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arXiv 2025年

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Federated Hybrid-Supervised Learning for Universal Medical Image Segmentation

Federated Hybrid-Supervised Learning for Universal Medical I...

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

作者： Shenhai Zheng Sian Wen Congyu Li Qing Chen Laquan Li College of Computer Science and Technology Chongqing University of Posts and Telecommunications China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education China School of Science Chongqing University of Posts and Telecommunications China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) is an advanced technology that tackles the challenge of blocked data arising from privacy concerns, enabling the training of deep learning models without the need for data sharing. However, FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a novel hybrid-supervised federated learning method (FedSLAG) that explores various types of annotations in medical imaging. To focus more on weakly-supervised and unsupervised scenarios within hybrid-supervised learning, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations (points and scribbles). The feature extraction module combines the features of multiple weakly-supervised clients and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models. Experiments on breast tumor and skin lesion segmentation tasks demonstrate significant efficiency gains as well as highly competitive segmentation accuracy in many hybrid-supervised situations. Our codes are available at: https://***/TrivenDev/FedSLAG.

关键词： Training Image segmentation Breast tumors Annotations Federated learning Feature extraction Skin Lesions Speech processing Biomedical imaging

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MAGIC: detecting advanced persistent threats via masked graph representation learning 24

MAGIC: detecting advanced persistent threats via masked grap...

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Proceedings of the 33rd USENIX Conference on security Symposium

作者： Zian Jia Yun Xiong Yuhong Nan Yao Zhang Jinjing Zhao Mi Wen Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Software Engineering Sun Yat-sen University China National Key Laboratory of Science and Technology on Information System Security China Shanghai University of Electric Power China

ISBN: (纸本)9781939133441

Advance Persistent Threats (APTs), adopted by most delicate attackers, are becoming increasing common and pose great threat to various enterprises and institutions. data provenance analysis on provenance graphs has emerged as a common approach in APT detection. However, previous works have exhibited several shortcomings: (1) requiring attack-containing data and a priori knowledge of APTs, (2) failing in extracting the rich contextual information buried within provenance graphs and (3) becoming impracticable due to their prohibitive computation overhead and memory *** this paper, we introduce MAGIC, a novel and flexible self-supervised APT detection approach capable of performing multi-granularity detection under different level of supervision. MAGIC leverages masked graph representation learning to model benign system entities and behaviors, performing efficient deep feature extraction and structure abstraction on provenance graphs. By ferreting out anomalous system behaviors via outlier detection methods, MAGIC is able to perform both system entity level and batched log level APT detection. MAGIC is specially designed to handle concept drift with a model adaption mechanism and successfully applies to universal conditions and detection scenarios. We evaluate MAGIC on three widely-used datasets, including both real-world and simulated attacks. Evaluation results indicate that MAGIC achieves promising detection results in all scenarios and shows enormous advantage over state-of-the-art APT detection approaches in performance overhead.

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DBSSL: A Scheme to Detect Backdoor Attacks in Self-Supervised Learning Models

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Huang, Yuxian Yang, Geng Yuan, Dong Yu, Shui Nanjing University of Posts and Telecommunication College of Computer Science and Software China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China University of Sydney School of Electrical and Information Engineering Australia University of Technology Sydney School of Computer Science Australia

Recently, self-supervised learning has garnered significant attention for its ability to extract high-quality features from unlabeled data. However, existing research indicates that backdoor attacks can pose significant threats to self-supervised learning. Additionally, due to the substantial training overhead, traditional backdoor detection methods in supervised learning, such as meta-learning, are not well-suited for self-supervised learning. Current backdoor detection methods for self-supervised learning can only defend against backdoor attacks triggered by patch. To address this challenge, we proposes DBSSL, a scheme that can efficiently detect backdoor attacks in self-supervised learning models. In DBSSL, we connect backdoor attacks with adversarial attacks and use adversarial perturbations for detection. Specifically, we first conduct targeted adversarial attacks on the samples, classifying them into different classes. Then, we employ the Z-Score method to analyze these adversarial perturbations. If outliers are present, we can infer that the self-supervised learning model has been backdoored. Unlike previous works, our scheme is better suited for downstream users with constrained computation resources and exhibits excellent detection capabilities for backdoor attacks caused by patch -type or global triggers. Abundant theoretical analysis has demonstrated the feasibility of our scheme. Moreover, extensive experiments show that our method performs well in detecting prevalent backdoor attacks in self-supervised learning, achieving detection accuracy exceeding 95%. © 2004-2012 IEEE.

关键词： Self-supervised learning

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Several classes of linear codes with few weights derived from Weil sums

arXiv

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arXiv 2025年

作者： Hu, Zhao Qiu, Mingxiu Li, Nian Tang, Xiaohu Wu, Liwei Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security School of Cyber Science and Technology Hubei University Wuhan430062 China State Key Laboratory of Integrated Service Networks Xi’an710071 China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan430062 China Southwest Jiaotong University Chengdu610031 China Wuhan Maritime Communication Research Institute China

Linear codes with few weights have applications in secret sharing, authentication codes, association schemes and strongly regular graphs. In this paper, several classes of t-weight linear codes over Fq are presented with the defining sets given by the intersection, difference and union of two certain sets, where t = 3, 4, 5, 6 and q is an odd prime power. By using Weil sums and Gauss sums, the parameters and weight distributions of these codes are determined completely. Moreover, three classes of optimal codes meeting the Griesmer bound are obtained, and computer experiments show that many (almost) optimal codes can be derived from our constructions. Copyright © 2025, The Authors. All rights reserved.

关键词： Weibull distribution

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Privacy Law Enforcement Under Centralized Governance: A Qualitative Analysis of Four Years’ Special Privacy Rectification Campaigns

arXiv

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arXiv 2025年

作者： Jing, Tao Li, Yao Ye, Jingzhou Wang, Jie Wang, Xueqiang School of Cyber Science and Engineering Huazhong University of Science and Technology China JinYinHu Laboratory China University of Central Florida United States Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

In recent years, major privacy laws like the GDPR have brought about positive changes. However, challenges remain in enforcing the laws, particularly due to under-resourced regulators facing a large number of potential privacy-violating software applications (apps) and the high costs of investigating them. Since 2019, China has launched a series of privacy enforcement campaigns known as Special Privacy Rectification Campaigns (SPRCs) to address widespread privacy violations in its mobile application (app) ecosystem. Unlike the enforcement of the GDPR, SPRCs are characterized by large-scale privacy reviews and strict sanctions, under the strong control of central authorities. In SPRCs, central government authorities issue administrative orders to mobilize various resources for market-wide privacy reviews of mobile apps. They enforce strict sanctions by requiring privacy-violating apps to rectify issues within a short timeframe or face removal from app stores. While there are a few reports on SPRCs, the effectiveness and potential problems of this campaign-style privacy enforcement approach remain unclear to the community. In this study, we conducted 18 semi-structured interviews with app-related engineers involved in SPRCs to better understand the campaign-style privacy enforcement. Based on the interviews, we reported our findings on a variety of aspects of SPRCs, such as the processes that app engineers regularly follow to achieve privacy compliance in SPRCs, the challenges they encounter, the solutions they adopt to address these challenges, and the impacts of SPRCs, etc. We found that app engineers face a series of challenges in achieving privacy compliance in their apps. For example, they receive inconsistent app privacy review reports from multiple app stores and have difficulties confirming the issues flagged by these reports;they also lack institutional support for studying privacy laws, self-validating privacy compliance of their apps, communicating effectiv

关键词： Differential privacy

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A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

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Vicinagearth 2025年第1期2卷 1-18页

作者： Wang, Bingshu Li, Changping Zou, Wenbin Zhang, Yongjun Chen, Xuhang Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Yongjun Zhang is with the State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou China

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.

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A Provably Secure Certificateless Signature Scheme With Anonymity for Healthcare IIoT

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IEEE Internet of Things Journal 2025年

作者： Qiao, Zirui Xu, Ran Zhou, Yanwei Yang, Bo Zhu, Tianqing Zhang, Mingwu Xi’an University of Posts and Telecommunications School of Cyberspace Security Xi’an China Ministry of Education Key Laboratory of Data Protection and Intelligent Management China Shaanxi Normal University School of Computer Science Xi’an China City University of Macau Faculty of Data Science Macau China Hubei University of Technology School of Computer Wuhan China

The industrial Internet of Things (IIoT) is changing our way of life and work. As the number of mobile devices connected to IIoT increases, users will face many security challenges such as insecure communication environments. The certificateless signature (CLS) scheme can ensure the integrity and validity of data and provide secure identity authentication for the IIoT, and several pairing-free CLS schemes have been proposed in recent years. However, we find they are vulnerable to the signature forgery attack of malicious key generation centers by safety analysis, which does not realize the security they have claimed. To solve this problem, we show an improved CLS scheme that achieves anonymity and formally proves its security under the hardness of the discrete logarithm problem in the random oracle. Comprehensive performance analysis and comparison show that our scheme has less communication and computation costs with higher security, and our construction is suitable for scenarios with limited resources. Finally, we apply this scheme to construct a mutual identity authentication protocol in the healthcare IIoT environment. 2014 IEEE.

关键词： Anonymity

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Mamba Adapter: Efficient Multi-Modal Fusion for Vision-Language Tracking

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Shi, Liangtao Zhong, Bineng Liang, Qihua Hu, Xiantao Mo, Zhiyi Song, Shuxiang Ministry of Education Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University School of Data Science and Software Engineering Wuzhou543002 China

Utilizing the high-level semantic information of language to compensate for the limitations of vision information is a highly regarded approach in single-object tracking. However, most existing vision-language (VL) trackers employ full-parameter fine-tuning, which can easily lead to catastrophic forgetting. Therefore, they fail to fully exploit the prior knowledge of pre-trained models from upstream tasks, resulting in unsatisfactory tracking performance. To alleviate the above problem, we propose a simple yet effective Vision-Language Tracking pipeline based on Mamba Adapter, named MAVLT, which adopts the idea of parameter-efficient fine-tuning (PEFT) to realize the interaction between vision-language modalities. This novel approach offers the following advantages: (1)The knowledge of the upstream pre-trained model is efficiently inherited by freezing its parameters. This ensures that the VL tracking framework only learns the modules for vision and language interaction, with a focus on the fusion between modalities. (2)The modal interaction between language and vision encoders is flexibly bridged in each encoder layer via proposed mamba adapter, enabling efficient interaction of visual and language information at multiple levels. Extensive experiments on five popular vision-language tracking benchmarks validate the effectiveness of the proposed MAVLT. Particularly, the MAVLT achieves 73.4% AUC score on the LaSOT benchmarks with only 0.18%(0.32M) of the total parameters updates. Code and models are available at https://***/GXNU-ZhongLab/MAVLT. © 1991-2012 IEEE.

关键词： Semantics

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VFLMonitor: Defending One-Party Hijacking Attacks in Vertical Federated Learning

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IEEE Transactions on Information Forensics and security 2025年 20卷 4828-4843页

作者： Xiangrui Xu Yiwen Zhao Yufei Han Yongsheng Zhu Zhen Han Guangquan Xu Bin Wang Shouling Ji Wei Wang School of Information Science and Technology Qingdao University of Science and Technology Qingdao China Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA INRIA Rennes Brittany France Institute of Computing Technologies China Academy of Railway Sciences Corporation Ltd. Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China Tianjin Key Laboratory of Advanced Networking (TANK) College of Intelligence and Computing Tianjin University Tianjin China Zhejiang Key Laboratory of Artificial Intelligence of Things (AIoT) Network and Data Security Hangzhou China College of Computer Science and Technology Zhejiang University Zhejiang China Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China

Vertical Federated Learning (VFL) is susceptible to various one-party hijacking attacks, such as Replay and Generation attacks, where a single malicious client can manipulate the model to produce attacker-specified results, thereby compromising its reliability in real-world deployments. In this paper, we first uncover the underlying mechanisms of these attacks and observe that successful attacks induce significant discrepancies in the embedding-label associations across different clients. We establish a theoretical framework demonstrating how these discrepancies can serve as reliable indicators for detecting hijacking attempts. Building upon this insight, we propose VFLMonitor, a robust defense mechanism that leverages these embedding-label discrepancies to detect and mitigate hijacking attacks. Specifically, VFLMonitor identifies suspicious queries by analyzing differences in label estimations from multiple clients and applies a majority voting rule to correct or filter out these malicious queries. Moreover, VFLMonitor introduces a novel regularization strategy during training to reduce intra-class variance in embeddings, thereby enhancing their discriminative power and improving defense effectiveness. Extensive experiments were conducted on 5 real-world datasets against 2 different attack types under 3 attack scenarios. The results demonstrate that VFLMonitor can effectively identify and exclude potential hijacked requests in all types of one-party hijacking attacks, while maintaining a meager false positive rate for legitimate queries.

关键词： Training Servers Electronic mail Artificial intelligence Reliability theory Predictive models Federated learning data models Computational modeling Collaboration

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