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Adversarial Camouflage for Node Injection Attack on Graphs 图上节点注入攻击的对抗伪装
Pub Date : 2022-08-03 DOI: 10.48550/arXiv.2208.01819
Shuchang Tao, Qi Cao, Huawei Shen, Yunfan Wu, Liang Hou, Xueqi Cheng
Node injection attacks on Graph Neural Networks (GNNs) have received increasing attention recently, due to their ability to degrade GNN performance with high attack success rates. However, our study indicates that these attacks often fail in practical scenarios, since defense/detection methods can easily identify and remove the injected nodes. To address this, we devote to camouflage node injection attack, making injected nodes appear normal and imperceptible to defense/detection methods. Unfortunately, the non-Euclidean structure of graph data and the lack of intuitive prior present great challenges to the formalization, implementation, and evaluation of camouflage. In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes. Then for implementation, we propose an adversarial CAmouflage framework for Node injection Attack, namely CANA, to improve attack performance under defense/detection methods in practical scenarios. A novel camouflage metric is further designed under the guide of distribution similarity. Extensive experiments demonstrate that CANA can significantly improve the attack performance under defense/detection methods with higher camouflage or imperceptibility. This work urges us to raise awareness of the security vulnerabilities of GNNs in practical applications.
近年来,针对图神经网络(GNN)的节点注入攻击以其较高的攻击成功率降低了GNN的性能,受到了越来越多的关注。然而,我们的研究表明,这些攻击在实际场景中经常失败,因为防御/检测方法可以很容易地识别和删除注入的节点。为了解决这个问题,我们致力于伪装节点注入攻击,使注入的节点看起来正常,防御/检测方法无法察觉。不幸的是,图数据的非欧几里得结构和缺乏直观先验给伪装的形式化、实现和评估带来了巨大的挑战。本文首先提出并定义伪装为注入节点与正常节点的自我网络之间的分布相似性。然后为了实现,我们提出了一个针对节点注入攻击的对抗性伪装框架,即CANA,以提高在实际场景中防御/检测方法下的攻击性能。在分布相似度的指导下,进一步设计了一种新的伪装度量。大量的实验表明,在具有更高伪装或不可感知性的防御/检测方法下,CANA可以显著提高攻击性能。这项工作促使我们提高对gnn在实际应用中的安全漏洞的认识。
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引用次数: 7
A new approach to generalized neighborhood system-based rough sets via convex structures and convex matroids 基于凸结构和凸拟阵的广义邻域系统粗糙集的新方法
Pub Date : 2022-08-01 DOI: 10.1016/j.ins.2022.08.084
Fangfang Zhao, B. Pang, Jusheng Mi
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引用次数: 2
Primitively visually meaningful image encryption: A new paradigm 原始视觉上有意义的图像加密:一种新范式
Pub Date : 2022-08-01 DOI: 10.1016/j.ins.2022.08.027
Ruoyu Zhao, Yushu Zhang, Y. Nan, Wenying Wen, Xiu-li Chai, Rushi Lan
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引用次数: 13
Optimized adaptive consensus control for multi-agent systems with prescribed performance 具有预定性能的多智能体系统的优化自适应共识控制
Pub Date : 2022-08-01 DOI: 10.1016/j.ins.2022.08.012
Lei Yan, Zhi Liu, C. L. Chen, Yun Zhang, Zongze Wu
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引用次数: 7
Rethinking Attention Mechanism in Time Series Classification 重新思考时间序列分类中的注意机制
Pub Date : 2022-07-14 DOI: 10.48550/arXiv.2207.07564
Bowen Zhao, Huanlai Xing, Xinhan Wang, Fuhong Song, Zhiwen Xiao
Attention-based models have been widely used in many areas, such as computer vision and natural language processing. However, relevant applications in time series classification (TSC) have not been explored deeply yet, causing a significant number of TSC algorithms still suffer from general problems of attention mechanism, like quadratic complexity. In this paper, we promote the efficiency and performance of the attention mechanism by proposing our flexible multi-head linear attention (FMLA), which enhances locality awareness by layer-wise interactions with deformable convolutional blocks and online knowledge distillation. What's more, we propose a simple but effective mask mechanism that helps reduce the noise influence in time series and decrease the redundancy of the proposed FMLA by masking some positions of each given series proportionally. To stabilize this mechanism, samples are forwarded through the model with random mask layers several times and their outputs are aggregated to teach the same model with regular mask layers. We conduct extensive experiments on 85 UCR2018 datasets to compare our algorithm with 11 well-known ones and the results show that our algorithm has comparable performance in terms of top-1 accuracy. We also compare our model with three Transformer-based models with respect to the floating-point operations per second and number of parameters and find that our algorithm achieves significantly better efficiency with lower complexity.
基于注意力的模型已广泛应用于计算机视觉和自然语言处理等领域。然而,相关在时间序列分类(TSC)中的应用尚未深入探索,导致相当多的TSC算法仍然存在一般性的注意机制问题,如二次复杂度。在本文中,我们提出了一种灵活的多头线性注意(FMLA),通过与可变形卷积块的分层交互和在线知识蒸馏来增强局部意识,从而提高了注意机制的效率和性能。此外,我们提出了一种简单而有效的掩膜机制,通过按比例掩膜每个给定序列的某些位置,有助于降低时间序列中的噪声影响并降低所提出的FMLA的冗余度。为了稳定这一机制,样本在随机蒙版层的模型中转发多次,并将其输出聚合到具有规则蒙版层的相同模型中。我们在85个UCR2018数据集上进行了大量的实验,将我们的算法与11个知名的算法进行了比较,结果表明我们的算法在top-1准确率方面具有相当的性能。我们还将我们的模型与三种基于transformer的模型在每秒浮点运算次数和参数数量方面进行了比较,发现我们的算法以更低的复杂性实现了明显更好的效率。
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引用次数: 10
Approximate synchronization of coupled multi-valued logical networks 耦合多值逻辑网络的近似同步
Pub Date : 2022-07-14 DOI: 10.48550/arXiv.2207.06615
Rong Zhao, Jun‐e Feng, Biao Wang
This article deals with the approximate synchronization of two coupled multi-valued logical networks. According to the initial state set from which both systems start, two kinds of approximate synchronization problem, local approximate synchronization and global approximate synchronization, are proposed for the first time. Three new notions: approximate synchronization state set (ASSS), the maximum approximate synchronization basin (MASB) and the shortest approximate synchronization time (SAST) are introduced and analyzed. Based on ASSS, several necessary and sufficient conditions are obtained for approximate synchronization. MASB, the set of all possible initial states, from which the systems are approximately synchronous, is investigated combining with the maximum invariant subset. And the calculation method of the SAST, associated with transient period, is presented. By virtue of MASB, pinning control scheme is investigated to make two coupled systems achieve global approximate synchronization. Furthermore, the related theories are also applied to the complete synchronization problem of $k$-valued ($kgeq2$) logical networks. Finally, four examples are given to illustrate the obtained results.
本文研究了两个耦合多值逻辑网络的近似同步问题。根据两个系统的初始状态集,首次提出了局部近似同步和全局近似同步两种近似同步问题。介绍并分析了三个新概念:近似同步状态集(ASSS)、最大近似同步池(MASB)和最短近似同步时间(SAST)。在此基础上,得到了近似同步的几个充要条件。结合最大不变子集,研究了系统近似同步的所有可能初始状态的集合MASB。给出了考虑暂态期的SAST的计算方法。利用MASB,研究了使两个耦合系统实现全局近似同步的钉住控制方案。此外,相关理论也应用于$k$ -value ($kgeq2$)逻辑网络的完全同步问题。最后,给出了四个算例来说明所得结果。
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引用次数: 1
An adaptive differential evolution framework based on population feature information 基于种群特征信息的自适应差分进化框架
Pub Date : 2022-07-01 DOI: 10.1016/j.ins.2022.07.043
Zijian Cao, Zhenyu Wang, Yanfang Fu, Haowen Jia, Feng Tian
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引用次数: 6
UHD Low-light image enhancement via interpretable bilateral learning 通过可解释的双边学习来增强UHD低光图像
Pub Date : 2022-07-01 DOI: 10.1016/j.ins.2022.07.051
Qiaowanni Lin, Zhuoran Zheng, Xiuyi Jia
{"title":"UHD Low-light image enhancement via interpretable bilateral learning","authors":"Qiaowanni Lin, Zhuoran Zheng, Xiuyi Jia","doi":"10.1016/j.ins.2022.07.051","DOIUrl":"https://doi.org/10.1016/j.ins.2022.07.051","url":null,"abstract":"","PeriodicalId":13641,"journal":{"name":"Inf. Sci.","volume":"39 1","pages":"1401-1415"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88062969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
A content-oriented no-reference perceptual video quality assessment method for computer graphics animation videos 面向内容的计算机图形动画视频无参考感知视频质量评价方法
Pub Date : 2022-07-01 DOI: 10.1016/j.ins.2022.07.053
Weizhi Xian, Mingliang Zhou, Bin Fang, S. Kwong
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引用次数: 3
A graph based approach for mining significant places in trajectory data 一种基于图的轨迹数据重要位置挖掘方法
Pub Date : 2022-07-01 DOI: 10.1016/j.ins.2022.07.046
Shimin Wang, Xinzheng Niu, Philippe Fournier-Viger, Dongmei Zhou, Fan Min
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引用次数: 4
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