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

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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Safeguarding Privacy in Real-Time Trajectory Releases for AI-Driven Consumer Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Cai, Sujin Tian, Wei Lv, Taizhi Wang, Xiangchen Li, Xin Jiangsu Maritime Institute School of Information and Engineering Nanjing211106 China Guizhou University of Commerce College of Computer and Information engineering Guiyang550014 China Southern University of Science and Technology College of Computer Science Shenzhen518055 China Hohai University College of Computer Science and Software Engineering Nanjing211106 China

Intelligent consumer applications optimize product recommendations by analyzing users' real-time trajectory information and mobility patterns to gain insights into their consumption preferences. However, privacy concerns often cause individuals to hesitate when sharing their locations. This paper introduces a real-time trajectory release scheme called Dynamic Trajectory Releasing based on Geo-indistinguishability (DTRG) that ensures client-side data protection. A significant contribution of this work is the introduction of a novel concept, w-event Geo-indistinguishability, which extends the measurement of privacy for real-time trajectories. Building upon the theoretical foundation of w-event Geo-indistinguishability, we design a dynamic privacy budget allocation strategy that doesn't require pre-setting trajectory length. This strategy recycles unused privacy budgets and progressively reallocating them in a backward cumulative manner, enhancing the utilization efficiency of the privacy budget. By incorporating users' travel information for prediction, DTRG adaptively perturbs real locations based on prediction results. Through the collaborative operation of the Prediction Mechanism (PM), Detection Mechanism (DM) and Noisy Mechanism (NM), this approach further optimizes the utility of the budget. Experimental results demonstrate that DTRG significantly improves data availability, achieving up to a 36% increase compared to some competing algorithms while maintaining the desired level of privacy. © 1975-2011 IEEE.

关键词： Differential privacy

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Multi-view aggregation and multi-relation alignment for few-shot fine-grained recognition

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Expert Systems with Applications 2025年 283卷

作者： Chen, Jiale Xu, Feng Lyu, Xin Zeng, Tao Li, Xin Chen, Shangjing College of Computer Science and Software Engineering Hohai University Nanjing210024 China School of Computer Engineering Jiangsu Ocean University Lianyungang222005 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing211106 China

Few-shot fine-grained recognition (FS-FGR) aims to recognize nuanced categories with a limited number of labeled samples that were not encountered during training. Previous work has made significant progress by enhancing the learning of foreground refined regions and the alignment of consistent semantics. However, the detrimental impact of insufficient background diversity on constructing representative category prototypes has been overlooked. Meanwhile, the alignment of semantically consistent features has been hampered by the reliance on singular metrics, resulting in suboptimal feature extraction. To address the limitations above, a novel framework with multi-view aggregation and multi-relation alignment (MVRA) is proposed. In this framework, we strive to refine category prototypes by generating and consolidating multiple views from limited learnable samples. Specifically, we generate foreground-refined views, pinpointing discriminative regions, and background-obfuscated views, broadening the landscape of background diversity. Further, without relying on the entire prior, a global label assignment module is designed to automatically assign reliable labels to the query set samples. Finally, armed with these credible labels, the multi-relation alignment module harnesses the enriched views and their semantic congruencies, facilitating robust feature extraction. The effectiveness and outstanding performance of MVRA are evaluated through extensive experiments conducted on three fine-grained benchmark datasets. © 2025

关键词： Feature extraction

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Efficient adaptive test case selection for DNNs robustness enhancement

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Journal of Systems and software 2025年 229卷

作者： Zhang, Zhiyi Meng, Huanze Ding, Yuchen Chen, Shuxian Yao, Yongming Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Jiangsu Nanjing China School of Mathematical Science Yangzhou University Jiangsu Yangzhou China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Army Engineering of PLA Jiangsu Nanjing China

Deep neural networks (DNNs) have been widely used in various fields, and testing for DNN-based software has become increasingly important. To discover potential faults in DNNs, a large number of test cases and their corresponding labels are required. However, labeling so many test cases consumes enormous costs. Although there have been many test case selection techniques for DNN models, these techniques still have problems such as high overhead, low efficiency, and poor diversity. To address this problem, this paper proposes an efficient adaptive test case selection method based on the principle of uniform distribution of test cases called EATS. Based on the idea of adaptive testing, EATS combines the uncertainty of the model and the diversity of faults to calculate the distance of test cases and sort them, then gives priority to test cases with a higher probability of causing faults. We conduct experiments on four popular datasets and four representative DNN models. Experiment results show that, compared with the existing eight methods, EATS performs better in uniformity of test case distribution, diversity of errors found, model optimization, and optimization efficiency. © 2025 Elsevier Inc.

关键词： software testing

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Adaptive Graph Structure Learning Neural Rough Differential Equations for Multivariate Time Series Forecasting

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IEEE Transactions on Big Data 2025年

作者： Su, Yuming Ma, Tinghuai Rong, Huan Wahab, Mohamed Magdy Abdel Nanjing University of Information Science & Technology The School of Cyber Science and Engineering School of Computer Science Nanjing210044 China Jiangsu Ocean University School of Computer Engineering Lianyungang222005 China Nanjing University of Information Science & Technology School of Software Nanjing210044 China Nanjing University of Information Science & Technology School of Artificial Intelligence School of Future Technology Nanjing210044 China Cairo University Faculty of Science Cario 12613 Egypt

Multivariate time series forecasting has extensive applications in urban computing, such as financial analysis, weather prediction, and traffic forecasting. Using graph structures to model the complex correlations among variables in time series, and leveraging graph neural networks and recurrent neural networks for temporal aggregation and spatial propagation stage, has shown promise. However, traditional methods' graph structure node learning and discrete neural architecture are not sensitive to issues such as sudden changes, time variance, and irregular sampling often found in real-world data. To address these challenges, we propose a method called Adaptive Graph structure Learning neural Rough Differential Equations (AGLRDE). Specifically, we combine dynamic and static graph structure learning to adaptively generate a more robust graph representation. Then we employ a spatio-temporal encoderdecoder based on Neural Rough Differential Equations (Neural RDE) to model spatio-temporal dependencies. Additionally, we introduce a path reconstruction loss to constrain the path generation stage. We conduct experiments on six benchmark datasets, demonstrating that our proposed method outperforms existing state-of-the-art methods. The results show that AGLRDE effectively handles aforementioned challenges, significantly improving the accuracy of multivariate time series forecasting. © 2015 IEEE.

关键词： Graph neural networks

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Generalizing Personalized Federated Graph Augmentation via Min-max Adversarial Learning 25

Generalizing Personalized Federated Graph Augmentation via M...

引用

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Liang Zhang Tao Long Yang Liu Lei Zhang Laizhong Cui Qingjiang Shi Shenzhen Research Institute of Big Data Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China Data Science Research Center Dukekunshan University Kunshan Jiangsu China School of Software Engineering Tongji University Yangpu Shanghai China

ISBN: (纸本)9798400712456

Federated learning (FL) enables the training of a global machine learning model among multiple local clients in a collaborative fashion without directly sharing the details of their data. Due to this advantage, it has been utilized in a wide range of applications where privacy is a critical concern and has attracted great attention for graph representation learning (GRL). Despite the offered advances, there still exist two major challenges in the FL for GRL across distributed graph data, including heterogeneity and complementarity. In order to tackle these challenges, a novel personalized federated graph augmentation (PFGA) framework is proposed in this work. Unlike existing techniques, it utilizes generative models as bridges to enable information sharing among clients, thereby facilitating the collaborative training of GRL models. Instead of directly using the generative model trained on each client individually, we aggregate them into the globally generative model to gain a global view of the entire graph, which effectively alleviates the heterogeneity and complementarity issues simultaneously. We formulate the training of the generative and GRL models as a min-max adversarial learning problem and theoretically prove the convergence. Furthermore, the effectiveness of the method is demonstrated using experimental results on six real-world datasets.

关键词： federated learning

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GNNMF-ATAC: A Web Server-Based ATAC-seq Motif Finding and Annotation Analysis 25

GNNMF-ATAC: A Web Server-Based ATAC-seq Motif Finding and An...

引用

Proceedings of the 2025 5th International Conference on Bioinformatics and Intelligent Computing

作者： Shuangquan Zhang Jinxin Jia Peng Gao Xuan Li Yan Wang School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China College of Software Jilin University Changchun Jilin China College of Computer Science and Technology Jilin University Changchun Jilin China

ISBN: (纸本)9798400712203

Transcription Factor Binding Sites (TFBSs) are specific DNA sequences that promote or inhibit gene expression via binding regions, thereby affecting protein synthesis. TFBSs play a crucial role in gene regulatory networks, complex diseases, and the growth of humans. Assay for Transposase-Accessible Chromatin with high-throughput Sequencing (ATAC-seq) is a high-throughput sequencing technology, providing a new perspective for studying transcription factor binding patterns (motifs). Different from sequencing ChIP-seq technologies, ATAC-seq is more efficient in detecting binding sites. Methods based on graph neural network have success on ATAC-seq motifs, but researchers make considerable effort to deploy these models. To address this issue, we developed a web server named GNNMF-ATAC for users. GNNMF-ATAC provides TFBSs prediction, motif finding, and enrichment analysis via deploying tools. Moreover, GNNMF-ATAC provides motif finding results and visualizations of 200 humans and 80 mouse ENCODE ATAC-seq datasets. GNNMF-ATAC is a valuable resource for experimental biologists seeking a user-friendly tool to find ATAC-seq motifs without programming expertise. GNNMF-ATAC is freely available at http://***.

关键词： ATAC-seq

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WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM with Sub-Class Exploration and Prompt Affinity Mining

arXiv

引用

arXiv 2025年

作者： Wang, Haoran Huai, Lian Li, Wenbin Qi, Lei Jiang, Xingqun Shi, Yinghuan National Key Laboratory for Novel Software Technology The National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing China National Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University Nanjing Drum Tower Hospital Jiangsu Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China Lab School of Computer Science and Engineering Southeast University Jiangsu Nanjing China BOE Technology Group Co. Ltd. Beijing China

We have witnessed remarkable progress in foundation models in vision tasks. Currently, several recent works have utilized the segmenting anything model (SAM) to boost the segmentation performance in medical images, where most of them focus on training an adaptor for fine-tuning a large amount of pixel-wise annotated medical images following a fully supervised manner. In this paper, to reduce the labeling cost, we investigate a novel weakly-supervised SAM-based segmentation model, namely WeakMedSAM. Specifically, our proposed WeakMedSAM contains two modules: 1) to mitigate severe co-occurrence in medical images, a sub-class exploration module is introduced to learn accurate feature representations. 2) to improve the quality of the class activation maps, our prompt affinity mining module utilizes the prompt capability of SAM to obtain an affinity map for random-walk refinement. Our method can be applied to any SAM-like backbone, and we conduct experiments with SAMUS and EfficientSAM. The experimental results on three popularly-used benchmark datasets, i.e., BraTS 2019, AbdomenCT-1K, and MSD Cardiac dataset, show the promising results of our proposed WeakMedSAM. Our code is available at https://***/wanghr64/WeakMedSAM. Copyright © 2025, The Authors. All rights reserved.

关键词： Medical imaging

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2M3DF: Advancing 3D Industrial Defect Detection with Multi Perspective Multimodal Fusion Network

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Asad, Mujtaba Azeem, Waqar Jiang, He Mustafa, Hafiz Tayyab Yang, Jie Liu, Wei Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Lahore Garrison University Department of Software Engineering Lahore54000 Pakistan China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

In the context of Industrial Anomaly Detection (IAD), ensuring the quality of manufactured products is critical. Traditional 2D based methods often fail to capture anomalies present in complex 3D shapes. For effective anomaly detection in 3D shapes, it is essential to incorporate global semantic context, local geometric structure, and color information of the object. To fully leverage these features, we propose a network named 2M3DF, that leverages knowledge from multi-view RGB images and corresponding point cloud information for enhanced anomaly detection performance. Our model initially employs pre-trained feature extractors that generate local features from multi-view RGB images and corresponding point clouds. The novel inter-modality feature representation and fusion module first adapts these inter-modality features and then effectively aligns and aggregates these multimodality features on a pixel-to-point basis. To learn the normality from point-wise fused multimodal features, we fit a multivariate Gaussian distribution to model the normal feature distribution. Comprehensive experimental evaluations using the MVTec3D-AD and Eyecandies dataset validate the effectiveness of our propose model and demonstrate significant improvements in comparison to existing state-of-the-art methods. Our model achieves a 96.6% mean I-AUROC while delivering real-time results. © 1991-2012 IEEE.

关键词： Normal distribution

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Embedded System Security Protocols: Latest Trends and Issues

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International Journal of High Speed Electronics and Systems 2025年第3期34卷

作者： Han, Huimin Gulzar, Yonis Nizamani, Ahsan Ahmed Nizamani, Abdul Haseeb Ali, Sijjad Aslam Bhatti, Mughair Mechanical and Electrical Engineering College Hainan Vocational University of Science and Technology Haikou571126 China Department of Management Information Systems King Faisal University P.O. Box 380 Al-Ahsa31982 Saudi Arabia School of Computer Science and Engineering Central South University Changsha410083 China College of Computer Science and Software Engineering Shenzhen University Guandong Shenzhen China School of Geography Nanjing Normal University Jiangsu Nanjing China

Given the increasingly serious security threats faced by embedded systems, an analysis has been conducted on the characteristics and vulnerabilities of embedded systems. Detailed discussions are provided on security threats faced by embedded systems from two major aspects: hardware-based attacks such as side-channel attacks, hardware Trojans, fault injection, microprobes, and hardware reverse engineering, and software attacks aimed at damaging, tampering, and stealing. The analysis includes existing security measures both domestically and internationally, highlighting their shortcomings. Based on the concept of trusted computing, the paper offers future research suggestions from several perspectives, including theoretical research models, trusted measurement, trusted reporting, trusted sharing, and isolation of resources. Finally, an ideal comprehensive joint security enhancement scheme is proposed, and future research directions are discussed. © 2025 World Scientific Publishing Company.

关键词： Embedded software

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