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学者姓名：刘西蒙
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Data is the essential fuel for deep neural networks (DNNs), and its quality affects the practical performance of DNNs. In real-world training scenarios, the successful generalization performance of DNNs is severely challenged by noisy samples with incorrect labels. To combat noisy samples in image classification, numerous methods based on sample selection and semi-supervised learning (SSL) have been developed, where sample selection is used to provide the supervision signal for SSL, achieving great success in resisting noisy samples. Due to the necessary warm-up training on noisy datasets and the basic sample selection mechanism, DNNs are still confronted with the challenge of memorizing noisy samples. However, existing methods do not address the memorization of noisy samples by DNNs explicitly, which hinders the generalization performance of DNNs. To alleviate this issue, we present a new approach to combat noisy samples. First, we propose a memorized noise detection method to detect noisy samples that DNNs have already memorized during the training process. Next, we design a noise-excluded sample selection method and a noise-alleviated MixMatch to alleviate the memorization of DNNs to noisy samples. Finally, we integrate our approach with the established method DivideMix, proposing Modified-DivideMix. The experimental results on CIFAR-10, CIFAR-100, and Clothing1M demonstrate the effectiveness of our approach.
Keyword ：
Accuracy Accuracy Artificial neural networks Artificial neural networks Deep neural networks Deep neural networks Entropy Entropy Filtering algorithms Filtering algorithms Image classification Image classification image classification. label flipping attack image classification. label flipping attack Noise Noise Noise measurement Noise measurement noisy label learning noisy label learning Reviews Reviews sample selection sample selection Semisupervised learning Semisupervised learning Training Training
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| GB/T 7714 | Yuan, Shunjie , Li, Xinghua , Miao, Yinbin et al. Combating Noisy Labels by Alleviating the Memorization of DNNs to Noisy Labels [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2025 , 27 : 597-609 . |
| MLA | Yuan, Shunjie et al. "Combating Noisy Labels by Alleviating the Memorization of DNNs to Noisy Labels" . | IEEE TRANSACTIONS ON MULTIMEDIA 27 (2025) : 597-609 . |
| APA | Yuan, Shunjie , Li, Xinghua , Miao, Yinbin , Zhang, Haiyan , Liu, Ximeng , Deng, Robert H. . Combating Noisy Labels by Alleviating the Memorization of DNNs to Noisy Labels . | IEEE TRANSACTIONS ON MULTIMEDIA , 2025 , 27 , 597-609 . |
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为空间众包中的工人分配任务是后续收集位置相关数据的重要前提 . 为了应对可能的位置隐私泄露 问题，研究者往往结合地理不可区分性进行保护. 现有满足地理不可区分性的任务分配方法通常针对一对一场景，其 研究目标一般集中在最小化平均旅行距离，而不是最大化任务分配数量；同时，它们假设工人能分配去执行任意的任 务. 此外，这些研究往往结合平面拉普拉斯机制实现地理不可区分性. 上述机制的随机性和无界性会导致工人上传的 位置数据包含过量噪音，进而降低任务分配的效用，导致工人平均旅行距离较大或者任务无法完全分配. 为解决以上 问题，本文提出满足地理不可区分性的偏好感知多对多任务分配算法 MONITOR（Many-to-many task allOcation under geo-iNdIsTinguishability for spatial crOwdsouRcing）. 该算法主要思想是对工人的偏好任务进行分组加噪并上传工人真 实位置到模糊偏好任务位置之间的距离以代替直接上传工人的模糊位置. 在MONITOR中，为了收集任务分配必需的 工人到任务的距离信息，设计了基于分组的模糊距离收集方法 GroCol（Group-based obfuscated distance Collection）；同 时为了提高任务分配的效用，设计了参数无关的模糊距离比较方法ParCom（Parameter-free obfuscated distance Compari⁃ son）. 此外，本文进一步从理论上分析了MONITOR的隐私、效用和复杂度. 在2个真实数据集和1个模拟数据集上的 实验结果表明MONITOR取得与非隐私任务分配类似的任务分配数量，且较基准方法的旅行距离降低了20%以上.
Keyword ：
任务分配 任务分配 地理不可区分性 地理不可区分性 平均旅行距离 平均旅行距离 空间众包 空间众包 隐私保护 隐私保护
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| GB/T 7714 | 张朋飞 , 翟睿辰 , 程 祥 et al. 满足地理不可区分性的偏好感知多对多 任务分配算法 [J]. | 电子学报 , 2025 . |
| MLA | 张朋飞 et al. "满足地理不可区分性的偏好感知多对多 任务分配算法" . | 电子学报 (2025) . |
| APA | 张朋飞 , 翟睿辰 , 程 祥 , 张治坤 , 刘西蒙 , 王 杰 . 满足地理不可区分性的偏好感知多对多 任务分配算法 . | 电子学报 , 2025 . |
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针对区块链技术在区块大小和生成速率上的固有限制导致的可扩展性问题,支付通道网络(pay-ment channel network,PCN)提供了链下扩容的有效方案.然而,传统的使用可锁定结构的PCN存在以下两个缺点:存在作恶方时该结构只能结束交易过程,却无法对恶意行为者实施识别与惩罚;某一笔交易需要复盘时,PCN中出现的所有交易都需要恢复,导致巨大的计算开销.鉴于此,提出了一种基于可追责断言性能优化的支付通道网络方案——AAPO-PCN(accountable assertions performance optimization-payment channel network).AAPO-PCN通过引进可追责断言算法,构建了一种可编辑的Merkle树结构.不同于传统Merkle树,该方案采用变色龙哈希函数替换原有哈希算法,并整合可追责断言机制,旨在有效识别恶意用户.通过这种方式,不仅令相关交易的恢复更加高效,同时也大幅减少了计算开销.最后提供了全面的安全性分析与实验,结果表明,AAPO-PCN在不牺牲安全性的情况下,具有更优的计算效率与通信开销.
Keyword ：
Merkle树 Merkle树 变色龙哈希函数 变色龙哈希函数 可追责断言 可追责断言 支付通道网络 支付通道网络
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| GB/T 7714 | 李雯琪 , 应作斌 , 臧嘉威 et al. 基于可追责断言的支付通道网络性能优化研究 [J]. | 网络与信息安全学报 , 2025 , 11 (1) : 66-78 . |
| MLA | 李雯琪 et al. "基于可追责断言的支付通道网络性能优化研究" . | 网络与信息安全学报 11 . 1 (2025) : 66-78 . |
| APA | 李雯琪 , 应作斌 , 臧嘉威 , 熊金波 , 刘西蒙 . 基于可追责断言的支付通道网络性能优化研究 . | 网络与信息安全学报 , 2025 , 11 (1) , 66-78 . |
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Graph Neural Networks (GNNs) have exhibited remarkable capabilities for dealing with graph-structured data. However, recent studies have revealed their fragility to adversarial attacks, where imperceptible perturbations to the graph structure can easily mislead predictions. To enhance adversarial robustness, some methods attempt to learn robust representation through improving GNN architectures. Subsequently, another approach suggests that these GNNs might taint feature information and have poor classifier performance, leading to the introduction of Graph Contrastive Learning (GCL) methods to build a refining-classifying pipeline. However, existing methods focus on global-local contrastive strategies, which fails to address the robustness issues inherent in the contexts of adversarial robustness. To address these challenges, we propose a novel paradigm named GRANCE to enhance the robustness of learned representations by shifting the focus to local neighborhoods. Specifically, a dual neighborhood contrastive learning strategy is designed to extract local topological and semantic information. Paired with a neighbor estimator, the strategy can learn robust representations that are resilient to adversarial edges. Additionally, we also provide an improved GNN as classifier. Theoretical analyses provide a stricter lower bound of mutual information, ensuring the convergence of GRANCE. Extensive experiments validate the effectiveness of GRANCE compared to state-of-the-art baselines against various adversarial attacks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Adversarial machine learning Adversarial machine learning Bayesian networks Bayesian networks Contrastive Learning Contrastive Learning Graph neural networks Graph neural networks
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| GB/T 7714 | Zhuang, Shuman , Wu, Zhihao , Chen, Zhaoliang et al. Refine then Classify: Robust Graph Neural Networks with Reliable Neighborhood Contrastive Refinement [C] . 2025 : 13473-13482 . |
| MLA | Zhuang, Shuman et al. "Refine then Classify: Robust Graph Neural Networks with Reliable Neighborhood Contrastive Refinement" . (2025) : 13473-13482 . |
| APA | Zhuang, Shuman , Wu, Zhihao , Chen, Zhaoliang , Dai, Hong-Ning , Liu, Ximeng . Refine then Classify: Robust Graph Neural Networks with Reliable Neighborhood Contrastive Refinement . (2025) : 13473-13482 . |
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Backdoor attacks and adversarial attacks are two major security threats to deep neural networks (DNNs), with the former one is a training-time data poisoning attack that aims to implant backdoor triggers into models by injecting trigger patterns into training samples, and the latter one is a testing-time attack trying to generate adversarial examples (AEs) from benign images to mislead a well-trained model. While previous works generally treat these two attacks separately, the inherent connection between these two attacks is rarely explored. In this paper, we focus on bridging backdoor and adversarial attacks and observe two intriguing phenomena when applying adversarial attacks on an infected model implanted with backdoors: 1) the sample is harder to be turned into an AE when the trigger is presented; 2) the AEs generated from backdoor samples are highly likely to be predicted as its true labels. Inspired by these observations, we proposed a novel backdoor defense method, dubbed Adversarial-Inspired Backdoor Defense (AIBD), to isolate the backdoor samples by leveraging a progressive top-q scheme and break the correlation between backdoor samples and their target labels using adversarial labels. Through extensive experiments on various datasets against six state-of-the-art backdoor attacks, the AIBD-trained models on poisoned data demonstrate superior performance over the existing defense methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Backpropagation Backpropagation Deep neural networks Deep neural networks
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| GB/T 7714 | Yin, Jia-Li , Wang, Weijian , Lyhwa et al. Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks [C] . 2025 : 9508-9516 . |
| MLA | Yin, Jia-Li et al. "Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks" . (2025) : 9508-9516 . |
| APA | Yin, Jia-Li , Wang, Weijian , Lyhwa , Lin, Wei , Liu, Ximeng . Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks . (2025) : 9508-9516 . |
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With the rapid growth of encrypted image data outsourced to cloud servers, achieving data confidentiality and searchability in cloud-assisted Internet of Things (IoT) environments has become increasingly feasible. However, achieving high efficiency and strong security simultaneously over large-scale encrypted image datasets remains a challenge. To address this, we propose a novel efficient and secure content-based image retrieval scheme in cloud-assisted IoT. Specifically, our scheme leverages lattice-based fully homomorphic encryption and homomorphic comparison techniques, utilizing Cheon-Kim-Kim-Song's batch processing and single-instruction-multiple-data capabilities. This approach significantly reduces the overhead of fully homomorphic computations, making the query process computational complexity independent of dataset size under certain conditions. Moreover, by integrating private information retrieval technology, the scheme enhances privacy by hiding access patterns of image data. Formal security analysis demonstrates that our scheme achieves indistinguishability against chosen-plaintext attack (IND-CPA), and extensive experiments based on real datasets confirm that our scheme is both practical and efficient for real-world applications.
Keyword ：
Cloud computing Cloud computing Content-based image retrieval (CBIR) Content-based image retrieval (CBIR) Data privacy Data privacy encrypted image data encrypted image data Feature extraction Feature extraction fully homomorphic encryption (HE) fully homomorphic encryption (HE) homomorphic comparison homomorphic comparison Homomorphic encryption Homomorphic encryption Image retrieval Image retrieval Indexes Indexes Internet of Things Internet of Things Nearest neighbor methods Nearest neighbor methods Privacy Privacy private information retrieval (PIR) private information retrieval (PIR) Security Security
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| GB/T 7714 | Chen, Lin , Yang, Yiwei , Yang, Li et al. Efficient and Secure Content-Based Image Retrieval in Cloud-Assisted Internet of Things [J]. | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (5) : 6001-6013 . |
| MLA | Chen, Lin et al. "Efficient and Secure Content-Based Image Retrieval in Cloud-Assisted Internet of Things" . | IEEE INTERNET OF THINGS JOURNAL 12 . 5 (2025) : 6001-6013 . |
| APA | Chen, Lin , Yang, Yiwei , Yang, Li , Xu, Chao , Miao, Yinbin , Liu, Zhiquan et al. Efficient and Secure Content-Based Image Retrieval in Cloud-Assisted Internet of Things . | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (5) , 6001-6013 . |
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Reconstruction attacks against federated learning (FL) aim to reconstruct users' samples through users' uploaded gradients. Local differential privacy (LDP) is regarded as an effective defense against various attacks, including sample reconstruction in FL, where gradients are clipped and perturbed. Existing attacks are ineffective in FL with LDP since clipped and perturbed gradients obliterate most sample information for reconstruction. Besides, existing attacks embed additional sample information into gradients to improve the attack effect and cause gradient expansion, leading to a more severe gradient clipping in FL with LDP. In this paper, we propose a sample reconstruction attack against LDP-based FL with any target models to reconstruct victims' sensitive samples to illustrate that FL with LDP is not flawless. Considering gradient expansion in reconstruction attacks and noise in LDP, the core of the proposed attack is gradient compression and reconstructed sample denoising. For gradient compression, an inference structure based on sample characteristics is presented to reduce redundant gradients against LDP. For reconstructed sample denoising, we artificially introduce zero gradients to observe noise distribution and scale confidence interval to filter the noise. Theoretical proof guarantees the effectiveness of the proposed attack. Evaluations show that the proposed attack is the only attack that reconstructs victims' training samples in LDP-based FL and has little impact on the target model's accuracy. We conclude that LDP-based FL needs further improvements to defend against sample reconstruction attacks effectively.
Keyword ：
Accuracy Accuracy Complexity theory Complexity theory data privacy data privacy differential privacy differential privacy Federated learning (FL) Federated learning (FL) Generative adversarial networks Generative adversarial networks Generators Generators Image reconstruction Image reconstruction Measurement Measurement Noise Noise Privacy Privacy Servers Servers Training Training
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| GB/T 7714 | You, Zhichao , Dong, Xuewen , Li, Shujun et al. Local Differential Privacy Is Not Enough: A Sample Reconstruction Attack Against Federated Learning With Local Differential Privacy [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2025 , 20 : 1519-1534 . |
| MLA | You, Zhichao et al. "Local Differential Privacy Is Not Enough: A Sample Reconstruction Attack Against Federated Learning With Local Differential Privacy" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 20 (2025) : 1519-1534 . |
| APA | You, Zhichao , Dong, Xuewen , Li, Shujun , Liu, Ximeng , Ma, Siqi , Shen, Yulong . Local Differential Privacy Is Not Enough: A Sample Reconstruction Attack Against Federated Learning With Local Differential Privacy . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2025 , 20 , 1519-1534 . |
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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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Recruiting users in mobile crowdsensing (MCS) can make the platform obtain high-quality data to provide better services. Although the privacy leakage during the process of user recruitment has received a lot of research attention, none of the existing work considers the evaluation of the sensing quality of privacy-preserving data submitted by users, which makes the platform incapable of recruiting users suitably to obtain high-quality sensing data, thereby reducing the reliability of MCS services. To solve this problem, we first propose a sensing quality evaluation method based on the deviation and variance of sensing data. According to it, the platform can obtain the sensing quality of privacy-preserving data for each user during the recruitment. Then we model the user recruitment with a limited budget platform as a Combinatorial Multi-Armed Bandit (CMAB) game to determine the recruited users based on the sensing quality of data obtained by evaluation. Finally, we theoretically prove that our algorithm satisfies differential privacy and the upper bound on the regret of rewards is restricted. Experimental results show that our proposal is superior in various properties, and our method has a 73.67% advantage in accumulated sensing qualities compared with comparison schemes.
Keyword ：
combinatorial multi-armed bandit combinatorial multi-armed bandit Differential privacy Differential privacy Mobile crowdsensing Mobile crowdsensing Perturbation methods Perturbation methods Privacy Privacy privacy protection privacy protection Protection Protection Recruitment Recruitment Sensors Sensors Task analysis Task analysis user recruitment user recruitment
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| GB/T 7714 | An, Jieying , Ren, Yanbing , Li, Xinghua et al. Privacy-Preserving User Recruitment With Sensing Quality Evaluation in Mobile Crowdsensing [J]. | IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING , 2025 , 22 (1) : 787-803 . |
| MLA | An, Jieying et al. "Privacy-Preserving User Recruitment With Sensing Quality Evaluation in Mobile Crowdsensing" . | IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 22 . 1 (2025) : 787-803 . |
| APA | An, Jieying , Ren, Yanbing , Li, Xinghua , Zhang, Man , Luo, Bin , Miao, Yinbin et al. Privacy-Preserving User Recruitment With Sensing Quality Evaluation in Mobile Crowdsensing . | IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING , 2025 , 22 (1) , 787-803 . |
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Due to the complexity of integrated circuit design and manufacturing process, an increasing number of third parties are outsourcing their untrusted intellectual property (IP) cores to pursue greater economic benefits, which may embed numerous security issues. The covert nature of hardware Trojans (HTs) poses a significant threat to cyberspace, and they may lead to catastrophic consequences for the national economy and personal privacy. To deal with HTs well, it is not enough to just detect whether they are included, like the existing studies. Same as malware, identifying the attack intentions of HTs, that is, analyzing the functions they implement, is of great scientific significance for the prevention and control of HTs. Based on the fined detection, for the first time, this article proposes a two-stage Graph Neural Network model for HTs' multifunctional classification, GNN4HT. In the first stage, GNN4HT localizes HTs, achieving a notable true positive rate (TPR) of 94.28% on the Trust-Hub dataset and maintaining high performance on the TRTC-IC dataset. GNN4HT further transforms the localization results into HT information graphs (HTIGs), representing the functional interaction graphs of HTs. In the second stage, the dataset is augmented through logical equivalence for training and HT functionalities are classified based on the extracted HTIG from the first stage. For the multifunctional classification of HTs, the correct classification rate reached as high as 80.95% at gate-level and 62.96% at register transfer level. This article marks a breakthrough in HT detection, and it is the first to address the multifunctional classification issue, holding significant practical importance and application prospects.
Keyword ：
Gate level Gate level golden free golden free Hardware Hardware hardware Trojan (HT) hardware Trojan (HT) HT information graph (HTIG) HT information graph (HTIG) HT location HT location HT multifunctional classification HT multifunctional classification Integrated circuit modeling Integrated circuit modeling Location awareness Location awareness Logic gates Logic gates register transfer level (RTL) register transfer level (RTL) Security Security Training Training Trojan horses Trojan horses
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| GB/T 7714 | Chen, Lihan , Dong, Chen , Wu, Qiaowen et al. GNN4HT: A Two-Stage GNN-Based Approach for Hardware Trojan Multifunctional Classification [J]. | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS , 2025 , 44 (1) : 172-185 . |
| MLA | Chen, Lihan et al. "GNN4HT: A Two-Stage GNN-Based Approach for Hardware Trojan Multifunctional Classification" . | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 44 . 1 (2025) : 172-185 . |
| APA | Chen, Lihan , Dong, Chen , Wu, Qiaowen , Liu, Ximeng , Guo, Xiaodong , Chen, Zhenyi et al. GNN4HT: A Two-Stage GNN-Based Approach for Hardware Trojan Multifunctional Classification . | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS , 2025 , 44 (1) , 172-185 . |
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