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Proceedings of the 33rd USENIX Conference on security Symposium

作者： Siyue Feng Yueming Wu Wenjie Xue Sikui Pan Deqing Zou Yang Liu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab and School of Cyber Science and Engineering Huazhong University of Science and Technology China Nanyang Technological University Singapore National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab and School of Cyber Science and Engineering Huazhong University of Science and Technology China and Jinyinhu Laboratory China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133441

With the continuous development of software open-sourcing, the reuse of open-source software has led to a significant increase in the occurrence of recurring vulnerabilities. These vulnerabilities often arise through the practice of copying and pasting existing vulnerabilities. Many methods have been proposed for detecting recurring vulnerabilities, but they often struggle to ensure both high efficiency and consideration of semantic information about vulnerabilities and patches. In this paper, we introduce FIRE, a scalable method for large-scale recurring vulnerability detection. It utilizes multi-stage filtering and differential taint paths to achieve precise clone vulnerability scanning at an extensive scale. In our evaluation across ten open-source software projects, FIRE demonstrates a precision of 90.0% in detecting 298 recurring vulnerabilities out of 385 ground truth instance. This surpasses the performance of existing advanced recurring vulnerability detection tools, detecting 31.4% more vulnerabilities than VUDDY and 47.0% more than MOVERY. When detecting vulnerabilities in large-scale software, FIRE outperforms MOVERY by saving about twice the time, enabling the scanning of recurring vulnerabilities on an ultra-large scale.

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Spatio-Temporal Transformer-Based Reinforcement Learning for Robot Crowd Navigation

Spatio-Temporal Transformer-Based Reinforcement Learning for...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： He, Haodong Fu, Hao Wang, Qiang Zhou, Shuai Liu, Wei Chen, Yang Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430081 China Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China

ISBN: (纸本)9798350325706

Ensuring robots can move safely and adhere to social norms in dynamic human environments is a crucial step towards robot autonomous decision-making. In existing work, double serial separate modules are generally used to capture spatial and temporal interactions, respectively. However, such methods lead to extra difficulties in improving the utilization of spatio-temporal features and reducing the conservatism of navigation policy. In light of this, this paper proposes a spatiotemporal transformer-based policy optimization algorithm to more effectively preserve the human-robot interactions. Specifically, a gated embedding mechanism is introduced to effectively fuses the spatial and temporal representations by integrating both modalities at the feature level. Then Transformer is leveraged to encode the spatio-temporal semantic information, with the hope of finding the optimal navigation policy. Finally, a combination of spatio-temporal Transformer and self-adjusting policy entropy significantly reduce the conservatism of navigation policies. Experimental results demonstrate the priority of the proposed algorithm over the state-of-the-art methods. © 2023 IEEE.

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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A generalized reliability analysis of 3-ary n-cubes

A generalized reliability analysis of 3-ary n-cubes

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International Conference on Communication Software and Networks, ICCSN

作者： Zefang Bai Guijuan Wang Meijie Ma School of Mathematics and Statistics Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331506322

ISBN: (纸本)9798331506339

Evaluating the reliability of interconnection networks is crucial. The edge-connectivity is a parameter used to assess reliability. In this paper, we discuss a conditional edge-connectivity called the h-extra g-component edge-connectivity. It combines the extra edge-connectivity and the component edge-connectivity. First, this paper proved that $c \lambda_{3}^{h}\left(Q_{n}^{3}\right)=4 n h-$ $4 e_{h}\left(Q_{n}^{3}\right)-h$ for $1 \leq h \leq \frac{1}{2} \cdot 3^{\left\lceil\frac{h}{2}\right\rceil}, n \geq 3$. Then, the paper given the upper bound of h-extra g-component edge-connectivity of the 3 -ary n-cubes for $g \in\left\{4,3^{c}+1\right\}, 1 \leq c \leq\left\lceil\frac{n}{2}\right\rceil-1, n \geq 3$.

关键词： Upper bound Multiprocessor interconnection Software Reliability

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GLCF: A Global-Local Multimodal Coherence Analysis Framework for Talking Face Generation Detection

arXiv

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arXiv 2024年

作者： Chen, Xiaocan Yin, Qilin Liu, Jiarui Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China Alibaba Group Hangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datasets. In this paper, we construct the first large-scale multi-scenario talking face dataset (MSTF), which contains 22 audio and video forgery techniques, filling the gap of datasets in this field. The dataset covers 11 generation scenarios and more than 20 semantic scenarios, closer to the practical application scenario of TFG. Besides, we also propose a TFG detection framework, which leverages the analysis of both global and local coherence in the multimodal content of TFG videos. Therefore, a region-focused smoothness detection module (RSFDM) and a discrepancy capture-time frame aggregation module (DCTAM) are introduced to evaluate the global temporal coherence of TFG videos, aggregating multi-grained spatial information. Additionally, a visual-audio fusion module (V-AFM) is designed to evaluate audiovisual coherence within a localized temporal perspective. Comprehensive experiments demonstrate the reasonableness and challenges of our datasets, while also indicating the superiority of our proposed method compared to the state-of-the-art deepfake detection approaches. © 2024, CC BY-NC-ND.

关键词： Semantics

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Disorder-enhanced layer Hall effect in a magnetic sandwich heterostructure

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Physical Review B 2024年第11期109卷 115301-115301页

作者： Xiao-Xia Yi Chun-Bo Hua Rui Chen Bin Zhou Department of Physics Hubei University Wuhan 430062 China School of Electronic and Information Engineering Hubei University of Science and Technology Xianning 437100 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan 430062 China

The layer Hall effect is a type of Hall effect where electrons on the top and bottom layers deflect in opposite directions. In this paper, we investigate the disorder effects on the layer Hall effect in a magnetic sandwich heterostructure that supports the axion insulator and normal insulator phases. We demonstrate that the layer Hall effect in the axion insulator phase is robust against weak disorder. Most interestingly, we find that the layer Hall effect in the normal insulator phase is significantly enhanced with increasing disorder strength. Theoretically, the disorder-enhanced layer Hall effect at weak disorder is explained by using the Born approximation. Experimentally, we suggest the disorder-enhanced layer Hall effect could be detected by applying a perpendicular electric field.

关键词： Topological Hall effect Dilute magnetic topological insulators Topological insulators

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SUMI-IFL: An information-Theoretic Framework for Image Forgery Localization with Sufficiency and Minimality Constraints

arXiv

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arXiv 2024年

作者： Sheng, Ziqi Lu, Wei Luo, Xiangyang Zhou, Jiantao Cao, Xiaochun School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University China State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus China

Image forgery localization (IFL) is a crucial technique for preventing tampered image misuse and protecting social safety. However, due to the rapid development of image tampering technologies, extracting more comprehensive and accurate forgery clues remains an urgent challenge. To address these challenges, we introduce a novel information-theoretic IFL framework named SUMI-IFL that imposes sufficiency-view and minimality-view constraints on forgery feature representation. First, grounded in the theoretical analysis of mutual information, the sufficiency-view constraint is enforced on the feature extraction network to ensure that the latent forgery feature contains comprehensive forgery clues. Considering that forgery clues obtained from a single aspect alone may be incomplete, we construct the latent forgery feature by integrating several individual forgery features from multiple perspectives. Second, based on the information bottleneck, the minimality-view constraint is imposed on the feature reasoning network to achieve an accurate and concise forgery feature representation that counters the interference of task-unrelated features. Extensive experiments show the superior performance of SUMI-IFL to existing state-of-the-art methods, not only on in-dataset comparisons but also on cross-dataset comparisons. © 2024, CC BY-NC-ND.

关键词： Clutter (information theory)

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How to Find Simple Conditions for Successful Sequence Reconstruction?

How to Find Simple Conditions for Successful Sequence Recons...

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IEEE (first ITW conference of the year) information Theory Workshop (ITW)

作者： Chen Wang Eitan Yaakobi Yiwei Zhang Key Laboratory of Cryptologic Technology and Information Security of Ministry of Education School of Cyber Science and Technology Shandong University Qingdao Shandong China Department of Computer Science Technion – Israel Institute of Technology Haifa Israel

ISBN: (数字)9798350348934

ISBN: (纸本)9798350348941

We study a model in which a codeword $x$ is transmitted through several identical channels, where each channel produces a noisy read of $x$ . The sequence reconstruction problem, proposed by Levenshtein, asks for how to uniquely re-construct $x$ based on these noisy reads. Most of previous works focused on the minimum number of reads which guarantees unique reconstruction of $x$ in the worst case. In this paper, we move on to a new perspective on the sequence reconstruction problem, and propose a different sufficient condition for unique reconstruction which takes both the number of reads and the distances among the reads into consideration. We offer both theoretical analysis and corresponding efficient reconstruction algorithms.

关键词： Sufficient conditions Conferences Reconstruction algorithms Noise measurement information theory

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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RoPA: Robust Privacy-Preserving Forward Aggregation for Split Vertical Federated Learning

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IEEE Transactions on Network and Service Management 2025年

作者： Wang, Lingling Zhang, Zhengyin Huang, Mei Gai, Keke Wang, Jingjing Shen, Yulong Qingdao University of Science and Technology School of Information Science and Technology Shandong Qingdao266061 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Xidian University School of Computer Science and Technology China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Split Vertical Federated Learning (Split VFL) is an increasingly popular framework for collaborative machine learning on vertically partitioned data. However, it is vulnerable to various attacks, resulting in privacy leakage and robust aggregation issues. Recent works have explored the privacy protection of raw data samples and labels, neglecting malicious attacks launched by dishonest passive parties. Since they may deviate from the protocol and launch embedding poisoning attacks and free-riding attacks, it will inevitably result in model performance loss. To address this issue, we propose a Robust Privacy-preserving forward Aggregation (RoPA) protocol, which can resist embedding poisoning attacks and free-riding attacks and protect the privacy of embedding vectors. Specifically, we first present a modified Secret-shared Non-Interactive Proofs (SNIP) algorithm to guarantee the integrity verification of embedding vectors. To prevent free-riding attacks, we also give a validity verification protocol using matrix commitment. In particular, we utilize probability checking and batch verification to improve the verification efficiency of the protocol. Moreover, we adopt arithmetic secret sharing to protect data privacy. Finally, we conduct rigorous theoretical analysis to prove the security of RoPA and evaluate the performance of RoPA. The experimental results show that the proof verification overhead of RoPA is approximately 8× lower than the original SNIP, and the model accuracy is improved by ranging from 3% to 15% under the above two malicious attacks. © 2004-2012 IEEE.

关键词： Federated learning

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