection Image edge detection Image manipulation localization Image manipulation localization Location awareness Location awareness Neural networks Neural networks Noise Noise Semantics Semantics Splicing Splicing Transformers Transformers
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| GB/T 7714 | Chen, Yun , Cheng, Hang , Wang, Haichou et al. EAN: Edge-Aware Network for Image Manipulation Localization [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (2) : 1591-1601 . |
| MLA | Chen, Yun et al. "EAN: Edge-Aware Network for Image Manipulation Localization" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 35 . 2 (2025) : 1591-1601 . |
| APA | Chen, Yun , Cheng, Hang , Wang, Haichou , Liu, Ximeng , Chen, Fei , Li, Fengyong et al. EAN: Edge-Aware Network for Image Manipulation Localization . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (2) , 1591-1601 . |
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Image hiding through the application of invertible neural network (INN) represents a significant branch within the realm of deep image hiding methodologies, characterized by a compact network architecture and a streamlined parameter count. Nonetheless, when juxtaposed with autoencoder-based approaches, existing INN methods often result in inferior image quality. To surmount this challenge, this paper introduces a novel masking-based image hiding paradigm, establishes a new spatial domain transformation for images, and refines the Swin-transformer block. By integrating these innovations, an INN architecture is crafted that is particularly adept for deep image hiding, termed NiNet. The experimental results demonstrate that NiNet can remarkably address the problem of image hiding. In terms of steganographic image quality, NiNet outperforms the current state-of-the-art (SOTA) algorithms by 0.26 dB on the DIV2K dataset, 1.49 dB on the COCO dataset, and 0.39 dB on the ImageNet dataset. Regarding the quality of secret image recovery, NiNet surpasses the SOTA algorithms by 2.06 dB on DIV2K, 1.98 dB on COCO, and 0.50 dB on ImageNet.
Keyword ：
Covert communication Covert communication Image hiding Image hiding Information security Information security Invertible neural networks Invertible neural networks Spatial domain transformation Spatial domain transformation
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| GB/T 7714 | Ni, Zishun , Cheng, Hang , Chen, Jiaoling et al. NiNet: A new invertible neural network architecture more suitable for deep image hiding [J]. | INFORMATION PROCESSING & MANAGEMENT , 2025 , 62 (6) . |
| MLA | Ni, Zishun et al. "NiNet: A new invertible neural network architecture more suitable for deep image hiding" . | INFORMATION PROCESSING & MANAGEMENT 62 . 6 (2025) . |
| APA | Ni, Zishun , Cheng, Hang , Chen, Jiaoling , Xu, Yongliang , Chen, Fei , Wang, Meiqing . NiNet: A new invertible neural network architecture more suitable for deep image hiding . | INFORMATION PROCESSING & MANAGEMENT , 2025 , 62 (6) . |
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With the widespread application of image editing software, image manipulation localization has become a focal point and promising research. Existing neural networks for image manipulation primarily rely on RGB and noise features to accurately identify tampered areas within images. However, in practical image manipulation localization tasks, noise features extracted from RGB images alone are often insufficient to effectively address tampering issues. Furthermore, existing encoder-decoder models for image manipulated localization often overlook the direct interactions between different layers during the decoding process, which hinders the effective transfer of deep semantic information to shallow features, thereby impacting the ability to accurately identify manipulated areas. To address the challenges previously identified, this paper presents a dynamically adaptive noise extraction module and achieves inter-layer information exchange in the decoder by fusing output features from different layers to extract edge information. We adaptively map RGB images to an appropriate color space using linear transformations and then extract noise features, leveraging the differences in color blocks to effectively uncover features of tampering. In addition, we integrate features across multiple decoder layers, employ deep multi-scale edge supervision to impose constraints, and introduce a dynamic ringed residual module to further enhance feature representation. Extensive experiments demonstrate that our approach achieves competitive results on diverse large-scale image datasets, exhibiting superior precision and robustness compared with most state-of-the-art methods.
Keyword ：
Digital images Digital images End-to-end neural networks End-to-end neural networks Image manipulation localization Image manipulation localization Multi-scale feature fusion Multi-scale feature fusion
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| GB/T 7714 | Wang, Haichou , Cheng, Hang , Chen, Yun et al. Image manipulation localization via semantic-guided feature enhancement and deep multi-scale edge supervision [J]. | NEUROCOMPUTING , 2025 , 639 . |
| MLA | Wang, Haichou et al. "Image manipulation localization via semantic-guided feature enhancement and deep multi-scale edge supervision" . | NEUROCOMPUTING 639 (2025) . |
| APA | Wang, Haichou , Cheng, Hang , Chen, Yun , Xu, Yongliang , Wang, Meiqing . Image manipulation localization via semantic-guided feature enhancement and deep multi-scale edge supervision . | NEUROCOMPUTING , 2025 , 639 . |
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Image steganography attempts to imperceptibly hide the secret image within the cover image. Most of the existing deep learning -based steganography approaches have excelled in payload capacity, visual quality, and steganographic security. However, they are difficult to losslessly reconstruct secret images from stego images with relatively large payload capacity. Recently, although some studies have introduced invertible neural networks (INNs) to achieve largecapacity image steganography, these methods still cannot reconstruct the secret image losslessly due to the existence of lost information on the output side of the concealing network. We present an INN -based framework in this paper for lossless image steganography. Specifically, we regard image steganography as an image super -resolution task that converts low -resolution cover images to high -resolution stego images while hiding secret images. The feature dimension of the generated stego image matches the total dimension of the input secret and cover images, thereby eliminating the lost information. Besides, a bijective secret projection module is designed to transform various secret images into a latent variable that follows a simple distribution, improving the imperceptibility of the secret image. Comprehensive experiments indicate that the proposed framework achieves secure hiding and lossless extraction of the secret image.
Keyword ：
Covert communication Covert communication Information security Information security Invertible neural networks Invertible neural networks Lossless steganography Lossless steganography
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| GB/T 7714 | Wang, Tingqiang , Cheng, Hang , Liu, Ximeng et al. Lossless image steganography: Regard steganography as super-resolution [J]. | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (4) . |
| MLA | Wang, Tingqiang et al. "Lossless image steganography: Regard steganography as super-resolution" . | INFORMATION PROCESSING & MANAGEMENT 61 . 4 (2024) . |
| APA | Wang, Tingqiang , Cheng, Hang , Liu, Ximeng , Xu, Yongliang , Chen, Fei , Wang, Meiqing et al. Lossless image steganography: Regard steganography as super-resolution . | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (4) . |
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The health-related Internet of Things (IoT) plays an irreplaceable role in the collection, analysis, and transmission of medical data. As a device of the health-related IoT, the electroencephalogram (EEG) has long been a powerful tool for physiological and clinical brain research, which contains a wealth of personal information. Due to its rich computational/storage resources, cloud computing is a promising solution to extract the sophisticated feature of massive EEG signals in the age of big data. However, it needs to solve both response latency and privacy leakage. To reduce latency between users and servers while ensuring data privacy, we propose a privacy-preserving feature extraction scheme, called LightPyFE, for EEG signals in the edge computing environment. In this scheme, we design an outsourced computing toolkit, which allows the users to achieve a series of secure integer and floating-point computing operations. During the implementation, LightPyFE can ensure that the users just perform the encryption and decryption operations, where all computing tasks are outsourced to edge servers for specific processing. Theoretical analysis and experimental results have demonstrated that our scheme can successfully achieve privacy-preserving feature extraction for EEG signals, and is practical yet effective.
Keyword ：
Additive secret sharing Additive secret sharing edge computing edge computing electroencephalogram (EEG) signal electroencephalogram (EEG) signal Internet of Things (IoT) Internet of Things (IoT) privacy-preserving privacy-preserving
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| GB/T 7714 | Yan, Nazhao , Cheng, Hang , Liu, Ximeng et al. Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (2) : 2520-2533 . |
| MLA | Yan, Nazhao et al. "Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing" . | IEEE INTERNET OF THINGS JOURNAL 11 . 2 (2024) : 2520-2533 . |
| APA | Yan, Nazhao , Cheng, Hang , Liu, Ximeng , Chen, Fei , Wang, Meiqing . Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (2) , 2520-2533 . |
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Due to enormous computing and storage overhead for well-trained Deep Neural Network (DNN) models, protecting the intellectual property of model owners is a pressing need. As the commercialization of deep models is becoming increasingly popular, the pre-trained models delivered to users may suffer from being illegally copied, redistributed, or abused. In this paper, we propose DeepDIST, the first end-to-end secure DNNs distribution framework in a black-box scenario. Specifically, our framework adopts a dual-level fingerprint (FP) mechanism to provide reliable ownership verification, and proposes two equivalent transformations that can resist collusion attacks, plus a newly designed similarity loss term to improve the security of the transformations. Unlike the existing passive defense schemes that detect colluding participants, we introduce an active defense strategy, namely damaging the performance of the model after the malicious collusion. The extensive experimental results show that DeepDIST can maintain the accuracy of the host DNN after embedding fingerprint conducted for true traitor tracing, and is robust against several popular model modifications. Furthermore, the anti-collusion effect is evaluated on two typical classification tasks (10-class and 100-class), and the proposed DeepDIST can drop the prediction accuracy of the collusion model to 10% and 1% (random guess), respectively.
Keyword ：
anti-collusion anti-collusion Deep neural networks Deep neural networks digital fingerprinting digital fingerprinting digital watermarking digital watermarking
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| GB/T 7714 | Cheng, Hang , Li, Xibin , Wang, Huaxiong et al. DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (1) : 97-109 . |
| MLA | Cheng, Hang et al. "DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34 . 1 (2024) : 97-109 . |
| APA | Cheng, Hang , Li, Xibin , Wang, Huaxiong , Zhang, Xinpeng , Liu, Ximeng , Wang, Meiqing et al. DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (1) , 97-109 . |
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